|
|
|
|
|
|
|
|
|
|
|
import json |
|
import logging |
|
import math |
|
from dataclasses import dataclass, field |
|
from typing import Optional |
|
|
|
import torch |
|
from fairseq import metrics |
|
from fairseq.tasks import register_task |
|
|
|
from tasks.ofa_task import OFAConfig, OFATask |
|
from data.mm_data.snli_ve_dataset import SnliVeDataset |
|
from data.file_dataset import FileDataset |
|
from data import data_utils |
|
from utils.trie import Trie |
|
|
|
logger = logging.getLogger(__name__) |
|
|
|
|
|
@dataclass |
|
class SnliVeConfig(OFAConfig): |
|
ans2label_dict: Optional[str] = field( |
|
default='{"no": 0, "yes":1, "maybe": 2}', |
|
metadata={"help": 'answer to label dict'}, |
|
) |
|
add_caption: bool = field( |
|
default=False, |
|
metadata={"help": "add caption to encoder"}, |
|
) |
|
valid_batch_size: int = field( |
|
default=20, |
|
metadata={"help": "valid batch size per step"}, |
|
) |
|
prompt_type: Optional[str] = field( |
|
default=None, |
|
metadata={"help": "prompt_type"}, |
|
) |
|
|
|
|
|
@register_task("snli_ve", dataclass=SnliVeConfig) |
|
class SnliVeTask(OFATask): |
|
def __init__(self, cfg: SnliVeConfig, 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] = SnliVeDataset( |
|
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, |
|
patch_image_size=self.cfg.patch_image_size, |
|
add_caption=self.cfg.add_caption, |
|
constraint_trie=self.constraint_trie, |
|
imagenet_default_mean_and_std=self.cfg.imagenet_default_mean_and_std, |
|
prompt_type=self.cfg.prompt_type |
|
) |
|
|
|
def build_model(self, cfg): |
|
model = super().build_model(cfg) |
|
answer_item_list = [] |
|
self.index2ans = {} |
|
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() |
|
answer_item_list.append(answer_item) |
|
self.index2ans[i] = answer |
|
self.constraint_trie.insert([self.tgt_dict.bos()] + answer_item.tolist() + [self.tgt_dict.eos()]) |
|
|
|
constraint_mask_list = [] |
|
for answer_item in answer_item_list: |
|
constraint_mask = torch.zeros((len(answer_item)+1, len(self.tgt_dict))).bool() |
|
for i in range(len(answer_item)+1): |
|
constraint_prefix_token = [self.src_dict.bos()] + answer_item[:i].tolist() |
|
constraint_nodes = self.constraint_trie.get_next_layer(constraint_prefix_token) |
|
constraint_mask[i][constraint_nodes] = True |
|
constraint_mask_list.append(constraint_mask) |
|
|
|
self.valid_answers_list = [] |
|
self.valid_constraint_masks_list = [] |
|
for i in range(0, len(answer_item_list), self.cfg.valid_batch_size): |
|
self.valid_answers_list += [answer_item_list[i:i+self.cfg.valid_batch_size]] |
|
self.valid_constraint_masks_list += [constraint_mask_list[i:i+self.cfg.valid_batch_size]] |
|
|
|
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, **extra_kwargs): |
|
loss, sample_size, logging_output = super().valid_step(sample, model, criterion) |
|
|
|
model.eval() |
|
with torch.no_grad(): |
|
encoder_out = model.encoder( |
|
sample["net_input"]["src_tokens"], |
|
src_lengths=sample["net_input"]["src_lengths"], |
|
patch_images=sample["net_input"]["patch_images"], |
|
patch_masks=sample["net_input"]["patch_masks"] |
|
) |
|
device = sample["net_input"]["src_tokens"].device |
|
eos_item = torch.tensor([self.src_dict.eos()]) |
|
pad = self.src_dict.pad() |
|
valid_result = [] |
|
for valid_answers, valid_constraint_masks in zip(self.valid_answers_list, self.valid_constraint_masks_list): |
|
valid_size = len(valid_answers) |
|
valid_tgt_items = [ |
|
torch.cat([torch.tensor(decoder_prompt[1:]), valid_answer, eos_item]) |
|
for decoder_prompt in sample["decoder_prompts"] for valid_answer in valid_answers |
|
] |
|
valid_prev_items = [ |
|
torch.cat([torch.tensor(decoder_prompt), valid_answer]) |
|
for decoder_prompt in sample["decoder_prompts"] for valid_answer in valid_answers |
|
] |
|
valid_constraint_mask_items = [ |
|
torch.cat([torch.zeros(len(decoder_prompt)-1, valid_constraint_mask.size(1)).bool(), valid_constraint_mask], dim=0) |
|
for decoder_prompt in sample["decoder_prompts"] for valid_constraint_mask in valid_constraint_masks |
|
] |
|
valid_tgt = data_utils.collate_tokens(valid_tgt_items, pad_idx=pad, left_pad=False).to(device) |
|
valid_prev_output = data_utils.collate_tokens(valid_prev_items, pad_idx=pad, left_pad=False).to(device) |
|
valid_constraint_masks = data_utils.collate_tokens(valid_constraint_mask_items, pad_idx=pad, left_pad=False).to(device) |
|
|
|
new_encoder_out = {} |
|
new_encoder_out["encoder_out"] = [ |
|
encoder_out["encoder_out"][0].repeat_interleave(valid_size, dim=1) |
|
] |
|
new_encoder_out["encoder_padding_mask"] = [ |
|
encoder_out["encoder_padding_mask"][0].repeat_interleave(valid_size, dim=0) |
|
] |
|
new_encoder_out["position_embeddings"] = [ |
|
encoder_out["position_embeddings"][0].repeat_interleave(valid_size, dim=0) |
|
] |
|
|
|
decoder_out = model.decoder(valid_prev_output, encoder_out=new_encoder_out) |
|
decoder_out[0].masked_fill_(~valid_constraint_masks, -math.inf) |
|
lprobs = model.get_normalized_probs(decoder_out, log_probs=True) |
|
scores = lprobs.gather(dim=-1, index=valid_tgt.unsqueeze(-1)).squeeze(-1) |
|
scores = scores.masked_fill(valid_tgt.eq(self.tgt_dict.pad()), 0) |
|
scores = scores.masked_fill((~valid_constraint_masks).all(2), 0) |
|
scores = scores.sum(1) |
|
scores = scores.view(-1, valid_size) |
|
valid_result.append(scores) |
|
|
|
valid_result = torch.cat(valid_result, dim=-1) |
|
predicts = valid_result.argmax(1).tolist() |
|
hyps = [self.index2ans[predict_index] for predict_index in predicts] |
|
scores = [ref_dict.get(hyp, 0) for ref_dict, hyp in zip(sample['ref_dict'], hyps)] |
|
logging_output["_snli_score_sum"] = sum(scores) |
|
logging_output["_snli_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_score(meters): |
|
score = meters["_snli_score_sum"].sum / meters["_snli_cnt"].sum |
|
score = score if isinstance(score, float) else score.item() |
|
return round(score, 4) |
|
|
|
if sum_logs("_snli_cnt") > 0: |
|
metrics.log_scalar("_snli_score_sum", sum_logs("_snli_score_sum")) |
|
metrics.log_scalar("_snli_cnt", sum_logs("_snli_cnt")) |
|
metrics.log_derived("snli_score", compute_score) |
|
|
