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from transformers import Qwen2Tokenizer
import bm25s
from bm25s.hf import BM25HF
# import Stemmer # optional: for stemming
# define the RexQwen2Tokenizer class that is a subclass of Qwen2Tokenizer
class RexQwen2Tokenizer(Qwen2Tokenizer):
# to simplify the __init__ method
def __init__(
self,
rex_index_name = "Llama-3-Magpie-Pro-1M-v0.1",
rex_size = 3,
**kwargs,
):
super().__init__(**kwargs)
self.rex_index_name = rex_index_name
hf_repo_name = f"yuchenlin/BM25S_index_{self.rex_index_name}"
self.retriever = BM25HF.load_from_hub(
hf_repo_name, revision="main", load_corpus=True
)
self.rex_size = rex_size
self.user_prefix = "<|im_start|>user"
def _rex_query(self, query):
k = self.rex_size
query_tokens = bm25s.tokenize(query, show_progress=False)
results, scores = self.retriever.retrieve(query_tokens, k=k, show_progress=False)
rex_chat_history = []
for i in range(results.shape[1]):
doc, score = results[0, i], scores[0, i]
rex_query = doc["query"]
rex_response = doc["response"]
rex_chat_history.append({"role": "user", "content": rex_query})
rex_chat_history.append({"role": "assistant", "content": rex_response})
rex_chat_history_tokens = self.apply_chat_template(rex_chat_history, tokenize=False, add_generation_prompt=False)
start_user = rex_chat_history_tokens.index(self.user_prefix)
rex_chat_history_tokens = rex_chat_history_tokens[start_user:]
return rex_chat_history_tokens
# redefine the _tokenize method
def tokenize(self, text, **kwargs):
# find the index for first user query
if self.user_prefix not in text or self.rex_size < 1:
# the query is not wrapped with chat template yet
# raise NotImplementedError
return super().tokenize(text, **kwargs)
start_index = text.index(self.user_prefix)
rex_chat_history_tokens = self._rex_query(text[start_index+len(self.user_prefix):])
rex_text = text[:start_index] + rex_chat_history_tokens + text[start_index:]
# print(rex_text)
tokens = super().tokenize(rex_text, **kwargs)
return tokens
if __name__ == "__main__":
from transformers import AutoTokenizer
model_path = "/net/nfs/mosaic/yuchenl/Qwen2-1.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, rex_size=3)
# tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct", trust_remote_code=True)
messages = [
{"role": "user", "content": "Who is Yuchen Lin?"},
{"role": "assistant", "content": "Yuchen Lin is a NLP researcher."},
{"role": "user", "content": "Can I ask him a question?"}
]
query = tokenizer.apply_chat_template(messages, tokenize=False)
print(tokenizer.tokenize(query))
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