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 TestTril(TestCase):
def test_tril(self):
dtype_list = [np.float32, np.float16]
format_list = [0, 3, 4]
shape_list = [[5, 5], [4, 5, 6]]
diagonal_list = [-1, 0, 1]
shape_format = [
[i, j, k, l] for i in dtype_list for j in format_list for k in shape_list for l in diagonal_list
]
for item in shape_format:
cpu_input, npu_input = create_common_tensor(item[:-1], 0, 100)
cpu_output = self.cpu_op_exec(cpu_input, item[-1])
npu_output = self.npu_op_exec(npu_input, item[-1])
self.assertRtolEqual(cpu_output, npu_output)
def test_tril_inplace(self):
dtype_list = [np.float32, np.float16]
format_list = [0, 3, 4]
shape_list = [[5, 5], [4, 5, 6]]
diagonal_list = [-1, 0, 1]
shape_format = [
[i, j, k, l] for i in dtype_list for j in format_list for k in shape_list for l in diagonal_list
]
for item in shape_format:
cpu_input, npu_input = create_common_tensor(item[:-1], 0, 100)
cpu_output = self.cpu_op_inplace_exec(cpu_input, item[-1])
npu_output = self.npu_op_inplace_exec(npu_input, item[-1])
self.assertRtolEqual(cpu_output, npu_output)
def cpu_op_exec(self, input1, diagonal=0):
output = torch.tril(input1, diagonal)
output = output.numpy()
return output
def npu_op_exec(self, input1, diagonal=0):
output = torch.tril(input1, diagonal)
output = output.to("cpu")
output = output.numpy()
return output
def cpu_op_inplace_exec(self, input1, diagonal=0):
output = input1.tril_(diagonal)
output = output.numpy()
return output
def npu_op_inplace_exec(self, input1, diagonal=0):
output = input1.tril_(diagonal)
output = output.to("cpu")
output = output.numpy()
return output
if __name__ == "__main__":
run_tests()