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
_TORCH_HAS_NAMED_TENSOR = hasattr(torch.Tensor, "refine_names")
class TestMin(TestCase):
def cpu_op_exec(self, input1):
output = torch.min(input1)
output = output.numpy()
return output
def npu_op_exec(self, input1):
output = torch.min(input1)
output = output.to("cpu")
output = output.numpy()
return output
def cpu_op_other_exec(self, input1, input2):
output = torch.min(input1, input2)
output = output.numpy()
return output
def npu_op_other_exec(self, input1, input2):
input1 = input1.to("npu")
input2 = input2.to("npu")
output = torch.min(input1, input2)
output = output.to("cpu")
output = output.numpy()
return output
def npu_op_other_exec_out(self, input1, input2, out):
torch.min(input1, input2, out=out)
output = out.to("cpu")
output = output.numpy()
return output
def cpu_op_dim_exec(self, input1, dim, keepdim):
output1, output2 = torch.min(input1, dim, keepdim)
output1 = output1.numpy()
output2 = output2.int().numpy()
return output1, output2
def npu_op_dim_exec(self, input1, dim, keepdim):
input1 = input1.to("npu")
output1, output2 = torch.min(input1, dim, keepdim)
output1 = output1.to("cpu")
output2 = output2.to("cpu")
output1 = output1.numpy()
output2 = output2.numpy()
return output1, output2
def cpu_op_dim_exec_out(self, input1, dim, keepdim):
out = torch.tensor(0).to(input1.dtype)
indices = torch.tensor(0).to(torch.long)
torch.min(input1, dim=dim, keepdim=keepdim, out=(out, indices))
out = out.numpy()
indices = indices.numpy()
return out, indices
def npu_op_dim_exec_out(self, input1, dim, keepdim):
out = torch.tensor(0).to(input1.dtype).npu()
indices = torch.tensor(0).to(torch.long).npu()
torch.min(input1, dim=dim, keepdim=keepdim, out=(out, indices))
out = out.to("cpu").numpy()
indices = indices.to("cpu").numpy()
return out, indices
def cpu_op_amin_exec(self, input1, dim, keepdim):
output = torch.amin(input1, dim, keepdim)
output = output.numpy()
return output
def npu_op_amin_exec(self, input1, dim, keepdim):
output = torch.amin(input1, dim, keepdim)
output = output.to("cpu")
output = output.numpy()
return output
def npu_op_amin_exec_out(self, input1, dim, keepdim, out):
torch.amin(input1, dim, keepdim, out=out)
output = out.to("cpu")
output = output.numpy()
return output
def min_result(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output = self.cpu_op_exec(cpu_input1)
npu_output = self.npu_op_exec(npu_input1)
cpu_output = cpu_output.astype(npu_output.dtype)
self.assertRtolEqual(cpu_output, npu_output)
def min_result_dim(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output_dim, cpu_output_indices = self.cpu_op_dim_exec(cpu_input1, item[1], item[2])
npu_output_dim, npu_output_indices = self.cpu_op_dim_exec(cpu_input1, item[1], item[2])
cpu_output_dim = cpu_output_dim.astype(npu_output_dim.dtype)
self.assertRtolEqual(cpu_output_dim, npu_output_dim)
def min_result_other(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[0], 0, 7)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_input2 = cpu_input2.to(torch.float32)
cpu_output_other = self.cpu_op_other_exec(cpu_input1, cpu_input2)
npu_output_other = self.npu_op_other_exec(npu_input1, npu_input2)
cpu_output_other = cpu_output_other.astype(npu_output_other.dtype)
self.assertRtolEqual(cpu_output_other, npu_output_other)
def min_out_result_other(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item[0], -100, 100)
cpu_input2, npu_input2 = create_common_tensor(item[0], -100, 100)
cpu_input3, npu_input3 = create_common_tensor(item[0], -100, 100)
cpu_input4, npu_input4 = create_common_tensor(item[1], -100, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
if cpu_input2.dtype == torch.float16:
cpu_input2 = cpu_input2.to(torch.float32)
cpu_output = self.cpu_op_other_exec(cpu_input1, cpu_input2)
npu_output_out1 = self.npu_op_other_exec(npu_input1, npu_input2)
npu_output_out2 = self.npu_op_other_exec_out(npu_input1, npu_input2, npu_input4)
cpu_output = cpu_output.astype(npu_output_out1.dtype)
self.assertRtolEqual(cpu_output, npu_output_out1)
self.assertRtolEqual(cpu_output, npu_output_out2)
cpu_out_dim, cpu_out_indices = self.cpu_op_dim_exec_out(cpu_input1, dim=0, keepdim=True)
npu_out_dim, npu_out_indices = self.npu_op_dim_exec_out(npu_input1, dim=0, keepdim=True)
npu_output_dim, npu_output_indices = self.npu_op_dim_exec(npu_input1, dim=0, keepdim=True)
cpu_out_dim = cpu_out_dim.astype(npu_out_dim.dtype)
if cpu_out_dim.dtype != np.float16:
self.assertRtolEqual(npu_out_dim, cpu_out_dim)
self.assertRtolEqual(npu_out_indices, cpu_out_indices)
else:
self.assertRtolEqual(npu_out_dim, npu_output_dim)
self.assertRtolEqual(npu_out_indices, npu_output_indices)
def min_name_result_other(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100)
cpu_input1.names = item[0][3]
npu_input1.names = item[0][3]
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output_dim, cpu_output_indices = self.cpu_op_dim_exec(cpu_input1, item[1], item[2])
npu_output_dim, npu_output_indices = self.npu_op_dim_exec(cpu_input1, item[1], item[2])
if npu_output_dim.dtype != np.float16:
self.assertRtolEqual(npu_output_dim, cpu_output_dim)
self.assertRtolEqual(npu_output_indices.astype(np.int32), cpu_output_indices.astype(np.int32))
else:
self.assertRtolEqual(npu_output_dim, cpu_output_dim.astype(np.float16))
self.assertRtolEqual(npu_output_indices.astype(np.int32), cpu_output_indices.astype(np.int32))
def min_name_out_result_other(self, shape_format):
for item in shape_format:
cpu_input1, npu_input1 = create_common_tensor(item[0], 0, 100)
cpu_input1.names = item[0][3]
npu_input1.names = item[0][3]
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output_dim, cpu_output_indices = self.cpu_op_dim_exec_out(cpu_input1, item[1], item[2])
npu_output_dim, npu_output_indices = self.npu_op_dim_exec_out(npu_input1, item[1], item[2])
if npu_output_dim.dtype != np.float16:
self.assertRtolEqual(npu_output_dim, cpu_output_dim)
self.assertRtolEqual(npu_output_indices.astype(np.int32), cpu_output_indices.astype(np.int32))
else:
self.assertRtolEqual(npu_output_dim, cpu_output_dim.astype(np.float16))
self.assertRtolEqual(npu_output_indices.astype(np.int32), cpu_output_indices.astype(np.int32))
def amin_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[0], 0, 100)
if cpu_input1.dtype == torch.float16:
cpu_input1 = cpu_input1.to(torch.float32)
cpu_output_amin = self.cpu_op_amin_exec(cpu_input1, item[1], item[2])
npu_output_amin = self.npu_op_amin_exec(npu_input1, item[1], item[2])
npu_output_amin_out = self.npu_op_amin_exec_out(npu_input1, item[1], item[2], npu_input2)
cpu_output_amin = cpu_output_amin.astype(npu_output_amin.dtype)
self.assertRtolEqual(cpu_output_amin, npu_output_amin)
self.assertRtolEqual(cpu_output_amin, npu_output_amin_out)
def test_min_out_result(self):
shape_format = [
[[np.float16, 0, [9, 10, 14, 14]], [np.float16, 0, [7, 10, 1, 1]]],
[[np.float16, 0, [9, 7, 12, 12]], [np.float16, 0, [7, 7, 1, 1]]],
[[np.float32, 0, [2, 3, 3, 3]], [np.float32, 0, [3, 1, 3]]],
[[np.float32, 0, [9, 13, 7, 7]], [np.float32, 0, [9, 13, 7, 7]]],
]
self.min_out_result_other(shape_format)
def test_min_shape_format_fp16_1d(self):
format_list = [0, 3]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8]], np.random.randint(0, 1), j] for i in format_list for j in keepdim_list
]
self.min_result(shape_format)
def test_min_shape_format_fp32_1d(self):
format_list = [0, 3]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.min_result(shape_format)
def test_min_shape_format_fp16_2d(self):
format_list = [0, 3]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.min_result(shape_format)
def test_min_shape_format_fp32_2d(self):
format_list = [0, 3]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.min_result(shape_format)
def test_min_shape_format_fp16_3d(self):
format_list = [0, 3, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.min_result(shape_format)
def test_min_shape_format_fp32_3d(self):
format_list = [0, 3, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.min_result(shape_format)
def test_min_shape_format_fp16_4d(self):
format_list = [0, 4, 3, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9, 10]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.min_result(shape_format)
def test_min_shape_format_fp32_4d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9, 10]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.min_result(shape_format)
def test_min_dim_shape_format_fp16_1d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8]], np.random.randint(0, 1), j] for i in format_list for j in keepdim_list
]
self.min_result_dim(shape_format)
def test_min_dim_shape_format_fp32_1d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.min_result_dim(shape_format)
def test_min_dim_shape_format_fp16_2d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7]], np.random.randint(0, 2), j] for i in format_list for j in
keepdim_list
]
self.min_result_dim(shape_format)
def test_min_dim_shape_format_fp32_2d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7]], np.random.randint(0, 2), j] for i in format_list for j in
keepdim_list
]
self.min_result_dim(shape_format)
def test_min_dim_shape_format_fp16_3d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9]], np.random.randint(0, 3), j] for i in format_list for j in
keepdim_list
]
self.min_result_dim(shape_format)
def test_min_dim_shape_format_fp32_3d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9]], np.random.randint(0, 3), j] for i in format_list for j in
keepdim_list
]
self.min_result_dim(shape_format)
def test_min_dim_shape_format_fp16_4d(self):
format_list = [0, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9, 10]], np.random.randint(0, 4), j] for i in format_list for j
in keepdim_list
]
self.min_result_dim(shape_format)
def test_min_dim_shape_format_fp32_4d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9, 10]], np.random.randint(0, 4), j] for i in format_list for j
in keepdim_list
]
self.min_result_dim(shape_format)
def test_min_other_shape_format_fp16_1d(self):
format_list = [0, 3, 4]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8]], np.random.randint(0, 1), j] for i in format_list for j in keepdim_list
]
self.min_result_other(shape_format)
def test_min_other_shape_format_fp32_1d(self):
format_list = [0, 3, 4]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.min_result_other(shape_format)
def test_min_other_shape_format_fp16_2d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7]], np.random.randint(0, 2), j] for i in format_list for j in
keepdim_list
]
self.min_result_other(shape_format)
def test_min_other_shape_format_fp32_2d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7]], np.random.randint(0, 2), j] for i in format_list for j in
keepdim_list
]
self.min_result_other(shape_format)
def test_min_other_shape_format_fp16_3d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9]], np.random.randint(0, 3), j] for i in format_list for j in
keepdim_list
]
self.min_result_other(shape_format)
def test_min_other_shape_format_fp32_3d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9]], np.random.randint(0, 3), j] for i in format_list for j in
keepdim_list
]
self.min_result_other(shape_format)
def test_min_other_shape_format_fp16_4d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9, 10]], np.random.randint(0, 4), j] for i in format_list for j
in keepdim_list
]
self.min_result_other(shape_format)
def test_min_other_shape_format_fp32_4d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9, 10]], np.random.randint(0, 4), j] for i in format_list for j
in keepdim_list
]
self.min_result_other(shape_format)
@unittest.skipUnless(_TORCH_HAS_NAMED_TENSOR, "Named tensor removed in PyTorch 2.13 (pytorch#173895)")
def test_min_dimname_shape_format(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9, 10], ('N', 'C', 'H', 'W')],
np.random.choice(['N', 'C', 'H', 'W']), j] for i in format_list for j
in
keepdim_list
]
self.min_name_result_other(shape_format)
@unittest.skipUnless(_TORCH_HAS_NAMED_TENSOR, "Named tensor removed in PyTorch 2.13 (pytorch#173895)")
def test_min_dimname_shape_format_fp16(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9, 10], ('N', 'C', 'H', 'W')],
np.random.choice(['N', 'C', 'H', 'W']), j] for i in format_list for j
in
keepdim_list
]
self.min_name_result_other(shape_format)
@unittest.skipUnless(_TORCH_HAS_NAMED_TENSOR, "Named tensor removed in PyTorch 2.13 (pytorch#173895)")
def test_min_dimname_out_shape_format(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9, 10], ('N', 'C', 'H', 'W')],
np.random.choice(['N', 'C', 'H', 'W']), j] for i in format_list for j
in
keepdim_list
]
self.min_name_out_result_other(shape_format)
@unittest.skipUnless(_TORCH_HAS_NAMED_TENSOR, "Named tensor removed in PyTorch 2.13 (pytorch#173895)")
def test_min_dimname_out_shape_format_fp16(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9, 10], ('N', 'C', 'H', 'W')],
np.random.choice(['N', 'C', 'H', 'W']), j] for i in format_list for j
in
keepdim_list
]
self.min_name_out_result_other(shape_format)
def test_amin_shape_format_fp16_1d(self):
format_list = [0, 3, 4]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8]], np.random.randint(0, 1), j] for i in format_list for j in keepdim_list
]
self.amin_result(shape_format)
def test_amin_shape_format_fp32_1d(self):
format_list = [0, 3, 4]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8]], np.random.randint(0, 1), j] for i in format_list for j in
keepdim_list
]
self.amin_result(shape_format)
def test_amin_shape_format_fp16_2d(self):
format_list = [0, 3, 4]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7]], np.random.randint(0, 2), j] for i in format_list for j in
keepdim_list
]
self.amin_result(shape_format)
def test_amin_shape_format_fp32_2d(self):
format_list = [0, 3, 4]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7]], np.random.randint(0, 2), j] for i in format_list for j in
keepdim_list
]
self.amin_result(shape_format)
def test_amin_shape_format_fp16_3d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9]], np.random.randint(0, 3), j] for i in format_list for j in
keepdim_list
]
self.amin_result(shape_format)
def test_amin_shape_format_fp32_3d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9]], np.random.randint(0, 3), j] for i in format_list for j in
keepdim_list
]
self.amin_result(shape_format)
def test_amin_shape_format_fp16_4d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float16, i, [8, 7, 9, 10]], np.random.randint(0, 4), j] for i in format_list for j
in keepdim_list
]
self.amin_result(shape_format)
def test_amin_shape_format_fp32_4d(self):
format_list = [0, 3, 4, 29]
keepdim_list = [True, False]
shape_format = [[[np.float32, i, [8, 7, 9, 10]], np.random.randint(0, 4), j] for i in format_list for j
in keepdim_list
]
self.amin_result(shape_format)
def test_minimum_scalar(self):
a = torch.randn(2, 3).npu()
b = torch.tensor(1)
res1 = torch.minimum(a, b)
res2 = torch.minimum(b, a)
self.assertEqual(res1, res2)
if __name__ == "__main__":
run_tests()
