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