NPU流式输出-torch_npu和transformers框架-多线程Streamer-昇腾910B-EE1001

前情提要

torch_npu框架不支持多线程自动set_device

报错详情

直接使用transformers的TextIteratorStreamer进行流式推理，会报错

Exception in thread Thread-6:
Traceback (most recent call last):File "/root/anaconda3/envs/AI/lib/python3.9/threading.py", line 954, in _bootstrap_innerself.run()File "/root/anaconda3/envs/AI/lib/python3.9/threading.py", line 892, in runself._target(*self._args, **self._kwargs)File "/root/anaconda3/envs/AI/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_contextreturn func(*args, **kwargs)File "/root/anaconda3/envs/AI/lib/python3.9/site-packages/transformers/generation/utils.py", line 1403, in generateand torch.sum(inputs_tensor[:, -1] == generation_config.pad_token_id) > 0
RuntimeError: getDevice:torch_npu/csrc/aten/common/CopyKernel.cpp:41 NPU error, error code is 107002
[Error]: The context is empty.Check whether acl.rt.set_context or acl.rt.set_device is called.
EE1001: The argument is invalid.Reason: rtGetDevMsg execute failed, reason=[context pointer null]Solution: 1.Check the input parameter range of the function. 2.Check the function invocation relationship.TraceBack (most recent call last):ctx is NULL![FUNC:GetDevErrMsg][FILE:api_impl.cc][LINE:4686]The argument is invalid.Reason: rtGetDevMsg execute failed, reason=[context pointer null]

设置好generation_config后，报错变为

Exception in thread Thread-6:
Traceback (most recent call last):File "/root/anaconda3/envs/sakura/lib/python3.9/threading.py", line 954, in _bootstrap_innerself.run()File "/root/anaconda3/envs/sakura/lib/python3.9/threading.py", line 892, in runself._target(*self._args, **self._kwargs)File "/root/anaconda3/envs/sakura/lib/python3.9/site-packages/torch/utils/_contextlib.py", line 115, in decorate_contextreturn func(*args, **kwargs)File "/root/anaconda3/envs/sakura/lib/python3.9/site-packages/transformers/generation/utils.py", line 1411, in generatestreamer.put(input_ids.cpu())
RuntimeError: getDevice:torch_npu/csrc/aten/common/CopyKernel.cpp:41 NPU error, error code is 107002
[Error]: The context is empty.Check whether acl.rt.set_context or acl.rt.set_device is called.
EE1001: The argument is invalid.Reason: rtGetDevMsg execute failed, reason=[context pointer null]Solution: 1.Check the input parameter range of the function. 2.Check the function invocation relationship.TraceBack (most recent call last):ctx is NULL![FUNC:GetDevErrMsg][FILE:api_impl.cc][LINE:4686]The argument is invalid.Reason: rtGetDevMsg execute failed, reason=[context pointer null]

为此咨询了transformers官方人员，issues-23042，但是他们无法处理
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后来经过不断debug发现在threading.py的Thread函数中，执行run函数后self._kwargs中的参数均未传递成功
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询问了torch_npu的官方人员

解决方案

在Thread函数中的target传入set_device
完整代码如下，以chatglm3-6b为例

import torch
import torch_npu
from torch_npu.contrib import transfer_to_npu
torch_device = "npu:3"
torch.npu.set_device(torch.device(torch_device))
torch.npu.set_compile_mode(jit_compile=False)
option = {}
option["NPU_FUZZY_COMPILE_BLACKLIST"] = "Tril"
torch.npu.set_option(option)
import os
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, AutoModel
from transformers import TextIteratorStreamer
from threading import Threadmodel_path = "/root/.cache/modelscope/hub/ZhipuAI/chatglm3-6b" 
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True, device_map=torch_device)
model = model.eval()def generate_with_npu_device(**generation_kwargs):torch.npu.set_device(torch.device(torch_device))model.generate(**generation_kwargs)if __name__ == "__main__":# TextIteratorStreamer实现streamer = TextIteratorStreamer(tokenizer)turn_count = 0while True:query = input("\n用户：")if query.strip() == "stop":breakinputs = tokenizer([query], return_tensors="pt")input_ids = inputs["input_ids"].to(torch_device)attention_mask = inputs["attention_mask"].to(torch_device)generation_kwargs = dict(input_ids=input_ids,attention_mask=attention_mask, streamer=streamer, max_new_tokens=512)thread = Thread(target=generate_with_npu_device, kwargs=generation_kwargs)thread.start()generated_text = ""position = 0# 流式输出for new_text in streamer:generated_text += new_textprint(generated_text[position:], end='', flush=True)position = len(generated_text)