import torch
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 TestLeakyReluBackward(TestCase):
def cpu_op_backward_exec(self, input1):
w = torch.ones_like(input1)
input1.requires_grad_(True)
output = torch.nn.functional.leaky_relu(input1)
output.backward(w)
res = input1.grad
res = res.numpy()
return res, output
def npu_op_backward_exec(self, input1):
w = torch.ones_like(input1)
w = w.to("npu")
input1 = input1.to("npu")
input1.requires_grad_(True)
output = torch.nn.functional.leaky_relu(input1)
output.backward(w)
output = output.to("cpu")
res = input1.grad
res = input1.grad.to("cpu")
res = res.numpy()
return res, output
def test_leaky_relu_backward_format_fp32(self):
format_list = [0, 3]
shape_list = [(5, 3)]
shape_format = [
[np.float32, i, j] for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 2)
cpu_output = self.cpu_op_backward_exec(cpu_input1)
npu_output = self.npu_op_backward_exec(npu_input1)
self.assertEqual(cpu_output, npu_output)
def test_leaky_relu_backward_format_fp16(self):
format_list = [0, 3]
shape_list = [(5, 3)]
shape_format = [
[np.float16, i, j] for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 2)
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output1, cpu_output2 = self.cpu_op_backward_exec(cpu_input1)
npu_output1, npu_output2 = self.npu_op_backward_exec(npu_input1)
cpu_output1 = cpu_output1.astype(np.float16)
self.assertEqual(cpu_output1, npu_output1)
self.assertEqual(cpu_output2, npu_output2)
if __name__ == "__main__":
run_tests()