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train_union.py
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train_union.py
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"""
Usage:
CUDA_VISIBLE_DEVICES=1,2,3 python -m torch.distributed.launch --nproc_per_node=3 train_union.py \
--corpus_path data/corpus/hotpot-paragraph-4.min.tsv \
--train_file data/hotpot-step-train.jsonl \
--predict_file data/hotpot-step-dev.jsonl \
--do_train \
--do_predict \
--encoder_name google/electra-base-discriminator \
--init_checkpoint "" \
--hard_negs_per_state 2 \
--memory_size 2 \
--max_distractors 2 \
--num_workers 1 \
--cmd_dropout_prob 0 \
--sp_weight 0.05 \
--per_gpu_train_batch_size 8 \
--learning_rate 5e-5 \
--warmup_ratio 0.1 \
--num_train_epochs 100 \
--eval_period -1 \
--criterion_metric action_acc \
--tag no-early-answer \
--debug
gpu216 gpu83 gpu79
export TOKENIZERS_PARALLELISM=true
CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 train_union.py \
--corpus_path data/corpus/hotpot-paragraph-5.strict.refined.tsv \
--train_file data/hotpot-step-train.strict.refined.jsonl \
--predict_file data/hotpot-step-dev.strict.refined.jsonl \
--do_train \
--do_predict \
--encoder_name google/electra-base-discriminator \
--init_checkpoint "" \
--hard_negs_per_state 2 \
--memory_size 2 \
--max_distractors 2 \
--strict \
--num_workers 0 \
--cmd_dropout_prob 0.5 \
--sp_weight 0.5 \
--per_gpu_train_batch_size 8 \
--per_gpu_infer_batch_size 16 \
--learning_rate 2e-5 \
--warmup_ratio 0.1 \
--num_train_epochs 30 \
--eval_period 1000 \
--criterion_metric cmd_acc \
--tag rand-state-per-quest \
--comment td5-exp1-ilma.4-cmd15
gpu21
CUDA_VISIBLE_DEVICES=1,2 python -m torch.distributed.launch --nproc_per_node=2 --master_port=13579 train_union.py \
--corpus_path data/corpus/hotpot-paragraph-5.strict.refined.tsv \
--train_file data/hotpot-step-train.strict.refined.jsonl \
--predict_file data/hotpot-step-dev.strict.refined.jsonl \
--do_train \
--do_predict \
--encoder_name google/electra-large-discriminator \
--init_checkpoint "" \
--hard_negs_per_state 2 \
--memory_size 2 \
--max_distractors 2 \
--strict \
--num_workers 0 \
--cmd_dropout_prob 0.5 \
--sp_weight 0.5 \
--gradient_accumulation_steps 2\
--per_gpu_train_batch_size 8 \
--per_gpu_infer_batch_size 16 \
--learning_rate 2e-5 \
--warmup_ratio 0.1 \
--num_train_epochs 30 \
--eval_period 1000 \
--criterion_metric cmd_acc \
--tag td5-exp1-ila.4 \
--comment pld-sb0-x1-f+a
CUDA_VISIBLE_DEVICES=3,2,0 python -m torch.distributed.launch --nproc_per_node=3 --master_port=24680 train_union.py \
--corpus_path data/corpus/hotpot-paragraph-5.strict.refined.tsv \
--train_file data/hotpot-step-train.strict.refined.jsonl \
--predict_file data/hotpot-step-dev.strict.refined.jsonl \
--do_train \
--do_predict \
--encoder_name google/electra-large-discriminator \
--init_checkpoint "" \
--hard_negs_per_state 2 \
--memory_size 2 \
--max_distractors 2 \
--strict \
--num_workers 0 \
--cmd_dropout_prob 0.5 \
--sp_weight 0.5 \
--gradient_accumulation_steps 2\
--per_gpu_train_batch_size 8 \
--per_gpu_infer_batch_size 16 \
--learning_rate 1e-5 \
--warmup_ratio 0.1 \
--num_train_epochs 30 \
--eval_period 1000 \
--criterion_metric cmd_acc \
--tag td5-exp1-ila.4-woin \
--comment pld-sb0-wo*-cmd10
CUDA_VISIBLE_DEVICES=0 python train_union.py \
--corpus_path data/corpus/hotpot-paragraph-5.strict.refined.tsv \
--train_file data/hotpot-step-train.strict.refined.jsonl \
--predict_file data/hotpot-step-dev.strict.refined.jsonl \
--do_train \
--do_predict \
--encoder_name google/electra-base-discriminator \
--init_checkpoint "" \
--hard_negs_per_state 2 \
--memory_size 2 \
--max_distractors 2 \
--strict \
--num_workers 0 \
--cmd_dropout_prob 0.5 \
--sp_weight 0.5 \
--gradient_accumulation_steps 1\
--per_gpu_train_batch_size 32 \
--per_gpu_infer_batch_size 32 \
--learning_rate 2e-5 \
--warmup_ratio 0.1 \
--num_train_epochs 30 \
--eval_period 1000 \
--criterion_metric cmd_acc \
--tag td5-exp1-ila.4 \
--comment pld-sb0-x1
export CKPT_PATH=ckpts/alpha_electra-base-discriminator_DP0.0_HN2_M2_D2_adamW_SP0.05_B24_LR5.0e-05-WU0.1-E100_S42_01150136/checkpoint_best.pt
CUDA_VISIBLE_DEVICES=1 python train_union.py \
--corpus_path data/corpus/hotpot-paragraph-4.min.tsv \
--train_file data/hotpot-step-train.jsonl \
--predict_file data/hotpot-step-dev.jsonl \
--do_predict \
--encoder_name google/electra-base-discriminator \
--init_checkpoint $CKPT_PATH \
--hard_negs_per_state 2 \
--memory_size 2 \
--max_distractors 2 \
--num_workers 1 \
--cmd_dropout_prob 0 \
--sp_weight 0.05 \
--per_gpu_train_batch_size 8 \
--learning_rate 5e-5 \
--warmup_ratio 0.1 \
--num_train_epochs 2 \
--eval_period 100 \
--criterion_metric action_acc \
--tag alpha
"""
from datetime import datetime
from functools import partial
import json
import logging
import os
from tqdm import tqdm, trange
# from tqdm.auto import tqdm, trange
import numpy as np
import torch
import torch.multiprocessing
from torch.optim import Adam
from torch.utils.data import DataLoader, RandomSampler, Subset
from torch.utils.data.distributed import DistributedSampler
try:
from torch.utils.tensorboard import SummaryWriter
except ImportError:
from tensorboardX import SummaryWriter
from transformers import AutoTokenizer, AdamW, get_linear_schedule_with_warmup # AutoConfig,
from config import train_args, ADDITIONAL_SPECIAL_TOKENS, FUNCTIONS, NA_POS
from models.union_model import UnionModel
from transition_data import collate_transitions, TransitionDataset, ConstantDataset
from utils.data_utils import load_corpus
from utils.model_utils import load_state, save_model
from utils.tensor_utils import to_cuda
from utils.text_utils import finetune_start
from utils.utils import set_seed, flatten_dict, collection_f1, harmonic_mean
from hotpot_evaluate_plus import exact_match_score, f1_score
logger = logging.getLogger(__name__)
torch.multiprocessing.set_sharing_strategy('file_system')
# if logger.hasHandlers():
# logger.handlers.clear()
l2a_from = 0
save_steps = (54, 59, 60, 66, 68, 72, 74, 79)
save_epochs = (22, 23, 24, 25, 26, 27, 28, 29)
# noinspection PyUnboundLocalVariable
def main():
args = train_args()
if args.fp16:
import apex
apex.amp.register_half_function(torch, 'einsum')
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = 0 if args.no_cuda else torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of synchronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
n_gpu = 1
args.train_batch_size = args.per_gpu_train_batch_size * max(1, n_gpu)
args.infer_batch_size = args.per_gpu_infer_batch_size * max(1, n_gpu)
if args.strict:
if 'strict' not in args.corpus_path:
args.corpus_path = f"{args.corpus_path[:-4]}-strict.tsv"
if 'strict' not in args.train_file:
args.train_file = f"{args.train_file[:-6]}-strict.jsonl"
if 'strict' not in args.predict_file:
args.predict_file = f"{args.predict_file[:-6]}-strict.jsonl"
# config logger and experiment dir
formatter = logging.Formatter('[%(asctime)s %(levelname)s %(name)s:%(lineno)s] %(message)s',
datefmt='%m/%d %H:%M:%S')
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(formatter)
stream_handler.setLevel(logging.INFO)
if args.do_train:
tt_batch_size = (args.train_batch_size *
args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.local_rank != -1 else 1))
hyper_params = {
"spw": args.sp_weight,
"hn": args.hard_negs_per_state,
"m": args.memory_size,
"d": args.max_distractors
}
exp_dir = (f"{args.encoder_name.split('/')[-1]}_DP{args.cmd_dropout_prob}_"
f"HN{args.hard_negs_per_state}_M{args.memory_size}_D{args.max_distractors}_"
f"{'adam' if args.use_adam else 'adamW'}_SP{args.sp_weight}_"
f"B{tt_batch_size}_LR{args.learning_rate:.1e}_WU{args.warmup_ratio}_E{args.num_train_epochs}_"
f"{f'fp16{args.fp16_opt_level}_' if args.fp16 else ''}"
f"S{args.seed}_{datetime.now().strftime('%m%d%H%M')}")
if args.tag:
exp_dir = f"{args.tag}_{exp_dir}"
if args.comment:
exp_dir = f"{exp_dir}_{args.comment}"
output_dir = os.path.join(args.output_dir, exp_dir)
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the process 0 mkdir and write args
else:
if os.path.exists(output_dir) and os.listdir(output_dir):
print(f"output directory {output_dir} already exists and is not empty.")
if not os.path.exists(output_dir):
os.makedirs(output_dir, exist_ok=True)
with open(os.path.join(output_dir, 'args.json'), 'w') as f:
json.dump(args.__dict__, f, indent=2)
if args.local_rank == 0:
torch.distributed.barrier() # Make sure only the process 0 mkdir and write args
file_handler = logging.FileHandler(os.path.join(output_dir, 'log.txt'))
file_handler.setFormatter(formatter)
file_handler.setLevel(logging.DEBUG)
# noinspection PyArgumentList
logging.basicConfig(
format='[%(asctime)s %(levelname)s %(name)s] %(message)s', datefmt='%m/%d %H:%M:%S',
level=logging.DEBUG if args.local_rank in [-1, 0] else logging.WARNING,
handlers=[file_handler, stream_handler]
)
else:
# noinspection PyArgumentList
logging.basicConfig(
format='[%(asctime)s %(levelname)s %(name)s] %(message)s', datefmt='%m/%d %H:%M:%S',
level=logging.DEBUG if args.local_rank in [-1, 0] else logging.WARNING,
handlers=[stream_handler]
)
# logger.setLevel(logging.DEBUG if args.local_rank in [-1, 0] else logging.WARNING)
logger.warning(f"Process rank: {args.local_rank}, device: {device}, n_gpu: {n_gpu}")
logger.info(args)
set_seed(args.seed)
# if args.local_rank not in [-1, 0]:
# torch.distributed.barrier() # Make sure only the process 0 will download model & vocab
tokenizer = AutoTokenizer.from_pretrained(args.encoder_name, use_fast=True,
additional_special_tokens=list(ADDITIONAL_SPECIAL_TOKENS.values()))
model = UnionModel(args.encoder_name, args.max_ans_len, args.sp_weight)
# if args.local_rank == 0:
# torch.distributed.barrier() # Make sure only the process 0 will download model & vocab
if args.init_checkpoint:
logger.info(f"Loading model from {args.init_checkpoint}...")
model = load_state(model, args.init_checkpoint)
model.to(device)
logger.info(f"number of trainable parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad):,d}")
logger.info(f"Loading corpus from {args.corpus_path}...")
corpus, title2id = load_corpus(args.corpus_path, for_hotpot=True, require_hyperlinks=True)
collate_func = partial(collate_transitions, pad_id=tokenizer.pad_token_id)
if args.local_rank in [-1, 0] or args.debug:
eval_dataset = TransitionDataset(args.predict_file, tokenizer, corpus, title2id,
args.max_seq_len, args.max_q_len, args.max_obs_len,
args.hard_negs_per_state, args.memory_size, args.max_distractors, args.strict)
eval_dataset_imgs = [ConstantDataset([eval_dataset[i] for i in range(len(eval_dataset))]) for _ in range(2)]
if args.debug:
eval_dataset_imgs = [Subset(data_image, indices=list(range(1024))) for data_image in eval_dataset_imgs]
eval_dataloaders = [
DataLoader(data_image, batch_size=args.infer_batch_size, collate_fn=collate_func,
pin_memory=True, num_workers=args.num_workers) for data_image in eval_dataset_imgs
]
if args.do_train:
no_decay = ['bias', 'LayerNorm.weight']
optimizer_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}
]
if args.use_adam:
optimizer = Adam(optimizer_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
else:
optimizer = AdamW(optimizer_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
if args.fp16:
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
else:
if args.fp16:
from apex import amp
model = amp.initialize(model, opt_level=args.fp16_opt_level)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.do_train:
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter(os.path.join('runs/', exp_dir))
train_dataset = TransitionDataset(args.train_file, tokenizer, corpus, title2id,
args.max_seq_len, args.max_q_len, args.max_obs_len,
args.hard_negs_per_state, args.memory_size, args.max_distractors, args.strict)
if args.debug:
train_dataset = eval_dataset_imgs[0]
train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset, sampler=train_sampler, batch_size=args.train_batch_size,
collate_fn=collate_func, pin_memory=True, num_workers=args.num_workers)
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
warmup_steps = t_total * args.warmup_ratio
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total
)
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {args.num_train_epochs}")
logger.info(f" Instantaneous batch size per GPU = {args.per_gpu_train_batch_size}")
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {tt_batch_size}")
logger.info(f" Total optimization steps = {t_total}")
global_step = 0 # gradient update step
batch_step = 0 # forward batch count
best_step, best_criterion, best_metrics = 0, 0, dict()
best_pr, best_qa, best_joint = 0, 0, 0
losses, last_losses = None, None
model.train()
epoch_iterator = trange(int(args.num_train_epochs), desc="Epoch", disable=args.local_rank not in [-1, 0])
for epoch in epoch_iterator:
batch_iterator = tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])
for batch in batch_iterator:
batch_step += 1
nn_input = batch['nn_input'] if args.no_cuda else to_cuda(batch['nn_input'])
if epoch < l2a_from:
nn_input['action_label'].fill_(-1)
_losses = model(nn_input)
if n_gpu > 1:
for k in _losses.keys(): # mean() to average on multi-gpu parallel (not distributed) training
_losses[k] = _losses[k].mean()
if args.gradient_accumulation_steps > 1:
for k in _losses.keys():
_losses[k] = _losses[k] / args.gradient_accumulation_steps
# criterion_loss = criterion_loss / args.gradient_accumulation_steps
criterion_loss = _losses['all']
if args.fp16:
with amp.scale_loss(criterion_loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
criterion_loss.backward()
if losses is None:
losses = {k: v.item() for k, v in _losses.items()}
last_losses = {k: 0.0 for k, v in _losses.items()}
else:
for k in losses.keys():
losses[k] += _losses[k].item()
if (batch_step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
scheduler.step()
model.zero_grad()
global_step += 1
# log losses
if args.local_rank in [-1, 0] and args.log_period > 0 and global_step % args.log_period == 0:
try:
tb_writer.add_scalar('lr', scheduler.get_last_lr()[0], global_step)
except:
tb_writer.add_scalar('lr', scheduler.get_lr()[0], global_step)
for k in losses:
tb_writer.add_scalar(f'loss/{k}', (losses[k] - last_losses[k]) / args.log_period,
global_step)
last_losses[k] = losses[k]
# last_losses = losses.copy()
# log metrics
if (args.local_rank in [-1, 0] and epoch >= 5 and
args.eval_period != -1 and global_step % args.eval_period == 0):
metrics = evaluates(args, model, tokenizer, eval_dataloaders, eval_dataset.examples)
criterion = metrics[args.criterion_metric]
logger.info(f"epoch:{epoch:2d} | step {global_step:5d} | "
f"loss:{losses['all'] / global_step:2.4f} | "
f"{args.criterion_metric} {criterion:2.2f}")
for k, v in metrics.items():
tb_writer.add_scalar(f"metric/{k}", v, global_step)
if best_criterion < criterion:
logger.info(f"Saving model with best {args.criterion_metric} "
f"{best_criterion:.2f} -> {criterion:.2f}")
save_model(model, os.path.join(output_dir, "checkpoint_best.pt"))
best_criterion = criterion
best_metrics = metrics
best_step = global_step
if best_pr < metrics['para_f1']:
logger.info(f"Saving model with best para_f1 {best_pr:.2f} -> {metrics['para_f1']:.2f}")
save_model(model, os.path.join(output_dir, "checkpoint_pr.pt"))
best_pr = metrics['para_f1']
if best_qa < metrics['answer_f1']:
logger.info(f"Saving model with best answer_f1 {best_qa:.2f} "
f"-> {metrics['answer_f1']:.2f}")
save_model(model, os.path.join(output_dir, "checkpoint_qa.pt"))
best_qa = metrics['answer_f1']
if best_joint < metrics['joint_acc']:
logger.info(f"Saving model with best joint_acc {best_joint:.2f} "
f"-> {metrics['joint_acc']:.2f}")
save_model(model, os.path.join(output_dir, "checkpoint_joint.pt"))
best_joint = metrics['joint_acc']
if global_step / 1000 in save_steps:
save_model(model, os.path.join(output_dir, f"checkpoint_{global_step}.pt"))
batch_iterator.set_description('Iteration(loss=%2.4f)' %
(criterion_loss.item() * args.gradient_accumulation_steps))
batch_iterator.close()
if args.local_rank in [-1, 0]:
save_model(model, os.path.join(output_dir, "checkpoint_last.pt"))
metrics = evaluates(args, model, tokenizer, eval_dataloaders, eval_dataset.examples)
criterion = metrics[args.criterion_metric]
logger.info(f"end of epoch {epoch:2d} | loss {losses['all'] / global_step:2.4f} | "
f"{args.criterion_metric} {criterion:2.2f}")
for k, v in metrics.items():
tb_writer.add_scalar(f"metric/{k}", v, global_step)
if best_criterion < criterion:
logger.info(f"Saving model with best {args.criterion_metric} "
f"{best_criterion:.2f} -> {criterion:.2f}")
save_model(model, os.path.join(output_dir, "checkpoint_best.pt"))
best_criterion = criterion
best_metrics = metrics
best_step = global_step
if best_pr < metrics['para_f1']:
logger.info(f"Saving model with best para_f1 {best_pr:.2f} -> {metrics['para_f1']:.2f}")
save_model(model, os.path.join(output_dir, "checkpoint_pr.pt"))
best_pr = metrics['para_f1']
if best_qa < metrics['answer_f1']:
logger.info(f"Saving model with best answer_f1 {best_qa:.2f} -> {metrics['answer_f1']:.2f}")
save_model(model, os.path.join(output_dir, "checkpoint_qa.pt"))
best_qa = metrics['answer_f1']
if best_joint < metrics['joint_acc']:
logger.info(f"Saving model with best joint_acc {best_joint:.2f} "
f"-> {metrics['joint_acc']:.2f}")
save_model(model, os.path.join(output_dir, "checkpoint_joint.pt"))
best_joint = metrics['joint_acc']
if epoch in save_epochs:
save_model(model, os.path.join(output_dir, f"checkpoint_{epoch}.pt"))
epoch_iterator.close()
if args.local_rank in [-1, 0]:
tb_writer.add_hparams(hyper_params,
{f"M/{k}": v for k, v in flatten_dict(best_metrics, sep='/').items()})
tb_writer.close()
logger.info(f"Achieve {best_criterion:.2f} {args.criterion_metric} at step {best_step}")
if best_step / 1000 in save_steps:
os.remove(os.path.join(output_dir, f"checkpoint_{best_step}.pt"))
elif args.do_predict:
metrics = evaluates(args, model, tokenizer, eval_dataloaders, eval_dataset.examples)
logger.info(f"test performance {metrics}")
elif args.do_test:
pass
def evaluates(args, model, tokenizer, eval_dataloaders, qas_examples):
assert len(eval_dataloaders) > 0
all_metrics = [evaluate(args, model, tokenizer, eval_dataloaders[i], qas_examples)
for i in range(len(eval_dataloaders))]
if len(all_metrics) == 1:
return all_metrics[0]
avg_metrics = {}
for k in all_metrics[0]:
avg_metrics[k] = np.mean([all_metrics[i][k] for i in range(len(all_metrics))])
return avg_metrics
def evaluate(args, model, tokenizer, eval_dataloader, qas_examples):
obs_accuracies = []
para_accuracies = []
para_f1_scores = []
sent_accuracies = []
sent_f1_scores = []
action_accuracies = []
cmd_accuracies = []
joint_accuracies = []
link_accuracies = {"all": [], "should": [], "practical": [], "act": []}
answer_ems = {"all": [], "should": [], "practical": [], "act": []}
answer_f1s = {"all": [], "should": [], "practical": [], "act": []}
model.eval()
for batch in tqdm(eval_dataloader):
nn_input = batch['nn_input'] if args.no_cuda else to_cuda(batch['nn_input'])
with torch.no_grad():
outputs = model(nn_input)
# (B, _P) (B, _S) (B,) (B,)
para_logits, sent_logits, para_threshold, sent_threshold = outputs[4:8]
# (B,) (B,) (B,) (B,) (B,)
pred_action, pred_start, pred_end, pred_link, pred_exp = outputs[8:13]
para_preds = [(_para_logits > _para_threshold).float() # (B, _P)
for _para_logits, _para_threshold in zip(para_logits, para_threshold)]
sent_preds = [(_sent_logits > _sent_threshold).float() # (B, _S)
for _sent_logits, _sent_threshold in zip(sent_logits, sent_threshold)]
obs_accuracies.extend(
[float(_para_preds[0] == _paras_label[0])
for _para_preds, _paras_label in zip(para_preds, nn_input['paras_label']) if len(_para_preds) > 0]
)
para_accuracies.extend(
(torch.cat(para_preds) == torch.cat(nn_input['paras_label'])).tolist()
)
para_f1_scores.extend(
[collection_f1(_para_preds.nonzero().squeeze_(1).tolist(), _paras_label.nonzero().squeeze_(1).tolist())
for _para_preds, _paras_label in zip(para_preds, nn_input['paras_label']) if len(_para_preds) > 0]
)
sent_accuracies.extend(
(torch.cat(sent_preds) == torch.cat(nn_input['sents_label'])).tolist()
)
sent_f1_scores.extend(
[collection_f1(_sent_preds.nonzero().squeeze_(1).tolist(), _sents_label.nonzero().squeeze_(1).tolist())
for _sent_preds, _sents_label in zip(sent_preds, nn_input['sents_label']) if len(_sent_preds) > 0]
)
action_accuracies.extend((pred_action == nn_input['action_label']).float().tolist())
# link_accuracies['all'].extend((pred_link == nn_input['link_label']).float().tolist())
pred_start = pred_start.tolist()
pred_end = pred_end.tolist()
for i, q_id in enumerate(batch['q_id']):
qas_example = qas_examples[q_id]
# predict answer
context_token_offset = nn_input['context_token_offset'][i].item()
start_token = pred_start[i] - context_token_offset
end_token = pred_end[i] - context_token_offset
token_ids = nn_input['input_ids'][i].tolist()
context_token_ids = token_ids[context_token_offset:]
context_token_spans = batch['context_token_spans'][i]
if start_token < 0:
if pred_start[i] != pred_end[i] or pred_start[i] not in [1, 2, NA_POS]:
logger.warning(f"predicted unexpected ans_span [{pred_start[i]}, {pred_end[i]}], "
f"gold: [{nn_input['answer_starts'][i][0]}, {nn_input['answer_ends'][i][0]}]")
if pred_start[i] == 1:
pred_ans = 'yes'
elif pred_start[i] == 2:
pred_ans = 'no'
else:
pred_ans = 'noanswer'
else:
start_token_ = finetune_start(start_token, context_token_ids, tokenizer)
star_char = context_token_spans[start_token_][0]
end_char = context_token_spans[end_token][1]
pred_ans = batch['context'][i][star_char:end_char + 1].strip()
if start_token_ != start_token:
logger.debug(f"finetune predicted answer "
f"『{batch['context'][i][context_token_spans[start_token][0]:end_char + 1]}』->"
f"『{pred_ans}』")
if nn_input['answer_starts'][i][0] in [-1, NA_POS]:
gold_answers = ['noanswer']
else:
gold_answers = qas_example.get('answers', [qas_example['answer']])
answer_ems['all'].append(
max(float(exact_match_score(pred_ans, gold_ans)) for gold_ans in gold_answers)
)
answer_f1s['all'].append(
max(float(f1_score(pred_ans, gold_ans)[0]) for gold_ans in gold_answers)
)
link_label = nn_input['link_label'][i].item() if nn_input['link_label'][i].item() != -1 else 0
link_accuracies['all'].append((pred_link[i] == link_label).float().item())
gold_action = FUNCTIONS[nn_input['action_label'][i].item()]
if gold_action == 'ANSWER':
ans_em = answer_ems['all'][-1]
ans_f1 = answer_f1s['all'][-1]
answer_ems['should'].append(ans_em)
answer_f1s['should'].append(ans_f1)
answer_ems['act'].append(ans_em * (pred_action[i] == nn_input['action_label'][i]).float().item())
answer_f1s['act'].append(ans_f1 * (pred_action[i] == nn_input['action_label'][i]).float().item())
cmd_accuracies.append(answer_f1s['act'][-1])
elif gold_action == 'LINK':
link_acc = link_accuracies['all'][-1]
link_accuracies['should'].append(link_acc)
link_accuracies['act'].append(link_acc * (pred_action[i] == nn_input['action_label'][i]).float().item())
cmd_accuracies.append(link_accuracies['act'][-1])
else:
cmd_accuracies.append((pred_action[i] == nn_input['action_label'][i]).float().item())
joint_accuracies.append(
cmd_accuracies[-1] * collection_f1(para_preds[i].nonzero().squeeze_(1).tolist(),
nn_input['paras_label'][i].nonzero().squeeze_(1).tolist())
)
if FUNCTIONS[pred_action[i].item()] == 'ANSWER':
answer_ems['practical'].append(answer_ems['all'][-1])
answer_f1s['practical'].append(answer_f1s['all'][-1])
elif FUNCTIONS[pred_action[i].item()] == 'LINK':
link_accuracies['practical'].append(link_accuracies['all'][-1])
model.train()
assert len(joint_accuracies) == len(cmd_accuracies) == len(action_accuracies)
metrics = {
"obs_acc": np.mean(obs_accuracies) * 100.,
"para_acc": np.mean(para_accuracies) * 100.,
"para_f1": np.mean(para_f1_scores) * 100.,
"sent_acc": np.mean(sent_accuracies) * 100.,
"sent_f1": np.mean(sent_f1_scores) * 100.,
"action_acc": np.mean(action_accuracies) * 100.,
"cmd_acc": np.mean(cmd_accuracies) * 100.,
"joint_acc": np.mean(joint_accuracies) * 100.
}
for k, v in link_accuracies.items():
metrics[f"link_acc_{k}"] = np.mean(v) * 100. if len(v) > 0 else 0.
metrics["link_acc"] = harmonic_mean(metrics["link_acc_practical"], metrics[f"link_acc_act"])
for k, v in answer_ems.items():
metrics[f"answer_em_{k}"] = np.mean(v) * 100. if len(v) > 0 else 0.
metrics["answer_em"] = harmonic_mean(metrics["answer_em_practical"], metrics[f"answer_em_act"])
for k, v in answer_f1s.items():
metrics[f"answer_f1_{k}"] = np.mean(v) * 100. if len(v) > 0 else 0.
metrics["answer_f1"] = harmonic_mean(metrics["answer_f1_practical"], metrics[f"answer_f1_act"])
return metrics
if __name__ == "__main__":
main()