Spaces:
Runtime error
Runtime error
File size: 8,650 Bytes
ee21b96 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 |
# 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)
|