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!16518 fix G.AsinhGrad, G.AcoshGrad on cpu 

From: @wangrao124
Reviewed-by: @wuxuejian,@liangchenghui
Signed-off-by: @wuxuejian
This commit is contained in:
mindspore-ci-bot 2021-05-19 10:40:18 +08:00 committed by Gitee
parent 1531000d05 e559ba6596
commit f3ce8af0d6
3 changed files with 8 additions and 8 deletions
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4
mindspore/ccsrc/backend/kernel_compiler/cpu/eltwise_grad_cpu_kernel.cc
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@ -170,7 +170,7 @@ template <typename T>
void EltWiseGradCPUKernel<T>::AsinhGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const {
for (size_t i = start; i < end; i++) {
T dividend = input2[i];
T divisor = sqrt(1 + input1[i] * input1[i]);
T divisor = cosh(input1[i]);
if (divisor == 0) {
if (dividend == 0) {
out[i] = std::numeric_limits<T>::quiet_NaN();
@ -191,7 +191,7 @@ template <typename T>
void EltWiseGradCPUKernel<T>::AcoshGrad(const T *input1, const T *input2, T *out, size_t start, size_t end) const {
for (size_t i = start; i < end; i++) {
T dividend = input2[i];
T divisor = sqrt(input1[i] * input1[i] - 1);
T divisor = sinh(input1[i]);
if (divisor == 0) {
if (dividend == 0) {
out[i] = std::numeric_limits<T>::quiet_NaN();

6
tests/st/ops/cpu/test_acosh_grad_op.py
View File

@ -37,10 +37,10 @@ class NetAcoshGrad(nn.Cell):
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_acosh_grad():
x = np.array([5, 4, 3]).astype('float32')
out = np.array([5, 4, 3]).astype('float32')
dy = np.array([1, 0, -1]).astype('float32')
acosh_grad = NetAcoshGrad()
output = acosh_grad(Tensor(x), Tensor(dy))
output = acosh_grad(Tensor(out), Tensor(dy))
print(output)
expect = dy / np.sqrt(x * x - 1)
expect = dy / np.sinh(out)
assert np.allclose(output.asnumpy(), expect)

6
tests/st/ops/cpu/test_asinh_grad_op.py
View File

@ -37,10 +37,10 @@ class NetAsinhGrad(nn.Cell):
@pytest.mark.platform_x86_cpu
@pytest.mark.env_onecard
def test_asinh_grad():
x = np.array([-0.5, 0, 0.5]).astype('float32')
out = np.array([-0.5, 0, 0.5]).astype('float32')
dy = np.array([1, 0, -1]).astype('float32')
asinh_grad = NetAsinhGrad()
output = asinh_grad(Tensor(x), Tensor(dy))
output = asinh_grad(Tensor(out), Tensor(dy))
print(output)
expect = dy / np.sqrt(1 + x * x)
expect = dy / np.cosh(out)
assert np.allclose(output.asnumpy(), expect)