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# Copyright 2022 The OFA-Sys Team.
# All rights reserved.
# This source code is licensed under the Apache 2.0 license
# found in the LICENSE file in the root directory.
import json
import logging
import math
from dataclasses import dataclass, field
from typing import Optional
import torch
from fairseq import metrics
from fairseq.dataclass import ChoiceEnum
from fairseq.tasks import register_task
from tasks.ofa_task import OFATask, OFAConfig
from data.nlu_data.mnli_dataset import MNLIDataset
from data.file_dataset import FileDataset
from utils.trie import Trie
logger = logging.getLogger(__name__)
@dataclass
class MNLIConfig(OFAConfig):
ans2label_dict: Optional[str] = field(
default='{"maybe": 0, "yes": 1, "no": 2}',
metadata={"help": 'answer to label dict'},
)
prompt_type: ChoiceEnum(["none", "src", "prev_output"]) = field(
default="none",
metadata={"help": "decoder prompt"},
)
@register_task("mnli", dataclass=MNLIConfig)
class MNLITask(OFATask):
def __init__(self, cfg: MNLIConfig, src_dict, tgt_dict):
super().__init__(cfg, src_dict, tgt_dict)
self.ans2label_dict = json.loads(self.cfg.ans2label_dict)
def load_dataset(self, split, epoch=1, combine=False, **kwargs):
paths = self.cfg.data.split(',')
assert len(paths) > 0
if split == 'train':
file_path = paths[(epoch - 1) % (len(paths) - 1)]
else:
file_path = paths[-1]
dataset = FileDataset(file_path, self.cfg.selected_cols)
self.datasets[split] = MNLIDataset(
split,
dataset,
self.bpe,
self.src_dict,
self.tgt_dict,
max_src_length=self.cfg.max_src_length,
max_tgt_length=self.cfg.max_tgt_length,
constraint_trie=self.constraint_trie,
prompt_type=self.cfg.prompt_type
)
def build_model(self, cfg):
model = super().build_model(cfg)
self.constraint_trie = Trie(self.tgt_dict.eos())
for i, answer in enumerate(self.ans2label_dict.keys()):
answer_item = self.tgt_dict.encode_line(
line=self.bpe.encode(' ' + answer),
add_if_not_exist=False,
append_eos=False
).long()
self.constraint_trie.insert([self.tgt_dict.bos()] + answer_item.tolist() + [self.tgt_dict.eos()])
return model
def build_generator(
self, models, args, seq_gen_cls=None, extra_gen_cls_kwargs=None, prefix_allowed_tokens_fn=None,
):
seq_generator = super().build_generator(models, args, seq_gen_cls, extra_gen_cls_kwargs, prefix_allowed_tokens_fn)
seq_generator.constraint_trie = self.constraint_trie
return seq_generator
def valid_step(self, sample, model, criterion):
loss, sample_size, logging_output = super().valid_step(sample, model, criterion)
model.eval()
with torch.no_grad():
net_output = model(**sample["net_input"])
net_output[0].masked_fill_(~sample["constraint_masks"], -math.inf)
last_token_ids = sample["net_input"]["prev_output_tokens"].ne(self.src_dict.pad()).sum(1, keepdim=True) - 1
logits = net_output[0].gather(1, last_token_ids.unsqueeze(2).expand(-1, -1, net_output[0].size(2)))
logits = logits.squeeze(1)
predicts = logits.argmax(1).tolist()
hyps = [self.bpe.decode(self.src_dict[predict]).strip() for predict in predicts]
scores = [ref_dict.get(hyp, 0) for ref_dict, hyp in zip(sample['ref_dict'], hyps)]
logging_output["_score_sum"] = sum(scores)
logging_output["_score_cnt"] = len(scores)
return loss, sample_size, logging_output
def reduce_metrics(self, logging_outputs, criterion):
super().reduce_metrics(logging_outputs, criterion)
def sum_logs(key):
import torch
result = sum(log.get(key, 0) for log in logging_outputs)
if torch.is_tensor(result):
result = result.cpu()
return result
def compute_acc(meters):
score = meters["_score_sum"].sum / meters["_score_cnt"].sum
score = score if isinstance(score, float) else score.item()
return round(score, 4)
if sum_logs("_score_cnt") > 0:
metrics.log_scalar("_score_sum", sum_logs("_score_sum"))
metrics.log_scalar("_score_cnt", sum_logs("_score_cnt"))
metrics.log_derived("acc", compute_acc)
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