File size: 3,065 Bytes
c01138f 7632e39 c01138f 7632e39 c01138f 7632e39 c01138f |
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 |
import torch
from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM
from typing import Dict, List, Any
class EndpointHandler():
def __init__(self, path=""):
self.tokenizer = AutoTokenizer.from_pretrained(path, padding_side='left')
self.model = AutoModelForCausalLM.from_pretrained(path, device_map="cpu", torch_dtype="auto").eval()
self.token_false_id = self.tokenizer.convert_tokens_to_ids("no")
self.token_true_id = self.tokenizer.convert_tokens_to_ids("yes")
prefix = "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n<|im_start|>user\n"
suffix = "<|im_end|>\n<|im_start|>assistant\n<think>\n\n</think>\n\n"
self.prefix_tokens = self.tokenizer.encode(prefix, add_special_tokens=False)
self.suffix_tokens = self.tokenizer.encode(suffix, add_special_tokens=False)
def format_instruction(self, instruction, query, doc):
if instruction is None:
instruction = 'Given a web search query, retrieve relevant passages that answer the query'
output = "<Instruct>: {instruction}\n<Query>: {query}\n<Document>: {doc}".format(instruction=instruction,query=query, doc=doc)
return output
def process_inputs(self, pairs):
max_length = 8192
inputs = self.tokenizer(
pairs, padding=False, truncation='longest_first',
return_attention_mask=False, max_length=max_length - len(self.prefix_tokens) - len(self.suffix_tokens)
)
for i, ele in enumerate(inputs['input_ids']):
inputs['input_ids'][i] = self.prefix_tokens + ele + self.suffix_tokens
inputs = self.tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=max_length)
for key in inputs:
inputs[key] = inputs[key].to(self.model.device)
return inputs
def compute_logits(self, inputs, **kwargs):
batch_scores = self.model(**inputs, cache_implementation="static").logits[:, -1, :]
true_vector = batch_scores[:, self.token_true_id]
false_vector = batch_scores[:, self.token_false_id]
batch_scores = torch.stack([false_vector, true_vector], dim=1)
batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
scores = batch_scores[:, 1].exp().tolist()
return scores
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
task = 'Given a web search query, retrieve relevant passages that answer the query'
inputs = data.pop("inputs", data)
if 'query' not in inputs or 'documents' not in inputs:
raise ValueError("query and documents are required.")
pairs = [self.format_instruction(task, inputs['query'], doc) for doc in inputs['documents']]
# Tokenize the input texts
inputs = self.process_inputs(pairs)
scores = self.compute_logits(inputs)
return dict(scores=scores)
|