File size: 5,065 Bytes
e02c258 c27af7d e02c258 c27af7d e02c258 c27af7d e02c258 c27af7d e02c258 c27af7d e02c258 c27af7d e02c258 |
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 |
from typing import Dict, List, Any
from pathlib import Path
import torch
from transformers import (
BartConfig,
BartForConditionalGeneration,
PreTrainedTokenizerFast,
)
class EndpointHandler():
def __init__(self, path=""):
# Load model from HuggingFace Hub
self.model_path = path + "/" + "kobartbasekosummary.pt"
config = BartConfig.from_pretrained("hyunwoongko/kobart")
self.model = BartForConditionalGeneration(config).eval().to('cpu')
self.model.model.load_state_dict(torch.load(
self.model_path,
map_location='cpu',
))
self.tokenizer = PreTrainedTokenizerFast.from_pretrained("hyunwoongko/kobart")
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
# destruct model and tokenizer
model = self.model
tokenizer = self.tokenizer
#parmeters
beam = 5
sampling = False
temperature = 1.0
sampling_topk = -1
sampling_topp = -1
length_penalty = 1.0
max_len_a = 1
max_len_b = 50
no_repeat_ngram_size = 4
return_tokens = False
bad_words_ids = None
dataPop = data.pop("inputs", data)
if isinstance(dataPop, str):
texts = [dataPop]
else:
texts = dataPop
tokenized = self.tokenize(tokenizer, texts)
input_ids = tokenized["input_ids"]
attention_mask = tokenized["attention_mask"]
generated = model.generate(
input_ids.to('cpu'),
attention_mask=attention_mask.to('cpu'),
use_cache=True,
early_stopping=False,
decoder_start_token_id=tokenizer.bos_token_id,
num_beams=beam,
do_sample=sampling,
temperature=temperature,
top_k=sampling_topk if sampling_topk > 0 else None,
top_p=sampling_topp if sampling_topk > 0 else None,
no_repeat_ngram_size=no_repeat_ngram_size,
bad_words_ids=[[tokenizer.convert_tokens_to_ids("<unk>")]]
if not bad_words_ids else bad_words_ids +
[[tokenizer.convert_tokens_to_ids("<unk>")]],
length_penalty=length_penalty,
max_length=max_len_a * len(input_ids[0]) + max_len_b,
)
summ_result = ''
if return_tokens:
output = [
tokenizer.convert_ids_to_tokens(_)
for _ in generated.tolist()
]
summ_result = (output[0] if isinstance(
dataPop,
str,
) else output)
else:
output = tokenizer.batch_decode(
generated.tolist(),
skip_special_tokens=True,
)
summ_result = (output[0].strip() if isinstance(
dataPop,
str,
) else [o.strip() for o in output])
return {"summarization": summ_result}
def tokenize(
self,
tokenizer,
texts: List[str],
max_len: int = 1024,
) -> Dict:
if isinstance(texts, str):
texts = [texts]
texts = [f"<s> {text}" for text in texts]
eos = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)
eos_list = [eos for _ in range(len(texts))]
tokens = tokenizer(
texts,
return_tensors="pt",
padding=True,
truncation=True,
add_special_tokens=False,
max_length=max_len - 1,
# result + <eos>
)
return self.add_bos_eos_tokens(tokenizer, tokens, eos_list)
def add_bos_eos_tokens(self, tokenizer, tokens, eos_list):
input_ids = tokens["input_ids"]
attention_mask = tokens["attention_mask"]
token_added_ids, token_added_masks = [], []
for input_id, atn_mask, eos in zip(
input_ids,
attention_mask,
eos_list,
):
maximum_idx = [
i for i, val in enumerate(input_id)
if val != tokenizer.convert_tokens_to_ids("<pad>")
]
if len(maximum_idx) == 0:
idx_to_add = 0
else:
idx_to_add = max(maximum_idx) + 1
eos = torch.tensor([eos], requires_grad=False)
additional_atn_mask = torch.tensor([1], requires_grad=False)
input_id = torch.cat([
input_id[:idx_to_add],
eos,
input_id[idx_to_add:],
]).long()
atn_mask = torch.cat([
atn_mask[:idx_to_add],
additional_atn_mask,
atn_mask[idx_to_add:],
]).long()
token_added_ids.append(input_id.unsqueeze(0))
token_added_masks.append(atn_mask.unsqueeze(0))
tokens["input_ids"] = torch.cat(token_added_ids, dim=0)
tokens["attention_mask"] = torch.cat(token_added_masks, dim=0)
return tokens
|