# 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.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)