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

在Ascend平台，调用grad_cmp函数时，绝大多数的用例都有精度问题

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 ascend

• Software Environment / 软件环境 (Mandatory / 必填):
  -- MindSpore version (e.g., 1.7.0.Bxxx) :
  -- Python version (e.g., Python 3.7.5) :
  -- OS platform and distribution (e.g., Linux Ubuntu 16.04):
  -- GCC/Compiler version (if compiled from source):

• 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
/mode graph

Related testcase / 关联用例 (Mandatory / 必填)

test_nn_marginrankingloss_reduction_default
test_nn_marginrankingloss_reduction_sum
test_nn_marginrankingloss_reduction_none
test_nn_marginrankingloss_input_dtype_float16
test_nn_marginrankingloss_input_dtype_float32
test_nn_marginrankingloss_input_dtype_float64
test_nn_marginrankingloss_input_attribute_margin
test_nn_marginrankingloss_1d_float32
test_nn_marginrankingloss_2d_float32
test_nn_marginrankingloss_3d_float16
test_nn_marginrankingloss_4d_float16
test_nn_marginrankingloss_5d_float32
test_nn_marginrankingloss_6d_float16
test_nn_marginrankingloss_7d_float16

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

1. def test_nn_marginrankingloss_reduction_default():
   logits1 = Tensor(np.random.randn(2, 2).astype(np.float32))
   logits2 = Tensor(np.random.randn(2, 2).astype(np.float32))
   labels = Tensor((np.random.randint(2, size=(2, 2)) * 2 - 1).astype(np.float32))
   input_list = [logits1, logits2, labels]
   fact = MarginRankingLossMock(inputs=input_list)
   fact.forward_cmp()

  fact.grad_cmp()

test_nn_marginrankingloss.py:30:

../share/ops/nn/marginrankingloss_ops.py:68: in grad_cmp
allclose_nparray(data_expected, data_me, self.loss, self.loss)
../share/utils.py:31: in allclose_nparray
_count_unequal_element(data_expected, data_me, rtol, atol)

data_expected = array([[0. , 0.07458219],
[0.07458219, 0. ]], dtype=float32)
data_me = array([[0., 0.],
[0., 0.]], dtype=float32), rtol = 0.001, atol = 0.001

def _count_unequal_element(data_expected, data_me, rtol, atol):
    assert data_expected.shape == data_me.shape
    total_count = len(data_expected.flatten())
    error = np.abs(data_expected - data_me)
    greater = np.greater(error, atol + np.abs(data_me) * rtol)
    loss_count = np.count_nonzero(greater)
    assert (loss_count / total_count) < rtol, \
        "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \

          format(data_expected[greater], data_me[greater], error[greater])

E AssertionError:
E data_expected_std:[0.07458219 0.07458219]
E data_me_error:[0. 0.]
E loss:[0.07458219 0.07458219]

../share/utils.py:24: AssertionError

2. _________________________________________________________ test_nn_marginrankingloss_2d_float32 ___________________________________________________________

   def test_nn_marginrankingloss_2d_float32():
   logits1 = Tensor(np.random.randn(2, 2).astype(np.float32))
   logits2 = Tensor(np.random.randn(2, 2).astype(np.float32))
   labels = Tensor((np.random.randint(2, size=(2, 2)) * 2 - 1).astype(np.float32))
   input_list = [logits1, logits2, labels]
   fact = MarginRankingLossMock(inputs=input_list)
   fact.forward_cmp()

  fact.grad_cmp()

test_nn_marginrankingloss.py:208:

../share/ops/nn/marginrankingloss_ops.py:68: in grad_cmp
allclose_nparray(data_expected, data_me, self.loss, self.loss)
../share/utils.py:31: in allclose_nparray
_count_unequal_element(data_expected, data_me, rtol, atol)

data_expected = array([[ 0.04290303, -0.04290303],
[ 0.04290303, 0.04290303]], dtype=float32)
data_me = array([[0., 0.],
[0., 0.]], dtype=float32), rtol = 0.001, atol = 0.001

def _count_unequal_element(data_expected, data_me, rtol, atol):
    assert data_expected.shape == data_me.shape
    total_count = len(data_expected.flatten())
    error = np.abs(data_expected - data_me)
    greater = np.greater(error, atol + np.abs(data_me) * rtol)
    loss_count = np.count_nonzero(greater)
    assert (loss_count / total_count) < rtol, \
        "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}". \

          format(data_expected[greater], data_me[greater], error[greater])

E AssertionError:
E data_expected_std:[ 0.04290303 -0.04290303 0.04290303 0.04290303]
E data_me_error:[0. 0. 0. 0.]
E loss:[0.04290303 0.04290303 0.04290303 0.04290303]

../share/utils.py:24: AssertionError
3.

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

all pass

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

Special notes for this issue/备注 (Optional / 选填)