import torch
import torch.nn.functional as F
import numpy as np
import torch_npu
from torch_npu.testing.testcase import TestCase, run_tests
from torch_npu.testing.common_utils import create_common_tensor
class TestMishBackward(TestCase):
def npu_op_exec(self, input1):
input1.requires_grad = True
output = torch.npu_mish(input1)
output.backward(torch.ones_like(output))
output_grad = input1.grad
output_grad = output_grad.to("cpu")
output_grad = output_grad.detach().numpy()
output = output.cpu().detach().numpy()
return output_grad, output
def cpu_op_exec(self, input1):
input1.requires_grad = True
output = input1 * (torch.tanh(F.softplus(input1)))
output.backward(torch.ones_like(output))
output_grad = input1.grad
output_grad = output_grad.to("cpu")
output_grad = output_grad.detach().numpy()
output = output.detach().numpy()
return output_grad, output
def test_mish_fp32(self):
npu_input = torch.tensor([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]).npu()
cpu_input = torch.tensor([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.])
output_grad, npu_output = self.npu_op_exec(npu_input)
ep_output_grad, ep_npu_output = self.cpu_op_exec(cpu_input)
self.assertRtolEqual(ep_output_grad, output_grad, prec=3.e-4)
self.assertRtolEqual(ep_npu_output, npu_output)
if __name__ == "__main__":
run_tests()