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c7a6fe6 | 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 | import transformers
import torch
import random
from datasets import load_dataset
import requests
question = "Mike Barnett negotiated many contracts including which player that went on to become general manager of CSKA Moscow of the Kontinental Hockey League?"
# Model ID and device setup
model_id = "PeterJinGo/SearchR1-nq_hotpotqa_train-qwen2.5-7b-em-ppo"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
question = question.strip()
if question[-1] != '?':
question += '?'
curr_eos = [151645, 151643] # for Qwen2.5 series models
curr_search_template = '\n\n{output_text}<information>{search_results}</information>\n\n'
# Prepare the message
prompt = f"""Answer the given question. \
You must conduct reasoning inside <think> and </think> first every time you get new information. \
After reasoning, if you find you lack some knowledge, you can call a search engine by <search> query </search> and it will return the top searched results between <information> and </information>. \
You can search as many times as your want. \
If you find no further external knowledge needed, you can directly provide the answer inside <answer> and </answer>, without detailed illustrations. For example, <answer> Beijing </answer>. Question: {question}\n"""
# Initialize the tokenizer and model
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
model = transformers.AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
# Define the custom stopping criterion
class StopOnSequence(transformers.StoppingCriteria):
def __init__(self, target_sequences, tokenizer):
# Encode the string so we have the exact token-IDs pattern
self.target_ids = [tokenizer.encode(target_sequence, add_special_tokens=False) for target_sequence in target_sequences]
self.target_lengths = [len(target_id) for target_id in self.target_ids]
self._tokenizer = tokenizer
def __call__(self, input_ids, scores, **kwargs):
# Make sure the target IDs are on the same device
targets = [torch.as_tensor(target_id, device=input_ids.device) for target_id in self.target_ids]
if input_ids.shape[1] < min(self.target_lengths):
return False
# Compare the tail of input_ids with our target_ids
for i, target in enumerate(targets):
if torch.equal(input_ids[0, -self.target_lengths[i]:], target):
return True
return False
def get_query(text):
import re
pattern = re.compile(r"<search>(.*?)</search>", re.DOTALL)
matches = pattern.findall(text)
if matches:
return matches[-1]
else:
return None
def search(query: str):
payload = {
"queries": [query],
"topk": 3,
"return_scores": True
}
results = requests.post("http://127.0.0.1:8000/retrieve", json=payload).json()['result']
def _passages2string(retrieval_result):
format_reference = ''
for idx, doc_item in enumerate(retrieval_result):
content = doc_item['document']['contents']
title = content.split("\n")[0]
text = "\n".join(content.split("\n")[1:])
format_reference += f"Doc {idx+1}(Title: {title}) {text}\n"
return format_reference
return _passages2string(results[0])
# Initialize the stopping criteria
target_sequences = ["</search>", " </search>", "</search>\n", " </search>\n", "</search>\n\n", " </search>\n\n"]
stopping_criteria = transformers.StoppingCriteriaList([StopOnSequence(target_sequences, tokenizer)])
cnt = 0
if tokenizer.chat_template:
prompt = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], add_generation_prompt=True, tokenize=False)
print('\n\n################# [Start Reasoning + Searching] ##################\n\n')
print(prompt)
# Encode the chat-formatted prompt and move it to the correct device
while True:
input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
attention_mask = torch.ones_like(input_ids)
# Generate text with the stopping criteria
outputs = model.generate(
input_ids,
attention_mask=attention_mask,
max_new_tokens=1024,
stopping_criteria=stopping_criteria,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.7
)
if outputs[0][-1].item() in curr_eos:
generated_tokens = outputs[0][input_ids.shape[1]:]
output_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
print(output_text)
break
generated_tokens = outputs[0][input_ids.shape[1]:]
output_text = tokenizer.decode(generated_tokens, skip_special_tokens=True)
tmp_query = get_query(tokenizer.decode(outputs[0], skip_special_tokens=True))
if tmp_query:
# print(f'searching "{tmp_query}"...')
search_results = search(tmp_query)
else:
search_results = ''
search_text = curr_search_template.format(output_text=output_text, search_results=search_results)
prompt += search_text
cnt += 1
print(search_text)
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