bugfix
This commit is contained in:
parent
f84f4f14f9
commit
da44517b80
@ -2,83 +2,86 @@ import torch
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model_pathMap = {
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model_pathMap = {
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}
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}
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def llm_register(model_key, Klass):
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def llm_register(model_key, Klass):
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model_pathMap[model_key] = Klass
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model_pathMap[model_key] = Klass
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def get_llm_class(model_path):
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def get_llm_class(model_path):
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for k,klass in model_pathMap.items():
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for k, klass in model_pathMap.items():
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if len(model_path.split(k)) > 1:
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if len(model_path.split(k)) > 1:
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return klass
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return klass
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print(f'{model_pathMap=}')
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print(f'{model_pathMap=}')
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return None
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return None
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class BaseReranker:
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class BaseReranker:
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def __init__(self, model_id, **kw):
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def __init__(self, model_id, **kw):
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self.model_id = model_id
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self.model_id = model_id
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def use_mps_if_prosible(self):
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if torch.backends.mps.is_available():
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device = torch.device("mps")
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self.model = self.model.to(device)
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def process_inputs(self, pairs):
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def use_mps_if_prosible(self):
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inputs = self.tokenizer(
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if torch.cuda.is_available():
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pairs, padding=False, truncation='longest_first',
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device = torch.device("cuda")
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return_attention_mask=False, max_length=self.max_length
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self.model = self.model.to(device, dtype=torch.float16)
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)
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else:
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inputs = self.tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=self.max_length)
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raise Exception("GPU not available, but required for FP16 inference")
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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 build_sys_prompt(self, sys_prompt):
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def process_inputs(self, pairs):
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return f"<|im_start|>system\n{sys_prompt}\n<|im_end|>"
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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=self.max_length
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)
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inputs = self.tokenizer.pad(inputs, padding=True, return_tensors="pt", max_length=self.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 build_user_prompt(self, query, doc, instruct=''):
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def build_sys_prompt(self, sys_prompt):
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return f'<|im_start|>user\n<Instruct>: {instruct}\n<Query>:{query}\n<Document>:\n{doc}<|im_end|>'
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return f"<|im_start|>system\n{sys_prompt}\n<|im_end|>"
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def build_assistant_prompt(self):
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return "<|im_start|>assistant\n<think>\n\n</think>\n\n"
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def compute_logits(self, inputs, **kwargs):
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def build_user_prompt(self, query, doc, instruct=''):
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batch_scores = self.model(**inputs).logits[:, -1, :]
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return f'<|im_start|>user\n<Instruct>: {instruct}\n<Query>:{query}\n<Document>:\n{doc}<|im_end|>'
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# true_vector = batch_scores[:, token_true_id]
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# false_vector = batch_scores[:, 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 build_pairs(self, query, docs, sys_prompt="", task=""):
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def build_assistant_prompt(self):
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sys_str = self.build_sys_prompt(sys_prompt)
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return "<|im_start|>assistant\n<think>\n\n</think>\n\n"
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ass_str = self.build_assistant_prompt()
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pairs = [ sys_str + '\n' + self.build_user_prompt(task, query, doc) + '\n' + ass_str for doc in docs ]
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return pairs
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def rerank(self, query, docs, top_n, sys_prompt="", task=""):
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def compute_logits(self, inputs, **kwargs):
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pairs = self.build_pairs(query, docs, sys_prompt=sys_prompt, task=task)
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batch_scores = self.model(**inputs).logits[:, -1, :]
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with torch.no_grad():
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batch_scores = torch.nn.functional.log_softmax(batch_scores, dim=1)
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inputs = self.process_inputs(pairs)
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scores = batch_scores[:, 1].exp().tolist()
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scores = self.compute_logits(inputs)
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return scores
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data = []
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for i, s in enumerate(scores):
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def build_pairs(self, query, docs, sys_prompt="", task=""):
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d = {
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sys_str = self.build_sys_prompt(sys_prompt)
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'index':i,
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ass_str = self.build_assistant_prompt()
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'relevance_score': s
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pairs = [sys_str + '\n' + self.build_user_prompt(task, query, doc) + '\n' + ass_str for doc in docs]
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}
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return pairs
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data.append(d)
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data = sorted(data,
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def rerank(self, query, docs, top_n, sys_prompt="", task=""):
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key=lambda x: x["relevance_score"],
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pairs = self.build_pairs(query, docs, sys_prompt=sys_prompt, task=task)
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reverse=True)
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with torch.no_grad():
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if len(data) > top_n:
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inputs = self.process_inputs(pairs)
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data = data[:top_n]
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scores = self.compute_logits(inputs)
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ret = {
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data = []
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"data": data,
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for i, s in enumerate(scores):
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"object": "rerank.result",
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d = {
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"model": self.model_name,
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'index': i,
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"usage": {
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'relevance_score': s
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"prompt_tokens": 0,
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}
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"total_tokens": 0
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data.append(d)
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}
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data = sorted(data,
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}
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key=lambda x: x["relevance_score"],
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return ret
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reverse=True)
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if len(data) > top_n:
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data = data[:top_n]
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ret = {
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"data": data,
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"object": "rerank.result",
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"model": self.model_name,
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"usage": {
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"prompt_tokens": 0,
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"total_tokens": 0
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}
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}
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return ret
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@ -1,31 +1,36 @@
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import torch
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import torch
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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from llmengine.base_reranker import BaseReranker, llm_register
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from llmengine.base_reranker import BaseReranker, llm_register
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from torch.cuda.amp import autocast
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class BgeReranker(BaseReranker):
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class BgeReranker(BaseReranker):
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def __init__(self, model_id, max_length=8096):
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def __init__(self, model_id, max_length=8096):
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if 'bge-reranker' not in model_id:
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if 'bge-reranker' not in model_id:
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e = Exception(f'{model_id} is not a bge-reranker')
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e = Exception(f'{model_id} is not a bge-reranker')
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raise e
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raise e
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self.tokenizer = AutoTokenizer.from_pretrained(model_id)
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self.tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForSequenceClassification.from_pretrained(model_id)
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model = AutoModelForSequenceClassification.from_pretrained(model_id, torch_dtype=torch.bfloat16)
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model.eval()
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model.eval()
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self.model = model
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if torch.cuda.is_available():
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self.model_id = model_id
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model = model.to('cuda')
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self.model_name = model_id.split('/')[-1]
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self.model = model
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self.model_id = model_id
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self.model_name = model_id.split('/')[-1]
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def build_pairs(self, query, docs, **kw):
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def build_pairs(self, query, docs, **kw):
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return [[query, doc] for doc in docs]
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return [[query, doc] for doc in docs]
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def process_inputs(self, pairs):
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def process_inputs(self, pairs):
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inputs = self.tokenizer(pairs, padding=True,
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inputs = self.tokenizer(pairs, padding=True,
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truncation=True, return_tensors='pt', max_length=512)
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truncation=True, return_tensors='pt', max_length=512)
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return inputs
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if torch.cuda.is_available():
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inputs = {k: v.to('cuda') for k, v in inputs.items()}
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return inputs
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def compute_logits(self, inputs):
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def compute_logits(self, inputs):
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scores = self.model(**inputs,
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with autocast():
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return_dict=True).logits.view(-1, ).float()
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scores = self.model(**inputs, return_dict=True).logits.view(-1,)
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scores = [ s.item() for s in scores ]
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scores = [s.item() for s in scores]
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return scores
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return scores
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llm_register('bge-reranker', BgeReranker)
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llm_register('bge-reranker', BgeReranker)
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@ -1,16 +1,122 @@
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import torch
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import torch
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from transformers import AutoModel, AutoTokenizer, AutoModelForCausalLM
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from llmengine.base_reranker import BaseReranker, llm_register
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from llmengine.base_reranker import BaseReranker, llm_register
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class Qwen3Reranker(BaseReranker):
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def __init__(self, model_id, max_length=8096):
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if 'Qwen3-Reranker' not in model_id:
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e = Exception(f'{model_id} is not a Qwen3-Reranker')
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raise e
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self.tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side='left')
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self.model = AutoModelForCausalLM.from_pretrained(model_id).eval()
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self.model_id = model_id
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self.model_name = model_id.split('/')[-1]
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self.max_length = 8192
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llm_register('Qwen3-Reranker', Qwen3Reranker)
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class Qwen3Reranker(BaseReranker):
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def __init__(self, model_id, max_length=1024): # 设置 max_length 为 1024
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if 'Qwen3-Reranker' not in model_id:
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raise Exception(f'{model_id} is not a Qwen3-Reranker')
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self.tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side='left')
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# 使用 FP16（GPU）
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self.model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16
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).eval()
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self.model_id = model_id
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self.model_name = model_id.split('/')[-1]
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self.max_length = max_length
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# 初始化前缀和后缀标记
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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(
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instruction=instruction, query=query, doc=doc
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)
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return output
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def process_inputs(self, pairs, batch_size=8):
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all_inputs = []
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for i in range(0, len(pairs), batch_size):
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batch_pairs = pairs[i:i + batch_size]
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inputs = self.tokenizer(
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batch_pairs,
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padding=False,
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truncation='longest_first',
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return_attention_mask=False,
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max_length=self.max_length - len(self.prefix_tokens) - len(self.suffix_tokens)
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)
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for j, ele in enumerate(inputs['input_ids']):
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inputs['input_ids'][j] = self.prefix_tokens + ele + self.suffix_tokens
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inputs = self.tokenizer.pad(
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inputs,
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padding=True,
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return_tensors="pt",
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max_length=self.max_length
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)
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for key in inputs:
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inputs[key] = inputs[key].to(self.model.device)
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all_inputs.append(inputs)
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# 清理内存
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return all_inputs
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def compute_logits(self, inputs, **kwargs):
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batch_scores = self.model(**inputs).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 build_pairs(self, query, docs, sys_prompt="", task=""):
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pairs = [self.format_instruction(task, query, doc) for doc in docs]
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return pairs
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def rerank(self, query, docs, top_n, sys_prompt="", task="", batch_size=8):
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if query is None:
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raise Exception("query is None")
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if docs is None or not docs:
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raise Exception("documents is None or empty")
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if not isinstance(docs, list):
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docs = [docs]
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if top_n is None or top_n <= 0:
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top_n = len(docs)
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pairs = self.build_pairs(query, docs, sys_prompt=sys_prompt, task=task)
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print(f"Number of documents: {len(docs)}")
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for i, p in enumerate(pairs):
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print(f"Pair {i} token length: {len(self.tokenizer.encode(p))}")
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scores = []
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with torch.no_grad():
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input_batches = self.process_inputs(pairs, batch_size=batch_size)
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for inputs in input_batches:
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batch_scores = self.compute_logits(inputs)
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scores.extend(batch_scores)
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# 清理内存
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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data = [
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{'index': i, 'relevance_score': s}
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for i, s in enumerate(scores)
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]
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data = sorted(data, key=lambda x: x["relevance_score"], reverse=True)
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if len(data) > top_n:
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data = data[:top_n]
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return {
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"data": data,
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"object": "rerank.result",
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"model": self.model_name,
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"usage": {
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"prompt_tokens": sum(len(self.tokenizer.encode(p)) for p in pairs),
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"total_tokens": sum(len(self.tokenizer.encode(p)) for p in pairs)
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}
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}
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llm_register('Qwen3-Reranker', Qwen3Reranker)
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@ -1,4 +1,3 @@
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#!/bin/bash
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#!/bin/bash
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# CUDA_VISIBLE_DEVICES=7 /share/vllm-0.8.5/bin/python -m llmengine.rerank -p 9997 /d/ymq/models/Qwen/Qwen3-Reranker-0___6B
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CUDA_VISIBLE_DEVICES=7 /share/vllm-0.8.5/bin/python -m llmengine.rerank -p 9997 /share/models/BAAI/bge-reranker-v2-m3
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CUDA_VISIBLE_DEVICES=7 /share/vllm-0.8.5/bin/python -m llmengine.rerank -p 9997 /share/models/BAAI/bge-reranker-v2-m3
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