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 TestNotEqual(TestCase):
def cpu_op_exec(self, input1, input2):
output = torch.not_equal(input1, input2)
output = output.numpy()
return output
def npu_op_inplace_exec(self, input1, input2):
input1.not_equal_(input2)
output = input1.to("cpu")
output = output.numpy()
return output
def cpu_op_inplace_exec(self, input1, input2):
input1.not_equal_(input2)
output = input1.numpy()
return output
def npu_op_exec(self, input1, input2):
output = torch.not_equal(input1, input2)
output = output.to("cpu")
output = output.numpy()
return output
def npu_op_exec_out(self, input1, input2):
input3 = torch.empty(0).bool().npu()
torch.not_equal(input1, input2, out=input3)
output = input3.to("cpu")
output = output.numpy()
return output
def test_not_equal_shape_format_fp32(self):
dtype_list = [np.float32]
format_list = [0, 3]
shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
shape_format = [
[d, i, j] for d in dtype_list for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 1, 100)
cpu_input2, npu_input2 = create_common_tensor(item, 1, 100)
cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
npu_output = self.npu_op_exec(npu_input1, npu_input2)
self.assertEqual(cpu_output, npu_output)
def test_not_equal_shape_format_fp16(self):
dtype_list = [np.float16]
format_list = [0, 3]
shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
shape_format = [
[d, i, j] for d in dtype_list for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 1, 100)
cpu_input2, npu_input2 = create_common_tensor(item, 1, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_input2 = cpu_input2.to(torch.float32)
cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
npu_output = self.npu_op_exec(npu_input1, npu_input2)
self.assertEqual(cpu_output, npu_output)
def test_not_equal_inp_shape_format(self):
dtype_list = [np.float16, np.float32]
format_list = [0, 3]
shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
shape_format = [
[d, i, j] for d in dtype_list for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 1, 100)
cpu_input2, npu_input2 = create_common_tensor(item, 1, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_input2 = cpu_input2.to(torch.float32)
cpu_output = self.cpu_op_inplace_exec(cpu_input1, cpu_input2)
npu_output = self.npu_op_inplace_exec(npu_input1, npu_input2)
self.assertEqual(cpu_output, npu_output)
def test_not_equal_inp_scalar_shape_format(self):
dtype_list = [np.float16, np.float32]
format_list = [0, 3]
shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
shape_format = [
[d, i, j] for d in dtype_list for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 1, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output = self.cpu_op_inplace_exec(cpu_input1, 5)
npu_output = self.npu_op_inplace_exec(npu_input1, 5)
cpu_output = cpu_output.astype(npu_output.dtype)
self.assertEqual(cpu_output, npu_output)
def test_not_equal_out_shape_format_fp32(self):
dtype_list = [np.float32]
format_list = [0]
shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
shape_format = [
[[d, i, j]] for d in dtype_list for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item[0], -10, 10)
cpu_input2, npu_input2 = create_common_tensor(item[0], -10, 10)
npu_output_out = self.npu_op_exec_out(npu_input1, npu_input2)
cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
self.assertEqual(cpu_output, npu_output_out)
def test_not_equal_scalar_out_shape_format_fp32(self):
dtype_list = [np.float32]
format_list = [0]
shape_list = [[1024], [8, 128], [2, 8, 128], [2, 8, 128, 512]]
shape_format = [
[[d, i, j]] for d in dtype_list for i in format_list for j in shape_list
]
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item[0], -10, 10)
npu_output_out = self.npu_op_exec_out(npu_input1, 5)
cpu_output = self.cpu_op_exec(cpu_input1, 5)
self.assertEqual(cpu_output, npu_output_out)
def test_not_equal_mix_dtype(self):
cpu_input1, npu_input1 = create_common_tensor([np.float16, 0, (2, 3)], 1, 100)
cpu_input2, npu_input2 = create_common_tensor([np.float32, 0, (2, 3)], 1, 100)
cpu_output = self.cpu_op_exec(cpu_input1, cpu_input2)
npu_output = self.npu_op_exec(npu_input1, npu_input2)
self.assertRtolEqual(cpu_output, npu_output)
def test_not_equal_first_arg_0d_cpu_tensor(self):
cpu_a = torch.tensor(2.0)
cpu_b = torch.tensor([1.0, 2.0, 3.0])
npu_b = cpu_b.npu()
cpu_output = torch.not_equal(cpu_a, cpu_b)
npu_output = torch.not_equal(cpu_a, npu_b)
self.assertEqual(cpu_output, npu_output.cpu())
def test_not_equal_inplace_second_arg_0d_cpu_tensor(self):
cpu_a = torch.tensor([1.0, 2.0, 3.0])
cpu_b = torch.tensor(2.0)
npu_a = cpu_a.clone().npu()
cpu_a.not_equal_(cpu_b)
npu_a.not_equal_(cpu_b)
self.assertEqual(cpu_a, npu_a.cpu())
if __name__ == "__main__":
run_tests()
