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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.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__) | |
| 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"}, | |
| ) | |
| 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) | |