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run.py
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run.py
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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import numpy as np
import json
import torch
import torch.distributed as dist
from tqdm import tqdm
from collections import defaultdict
from transformers import BartTokenizer, AlbertTokenizer, BertTokenizer, T5Tokenizer
from transformers import BartConfig, AlbertConfig, BertConfig, T5Config
from transformers import AdamW, get_linear_schedule_with_warmup
from QAData import QAData, AmbigQAData, DisAmbigQAData, AmbigQADataLeaderboard, AmbigQACoTrainingLabelData, AmbigQACoTrainingTrainData
from QGData import QGData, AmbigQGData, QGMaskedData, AmbigQGRewriteData, AmbigQGWeightedData, AmbigQGNoPromptData
from QGInferenceData import AmbigQGInferenceData
from QAInferenceData import AmbigQAInferenceData
from QALMFilteringData import AmbigQALMFilteringData
from QAEMFilteringData import AmbigQAEMFilteringData
from PassageData import PassageData
from RerankData import NQRerankerData, AQRerankerData
from models.span_predictor import SpanPredictor, AlbertSpanPredictor
from models.reranker import BertReranker
from models.seq2seq import MyBart, MyT5, MyBartS2S, MyBartDynamic, MyBartDynamicWeightedLoss, MyBartWeightedLoss
from models.seq2seq_with_prefix import MyBartWithPrefix
from models.lm_filtering import MyBartLMFiltering
from models.biencoder import MyBiEncoder
from models.qagen import MyBart_QAGen
from QAGenData import QAGenPassageData, QAGenData
from ambigqa_evaluate_script import get_exact_match
# from IPython import embed
def _average_gradients(model):
# Gradient averaging.
size = float(dist.get_world_size())
for param in model.parameters():
dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM)
param.grad.data /= size
def run(args, logger):
if args.is_distributed == 1:
logger.debug("Distributed training - {}".format(bool(args.is_distributed)))
logger.debug("Number of gpus available - {}".format(args.num_gpus))
kwargs = {'num_workers': 1, 'pin_memory': True}
# Initialize the distributed environment.
world_size = len(args.hosts)
os.environ['WORLD_SIZE'] = str(world_size)
host_rank = args.hosts.index(args.current_host)
os.environ['RANK'] = str(host_rank)
# dist.init_process_group(backend='nccl')
dist.init_process_group(backend='NCCL', rank=host_rank, world_size=world_size)
logger.info('Initialized the distributed environment: \'{}\' backend on {} nodes. '.format(
'nccl', dist.get_world_size()) + 'Current host rank is {}. Number of gpus: {}'.format(
dist.get_rank(), args.num_gpus))
else:
kwargs = {}
# args.dpr = args.task=="dpr"
# args.is_seq2seq = 'bart' in args.bert_name
if 'bart' in args.bert_name:
tokenizer = BartTokenizer.from_pretrained(args.bert_name)
tokenizer.add_tokens(["<SEP>"])
if args.do_predict and args.nq_answer_as_prefix:
Model = MyBartWithPrefix
elif args.task in ['qg'] and args.filter_not_found_answer_passages:
Model = MyBartDynamic
elif args.task in ["qg_weighted_loss"]:
if args.filter_not_found_answer_passages:
Model = MyBartDynamicWeightedLoss
else:
Model = MyBartWeightedLoss
else:
Model = MyBart
Config = BartConfig
if args.task == 'qa_gen':
tokenizer.add_tokens(["<QAGEN-Q>"])
tokenizer.add_tokens(["<QAGEN-A>"])
# args.append_another_bos = True
elif 'albert' in args.bert_name:
tokenizer = AlbertTokenizer.from_pretrained(args.bert_name)
Model = AlbertSpanPredictor
Config = AlbertConfig
elif 'bert' in args.bert_name:
tokenizer = BertTokenizer.from_pretrained(args.bert_name)
Model = MyBiEncoder if args.dpr else BertReranker
Config = BertConfig
elif 't5' in args.bert_name:
logger.info('Usage: https://github.com/huggingface/transformers/issues/4092')
# https://huggingface.co/transformers/model_doc/t5.html#training
#
# input_ids = tokenizer.encode('translate English to German: The house is wonderful. </s>', return_tensors='pt')
# labels = tokenizer.encode('Das Haus ist wunderbar. </s>', return_tensors='pt')
# # the forward function automatically creates the correct decoder_input_ids
# model(input_ids=input_ids, labels=labels)
#
# Gotcha for me was that the decoder_input_ids at inference should be prepended by the padding token as stated in the docs for T5ForConditionalGeneration.
# During training, there is no need to prepend the padding token since this is done automatically in T5 when lm_labels is provided.
# During evaluation, one has to prepend the PAD token as you stated in your example.
# After training, the mode can be used with the generate() method (which actually powers the summarization, translation and text-generation pipeline).
# In the generate() method, the padding token is automatically prepended.
# from transformers import T5Tokenizer, T5Model
# tokenizer = T5Tokenizer.from_pretrained('t5-small')
# model = T5Model.from_pretrained('t5-small')
# input_ids = tokenizer.encode("summarize: Hello, my dog is cute", return_tensors="pt")
# decoder_input_ids = tokenizer.encode("<pad>", return_tensors="pt")
# outputs = model(input_ids=input_ids, decoder_input_ids=decoder_input_ids)
# outputs[0]
# Do note that T5ForConditionalGeneration already prepends the padding by default. Above is only necessary if you're doing a forward pass straight from T5Model.
#
# you should add the </s> token to the end of a sentence.
tokenizer = T5Tokenizer.from_pretrained(args.bert_name)
tokenizer.add_tokens(["<SEP>"])
Model = MyT5
Config = T5Config
# args.append_another_bos = True
else:
raise NotImplementedError()
if args.dpr:
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
Model = MyBiEncoder
args.checkpoint = os.path.join(args.dpr_data_dir, "checkpoint/retriever/{}/bert-base-encoder.cp".format(args.dpr_checkpoint))
assert not args.do_train, "Training DPR is not supported yet"
if args.task == 'qa_gen':
passages = QAGenPassageData(logger, args, tokenizer)
else:
passages = PassageData(logger, args, tokenizer)
def _getData():
if args.task == 'rrk':
return AQRerankerData if args.ambigqa and not args.leaderboard else NQRerankerData
elif args.task == 'qa_noamb_aq':
return DisAmbigQAData
elif args.task == "qg_weighted_loss":
assert args.ambigqa == True, 'Not support NQG pretraining!'
return AmbigQGWeightedData
elif args.task == 'qg_noprompt':
return AmbigQGNoPromptData
elif args.task == "qg":
return AmbigQGData if args.ambigqa else QGData
elif args.task == 'qg_mask':
return QGMaskedData
elif args.task == 'cotraining_label':
return AmbigQACoTrainingLabelData
elif args.task == 'cotraining_train':
return AmbigQACoTrainingTrainData
else:
if args.leaderboard:
# for the use of dpr
return AmbigQADataLeaderboard
else:
return AmbigQAData if args.ambigqa else QAData
def _load_from_checkpoint(checkpoint):
def convert_to_single_gpu(state_dict):
if "model_dict" in state_dict:
state_dict = state_dict["model_dict"]
def _convert(key):
if key.startswith('module.'):
return key[7:]
return key
return {_convert(key):value for key, value in state_dict.items()}
state_dict = convert_to_single_gpu(torch.load(checkpoint))
model = Model(Config.from_pretrained(args.bert_name))
if "bart" in args.bert_name:
model.resize_token_embeddings(len(tokenizer))
logger.info("Loading from {}".format(checkpoint))
return model.from_pretrained(None, config=model.config, state_dict=state_dict)
dev_data = _getData()(logger, args, args.predict_file, False, passages)
dev_data.load_dataset(tokenizer)
dev_data.load_dataloader()
if args.do_train:
train_data = _getData()(logger, args, args.train_file, True, passages)
train_data.load_dataset(tokenizer)
train_data.load_dataloader(**kwargs)
if args.checkpoint is not None:
model = _load_from_checkpoint(args.checkpoint)
else:
model = Model.from_pretrained(args.bert_name)
if "bart" in args.bert_name:
# see https://github.com/pytorch/fairseq/issues/1389
model.decoder_start_token_id = args.decoder_start_token_id
model.resize_token_embeddings(len(tokenizer))
if args.is_distributed == 1:
logger.info('Use DDP!')
model.to(torch.device("cuda"))
model = torch.nn.parallel.DistributedDataParallel(model)
# model.to(torch.device("cuda", args.local_rank))
# model = torch.nn.parallel.DistributedDataParallel(model,
# device_ids=[args.local_rank],
# output_device=args.local_rank)
else:
if args.n_gpu>1:
logger.info('Use DP!')
model = torch.nn.DataParallel(model)
model.to(torch.device("cuda"))
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},
{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
if args.task == 'qa_gen':
num_training_steps = args.num_train_epochs * min(
[int(len(train_data.dataset_task_1) / args.train_batch_size_task_1),
int(len(train_data.dataset_task_2_1) / args.train_batch_size_task_2_1),
int(len(train_data.dataset_task_2_2) / args.train_batch_size_task_2_2),
int(len(train_data.dataset_task_3_1) / args.train_batch_size_task_3_1),
int(len(train_data.dataset_task_3_2) / args.train_batch_size_task_3_2), ])
args.warmup_steps = int(num_training_steps * args.warmup_proportion)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=num_training_steps)
train_qa_gen(args, logger, model, train_data, dev_data, optimizer, scheduler)
else:
num_training_steps = args.num_train_epochs * int(len(train_data) / args.train_batch_size)
args.warmup_steps = int(num_training_steps * args.warmup_proportion)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=num_training_steps)
if args.task == 'rrk':
train_reranker(args, logger, model, train_data, dev_data, optimizer, scheduler)
else:
train(args, logger, model, train_data, dev_data, optimizer, scheduler)
if args.do_predict:
checkpoint = os.path.join(args.output_dir, 'best-model.pt') if args.checkpoint is None else args.checkpoint
model = _load_from_checkpoint(checkpoint)
logger.info("Loading checkpoint from {}".format(checkpoint))
if "bart" in args.bert_name:
model.decoder_start_token_id = args.decoder_start_token_id
if "bart" in args.bert_name:
model.resize_token_embeddings(len(tokenizer))
if args.n_gpu>1 and 'bert' in args.bert_name:
model = torch.nn.DataParallel(model)
model.to(torch.device("cuda"))
model.eval()
ems, result = inference(model, dev_data, save_predictions=False, logger=logger)
logger.info("%s on test data = %.2f" % (dev_data.metric, ems))
if dev_data.args.task not in ['dpr', 'rrk', 'cotraining_label']:
with open(os.path.join(args.output_dir, "{}{}-best-result-{}.json".format(args.task, "-aq" if args.ambigqa else "", dev_data.data_type)), 'w') as f:
json.dump(result, f, indent=4)
def run_over_generate(args, logger):
def _load_from_checkpoint(checkpoint, Model):
def convert_to_single_gpu(state_dict):
if "model_dict" in state_dict:
state_dict = state_dict["model_dict"]
def _convert(key):
if key.startswith('module.'):
return key[7:]
return key
return {_convert(key):value for key, value in state_dict.items()}
state_dict = convert_to_single_gpu(torch.load(checkpoint))
model = Model(Config.from_pretrained(args.bert_name))
if "bart" in args.bert_name:
model.resize_token_embeddings(len(tokenizer))
return model.from_pretrained(None, config=model.config, state_dict=state_dict)
if 'bart' in args.bert_name:
tokenizer = BartTokenizer.from_pretrained(args.bert_name)
tokenizer.add_tokens(["<SEP>"])
MAP_Model = MyBart
QD_Model = MyBart
Config = BartConfig
else:
raise NotImplementedError()
passages = PassageData(logger, args, tokenizer)
map_dev_data = AmbigQAInferenceData(logger, args, args.predict_file, False, passages)
map_dev_data.load_dataset(tokenizer)
map_dev_data.load_dataloader()
map_prediction_file = '/' + os.path.join(*args.map_ckpt.split('/')[:-3], '{}.json'.format(map_dev_data.data_type))
if os.path.exists(map_prediction_file) and args.over_generate_pass == 0 and not args.leaderboard:
with open(map_prediction_file) as f:
map_dev_data.data = json.load(f)
logger.info('Loading MAP Prediction File from {}!'.format(map_prediction_file))
else:
map_model = _load_from_checkpoint(args.map_ckpt, MAP_Model)
logger.info("Loading map checkpoint from {}".format(args.map_ckpt))
if "bart" in args.bert_name:
map_model.decoder_start_token_id = args.decoder_start_token_id
map_model.resize_token_embeddings(len(tokenizer))
map_model.to(torch.device("cuda"))
map_model.eval()
map_predictions_metadata = map_dev_data.tokenized_data[-1]
inference_overgenerate('qa', map_model, map_dev_data, map_predictions_metadata)
del map_model; torch.cuda.empty_cache()
if args.over_generate_pass == 0 and not args.leaderboard:
# save map data
with open(map_prediction_file, 'w') as f:
json.dump(map_dev_data.data, f)
logger.info('Saving MAP Prediction File to {}!'.format(map_prediction_file))
# 2. Question Disambiguation
# reduce the batch size
args.predict_batch_size = int(args.predict_batch_size/4)
qd_dev_data = AmbigQGInferenceData(logger, args, args.predict_file, False, passages, map_dev_data.data, map_dev_data.dpr_reranked_tokenized_data)
qd_dev_data.load_dataset(tokenizer)
qd_dev_data.load_dataloader()
qd_model = _load_from_checkpoint(args.qd_ckpt, QD_Model)
logger.info("Loading qd checkpoint from {}".format(args.qd_ckpt))
if "bart" in args.bert_name:
qd_model.decoder_start_token_id = args.decoder_start_token_id
qd_model.resize_token_embeddings(len(tokenizer))
qd_model.to(torch.device("cuda"))
qd_model.eval()
qd_predictions_metadata = qd_dev_data.tokenized_data[2]
inference_overgenerate('qg', qd_model, qd_dev_data, qd_predictions_metadata)
# evaluate un-filtered data
if not args.leaderboard:
results = qd_dev_data.evaluate()
with open(os.path.join(args.output_dir, "{}.json".format(qd_dev_data.data_type)), 'w') as f:
json.dump(qd_dev_data.data, f)
logger.info('Saving predictions to \n{}'.format(os.path.join(args.output_dir, "{}.json".format(qd_dev_data.data_type))))
with open(os.path.join(args.output_dir, "{}_over_generate_pass_{}_result.json".format(qd_dev_data.data_type, args.over_generate_pass)), 'w') as f:
json.dump(results, f, indent=4)
logger.info('Saving results to \n{}'.format(os.path.join(args.output_dir, "{}_over_generate_pass_{}_result.json".format(qd_dev_data.data_type, args.over_generate_pass))))
else:
if args.over_generate_pass == 0:
# save pass 0 predictions
e2e_predictions = {}
for d in qd_dev_data.data:
e2e_predictions[d['id']] = [{"question": x[0], "answer": x[1]} for x in d['over_generate_0_noambq_answer']]
with open(os.path.join(args.output_dir, "{}_e2e_leaderboard.json".format(qd_dev_data.data_type)), 'w') as f:
json.dump(e2e_predictions, f, indent=2)
logger.info('Saving e2e predictions (pass=0) to \n{}'.format(os.path.join(args.output_dir, "{}_e2e_leaderboard.json".format(qd_dev_data.data_type))))
with open(os.path.join(args.output_dir, "{}.json".format(qd_dev_data.data_type)), 'w') as f:
json.dump(qd_dev_data.data, f)
logger.info('Saving all predictions to \n{}'.format(os.path.join(args.output_dir, "{}.json".format(qd_dev_data.data_type))))
def run_lm_filtering(args, logger):
def _load_from_checkpoint(checkpoint, Model):
def convert_to_single_gpu(state_dict):
if "model_dict" in state_dict:
state_dict = state_dict["model_dict"]
def _convert(key):
if key.startswith('module.'):
return key[7:]
return key
return {_convert(key):value for key, value in state_dict.items()}
state_dict = convert_to_single_gpu(torch.load(checkpoint))
model = Model(BartConfig.from_pretrained(args.bert_name))
if "bart" in args.bert_name:
model.resize_token_embeddings(len(tokenizer))
return model.from_pretrained(None, config=model.config, state_dict=state_dict)
assert 'bart' in args.bert_name, 'only support bart!'
tokenizer = BartTokenizer.from_pretrained(args.bert_name)
tokenizer.add_tokens(["<SEP>"])
passages = PassageData(logger, args, tokenizer)
dev_data = AmbigQALMFilteringData(logger, args, args.predict_file, False, passages)
dev_data.load_dataset(tokenizer)
dev_data.load_dataloader()
verifier = _load_from_checkpoint(args.verifier_ckpt, MyBartLMFiltering)
logger.info("Loading checkpoint from {}".format(args.verifier_ckpt))
verifier.decoder_start_token_id = args.decoder_start_token_id
verifier.resize_token_embeddings(len(tokenizer))
verifier.to(torch.device("cuda"))
verifier.eval()
lm_scores = inference_lm_filtering(verifier, dev_data, logger=logger)
del verifier; torch.cuda.empty_cache()
if not args.leaderboard:
# evaluate un-filtered data
predictions, results = dev_data.evaluate(lm_scores)
with open(os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_prediction.json".format(dev_data.data_type, args.over_generate_pass)), 'w') as f:
json.dump(predictions, f)
logger.info('Saving predictions to \n{}'.format(os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_prediction.json".format(dev_data.data_type, args.over_generate_pass))))
with open(os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_result.json".format(dev_data.data_type, args.over_generate_pass)), 'w') as f:
json.dump(results, f, indent=4)
logger.info('Saving results to \n{}'.format(os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_result.json".format(dev_data.data_type, args.over_generate_pass))))
else:
predictions = dev_data.predict(lm_scores, dev_data.args.leaderboard_threshold, dev_data.args.leaderboard_threshold_mode)
path_predictions = os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_prediction_leaderboard_{}.json".format(
dev_data.data_type, args.over_generate_pass, "{}{}".format(
dev_data.args.leaderboard_threshold_mode, dev_data.args.leaderboard_threshold if dev_data.args.leaderboard_threshold_mode == 'fixed' else "")))
with open(path_predictions, 'w') as f:
json.dump(predictions, f, indent=2)
logger.info('Saving predictions to \n{}'.format(path_predictions))
#
# path_global_threshold_avg_std = os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_prediction_leaderboard_global_avg_std.json".format(dev_data.data_type, args.over_generate_pass))
# with open(path_global_threshold_avg_std, 'w') as f:
# json.dump(predictions_avg_std, f, indent=2)
# logger.info('Saving global avg std predictions to \n{}'.format(path_global_threshold_avg_std))
#
#
# path_global_threshold_predefined = os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_prediction_leaderboard_global_threshold_{:.2f}.json".format(dev_data.data_type, args.over_generate_pass, dev_data.args.leaderboard_threshold))
# with open(path_global_threshold_predefined, 'w') as f:
# json.dump(predictions_threshold, f, indent=2)
# logger.info('Saving global threshold predictions to \n{}'.format(path_global_threshold_predefined))
def run_em_filtering(args, logger):
def _load_from_checkpoint(checkpoint, Model):
def convert_to_single_gpu(state_dict):
if "model_dict" in state_dict:
state_dict = state_dict["model_dict"]
def _convert(key):
if key.startswith('module.'):
return key[7:]
return key
return {_convert(key): value for key, value in state_dict.items()}
state_dict = convert_to_single_gpu(torch.load(checkpoint))
model = Model(BartConfig.from_pretrained(args.bert_name))
if "bart" in args.bert_name:
model.resize_token_embeddings(len(tokenizer))
return model.from_pretrained(None, config=model.config, state_dict=state_dict)
assert 'bart' in args.bert_name, 'only support bart!'
tokenizer = BartTokenizer.from_pretrained(args.bert_name)
tokenizer.add_tokens(["<SEP>"])
passages = PassageData(logger, args, tokenizer)
dev_data = AmbigQAEMFilteringData(logger, args, args.predict_file, False, passages)
dev_data.load_dataset(tokenizer)
dev_data.load_dataloader()
verifier = _load_from_checkpoint(args.verifier_ckpt, MyBart)
logger.info("Loading checkpoint from {}".format(args.verifier_ckpt))
verifier.decoder_start_token_id = args.decoder_start_token_id
verifier.resize_token_embeddings(len(tokenizer))
verifier.to(torch.device("cuda"))
verifier.eval()
em_predictions = inference_em_filtering(verifier, dev_data, logger=logger)
# evaluate un-filtered data
dev_data.evaluate(em_predictions)
def run_over_generate_lm_filtering(args, logger):
def _load_from_checkpoint(checkpoint, Model):
def convert_to_single_gpu(state_dict):
if "model_dict" in state_dict:
state_dict = state_dict["model_dict"]
def _convert(key):
if key.startswith('module.'):
return key[7:]
return key
return {_convert(key):value for key, value in state_dict.items()}
state_dict = convert_to_single_gpu(torch.load(checkpoint))
model = Model(Config.from_pretrained(args.bert_name))
if "bart" in args.bert_name:
model.resize_token_embeddings(len(tokenizer))
return model.from_pretrained(None, config=model.config, state_dict=state_dict)
if 'bart' in args.bert_name:
tokenizer = BartTokenizer.from_pretrained(args.bert_name)
tokenizer.add_tokens(["<SEP>"])
MAP_Model = MyBart
QD_Model = MyBart
Config = BartConfig
else:
raise NotImplementedError()
passages = PassageData(logger, args, tokenizer)
map_dev_data = AmbigQAInferenceData(logger, args, args.predict_file, False, passages)
map_dev_data.load_dataset(tokenizer)
map_dev_data.load_dataloader()
map_prediction_file = '/' + os.path.join(*args.map_ckpt.split('/')[:-3], '{}.json'.format(map_dev_data.data_type))
if os.path.exists(map_prediction_file) and args.over_generate_pass == 0 and not args.leaderboard:
with open(map_prediction_file) as f:
map_dev_data.data = json.load(f)
logger.info('Loading MAP Prediction File from {}!'.format(map_prediction_file))
else:
map_model = _load_from_checkpoint(args.map_ckpt, MAP_Model)
logger.info("Loading map checkpoint from {}".format(args.map_ckpt))
if "bart" in args.bert_name:
map_model.decoder_start_token_id = args.decoder_start_token_id
map_model.resize_token_embeddings(len(tokenizer))
map_model.to(torch.device("cuda"))
map_model.eval()
map_predictions_metadata = map_dev_data.tokenized_data[-1]
inference_overgenerate('qa', map_model, map_dev_data, map_predictions_metadata)
del map_model; torch.cuda.empty_cache()
if args.over_generate_pass == 0 and not args.leaderboard:
# save map data
with open(map_prediction_file, 'w') as f:
json.dump(map_dev_data.data, f)
logger.info('Saving MAP Prediction File to {}!'.format(map_prediction_file))
# 2. Question Disambiguation
# reduce the batch size
args.predict_batch_size = int(args.predict_batch_size/4)
qd_dev_data = AmbigQGInferenceData(logger, args, args.predict_file, False, passages, map_dev_data.data, map_dev_data.dpr_reranked_tokenized_data)
qd_dev_data.load_dataset(tokenizer)
qd_dev_data.load_dataloader()
qd_model = _load_from_checkpoint(args.qd_ckpt, QD_Model)
logger.info("Loading qd checkpoint from {}".format(args.qd_ckpt))
if "bart" in args.bert_name:
qd_model.decoder_start_token_id = args.decoder_start_token_id
qd_model.resize_token_embeddings(len(tokenizer))
qd_model.to(torch.device("cuda"))
qd_model.eval()
qd_predictions_metadata = qd_dev_data.tokenized_data[2]
inference_overgenerate('qg', qd_model, qd_dev_data, qd_predictions_metadata)
del qd_model; torch.cuda.empty_cache()
# 3. LM Filtering
args.predict_batch_size = args.predict_batch_size * 4
verifier_data = AmbigQALMFilteringData(logger, args, args.predict_file, False, passages, over_generate_data=qd_dev_data.data)
verifier_data.load_dataset(tokenizer)
verifier_data.load_dataloader()
verifier = _load_from_checkpoint(args.verifier_ckpt, MyBartLMFiltering)
logger.info("Loading checkpoint from {}".format(args.verifier_ckpt))
verifier.decoder_start_token_id = args.decoder_start_token_id
verifier.resize_token_embeddings(len(tokenizer))
verifier.to(torch.device("cuda"))
verifier.eval()
lm_scores = inference_lm_filtering(verifier, verifier_data, logger=logger)
del verifier; torch.cuda.empty_cache()
# evaluate un-filtered data
predictions, results = verifier_data.evaluate(lm_scores)
# replace the original predictions, and save
for ex in verifier_data.data:
filtered_qapairs = predictions['th_best'][ex['id']]
ex['over_generate_{}_noambq_answer'.format(args.over_generate_pass)] = [(qapair['question'], qapair['answer']) for qapair in filtered_qapairs]
with open(os.path.join(args.output_dir, "{}.json".format(verifier_data.data_type)), 'w') as f:
json.dump(verifier_data.data, f)
logger.info('Saving data file to \n{}'.format(os.path.join(args.output_dir, "{}.json".format(verifier_data.data_type))))
with open(os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_prediction.json".format(verifier_data.data_type, args.over_generate_pass)), 'w') as f:
json.dump(predictions, f)
logger.info('Saving predictions to \n{}'.format(os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_prediction.json".format(verifier_data.data_type, args.over_generate_pass))))
with open(os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_result.json".format(verifier_data.data_type, args.over_generate_pass)), 'w') as f:
json.dump(results, f, indent=4)
logger.info('Saving results to \n{}'.format(os.path.join(args.output_dir, "{}_over_generate_pass_{}_lm_filtered_result.json".format(verifier_data.data_type, args.over_generate_pass))))
def train(args, logger, model, train_data, dev_data, optimizer, scheduler):
model.train()
global_step = 0
train_losses = []
train_preds = []
best_accuracy = -1
stop_training=False
wait_step = 0
logger.info("Start training!")
for epoch in range(int(args.num_train_epochs)):
for batch in train_data.dataloader:
# debug code
if global_step == 0:
logger.info('Batch size {}'.format(len(batch[0])))
global_step += 1
# total number of predictions
all_decoder_attention_mask = batch[3]
if 't5' in args.bert_name:
num_preds = torch.sum(torch.ne(all_decoder_attention_mask, 0))
elif 'bart' in args.bert_name:
num_preds = torch.sum(torch.ne(all_decoder_attention_mask[..., 1:], 0))
else:
raise NotImplementedError
# actual batchsize for grad step
batch_losses = []
actual_train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
for grad_step in range(0, len(batch[0]), actual_train_batch_size):
actual_batch = [b[grad_step: grad_step + actual_train_batch_size].to(torch.device("cuda")) for b in batch]
assert args.is_seq2seq is True
if 't5' in args.bert_name:
lm_labels = actual_batch[2]
lm_labels[lm_labels == 0] = -100
loss = model(input_ids=actual_batch[0], attention_mask=actual_batch[1],
lm_labels=lm_labels, decoder_attention_mask=actual_batch[3],
is_training=True)
elif 'bart' in args.bert_name:
if args.filter_not_found_answer_passages:
if args.task == "qg_weighted_loss":
loss = model(input_ids=actual_batch[0], attention_mask=actual_batch[1],
decoder_input_ids=actual_batch[2], decoder_attention_mask=actual_batch[3],
is_training=True, is_discard=actual_batch[4], weighted_positions=actual_batch[5],
insert_loss_weight=args.lambda_qg_loss_weight)
else:
loss = model(input_ids=actual_batch[0], attention_mask=actual_batch[1],
decoder_input_ids=actual_batch[2], decoder_attention_mask=actual_batch[3],
is_training=True, is_discard=actual_batch[4])
else:
if args.task == "qg_weighted_loss":
loss = model(input_ids=actual_batch[0], attention_mask=actual_batch[1],
decoder_input_ids=actual_batch[2], decoder_attention_mask=actual_batch[3],
is_training=True, weighted_positions=actual_batch[4],
insert_loss_weight=args.lambda_qg_loss_weight)
else:
loss = model(input_ids=actual_batch[0], attention_mask=actual_batch[1],
decoder_input_ids=actual_batch[2], decoder_attention_mask=actual_batch[3],
is_training=True)
else:
raise NotImplementedError
# if we average over all gpus, then the model will be inclined to generate shorter answers
loss = torch.sum(loss) / num_preds
if torch.isnan(loss).data:
logger.info("Stop training because loss=%s" % (loss.data))
stop_training = True
break
batch_losses.append(loss.detach().cpu().item())
loss.backward()
# TODO, check if needed
if args.average_gradient == 1:
_average_gradients(model)
train_losses.append(sum(batch_losses) * num_preds.item())
train_preds.append(num_preds.item())
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step() # We have accumulated enough gradients
scheduler.step()
model.zero_grad()
if global_step % args.eval_period == 0:
if args.skip_inference:
avg_train_loss = sum(train_losses) / sum(train_preds)
logger.info("Epoch=%d, Global-step=%d, Train-loss=%.2f" % (
epoch,
global_step,
avg_train_loss,
))
model_state_dict = {k: v.cpu() for (k, v) in model.state_dict().items()}
torch.save(model_state_dict, os.path.join(args.output_dir, "model-step{}.pt".format(global_step)))
train_losses = []
train_preds = []
wait_step = 0
stop_training = False
logger.info("=" * 20 + '\n')
model.train()
else:
model.eval()
curr_em = inference(model, dev_data, save_predictions=False, logger=logger)
if type(curr_em) == tuple:
curr_em, curr_results = curr_em
else:
curr_results = None
avg_train_loss = sum(train_losses) / sum(train_preds)
logger.info("Epoch=%d, Global-step=%d, Train-loss=%.2f, %s=%.2f%%" % (
epoch,
global_step,
avg_train_loss,
dev_data.metric,
curr_em,
))
train_losses = []
train_preds = []
if best_accuracy < curr_em:
model_state_dict = {k:v.cpu() for (k, v) in model.state_dict().items()}
torch.save(model_state_dict, os.path.join(args.output_dir, "best-model.pt"))
if curr_results:
curr_results['epoch'] = epoch
curr_results['global_step'] = global_step
curr_results['train_loss'] = avg_train_loss
with open(os.path.join(args.output_dir, "best-result.json"), 'w') as f:
json.dump(curr_results, f, indent=4)
logger.info("New best %s: %.2f%% -> %.2f%%" % (dev_data.metric, best_accuracy, curr_em,))
best_accuracy = curr_em
wait_step = 0
stop_training = False
else:
wait_step += 1
if wait_step >= args.wait_step:
stop_training = True
break
logger.info("=" * 20 + '\n')
model.train()
if stop_training:
break
def train_qa_gen(args, logger, model, train_data, dev_data, optimizer, scheduler):
model.train()
global_step = 0
train_losses = [[], [], []] # task 1,2,3
train_preds = [[], [], []] # task 1,2,3
best_accuracy = -1
stop_training=False
wait_step = 0
logger.info("Start training!")
for epoch in range(int(args.num_train_epochs)):
for batch_task_1, batch_task_2_1, batch_task_2_2, batch_task_3_1, batch_task_3_2 in \
zip(train_data.dataloader_task_1, train_data.dataloader_task_2_1, train_data.dataloader_task_2_2,
train_data.dataloader_task_3_1, train_data.dataloader_task_3_2):
global_step += 1
# total number of predictions
all_decoder_attention_mask_task_1 = batch_task_1[3]
all_decoder_attention_mask_task_2_1 = batch_task_2_1[3]
all_decoder_attention_mask_task_3_1 = batch_task_3_1[3]
all_decoder_attention_mask_task_3_2 = batch_task_3_2[3]
if 'bart' in args.bert_name:
num_preds_task_1 = torch.sum(torch.ne(all_decoder_attention_mask_task_1[..., 1:], 0))
num_preds_task_2_1 = torch.sum(torch.ne(all_decoder_attention_mask_task_2_1[..., 1:], 0))
num_preds_task_3_1 = torch.sum(torch.ne(all_decoder_attention_mask_task_3_1[..., 1:], 0))
num_preds_task_3_2 = torch.sum(torch.ne(all_decoder_attention_mask_task_3_2[..., 1:], 0))
else:
raise NotImplementedError
# actual batchsize for grad step
batch_losses = [[], [], [],] # batch 1,2,3
actual_train_batch_size_task_1 = int(args.train_batch_size_task_1 / args.task_1_gradient_accumulation_steps)
actual_train_batch_size_task_2_1 = int(args.train_batch_size_task_2_1 / args.gradient_accumulation_steps)
actual_train_batch_size_task_3_1 = int(args.train_batch_size_task_3_1 / args.gradient_accumulation_steps)
actual_train_batch_size_task_3_2 = int(args.train_batch_size_task_3_2 / args.gradient_accumulation_steps)
for task_name in ['task_1', 'task_2', 'task_3']:
if task_name == 'task_1':
for grad_step in range(args.task_1_gradient_accumulation_steps):
decoder_start_token_id = train_data.tokenizer.convert_tokens_to_ids(train_data.QBOS)
actual_batch = [b[grad_step * actual_train_batch_size_task_1: (grad_step+1) * actual_train_batch_size_task_1].to(torch.device("cuda")) for b in batch_task_1]
loss = model(input_ids=actual_batch[0], attention_mask=actual_batch[1],
decoder_input_ids=actual_batch[2], decoder_attention_mask=actual_batch[3],
is_training=True, decoder_start_token_id=decoder_start_token_id)
loss = torch.sum(loss) / num_preds_task_1
batch_losses[0].append(loss.detach().cpu().item())
if torch.isnan(loss).data:
logger.info("Stop training because loss=%s" % (loss.data))
stop_training = True
break
loss.backward()
else:
for grad_step in range(args.gradient_accumulation_steps):
if task_name == 'task_2':
decoder_start_token_id = train_data.tokenizer.convert_tokens_to_ids(train_data.QBOS)
actual_batch = [b[grad_step * actual_train_batch_size_task_2_1: (grad_step + 1) * actual_train_batch_size_task_2_1].to(torch.device("cuda")) for b in batch_task_2_1]
elif task_name == 'task_3':
decoder_start_token_id = train_data.tokenizer.convert_tokens_to_ids(train_data.ABOS)
actual_batch_task_3_1 = [b[grad_step * actual_train_batch_size_task_3_1: (grad_step + 1) * actual_train_batch_size_task_3_1].to(torch.device("cuda")) for b in batch_task_3_1]
actual_batch_task_3_2 = [b[grad_step * actual_train_batch_size_task_3_2: (grad_step + 1) * actual_train_batch_size_task_3_2].to(torch.device("cuda")) for b in batch_task_3_2]
actual_batch = [torch.cat([b1,b2], dim=0) for b1, b2 in zip(actual_batch_task_3_1, actual_batch_task_3_2)]
else:
raise NotImplementedError
loss = model(input_ids=actual_batch[0], attention_mask=actual_batch[1],
decoder_input_ids=actual_batch[2], decoder_attention_mask=actual_batch[3],
is_training=True, decoder_start_token_id=decoder_start_token_id)
# if we average over all gpus, then the model will be inclined to generate shorter answers
if task_name == 'task_2':
loss = torch.sum(loss) / num_preds_task_2_1
batch_losses[1].append(loss.detach().cpu().item())
elif task_name == 'task_3':
loss = torch.sum(loss) / (num_preds_task_3_1+num_preds_task_3_2)
batch_losses[2].append(loss.detach().cpu().item())
else:
raise NotImplementedError
if torch.isnan(loss).data:
logger.info("Stop training because loss=%s" % (loss.data))
stop_training = True
break
loss.backward()
train_losses[0].append(sum(batch_losses[0]) * num_preds_task_1.item())
train_preds[0].append(num_preds_task_1.item())
train_losses[1].append(sum(batch_losses[1]) * num_preds_task_2_1.item())
train_preds[1].append(num_preds_task_2_1.item())
train_losses[2].append(sum(batch_losses[2]) * (num_preds_task_3_1+num_preds_task_3_2).item())
train_preds[2].append((num_preds_task_3_1+num_preds_task_3_2).item())
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step() # We have accumulated enought gradients
scheduler.step()
model.zero_grad()
if global_step % args.eval_period == 0:
model.eval()
curr_f1_ans, curr_f1_ans_multi, curr_ques_f1_bleu, curr_ques_f1_edit_f1, dev_predictions = inference_qa_gen(model.module, dev_data, logger=logger)
logger.info(
"Epoch={}, Global-step={}, Loss-T1={:.2f}, Loss-T2={:.2f}, Loss-T3={:.2f}, A-All={:.2f}, A-Multi={:.2f}, Q-Bleu={:.2f}, Q-Edit={:.2f}".format(
epoch,
global_step,
sum(train_losses[0]) / sum(train_preds[0]),
sum(train_losses[1]) / sum(train_preds[1]),
sum(train_losses[2]) / sum(train_preds[2]),
curr_f1_ans * 100.0,
curr_f1_ans_multi * 100.0,
curr_ques_f1_bleu * 100.0,
curr_ques_f1_edit_f1 * 100.0,
))
train_losses = [[],[],[]]
train_preds = [[],[],[]]
if best_accuracy < curr_f1_ans+curr_ques_f1_edit_f1:
model_state_dict = {k:v.cpu() for (k, v) in model.state_dict().items()}
torch.save(model_state_dict, os.path.join(args.output_dir, "best-model.pt"))
logger.info("New best %s: %.2f%% -> %.2f%%" % ('Ans-All + Ques-Edit', best_accuracy*100.0, (curr_f1_ans+curr_ques_f1_edit_f1)*100.0,))
best_accuracy = curr_f1_ans+curr_ques_f1_edit_f1
dev_data.save_predictions(dev_predictions, mode='_{}'.format(dev_data.args.task))
wait_step = 0
stop_training = False
else:
wait_step += 1
if wait_step >= args.wait_step:
stop_training = True
break
model.train()
if stop_training:
break
def train_reranker(args, logger, model, train_data, dev_data, optimizer, scheduler):
model.train()
global_step = 0
train_losses = []
best_accuracy = -1
stop_training=False
wait_step = 0
logger.info("Start training!")
for epoch in range(int(args.num_train_epochs)):
for batch in train_data.dataloader:
global_step += 1
# actual batchsize for grad step
batch_losses = []
actual_train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
for grad_step in range(0, len(batch[0]), actual_train_batch_size):
actual_batch = [b[grad_step: grad_step + actual_train_batch_size].to(torch.device("cuda")) for b in batch]
loss = model(input_ids=actual_batch[0], attention_mask=actual_batch[1], token_type_ids=actual_batch[2],
labels=actual_batch[3], is_training=True)
if args.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if torch.isnan(loss).data:
logger.info("Stop training because loss=%s" % (loss.data))
stop_training=True
break
batch_losses.append(loss.detach().cpu().item())
loss.backward()
train_losses.append(np.mean(batch_losses))
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step() # We have accumulated enought gradients
scheduler.step()
model.zero_grad()
if global_step % args.eval_period == 0:
model.eval()
curr_em = inference(model, dev_data, save_predictions=False, logger=logger)
logger.info("Epoch=%d, Global-step=%d, Train-loss=%.2f, %s=%.2f%%" % (
epoch,
global_step,
np.mean(train_losses),
dev_data.metric,
curr_em * 100.0,
))
train_losses = []
if best_accuracy < curr_em:
model_state_dict = {k:v.cpu() for (k, v) in model.state_dict().items()}
torch.save(model_state_dict, os.path.join(args.output_dir, "best-model.pt"))
logger.info("New best %s: %.2f%% -> %.2f%%" % (dev_data.metric, best_accuracy * 100.0, curr_em * 100.0,))
best_accuracy = curr_em
wait_step = 0
stop_training = False
else:
wait_step += 1
if wait_step >= args.wait_step:
stop_training = True
break
model.train()
if stop_training:
break
def inference(model, dev_data, save_predictions=False, logger=None):
if dev_data.args.task == 'dpr':
return inference_dpr(model, dev_data, save_predictions=True)
elif dev_data.args.task == 'rrk':
return inference_reranker(model, dev_data, save_predictions=True, logger=logger)
elif dev_data.args.task in ["qa", "qg", "qg_mask", "qa_noamb_aq", "qg_rewrite", "qg_weighted_loss", "qg_noprompt", "cotraining_label", "cotraining_train"]:
if "bart" in dev_data.args.bert_name:
if dev_data.args.ambigqa_editqg:
return inference_seq2seq_editqg(model if dev_data.args.n_gpu == 1 or dev_data.args.do_predict or dev_data.args.do_e2e_predict else model.module, dev_data, save_predictions=save_predictions, logger=logger)
if dev_data.args.filter_not_found_answer_passages:
return inference_seq2seq_dynamic(model if dev_data.args.n_gpu == 1 or dev_data.args.do_predict or dev_data.args.do_e2e_predict else model.module, dev_data, save_predictions=save_predictions, logger=logger)
return inference_seq2seq(model if dev_data.args.n_gpu==1 or dev_data.args.do_predict or dev_data.args.do_e2e_predict else model.module, dev_data, save_predictions=save_predictions, logger=logger)
if "t5" in dev_data.args.bert_name:
return inference_seq2seq_t5(model if dev_data.args.n_gpu == 1 or dev_data.args.do_predict or dev_data.args.do_e2e_predict else model.module, dev_data, save_predictions=save_predictions, logger=logger)
else:
raise NotImplementedError
def inference_dpr(model, dev_data, save_predictions):
def _inference(dataloader, is_passages):
if dev_data.args.n_gpu>1:
curr_model = model.module.ctx_model if is_passages else model.module.question_model
curr_model = torch.nn.DataParallel(curr_model)
else:
curr_model = model.ctx_model if is_passages else model.question_model
vectors = []
for i, batch in tqdm(enumerate(dataloader)):
with torch.no_grad():
batch = [b.to(torch.device("cuda")) for b in batch]
outputs = curr_model(input_ids=batch[0], attention_mask=batch[1])[0][:,0,:]
vectors.append(outputs.detach().cpu().numpy())
return np.concatenate(vectors, axis=0)
checkpoint = dev_data.args.checkpoint
assert checkpoint is not None
import faiss
postfix = "_20200201" if dev_data.args.wiki_2020 else ""
index_path = checkpoint[:checkpoint.index(".")] + "{}.IndexFlatIP".format(postfix)
if os.path.exists(index_path):
index = faiss.read_index(index_path)
else:
checkpoint = dev_data.args.checkpoint
# load passage vectors
index = dev_data.args.db_index
if index==-1:
for index in range(10):
pvec_path = checkpoint[:checkpoint.index(".")] + ".psgs_w100{}_{}.npy".format(postfix, index)
assert os.path.exists(pvec_path)
if index==0:
pvec = np.load(pvec_path)
else:
pvec = np.concatenate([pvec, np.load(pvec_path)], axis=0)
else:
pvec_path = checkpoint[:checkpoint.index(".")] + ".psgs_w100{}_{}.npy".format(postfix, index)
print (pvec_path)
if os.path.exists(pvec_path):
pvec = np.load(pvec_path)
else:
dev_data.passages.load_tokenized_data("bert")
dev_data.passages.load_dataset("bert")
dataloader = dev_data.passages.load_dataloader(
dev_data.args.predict_batch_size,
is_training=False,
do_return=True)
if dev_data.args.verbose:
dataloader = tqdm(dataloader)
pvec = _inference(dataloader, is_passages=True)
np.save(pvec_path, pvec)
exit()
print (pvec.shape)
index = faiss.IndexFlatIP(pvec.shape[1])
index.add(pvec)
faiss.write_index(index, index_path)
# load question vectors
qvec = _inference(dev_data.dataloader, is_passages=False) #model.inference(dev_data.dataloader, is_passages=False)
print (qvec.shape)
D, I = index.search(qvec, dev_data.args.dpr_retrieval_topk_psgs)
assert D.shape == I.shape == (qvec.shape[0], dev_data.args.dpr_retrieval_topk_psgs)
predictions = I.tolist()
accuracy = dev_data.passages.evaluate(predictions, dev_data.get_answers())
if save_predictions:
dev_data.save_predictions(predictions, mode="_{}".format(dev_data.args.dpr_checkpoint))
return np.mean(accuracy), None
def inference_reranker(model, dev_data, save_predictions=False, logger=None):
outputs = []
if dev_data.args.verbose:
dev_data.dataloader = tqdm(dev_data.dataloader)
for i, batch in enumerate(dev_data.dataloader):
with torch.no_grad():
batch = [b.to(torch.device("cuda")) for b in batch]
batch_sel_logits = model(input_ids=batch[0], attention_mask=batch[1], token_type_ids=batch[2])
batch_sel_logits = batch_sel_logits.detach().cpu().tolist()
for sel_logit in batch_sel_logits: