import copy
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 TestRemainder(TestCase):
def cpu_op_exec(self, input1, input2):
output = torch.remainder(input1, input2)
output = output.numpy()
return output
def npu_op_exec(self, input1, input2):
output = torch.remainder(input1, input2)
output = output.to("cpu")
output = output.numpy()
return output
def npu_op_exec_out(self, input1, input2, out):
output = torch.remainder(input1, input2, out=out)
output = out.to("cpu")
output = output.numpy()
return output
def cpu_op_inplace_exec(self, input1, input2):
output = input1.remainder_(input2)
output = output.numpy()
return output
def npu_op_inplace_exec(self, input1, input2):
output = input1.remainder_(input2)
output = input1.to("cpu")
output = output.numpy()
return output
def npu_op_exec_scalar(self, input1, input2):
output = torch.remainder(input1, input2)
output = output.to("cpu")
output = output.numpy()
return output
def remainder_out_result(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 100)
cpu_input2, npu_input2 = create_common_tensor(item, 0, 100)
npu_input3 = torch.randn(6).to("npu")
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_out = self.npu_op_exec_out(npu_input1, npu_input2, npu_input3)
self.assertRtolEqual(cpu_output, npu_output_out)
def remainder_result(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item, 0, 100)
cpu_input2, npu_input2 = create_common_tensor(item, 0, 100)
npu_input3 = copy.deepcopy(cpu_input1).to("npu")
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)
npu_output_out = self.npu_op_exec_out(npu_input1, npu_input2, npu_input3)
cpu_output_inplace = self.cpu_op_inplace_exec(cpu_input1, cpu_input2)
npu_output_inplace = self.npu_op_inplace_exec(npu_input1, npu_input2)
cpu_output = cpu_output.astype(npu_output.dtype)
cpu_output_inplace = cpu_output_inplace.astype(npu_output_inplace.dtype)
self.assertRtolEqual(cpu_output, npu_output)
self.assertRtolEqual(cpu_output_inplace, npu_output_inplace)
self.assertRtolEqual(cpu_output, npu_output_out)
def remainder_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, 2)
npu_input3 = copy.deepcopy(cpu_input1).to("npu")
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output = self.cpu_op_exec(cpu_input1, scalar)
npu_output_scalar = self.npu_op_exec_scalar(npu_input1, scalar)
npu_output_out = self.npu_op_exec_out(npu_input1, scalar, npu_input3)
cpu_output = cpu_output.astype(npu_output_scalar.dtype)
self.assertRtolEqual(cpu_output, npu_output_scalar)
self.assertRtolEqual(cpu_output, npu_output_out)
def test_remainder_shape_format_fp16_1d(self):
format_list = [0, 3]
shape_format = [[np.float16, i, [4]] for i in format_list
]
self.remainder_result(shape_format)
def test_remainder_shape_format_fp32_1d(self):
format_list = [0, 3]
shape_format = [[np.float32, i, [4]] for i in format_list
]
self.remainder_result(shape_format)
def test_remainder_shape_format_fp16_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [4, 18]] for i in format_list
]
self.remainder_result(shape_format)
def test_remainder_shape_format_fp32_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [4, 18]] for i in format_list
]
self.remainder_result(shape_format)
def test_remainder_shape_format_fp16_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [4, 18, 32]] for i in format_list
]
self.remainder_result(shape_format)
def test_remainder_shape_format_fp32_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [4, 18, 32]] for i in format_list
]
self.remainder_result(shape_format)
def test_remainder_shape_format_fp16_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [4, 18, 32, 128]] for i in format_list
]
self.remainder_result(shape_format)
def test_remainder_shape_format_fp32_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [4, 18, 32, 128]] for i in format_list
]
self.remainder_result(shape_format)
def test_remainder_scalar_shape_format_fp16_1d(self):
format_list = [0, 3]
shape_format = [[np.float16, i, [4]] for i in format_list
]
self.remainder_scalar_result(shape_format)
def test_remainder_scalar_shape_format_fp32_1d(self):
format_list = [0, 3]
shape_format = [[np.float32, i, [4]] for i in format_list
]
self.remainder_scalar_result(shape_format)
def test_remainder_scalar_shape_format_fp16_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [4, 18]] for i in format_list
]
self.remainder_scalar_result(shape_format)
def test_remainder_scalar_shape_format_fp32_2d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [4, 18]] for i in format_list
]
self.remainder_scalar_result(shape_format)
def test_remainder_scalar_shape_format_fp16_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [4, 18, 32]] for i in format_list
]
self.remainder_scalar_result(shape_format)
def test_remainder_scalar_shape_format_fp32_3d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [4, 18, 32]] for i in format_list
]
self.remainder_scalar_result(shape_format)
def test_remainder_scalar_shape_format_fp16_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float16, i, [4, 18, 32, 128]] for i in format_list
]
self.remainder_scalar_result(shape_format)
def test_remainder_scalar_shape_format_fp32_4d(self):
format_list = [0, 3, 29]
shape_format = [[np.float32, i, [4, 18, 32, 128]] for i in format_list
]
self.remainder_scalar_result(shape_format)
def test_remainder_mix_dtype_1(self):
npu_input1, npu_input2 = create_common_tensor([np.int32, 0, (2, 3)], 1, 100)
npu_input3, npu_input4 = create_common_tensor([np.float32, 0, (2, 3)], 1, 100)
cpu_output = self.cpu_op_exec(npu_input1, npu_input3)
npu_output = self.npu_op_exec(npu_input1, npu_input3)
self.assertRtolEqual(cpu_output, npu_output)
def test_remainder_mix_dtype_2(self):
npu_input1, npu_input2 = create_common_tensor([np.float32, 0, (2, 3)], 1, 100)
npu_input3 = torch.tensor(3).int()
cpu_output = self.cpu_op_exec(npu_input1, npu_input3)
npu_output = self.npu_op_exec(npu_input1, npu_input3)
self.assertRtolEqual(cpu_output, npu_output)
def test_remainder_scalar_shape_format_fp32_out_4d(self):
format_list = [0]
shape_format = [[np.float32, i, [4, 18, 32, 128]] for i in format_list
]
self.remainder_out_result(shape_format)
def test_remainder_second_arg_0d_cpu_tensor(self):
cpu_a = torch.tensor([5.0, 7.0, 9.0])
cpu_b = torch.tensor(3.0)
npu_a = cpu_a.npu()
cpu_output = torch.remainder(cpu_a, cpu_b)
npu_output = torch.remainder(npu_a, cpu_b)
self.assertRtolEqual(cpu_output, npu_output.cpu())
def test_remainder_first_arg_0d_cpu_tensor(self):
cpu_a = torch.tensor(7.0)
cpu_b = torch.tensor([3.0, 4.0, 5.0])
npu_b = cpu_b.npu()
cpu_output = torch.remainder(cpu_a, cpu_b)
npu_output = torch.remainder(cpu_a, npu_b)
self.assertRtolEqual(cpu_output, npu_output.cpu())
def test_remainder_inplace_second_arg_0d_cpu_tensor(self):
cpu_a = torch.tensor([5.0, 7.0, 9.0])
cpu_b = torch.tensor(3.0)
npu_a = cpu_a.clone().npu()
cpu_a.remainder_(cpu_b)
npu_a.remainder_(cpu_b)
self.assertRtolEqual(cpu_a, npu_a.cpu())
if __name__ == "__main__":
run_tests()