import unittest
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 TestRsub(TestCase):
def cpu_op_exec(self, input1, input2):
output = torch.rsub(input1, input2)
output = output.numpy()
return output
def npu_op_exec(self, input1, input2):
output = torch.rsub(input1, input2)
output = output.to("cpu")
output = output.numpy()
return output
def npu_op_exec_scalar(self, input1, input2):
output = torch.rsub(input1, input2)
output = output.to("cpu")
output = output.numpy()
return output
def rsub_result(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100)
cpu_input2, npu_input2 = create_common_tensor(item[1], 0, 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)
cpu_output = cpu_output.astype(npu_output.dtype)
self.assertRtolEqual(cpu_output, npu_output)
def rsub_scalar_result(self, shape_format):
for item in shape_format:
scalar = np.random.uniform(0, 100)
cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100)
cpu_output = self.cpu_op_exec(cpu_input1, scalar)
npu_output_scalar = self.npu_op_exec_scalar(npu_input1, scalar)
self.assertRtolEqual(cpu_output, npu_output_scalar)
def test_sub_shape_format_fp16_1d(self):
format_list = [-1, 0, 3]
shape_format = [[[np.float16, i, [32]], [np.float16, i, [32]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_fp32_1d(self):
format_list = [-1, 0, 3]
shape_format = [[[np.float16, i, [32]], [np.float16, i, [32]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_fp16_2d(self):
format_list = [-1, 0, 3, 29]
shape_format = [[[np.float16, i, [5, 3]], [np.float16, i, [5, 3]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_fp32_2d(self):
format_list = [-1, 0, 3, 29]
shape_format = [[[np.float16, i, [5, 3]], [np.float16, i, [5, 3]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_fp16_3d(self):
format_list = [-1, 0, 3, 29]
shape_format = [[[np.float16, i, [256, 480, 14]], [np.float16, i, [256, 480, 14]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_fp32_3d(self):
format_list = [-1, 0, 3, 29]
shape_format = [[[np.float16, i, [256, 480, 14]], [np.float16, i, [256, 480, 14]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_fp16_4d(self):
format_list = [-1, 0, 3, 29]
shape_format = [[[np.float16, i, [32, 3, 3, 3]], [np.float16, i, [32, 3, 3, 3]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_fp32_4d(self):
format_list = [-1, 0, 3, 29]
shape_format = [[[np.float16, i, [32, 3, 3, 3]], [np.float16, i, [32, 3, 3, 3]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_int32_1d(self):
format_list = [-1, 0]
shape_format = [[[np.int32, i, [32]], [np.int32, i, [32]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_int32_2d(self):
format_list = [-1, 0]
shape_format = [[[np.int32, i, [5, 3]], [np.int32, i, [5, 3]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_int32_3d(self):
format_list = [-1, 0]
shape_format = [[[np.int32, i, [256, 480, 14]], [np.int32, i, [256, 480, 14]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_shape_format_int32_4d(self):
format_list = [-1, 0]
shape_format = [[[np.int32, i, [32, 3, 3, 3]], [np.int32, i, [32, 3, 3, 3]]] for i in format_list]
self.rsub_result(shape_format)
def test_sub_scalar_shape_format_fp16_1d(self):
format_list = [-1, 0]
shape_format = [[[np.float16, i, [32]]] for i in format_list]
self.rsub_scalar_result(shape_format)
def test_sub_scalar_shape_format_fp32_1d(self):
format_list = [-1, 0]
shape_format = [[[np.float16, i, [32]]] for i in format_list]
self.rsub_scalar_result(shape_format)
def test_sub_scalar_shape_format_fp16_2d(self):
format_list = []
shape_format = [[[np.float16, i, [32, 64]]] for i in format_list]
self.rsub_scalar_result(shape_format)
def test_sub_scalar_shape_format_fp32_2d(self):
format_list = []
shape_format = [[[np.float16, i, [32, 64]]] for i in format_list]
self.rsub_scalar_result(shape_format)
def test_sub_scalar_shape_format_fp16_3d(self):
format_list = []
shape_format = [[[np.float16, i, [32, 64, 128]]] for i in format_list]
self.rsub_scalar_result(shape_format)
def test_sub_scalar_shape_format_fp32_3d(self):
format_list = []
shape_format = [[[np.float16, i, [32, 64, 128]]] for i in format_list]
self.rsub_scalar_result(shape_format)
def test_sub_scalar_shape_format_fp16_4d(self):
format_list = []
shape_format = [[[np.float16, i, [32, 64, 128, 28]]] for i in format_list]
self.rsub_scalar_result(shape_format)
def test_sub_scalar_shape_format_fp32_4d(self):
format_list = []
shape_format = [[[np.float16, i, [32, 64, 128, 28]]] for i in format_list]
self.rsub_scalar_result(shape_format)
def test_scalar_sub_byte(self):
s_cpu = torch.tensor([0, 1, 2, 3, 4]).byte()
s_npu = s_cpu.npu()
c_out = 1 - s_cpu
n_out = 1 - s_npu
self.assertRtolEqual(c_out.numpy(), n_out.cpu().numpy())
def test_rsub_second_arg_0d_cpu_tensor(self):
cpu_a = torch.tensor([1.0, 2.0, 3.0])
cpu_b = torch.tensor(5.0)
npu_a = cpu_a.npu()
cpu_output = torch.rsub(cpu_a, cpu_b)
npu_output = torch.rsub(npu_a, cpu_b)
self.assertRtolEqual(cpu_output, npu_output.cpu())
def test_rsub_first_arg_0d_cpu_tensor(self):
cpu_a = torch.tensor(5.0)
cpu_b = torch.tensor([1.0, 2.0, 3.0])
npu_b = cpu_b.npu()
cpu_output = torch.rsub(cpu_a, cpu_b)
npu_output = torch.rsub(cpu_a, npu_b)
self.assertRtolEqual(cpu_output, npu_output.cpu())
if __name__ == "__main__":
run_tests()
