torch-npu 不支持算子 aten::isin.Tensor_Tensor_out

[natureLanguageQing]

torch-npu 2.2.0
`import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "npu"

THUDM/glm49bchat

tokenizer = AutoTokenizer.from_pretrained("/home/ma-user/THUDM/glm49bchat",trust_remote_code=True)

query = "你好"

inputs = tokenizer.apply_chat_template([{"role": "user", "content": query}],
add_generation_prompt=True,
tokenize=True,
return_tensors="pt",
return_dict=True
)

inputs = inputs.npu()

for i,j in inputs.items():

inputs[i] = j.npu()

model = AutoModelForCausalLM.from_pretrained(
"/home/ma-user/THUDM/glm49bchat",
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True
).npu().eval()
gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1}

input_ids = input_ids.to('npu')

inputs['input_ids'] = inputs['input_ids'].npu()
inputs['attention_mask'] = inputs['attention_mask'].npu()
inputs['position_ids'] = inputs['position_ids'].npu()
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0], skip_special_tokens=True))`

[W AddKernelNpu.cpp:82] Warning: The oprator of add is executed, Currently High Accuracy but Low Performance OP with 64-bit has been used, Please Do Some Cast at Python Functions with 32-bit for Better Performance! (function operator())
[W VariableFallbackKernel.cpp:51] Warning: CAUTION: The operator 'aten::isin.Tensor_Tensor_out' is not currently supported on the NPU backend and will fall back to run on the CPU. This may have performance implications. (function npu_cpu_fallback)

[yunyiyun]

已有issue跟踪 https://gitee.com/ascend/pytorch/issues/I9JGYU?from=project-issue