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train.py
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train.py
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# Copyright 2019-present NAVER Corp.
# Apache License v2.0
# Wonseok Hwang
# Sep30, 2018
import os, sys, argparse, re, json
from matplotlib.pylab import *
import torch.nn as nn
import torch
import torch.nn.functional as F
import random as python_random
# import torchvision.datasets as dsets
# BERT
import bert.tokenization as tokenization
from bert.modeling import BertConfig, BertModel
from sqlova.utils.utils_wikisql import *
from sqlova.utils.utils import load_jsonl
from sqlova.model.nl2sql.wikisql_models import *
from sqlnet.dbengine import DBEngine
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def construct_hyper_param(parser):
parser.add_argument("--do_train", default=False, action='store_true')
parser.add_argument('--do_infer', default=False, action='store_true')
parser.add_argument('--infer_loop', default=False, action='store_true')
parser.add_argument("--trained", default=False, action='store_true')
parser.add_argument('--tepoch', default=200, type=int)
parser.add_argument("--bS", default=32, type=int,
help="Batch size")
parser.add_argument("--accumulate_gradients", default=1, type=int,
help="The number of accumulation of backpropagation to effectivly increase the batch size.")
parser.add_argument('--fine_tune',
default=False,
action='store_true',
help="If present, BERT is trained.")
parser.add_argument("--model_type", default='Seq2SQL_v1', type=str,
help="Type of model.")
# 1.2 BERT Parameters
parser.add_argument("--vocab_file",
default='vocab.txt', type=str,
help="The vocabulary file that the BERT model was trained on.")
parser.add_argument("--max_seq_length",
default=222, type=int, # Set based on maximum length of input tokens.
help="The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded.")
parser.add_argument("--num_target_layers",
default=2, type=int,
help="The Number of final layers of BERT to be used in downstream task.")
parser.add_argument('--lr_bert', default=1e-5, type=float, help='BERT model learning rate.')
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--no_pretraining', action='store_true', help='Use BERT pretrained model')
parser.add_argument("--bert_type_abb", default='uS', type=str,
help="Type of BERT model to load. e.g.) uS, uL, cS, cL, and mcS")
# 1.3 Seq-to-SQL module parameters
parser.add_argument('--lS', default=2, type=int, help="The number of LSTM layers.")
parser.add_argument('--dr', default=0.3, type=float, help="Dropout rate.")
parser.add_argument('--lr', default=1e-3, type=float, help="Learning rate.")
parser.add_argument("--hS", default=100, type=int, help="The dimension of hidden vector in the seq-to-SQL module.")
# 1.4 Execution-guided decoding beam-size. It is used only in test.py
parser.add_argument('--EG',
default=False,
action='store_true',
help="If present, Execution guided decoding is used in test.")
parser.add_argument('--beam_size',
type=int,
default=4,
help="The size of beam for smart decoding")
args = parser.parse_args()
map_bert_type_abb = {'uS': 'uncased_L-12_H-768_A-12',
'uL': 'uncased_L-24_H-1024_A-16',
'cS': 'cased_L-12_H-768_A-12',
'cL': 'cased_L-24_H-1024_A-16',
'mcS': 'multi_cased_L-12_H-768_A-12'}
args.bert_type = map_bert_type_abb[args.bert_type_abb]
print(f"BERT-type: {args.bert_type}")
# Decide whether to use lower_case.
if args.bert_type_abb == 'cS' or args.bert_type_abb == 'cL' or args.bert_type_abb == 'mcS':
args.do_lower_case = False
else:
args.do_lower_case = True
# Seeds for random number generation
seed(args.seed)
python_random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
# args.toy_model = not torch.cuda.is_available()
args.toy_model = False
args.toy_size = 12
return args
def get_bert(BERT_PT_PATH, bert_type, do_lower_case, no_pretraining):
bert_config_file = os.path.join(BERT_PT_PATH, f'bert_config_{bert_type}.json')
vocab_file = os.path.join(BERT_PT_PATH, f'vocab_{bert_type}.txt')
init_checkpoint = os.path.join(BERT_PT_PATH, f'pytorch_model_{bert_type}.bin')
bert_config = BertConfig.from_json_file(bert_config_file)
tokenizer = tokenization.FullTokenizer(
vocab_file=vocab_file, do_lower_case=do_lower_case)
bert_config.print_status()
model_bert = BertModel(bert_config)
if no_pretraining:
pass
else:
model_bert.load_state_dict(torch.load(init_checkpoint, map_location='cpu'))
print("Load pre-trained parameters.")
model_bert.to(device)
return model_bert, tokenizer, bert_config
def get_opt(model, model_bert, fine_tune):
if fine_tune:
opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=0)
opt_bert = torch.optim.Adam(filter(lambda p: p.requires_grad, model_bert.parameters()),
lr=args.lr_bert, weight_decay=0)
else:
opt = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr, weight_decay=0)
opt_bert = None
return opt, opt_bert
def get_models(args, BERT_PT_PATH, trained=False, path_model_bert=None, path_model=None):
# some constants
agg_ops = ['', 'MAX', 'MIN', 'COUNT', 'SUM', 'AVG']
cond_ops = ['=', '>', '<', 'OP'] # do not know why 'OP' required. Hence,
print(f"Batch_size = {args.bS * args.accumulate_gradients}")
print(f"BERT parameters:")
print(f"learning rate: {args.lr_bert}")
print(f"Fine-tune BERT: {args.fine_tune}")
# Get BERT
model_bert, tokenizer, bert_config = get_bert(BERT_PT_PATH, args.bert_type, args.do_lower_case,
args.no_pretraining)
args.iS = bert_config.hidden_size * args.num_target_layers # Seq-to-SQL input vector dimenstion
# Get Seq-to-SQL
n_cond_ops = len(cond_ops)
n_agg_ops = len(agg_ops)
print(f"Seq-to-SQL: the number of final BERT layers to be used: {args.num_target_layers}")
print(f"Seq-to-SQL: the size of hidden dimension = {args.hS}")
print(f"Seq-to-SQL: LSTM encoding layer size = {args.lS}")
print(f"Seq-to-SQL: dropout rate = {args.dr}")
print(f"Seq-to-SQL: learning rate = {args.lr}")
model = Seq2SQL_v1(args.iS, args.hS, args.lS, args.dr, n_cond_ops, n_agg_ops)
model = model.to(device)
if trained:
assert path_model_bert != None
assert path_model != None
if torch.cuda.is_available():
res = torch.load(path_model_bert)
else:
res = torch.load(path_model_bert, map_location='cpu')
model_bert.load_state_dict(res['model_bert'])
model_bert.to(device)
if torch.cuda.is_available():
res = torch.load(path_model)
else:
res = torch.load(path_model, map_location='cpu')
model.load_state_dict(res['model'])
return model, model_bert, tokenizer, bert_config
def get_data(path_wikisql, args):
train_data, train_table, dev_data, dev_table, _, _ = load_wikisql(path_wikisql, args.toy_model, args.toy_size,
no_w2i=True, no_hs_tok=True)
train_loader, dev_loader = get_loader_wikisql(train_data, dev_data, args.bS, shuffle_train=True)
return train_data, train_table, dev_data, dev_table, train_loader, dev_loader
def train(train_loader, train_table, model, model_bert, opt, bert_config, tokenizer,
max_seq_length, num_target_layers, accumulate_gradients=1, check_grad=True,
st_pos=0, opt_bert=None, path_db=None, dset_name='train'):
model.train()
model_bert.train()
ave_loss = 0
cnt = 0 # count the # of examples
cnt_sc = 0 # count the # of correct predictions of select column
cnt_sa = 0 # of selectd aggregation
cnt_wn = 0 # of where number
cnt_wc = 0 # of where column
cnt_wo = 0 # of where operator
cnt_wv = 0 # of where-value
cnt_wvi = 0 # of where-value index (on question tokens)
cnt_lx = 0 # of logical form acc
cnt_x = 0 # of execution acc
# Engine for SQL querying.
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
for iB, t in enumerate(train_loader):
cnt += len(t)
if cnt < st_pos:
continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, train_table, no_hs_t=True, no_sql_t=True)
# nlu : natural language utterance
# nlu_t: tokenized nlu
# sql_i: canonical form of SQL query
# sql_q: full SQL query text. Not used.
# sql_t: tokenized SQL query
# tb : table
# hs_t : tokenized headers. Not used.
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
# get ground truth where-value index under CoreNLP tokenization scheme. It's done already on trainset.
g_wvi_corenlp = get_g_wvi_corenlp(t)
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
# wemb_n: natural language embedding
# wemb_h: header embedding
# l_n: token lengths of each question
# l_hpu: header token lengths
# l_hs: the number of columns (headers) of the tables.
try:
#
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
# e.g. train: 32.
continue
# score
s_sc, s_sa, s_wn, s_wc, s_wo, s_wv = model(wemb_n, l_n, wemb_h, l_hpu, l_hs,
g_sc=g_sc, g_sa=g_sa, g_wn=g_wn, g_wc=g_wc, g_wvi=g_wvi)
# Calculate loss & step
loss = Loss_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi)
# Calculate gradient
if iB % accumulate_gradients == 0: # mode
# at start, perform zero_grad
opt.zero_grad()
if opt_bert:
opt_bert.zero_grad()
loss.backward()
if accumulate_gradients == 1:
opt.step()
if opt_bert:
opt_bert.step()
elif iB % accumulate_gradients == (accumulate_gradients - 1):
# at the final, take step with accumulated graident
loss.backward()
opt.step()
if opt_bert:
opt_bert.step()
else:
# at intermediate stage, just accumulates the gradients
loss.backward()
# Prediction
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi = pred_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, )
pr_wv_str, pr_wv_str_wp = convert_pr_wvi_to_string(pr_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
# Sort pr_wc:
# Sort pr_wc when training the model as pr_wo and pr_wvi are predicted using ground-truth where-column (g_wc)
# In case of 'dev' or 'test', it is not necessary as the ground-truth is not used during inference.
pr_wc_sorted = sort_pr_wc(pr_wc, g_wc)
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc_sorted, pr_wo, pr_wv_str, nlu)
# Cacluate accuracy
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, \
cnt_wc1_list, cnt_wo1_list, \
cnt_wvi1_list, cnt_wv1_list = get_cnt_sw_list(g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi,
sql_i, pr_sql_i,
mode='train')
cnt_lx1_list = get_cnt_lx_list(cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list,
cnt_wo1_list, cnt_wv1_list)
# lx stands for logical form accuracy
# Execution accuracy test.
cnt_x1_list, g_ans, pr_ans = get_cnt_x_list(engine, tb, g_sc, g_sa, sql_i, pr_sc, pr_sa, pr_sql_i)
# statistics
ave_loss += loss.item()
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wvi / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
aux_out = 1
return acc, aux_out
def report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_q, pr_ans,
cnt_list, current_cnt):
cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x = current_cnt
print(f'cnt = {cnt} / {cnt_tot} ===============================')
print(f'headers: {hds}')
print(f'nlu: {nlu}')
# print(f's_sc: {s_sc[0]}')
# print(f's_sa: {s_sa[0]}')
# print(f's_wn: {s_wn[0]}')
# print(f's_wc: {s_wc[0]}')
# print(f's_wo: {s_wo[0]}')
# print(f's_wv: {s_wv[0][0]}')
print(f'===============================')
print(f'g_sc : {g_sc}')
print(f'pr_sc: {pr_sc}')
print(f'g_sa : {g_sa}')
print(f'pr_sa: {pr_sa}')
print(f'g_wn : {g_wn}')
print(f'pr_wn: {pr_wn}')
print(f'g_wc : {g_wc}')
print(f'pr_wc: {pr_wc}')
print(f'g_wo : {g_wo}')
print(f'pr_wo: {pr_wo}')
print(f'g_wv : {g_wv}')
# print(f'pr_wvi: {pr_wvi}')
print('g_wv_str:', g_wv_str)
print('p_wv_str:', pr_wv_str)
print(f'g_sql_q: {g_sql_q}')
print(f'pr_sql_q: {pr_sql_q}')
print(f'g_ans: {g_ans}')
print(f'pr_ans: {pr_ans}')
print(f'--------------------------------')
print(cnt_list)
print(f'acc_lx = {cnt_lx / cnt:.3f}, acc_x = {cnt_x / cnt:.3f}\n',
f'acc_sc = {cnt_sc / cnt:.3f}, acc_sa = {cnt_sa / cnt:.3f}, acc_wn = {cnt_wn / cnt:.3f}\n',
f'acc_wc = {cnt_wc / cnt:.3f}, acc_wo = {cnt_wo / cnt:.3f}, acc_wv = {cnt_wv / cnt:.3f}')
print(f'===============================')
def test(data_loader, data_table, model, model_bert, bert_config, tokenizer,
max_seq_length,
num_target_layers, detail=False, st_pos=0, cnt_tot=1, EG=False, beam_size=4,
path_db=None, dset_name='test'):
model.eval()
model_bert.eval()
ave_loss = 0
cnt = 0
cnt_sc = 0
cnt_sa = 0
cnt_wn = 0
cnt_wc = 0
cnt_wo = 0
cnt_wv = 0
cnt_wvi = 0
cnt_lx = 0
cnt_x = 0
cnt_list = []
engine = DBEngine(os.path.join(path_db, f"{dset_name}.db"))
results = []
for iB, t in enumerate(data_loader):
cnt += len(t)
if cnt < st_pos:
continue
# Get fields
nlu, nlu_t, sql_i, sql_q, sql_t, tb, hs_t, hds = get_fields(t, data_table, no_hs_t=True, no_sql_t=True)
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv = get_g(sql_i)
g_wvi_corenlp = get_g_wvi_corenlp(t)
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
try:
g_wvi = get_g_wvi_bert_from_g_wvi_corenlp(t_to_tt_idx, g_wvi_corenlp)
g_wv_str, g_wv_str_wp = convert_pr_wvi_to_string(g_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
except:
# Exception happens when where-condition is not found in nlu_tt.
# In this case, that train example is not used.
# During test, that example considered as wrongly answered.
for b in range(len(nlu)):
results1 = {}
results1["error"] = "Skip happened"
results1["nlu"] = nlu[b]
results1["table_id"] = tb[b]["id"]
results.append(results1)
continue
# model specific part
# score
if not EG:
# No Execution guided decoding
s_sc, s_sa, s_wn, s_wc, s_wo, s_wv = model(wemb_n, l_n, wemb_h, l_hpu, l_hs)
# get loss & step
loss = Loss_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi)
# prediction
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi = pred_sw_se(s_sc, s_sa, s_wn, s_wc, s_wo, s_wv, )
pr_wv_str, pr_wv_str_wp = convert_pr_wvi_to_string(pr_wvi, nlu_t, nlu_tt, tt_to_t_idx, nlu)
# g_sql_i = generate_sql_i(g_sc, g_sa, g_wn, g_wc, g_wo, g_wv_str, nlu)
pr_sql_i = generate_sql_i(pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, nlu)
else:
# Execution guided decoding
prob_sca, prob_w, prob_wn_w, pr_sc, pr_sa, pr_wn, pr_sql_i = model.beam_forward(wemb_n, l_n, wemb_h, l_hpu,
l_hs, engine, tb,
nlu_t, nlu_tt,
tt_to_t_idx, nlu,
beam_size=beam_size)
# sort and generate
pr_wc, pr_wo, pr_wv, pr_sql_i = sort_and_generate_pr_w(pr_sql_i)
# Follosing variables are just for the consistency with no-EG case.
pr_wvi = None # not used
pr_wv_str = None
pr_wv_str_wp = None
loss = torch.tensor([0])
g_sql_q = generate_sql_q(sql_i, tb)
pr_sql_q = generate_sql_q(pr_sql_i, tb)
# Saving for the official evaluation later.
for b, pr_sql_i1 in enumerate(pr_sql_i):
results1 = {}
results1["query"] = pr_sql_i1
results1["table_id"] = tb[b]["id"]
results1["nlu"] = nlu[b]
results.append(results1)
cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, \
cnt_wc1_list, cnt_wo1_list, \
cnt_wvi1_list, cnt_wv1_list = get_cnt_sw_list(g_sc, g_sa, g_wn, g_wc, g_wo, g_wvi,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wvi,
sql_i, pr_sql_i,
mode='test')
cnt_lx1_list = get_cnt_lx_list(cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list,
cnt_wo1_list, cnt_wv1_list)
# Execution accura y test
cnt_x1_list = []
# lx stands for logical form accuracy
# Execution accuracy test.
cnt_x1_list, g_ans, pr_ans = get_cnt_x_list(engine, tb, g_sc, g_sa, sql_i, pr_sc, pr_sa, pr_sql_i)
# stat
ave_loss += loss.item()
# count
cnt_sc += sum(cnt_sc1_list)
cnt_sa += sum(cnt_sa1_list)
cnt_wn += sum(cnt_wn1_list)
cnt_wc += sum(cnt_wc1_list)
cnt_wo += sum(cnt_wo1_list)
cnt_wv += sum(cnt_wv1_list)
cnt_wvi += sum(cnt_wvi1_list)
cnt_lx += sum(cnt_lx1_list)
cnt_x += sum(cnt_x1_list)
current_cnt = [cnt_tot, cnt, cnt_sc, cnt_sa, cnt_wn, cnt_wc, cnt_wo, cnt_wv, cnt_wvi, cnt_lx, cnt_x]
cnt_list1 = [cnt_sc1_list, cnt_sa1_list, cnt_wn1_list, cnt_wc1_list, cnt_wo1_list, cnt_wv1_list, cnt_lx1_list,
cnt_x1_list]
cnt_list.append(cnt_list1)
# report
if detail:
report_detail(hds, nlu,
g_sc, g_sa, g_wn, g_wc, g_wo, g_wv, g_wv_str, g_sql_q, g_ans,
pr_sc, pr_sa, pr_wn, pr_wc, pr_wo, pr_wv_str, pr_sql_q, pr_ans,
cnt_list1, current_cnt)
ave_loss /= cnt
acc_sc = cnt_sc / cnt
acc_sa = cnt_sa / cnt
acc_wn = cnt_wn / cnt
acc_wc = cnt_wc / cnt
acc_wo = cnt_wo / cnt
acc_wvi = cnt_wvi / cnt
acc_wv = cnt_wv / cnt
acc_lx = cnt_lx / cnt
acc_x = cnt_x / cnt
acc = [ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x]
return acc, results, cnt_list
def tokenize_corenlp(client, nlu1):
nlu1_tok = []
for sentence in client.annotate(nlu1):
for tok in sentence:
nlu1_tok.append(tok.originalText)
return nlu1_tok
def tokenize_corenlp_direct_version(client, nlu1):
nlu1_tok = []
for sentence in client.annotate(nlu1).sentence:
for tok in sentence.token:
nlu1_tok.append(tok.originalText)
return nlu1_tok
def infer(nlu1,
table_name, data_table, path_db, db_name,
model, model_bert, bert_config, max_seq_length, num_target_layers,
beam_size=4, show_table=False, show_answer_only=False):
# I know it is of against the DRY principle but to minimize the risk of introducing bug w, the infer function introuced.
model.eval()
model_bert.eval()
engine = DBEngine(os.path.join(path_db, f"{db_name}.db"))
# Get inputs
nlu = [nlu1]
# nlu_t1 = tokenize_corenlp(client, nlu1)
nlu_t1 = tokenize_corenlp_direct_version(client, nlu1)
nlu_t = [nlu_t1]
tb1 = data_table[0]
hds1 = tb1['header']
tb = [tb1]
hds = [hds1]
hs_t = [[]]
wemb_n, wemb_h, l_n, l_hpu, l_hs, \
nlu_tt, t_to_tt_idx, tt_to_t_idx \
= get_wemb_bert(bert_config, model_bert, tokenizer, nlu_t, hds, max_seq_length,
num_out_layers_n=num_target_layers, num_out_layers_h=num_target_layers)
prob_sca, prob_w, prob_wn_w, pr_sc, pr_sa, pr_wn, pr_sql_i = model.beam_forward(wemb_n, l_n, wemb_h, l_hpu,
l_hs, engine, tb,
nlu_t, nlu_tt,
tt_to_t_idx, nlu,
beam_size=beam_size)
# sort and generate
pr_wc, pr_wo, pr_wv, pr_sql_i = sort_and_generate_pr_w(pr_sql_i)
if len(pr_sql_i) != 1:
raise EnvironmentError
pr_sql_q1 = generate_sql_q(pr_sql_i, [tb1])
pr_sql_q = [pr_sql_q1]
try:
pr_ans, _ = engine.execute_return_query(tb[0]['id'], pr_sc[0], pr_sa[0], pr_sql_i[0]['conds'])
except:
pr_ans = ['Answer not found.']
pr_sql_q = ['Answer not found.']
if show_answer_only:
print(f'Q: {nlu[0]}')
print(f'A: {pr_ans[0]}')
print(f'SQL: {pr_sql_q}')
else:
print(f'START ============================================================= ')
print(f'{hds}')
if show_table:
print(engine.show_table(table_name))
print(f'nlu: {nlu}')
print(f'pr_sql_i : {pr_sql_i}')
print(f'pr_sql_q : {pr_sql_q}')
print(f'pr_ans: {pr_ans}')
print(f'---------------------------------------------------------------------')
return pr_sql_i, pr_ans
def print_result(epoch, acc, dname):
ave_loss, acc_sc, acc_sa, acc_wn, acc_wc, acc_wo, acc_wvi, acc_wv, acc_lx, acc_x = acc
print(f'{dname} results ------------')
print(
f" Epoch: {epoch}, ave loss: {ave_loss}, acc_sc: {acc_sc:.3f}, acc_sa: {acc_sa:.3f}, acc_wn: {acc_wn:.3f}, \
acc_wc: {acc_wc:.3f}, acc_wo: {acc_wo:.3f}, acc_wvi: {acc_wvi:.3f}, acc_wv: {acc_wv:.3f}, acc_lx: {acc_lx:.3f}, acc_x: {acc_x:.3f}"
)
if __name__ == '__main__':
## 1. Hyper parameters
parser = argparse.ArgumentParser()
args = construct_hyper_param(parser)
## 2. Paths
path_h = './data_and_model' # '/home/wonseok'
path_wikisql = './data_and_model' # os.path.join(path_h, 'data', 'wikisql_tok')
BERT_PT_PATH = path_wikisql
path_save_for_evaluation = './'
## 3. Load data
train_data, train_table, dev_data, dev_table, train_loader, dev_loader = get_data(path_wikisql, args)
# test_data, test_table = load_wikisql_data(path_wikisql, mode='test', toy_model=args.toy_model, toy_size=args.toy_size, no_hs_tok=True)
# test_loader = torch.utils.data.DataLoader(
# batch_size=args.bS,
# dataset=test_data,
# shuffle=False,
# num_workers=4,
# collate_fn=lambda x: x # now dictionary values are not merged!
# )
## 4. Build & Load models
if not args.trained:
model, model_bert, tokenizer, bert_config = get_models(args, BERT_PT_PATH)
else:
# To start from the pre-trained models, un-comment following lines.
path_model_bert = './data_and_model/model_bert_best.pt'
path_model = './data_and_model/model_best.pt'
model, model_bert, tokenizer, bert_config = get_models(args, BERT_PT_PATH, trained=True,
path_model_bert=path_model_bert, path_model=path_model)
## 5. Get optimizers
if args.do_train:
opt, opt_bert = get_opt(model, model_bert, args.fine_tune)
## 6. Train
acc_lx_t_best = -1
epoch_best = -1
for epoch in range(args.tepoch):
# train
acc_train, aux_out_train = train(train_loader,
train_table,
model,
model_bert,
opt,
bert_config,
tokenizer,
args.max_seq_length,
args.num_target_layers,
args.accumulate_gradients,
opt_bert=opt_bert,
st_pos=0,
path_db=path_wikisql,
dset_name='train')
# check DEV
with torch.no_grad():
acc_dev, results_dev, cnt_list = test(dev_loader,
dev_table,
model,
model_bert,
bert_config,
tokenizer,
args.max_seq_length,
args.num_target_layers,
detail=False,
path_db=path_wikisql,
st_pos=0,
dset_name='dev', EG=args.EG)
print_result(epoch, acc_train, 'train')
print_result(epoch, acc_dev, 'dev')
# save results for the official evaluation
save_for_evaluation(path_save_for_evaluation, results_dev, 'dev')
# save best model
# Based on Dev Set logical accuracy lx
acc_lx_t = acc_dev[-2]
if acc_lx_t > acc_lx_t_best:
acc_lx_t_best = acc_lx_t
epoch_best = epoch
# save best model
state = {'model': model.state_dict()}
torch.save(state, os.path.join('.', 'model_best.pt'))
state = {'model_bert': model_bert.state_dict()}
torch.save(state, os.path.join('.', 'model_bert_best.pt'))
print(f" Best Dev lx acc: {acc_lx_t_best} at epoch: {epoch_best}")
if args.do_infer:
# To use recent corenlp: https://github.com/stanfordnlp/python-stanford-corenlp
# 1. pip install stanford-corenlp
# 2. download java crsion
# 3. export CORENLP_HOME=/Users/wonseok/utils/stanford-corenlp-full-2018-10-05
# from stanza.nlp.corenlp import CoreNLPClient
# client = CoreNLPClient(server='http://localhost:9000', default_annotators='ssplit,tokenize'.split(','))
import corenlp
client = corenlp.CoreNLPClient(annotators='ssplit,tokenize'.split(','))
nlu1 = "Which company have more than 100 employees?"
path_db = './data_and_model'
db_name = 'ctable'
data_table = load_jsonl('./data_and_model/ctable.tables.jsonl')
table_name = 'ftable1'
n_Q = 100000 if args.infer_loop else 1
for i in range(n_Q):
if n_Q > 1:
nlu1 = input('Type question: ')
pr_sql_i, pr_ans = infer(
nlu1,
table_name, data_table, path_db, db_name,
model, model_bert, bert_config, max_seq_length=args.max_seq_length,
num_target_layers=args.num_target_layers,
beam_size=1, show_table=False, show_answer_only=False
)