ValueError: type to initialized an Operator can not be None.

[hyunsir]

我在试图将rocketqa配合haystack中的milvus document使用。我参考教程，在DualEncoder的模型中，对rocketqa生成的问题向量进行序列化的时候，在我的项目部署过程中产生了bug。其中，query_emb= model.encode_query(query)。
File "/*****************************************/******.py", line 177, in retrieve query_emb = np.array(list(query_emb)) File "/usr/local/lib/python3.8/site-packages/rocketqa/encoder/dual_encoder.py", line 168, in encode_query q_rep = self.exe.run(program=self.test_prog, File "/usr/local/lib/python3.8/site-packages/paddle/fluid/executor.py", line 1299, in run six.reraise(*sys.exc_info()) File "/usr/local/lib/python3.8/site-packages/six.py", line 719, in reraise raise value File "/usr/local/lib/python3.8/site-packages/paddle/fluid/executor.py", line 1285, in run res = self._run_impl( File "/usr/local/lib/python3.8/site-packages/paddle/fluid/executor.py", line 1431, in _run_impl program = self._add_feed_fetch_ops( File "/usr/local/lib/python3.8/site-packages/paddle/fluid/executor.py", line 767, in _add_feed_fetch_ops tmp_program = program.clone() File "/usr/local/lib/python3.8/site-packages/paddle/fluid/framework.py", line 5443, in clone p._sync_with_cpp() File "/usr/local/lib/python3.8/site-packages/paddle/fluid/framework.py", line 5983, in _sync_with_cpp block._sync_with_cpp() File "/usr/local/lib/python3.8/site-packages/paddle/fluid/framework.py", line 3771, in _sync_with_cpp op = Operator(self, op_desc) File "/usr/local/lib/python3.8/site-packages/paddle/fluid/framework.py", line 2594, in __init__ raise ValueError( ValueError:typeto initialized an Operator can not be None.
另外这个bug只有在远程部署的时候会报错。我很疑惑，有人能给我一些帮助吗

[hyunsir]

上面的bug报错看不清楚可以看这个 ：
File "/***********************************/.py", line 177, in retrieve
query_emb = np.array(list(query_emb))
File "/usr/local/lib/python3.8/site-packages/rocketqa/encoder/dual_encoder.py", line 168, in encode_query
q_rep = self.exe.run(program=self.test_prog,
File "/usr/local/lib/python3.8/site-packages/paddle/fluid/executor.py", line 1299, in run
six.reraise(*sys.exc_info())
File "/usr/local/lib/python3.8/site-packages/six.py", line 719, in reraise
raise value
File "/usr/local/lib/python3.8/site-packages/paddle/fluid/executor.py", line 1285, in run
res = self._run_impl(
File "/usr/local/lib/python3.8/site-packages/paddle/fluid/executor.py", line 1431, in _run_impl
program = self._add_feed_fetch_ops(
File "/usr/local/lib/python3.8/site-packages/paddle/fluid/executor.py", line 767, in _add_feed_fetch_ops
tmp_program = program.clone()
File "/usr/local/lib/python3.8/site-packages/paddle/fluid/framework.py", line 5443, in clone
p._sync_with_cpp()
File "/usr/local/lib/python3.8/site-packages/paddle/fluid/framework.py", line 5983, in _sync_with_cpp
block._sync_with_cpp()
File "/usr/local/lib/python3.8/site-packages/paddle/fluid/framework.py", line 3771, in _sync_with_cpp
op = Operator(self, op_desc)
File "/usr/local/lib/python3.8/site-packages/paddle/fluid/framework.py", line 2594, in init
raise ValueError( ValueError:typeto initialized an Operator can not be None.

[sfwydyc]

能贴一下报错代码片段嘛？

[hyunsir]

这是我使用rocket服务的地方
class _RocketQADul(_BaseEmbeddingEncoder):
def init(self, retriever: "RocketQARetriever"):
# pretrained embedding models coming from: https://github.com/UKPLab/sentence-transformers#pretrained-models
# e.g. 'roberta-base-nli-stsb-mean-tokens'
self.batch_size = retriever.batch_size
self.rocketqa_embedding_model = rocketqa.load_model(retriever.rocket_qa_embedding_model, use_cuda=False, device_id=0, batch_size=self.batch_size)
# self.embedding_model.max_seq_length = retriever.max_seq_len
self.show_progress_bar = retriever.progress_bar
document_store = retriever.document_store
if document_store.similarity != "cosine":
logger.warning(
f"You are using a Sentence Transformer with the {document_store.similarity} function. "
f"We recommend using cosine instead. "
f"This can be set when initializing the DocumentStore"
)

def embed(self, texts: List[List[str]]) -> List[np.ndarray]:
    # texts can be a list of [title, text]
    # get back list of numpy embedding vectors
    # rocketqa change
    title = []
    para = []
    for entity in texts:
        title.append(entity[0])
        para.append(entity[1])
    emb = self.rocketqa_embedding_model.encode_para(para, title)
    emb = [r for r in emb]
    # emb = np.array(list(emb))
    return emb

def embed_queries(self, texts: List[str]) -> List[np.ndarray]:
    return self.rocketqa_embedding_model.encode_query(texts)

def embed_documents(self, docs: List[Document]) -> List[np.ndarray]:
    passages = [[d.meta["name"] if d.meta and "name" in d.meta else "", d.content] for d in docs]  # type: ignore
    # passage=[[name,content],[name2,content2],...]
    return self.embed(passages)

这是我对返回结果进行处理的然后报错的地方
def retrieve(
self,
query: str,
filters: dict = None,
top_k: Optional[int] = None,
index: str = None,
headers: Optional[Dict[str, str]] = None,
) -> List[Document]:
if top_k is None:
top_k = self.top_k
if index is None:
index = self.document_store.index
query_emb = self.embed_queries(texts=[query])
print(query_emb)
query_emb = np.array(list(list(query_emb)[0]))
# print(type(query_emb)) # <class 'numpy.ndarray'>
# print(query_emb) # [-1.1523590,..., 1.43632686e+00]
documents = self.document_store.query_by_embedding(
query_emb=query_emb, filters=filters, top_k=top_k, index=index, headers=headers
)
return documents