llmengine/build/lib/llmengine/base_reranker.py

85 lines
2.5 KiB
Python

import torch
model_pathMap = {
}
def llm_register(model_key, Klass):
model_pathMap[model_key] = Klass
def get_llm_class(model_path):
for k,klass in model_pathMap.items():
if len(model_path.split(k)) > 1:
return klass
print(f'{model_pathMap=}')
return None
class BaseReranker:
def __init__(self, model_id, **kw):
self.model_id = model_id
def use_mps_if_prosible(self):
if torch.backends.mps.is_available():
device = torch.device("mps")
self.model = self.model.to(device)
def process_inputs(self, pairs):
inputs = self.tokenizer(
pairs, padding=False, truncation='longest_first',
return_attention_mask=False, max_length=self.max_length
)
inputs = self.tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=self.max_length)
for key in inputs:
inputs[key] = inputs[key].to(self.model.device)
return inputs
def build_sys_prompt(self, sys_prompt):
return f"<|im_start|>system\n{sys_prompt}\n<|im_end|>"
def build_user_prompt(self, query, doc, instruct=''):
return f'<|im_start|>user\n<Instruct>: {instruct}\n<Query>:{query}\n<Document>:\n{doc}<|im_end|>'
def build_assistant_prompt(self):
return "<|im_start|>assistant\n<think>\n\n</think>\n\n"
def compute_logits(self, inputs, **kwargs):
batch_scores = self.model(**inputs).logits[:, -1, :]
# true_vector = batch_scores[:, token_true_id]
# false_vector = batch_scores[:, 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 build_pairs(self, query, docs, sys_prompt="", task=""):
sys_str = self.build_sys_prompt(sys_prompt)
ass_str = self.build_assistant_prompt()
pairs = [ sys_str + '\n' + self.build_user_prompt(task, query, doc) + '\n' + ass_str for doc in docs ]
return pairs
def rerank(self, query, docs, top_n, sys_prompt="", task=""):
pairs = self.build_pairs(query, docs, sys_prompt=sys_prompt, task=task)
with torch.no_grad():
inputs = self.process_inputs(pairs)
scores = self.compute_logits(inputs)
data = []
for i, s in enumerate(scores):
d = {
'index':i,
'relevance_score': s
}
data.append(d)
data = sorted(data,
key=lambda x: x["relevance_score"],
reverse=True)
if len(data) > top_n:
data = data[:top_n]
ret = {
"data": data,
"object": "rerank.result",
"model": self.model_name,
"usage": {
"prompt_tokens": 0,
"total_tokens": 0
}
}
return ret