Describe the current behavior / 问题描述 (Mandatory / 必填)

The value of the relu operator on mindspore after passing through nan is 0. And the value of the relu operator on pytorch after passing through nan is still nan.

Environment / 环境信息 (Mandatory / 必填)

• Hardware Environment(Ascend/GPU/CPU) / 硬件环境:

Please delete the backend not involved / 请删除不涉及的后端:
/device /CPU/

• Software Environment / 软件环境 (Mandatory / 必填):
  -- MindSpore version (e.g., 1.7.0.Bxxx) :2.2.0
  -- Torch version (e.g., 1.7.0.Bxxx) :2.0.0+cpu
  -- Python version (e.g., Python 3.7.5) :3.8.16
  -- OS platform and distribution (e.g., Linux Ubuntu 16.04):Linux Ubuntu 18.04.6 LTS

• Excute Mode / 执行模式 (Mandatory / 必填)(PyNative/Graph):

Please delete the mode not involved / 请删除不涉及的模式:
/mode pynative

Related testcase / 关联用例 (Mandatory / 必填)

import mindspore.ops
import torch
import mindspore
import numpy as np


def mindspore_relu():
    print("*************mindspore_relu()**************")
    input_np = np.load("./tensor_50.npy")

    input_tensor = mindspore.Tensor(input_np)
    output = mindspore.ops.relu(input=input_tensor)
    is_nan = mindspore.ops.IsNan()
    print("input has nan",is_nan(input_tensor).any())
    print("output has nan",is_nan(output).any())

def torch_relu():
    print("*************torch_relu()**************")
    input_np = np.load("./tensor_50.npy")
    input_tensor = torch.Tensor(input_np)
    output = torch.relu(input_tensor)
    print("input has nan",torch.isnan(input_tensor).any())
    print("output has nan",torch.isnan(output).any())

    
if __name__ == "__main__":
    mindspore_relu()
    torch_relu()

tensor_50.npy

Steps to reproduce the issue / 重现步骤 (Mandatory / 必填)

1. running code

Describe the expected behavior / 预期结果 (Mandatory / 必填)

After the relu operator on the mindspore has been computed by nan, the output should still be nan

Related log / screenshot / 日志 / 截图 (Mandatory / 必填)