def test_dot(self):
types = {
'torch.DoubleTensor': 1e-8,
'torch.FloatTensor': 1e-4,
}
for tname, _prec in types.items():
v1 = torch.randn(100).type(tname)
v2 = torch.randn(100).type(tname)
res1 = torch.dot(v1, v2)
res2 = 0
for i, j in zip(v1, v2):
res2 += i * j
self.assertEqual(res1, res2)
# Test 0-strided
for tname, _prec in types.items():
v1 = torch.randn(1).type(tname).expand(100)
v2 = torch.randn(100).type(tname)
res1 = torch.dot(v1, v2)
res2 = 0
for i, j in zip(v1, v2):
res2 += i * j
self.assertEqual(res1, res2)
python类DoubleTensor()的实例源码
def default_collate(batch):
"Puts each data field into a tensor with outer dimension batch size"
if torch.is_tensor(batch[0]):
return torch.cat([t.view(1, *t.size()) for t in batch], 0)
elif isinstance(batch[0], int):
return torch.LongTensor(batch)
elif isinstance(batch[0], float):
return torch.DoubleTensor(batch)
elif isinstance(batch[0], str):
return batch
elif isinstance(batch[0], collections.Iterable):
# if each batch element is not a tensor, then it should be a tuple
# of tensors; in that case we collate each element in the tuple
transposed = zip(*batch)
return [default_collate(samples) for samples in transposed]
raise TypeError(("batch must contain tensors, numbers, or lists; found {}"
.format(type(batch[0]))))
def test_cuda(self, test_case):
if not TEST_CUDA or not self.should_test_cuda:
raise unittest.SkipTest('Excluded from CUDA tests')
try:
cpu_input = self._get_input()
type_map = {
torch.DoubleTensor: torch.cuda.FloatTensor,
}
gpu_input = to_gpu(cpu_input, type_map=type_map)
cpu_target = self.target
gpu_target = to_gpu(self.target, type_map=type_map)
cpu_module = self.constructor(*self.constructor_args)
gpu_module = self.constructor(*self.constructor_args).float().cuda()
cpu_output = test_case._forward_criterion(cpu_module, cpu_input, cpu_target)
gpu_output = test_case._forward_criterion(gpu_module, gpu_input, gpu_target)
test_case.assertEqual(cpu_output, gpu_output, 2e-4)
cpu_gradInput = test_case._backward_criterion(cpu_module, cpu_input, cpu_target)
gpu_gradInput = test_case._backward_criterion(gpu_module, gpu_input, gpu_target)
test_case.assertEqual(cpu_gradInput, gpu_gradInput, 2e-4)
except NotImplementedError:
pass
def test_Copy(self):
input = torch.randn(3,4).double()
c = nn.Copy(torch.DoubleTensor, torch.FloatTensor)
output = c.forward(input)
self.assertEqual(torch.typename(output), 'torch.FloatTensor')
self.assertEqual(output, input.float(), 1e-6)
gradInput = c.backward(input, output.fill_(1))
self.assertEqual(torch.typename(gradInput), 'torch.DoubleTensor')
self.assertEqual(gradInput, output.double(), 1e-6)
c.dontCast = True
c.double()
self.assertEqual(torch.typename(output), 'torch.FloatTensor')
# Check that these don't raise errors
c.__repr__()
str(c)
def test_abs(self):
size = 1000
max_val = 1000
original = torch.rand(size).mul(max_val)
# Tensor filled with values from {-1, 1}
switch = torch.rand(size).mul(2).floor().mul(2).add(-1)
types = ['torch.DoubleTensor', 'torch.FloatTensor', 'torch.LongTensor', 'torch.IntTensor']
for t in types:
data = original.type(t)
switch = switch.type(t)
res = torch.mul(data, switch)
self.assertEqual(res.abs(), data, 1e-16)
# Checking that the right abs function is called for LongTensor
bignumber = 2^31 + 1
res = torch.LongTensor((-bignumber,))
self.assertGreater(res.abs()[0], 0)
def __init__(self, n, Qpenalty, nineq):
super().__init__()
nx = (n**2)**3
self.Q = Variable(Qpenalty*torch.eye(nx).double().cuda())
self.G1 = Variable(-torch.eye(nx).double().cuda())
self.h1 = Variable(torch.zeros(nx).double().cuda())
# if trueInit:
# self.A = Parameter(torch.DoubleTensor(get_sudoku_matrix(n)).cuda())
# else:
# # t = get_sudoku_matrix(n)
# # self.A = Parameter(torch.rand(t.shape).double().cuda())
# # import IPython, sys; IPython.embed(); sys.exit(-1)
self.A = Parameter(torch.rand(50,nx).double().cuda())
self.G2 = Parameter(torch.Tensor(128, nx).uniform_(-1,1).double().cuda())
self.z2 = Parameter(torch.zeros(nx).double().cuda())
self.s2 = Parameter(torch.ones(128).double().cuda())
# self.b = Variable(torch.ones(self.A.size(0)).double().cuda())
def forward(self, puzzles):
nBatch = puzzles.size(0)
x = puzzles.view(nBatch,-1)
x = self.fc_in(x)
e = Variable(torch.Tensor())
h = self.G.mv(self.z)+self.s
x = QPFunction(verbose=False)(
self.Q, x, self.G, h, e, e,
)
x = self.fc_out(x)
x = x.view_as(puzzles)
return x
# if __name__=="__main__":
# sudoku = SolveSudoku(2, 0.2)
# puzzle = [[4, 0, 0, 0], [0,0,4,0], [0,2,0,0], [0,0,0,1]]
# Y = Variable(torch.DoubleTensor(np.array([[np.array(np.eye(5,4,-1)[i,:]) for i in row] for row in puzzle])).cuda())
# solution = sudoku(Y.unsqueeze(0))
# print(solution.view(1,4,4,4))
def test_type(self):
l = nn.Linear(10, 20)
net = nn.Module()
net.l = l
net.l2 = l
net.add_module('empty', None)
net.float()
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
net.double()
self.assertIsInstance(l.weight.data, torch.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.DoubleTensor)
net.type(torch.FloatTensor)
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
net.type(torch.DoubleTensor)
self.assertIsInstance(l.weight.data, torch.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.DoubleTensor)
if TEST_CUDA:
net.type(torch.cuda.FloatTensor)
self.assertIsInstance(l.weight.data, torch.cuda.FloatTensor)
self.assertIsInstance(l.bias.data, torch.cuda.FloatTensor)
def test_type_conversions(self):
x = Variable(torch.randn(5, 5))
self.assertIs(type(x.float().data), torch.FloatTensor)
self.assertIs(type(x.int().data), torch.IntTensor)
if torch.cuda.is_available():
self.assertIs(type(x.float().cuda().data), torch.cuda.FloatTensor)
self.assertIs(type(x.int().cuda().data), torch.cuda.IntTensor)
self.assertIs(type(x.int().cuda().cpu().data), torch.IntTensor)
if torch.cuda.device_count() > 2:
x2 = x.float().cuda(1)
self.assertIs(type(x2.data), torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 1)
x2 = x.float().cuda()
self.assertIs(type(x2.data), torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 0)
x2 = x2.cuda(1)
self.assertIs(type(x2.data), torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 1)
for t in [torch.DoubleTensor, torch.FloatTensor, torch.IntTensor, torch.ByteTensor]:
y = Variable(torch.randn(5, 5).type(t))
self.assertIs(type(x.type_as(y).data), t)
def _test_barrier_helper(self, group, group_id, rank):
WAIT_TIME = 0.3 # seconds
for dest in group:
expected_time = torch.DoubleTensor(1).fill_(0.0)
if dest == rank:
expected_time.fill_(time.time() + WAIT_TIME)
dist.broadcast(expected_time, dest, group_id)
time.sleep(WAIT_TIME + 0.1) # sleep a little bit longer
dist.barrier(group_id)
else:
dist.broadcast(expected_time, dest, group_id)
dist.barrier(group_id)
self.assertGreaterEqual(time.time(), expected_time[0])
self._barrier()
def test_serialization_array_with_storage(self):
x = torch.randn(5, 5).cuda()
y = torch.IntTensor(2, 5).fill_(0).cuda()
q = [x, y, x, y.storage()]
with tempfile.NamedTemporaryFile() as f:
torch.save(q, f)
f.seek(0)
q_copy = torch.load(f)
self.assertEqual(q_copy, q, 0)
q_copy[0].fill_(5)
self.assertEqual(q_copy[0], q_copy[2], 0)
self.assertTrue(isinstance(q_copy[0], torch.cuda.DoubleTensor))
self.assertTrue(isinstance(q_copy[1], torch.cuda.IntTensor))
self.assertTrue(isinstance(q_copy[2], torch.cuda.DoubleTensor))
self.assertTrue(isinstance(q_copy[3], torch.cuda.IntStorage))
q_copy[1].fill_(10)
self.assertTrue(q_copy[3], torch.cuda.IntStorage(10).fill_(10))
def test_cuda(self, test_case):
if not TEST_CUDA or not self.should_test_cuda:
raise unittest.SkipTest('Excluded from CUDA tests')
try:
cpu_input = self._get_input()
type_map = {
torch.DoubleTensor: torch.cuda.FloatTensor,
}
gpu_input = to_gpu(cpu_input, type_map=type_map)
cpu_target = self.target
gpu_target = to_gpu(self.target, type_map=type_map)
cpu_module = self.constructor(*self.constructor_args)
gpu_module = self.constructor(*self.constructor_args).float().cuda()
cpu_output = test_case._forward_criterion(cpu_module, cpu_input, cpu_target)
gpu_output = test_case._forward_criterion(gpu_module, gpu_input, gpu_target)
test_case.assertEqual(cpu_output, gpu_output, 4e-4)
cpu_gradInput = test_case._backward_criterion(cpu_module, cpu_input, cpu_target)
gpu_gradInput = test_case._backward_criterion(gpu_module, gpu_input, gpu_target)
test_case.assertEqual(cpu_gradInput, gpu_gradInput, 4e-4)
except NotImplementedError:
pass
def test_Copy(self):
input = torch.randn(3, 4).double()
c = nn.Copy(torch.DoubleTensor, torch.FloatTensor)
output = c.forward(input)
self.assertEqual(torch.typename(output), 'torch.FloatTensor')
self.assertEqual(output, input.float(), 1e-6)
gradInput = c.backward(input, output.fill_(1))
self.assertEqual(torch.typename(gradInput), 'torch.DoubleTensor')
self.assertEqual(gradInput, output.double(), 1e-6)
c.dontCast = True
c.double()
self.assertEqual(torch.typename(output), 'torch.FloatTensor')
# Check that these don't raise errors
c.__repr__()
str(c)
def test_type(self):
l = nn.Linear(10, 20)
net = nn.Module()
net.l = l
net.l2 = l
net.add_module('empty', None)
net.float()
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
net.double()
self.assertIsInstance(l.weight.data, torch.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.DoubleTensor)
net.type(torch.FloatTensor)
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
net.type(torch.DoubleTensor)
self.assertIsInstance(l.weight.data, torch.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.DoubleTensor)
if TEST_CUDA:
net.type(torch.cuda.FloatTensor)
self.assertIsInstance(l.weight.data, torch.cuda.FloatTensor)
self.assertIsInstance(l.bias.data, torch.cuda.FloatTensor)
def test_mark_non_differentiable(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, input):
output = input > 0
ctx.mark_non_differentiable(output)
return output
@staticmethod
def backward(ctx, grad_output):
return (grad_output * 0).type(torch.DoubleTensor)
x = Variable(torch.randn(5, 5), requires_grad=True)
mask = MyFunction.apply(x)
self.assertFalse(mask.requires_grad)
y = x.masked_fill(mask, 0)
y.sum().backward()
def test_type_conversions(self):
x = Variable(torch.randn(5, 5))
self.assertIs(type(x.float().data), torch.FloatTensor)
self.assertIs(type(x.int().data), torch.IntTensor)
if torch.cuda.is_available():
self.assertIs(type(x.float().cuda().data), torch.cuda.FloatTensor)
self.assertIs(type(x.int().cuda().data), torch.cuda.IntTensor)
self.assertIs(type(x.int().cuda().cpu().data), torch.IntTensor)
if torch.cuda.device_count() > 2:
x2 = x.float().cuda(1)
self.assertIs(type(x2.data), torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 1)
x2 = x.float().cuda()
self.assertIs(type(x2.data), torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 0)
x2 = x2.cuda(1)
self.assertIs(type(x2.data), torch.cuda.FloatTensor)
self.assertIs(x2.get_device(), 1)
for t in [torch.DoubleTensor, torch.FloatTensor, torch.IntTensor, torch.ByteTensor]:
for var in (True, False):
y = torch.randn(5, 5).type(t)
if var:
y = Variable(y)
self.assertIs(type(x.type_as(y).data), t)
def _test_barrier_helper(self, group, group_id, rank):
WAIT_TIME = 0.3 # seconds
for dest in group:
expected_time = torch.DoubleTensor(1).fill_(0.0)
if dest == rank:
expected_time.fill_(time.time() + WAIT_TIME)
dist.broadcast(expected_time, dest, group_id)
time.sleep(WAIT_TIME + 0.1) # sleep a little bit longer
dist.barrier(group_id)
else:
dist.broadcast(expected_time, dest, group_id)
dist.barrier(group_id)
self.assertGreaterEqual(time.time(), expected_time[0])
self._barrier()
def test_serialization_array_with_storage(self):
x = torch.randn(5, 5).cuda()
y = torch.IntTensor(2, 5).fill_(0).cuda()
q = [x, y, x, y.storage()]
with tempfile.NamedTemporaryFile() as f:
torch.save(q, f)
f.seek(0)
q_copy = torch.load(f)
self.assertEqual(q_copy, q, 0)
q_copy[0].fill_(5)
self.assertEqual(q_copy[0], q_copy[2], 0)
self.assertTrue(isinstance(q_copy[0], torch.cuda.DoubleTensor))
self.assertTrue(isinstance(q_copy[1], torch.cuda.IntTensor))
self.assertTrue(isinstance(q_copy[2], torch.cuda.DoubleTensor))
self.assertTrue(isinstance(q_copy[3], torch.cuda.IntStorage))
q_copy[1].fill_(10)
self.assertTrue(q_copy[3], torch.cuda.IntStorage(10).fill_(10))
def test_cuda(self, test_case):
if not TEST_CUDA or not self.should_test_cuda:
raise unittest.SkipTest('Excluded from CUDA tests')
try:
cpu_input = self._get_input()
type_map = {
torch.DoubleTensor: torch.cuda.FloatTensor,
}
gpu_input = to_gpu(cpu_input, type_map=type_map)
cpu_target = self.target
gpu_target = to_gpu(self.target, type_map=type_map)
cpu_module = self.constructor(*self.constructor_args)
gpu_module = self.constructor(*self.constructor_args).float().cuda()
cpu_output = test_case._forward_criterion(cpu_module, cpu_input, cpu_target)
gpu_output = test_case._forward_criterion(gpu_module, gpu_input, gpu_target)
test_case.assertEqual(cpu_output, gpu_output, 4e-4)
cpu_gradInput = test_case._backward_criterion(cpu_module, cpu_input, cpu_target)
gpu_gradInput = test_case._backward_criterion(gpu_module, gpu_input, gpu_target)
test_case.assertEqual(cpu_gradInput, gpu_gradInput, 4e-4)
except NotImplementedError:
pass
def test_Copy(self):
input = torch.randn(3, 4).double()
c = nn.Copy(torch.DoubleTensor, torch.FloatTensor)
output = c.forward(input)
self.assertEqual(torch.typename(output), 'torch.FloatTensor')
self.assertEqual(output, input.float(), 1e-6)
gradInput = c.backward(input, output.fill_(1))
self.assertEqual(torch.typename(gradInput), 'torch.DoubleTensor')
self.assertEqual(gradInput, output.double(), 1e-6)
c.dontCast = True
c.double()
self.assertEqual(torch.typename(output), 'torch.FloatTensor')
# Check that these don't raise errors
c.__repr__()
str(c)
def test_abs(self):
size = 1000
max_val = 1000
original = torch.rand(size).mul(max_val)
# Tensor filled with values from {-1, 1}
switch = torch.rand(size).mul(2).floor().mul(2).add(-1)
types = ['torch.DoubleTensor', 'torch.FloatTensor', 'torch.LongTensor', 'torch.IntTensor']
for t in types:
data = original.type(t)
switch = switch.type(t)
res = torch.mul(data, switch)
# abs is used in assertEqual so we use the slow version instead
self.assertTensorsSlowEqual(res.abs(), data, 1e-16)
# Checking that the right abs function is called for LongTensor
bignumber = 2 ^ 31 + 1
res = torch.LongTensor((-bignumber,))
self.assertGreater(res.abs()[0], 0)
def test_type(self):
l = nn.Linear(10, 20)
net = nn.Module()
net.l = l
net.l2 = l
net.add_module('empty', None)
net.float()
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
net.double()
self.assertIsInstance(l.weight.data, torch.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.DoubleTensor)
net.type(torch.FloatTensor)
self.assertIsInstance(l.weight.data, torch.FloatTensor)
self.assertIsInstance(l.bias.data, torch.FloatTensor)
net.type(torch.DoubleTensor)
self.assertIsInstance(l.weight.data, torch.DoubleTensor)
self.assertIsInstance(l.bias.data, torch.DoubleTensor)
if TEST_CUDA:
net.type(torch.cuda.FloatTensor)
self.assertIsInstance(l.weight.data, torch.cuda.FloatTensor)
self.assertIsInstance(l.bias.data, torch.cuda.FloatTensor)
def test_mark_non_differentiable(self):
class MyFunction(Function):
@staticmethod
def forward(ctx, input):
output = input > 0
ctx.mark_non_differentiable(output)
return output
@staticmethod
def backward(ctx, grad_output):
return (grad_output * 0).type(torch.DoubleTensor)
x = Variable(torch.randn(5, 5), requires_grad=True)
mask = MyFunction.apply(x)
self.assertFalse(mask.requires_grad)
y = x.masked_fill(mask, 0)
y.sum().backward()
def run_functional_checks(test_case, test_name, name, apply_fn, run_grad_checks,
f_args_variable, f_args_tensor):
output_variable = apply_fn(*f_args_variable)
if not exclude_tensor_method(name, test_name):
output_tensor = apply_fn(*f_args_tensor)
if not torch.is_tensor(output_tensor) and not isinstance(output_tensor, tuple):
output_tensor = torch.DoubleTensor((output_tensor,))
test_case.assertEqual(unpack_variables(output_variable), output_tensor)
if run_grad_checks:
run_grad_and_gradgrad_checks(test_case, test_name, apply_fn,
output_variable, f_args_variable)
self_variable = f_args_variable[0]
if isinstance(output_variable, torch.autograd.Variable) and self_variable is not None:
output_variable.backward(torch.randn(*output_variable.size()).type_as(output_variable.data))
test_case.assertTrue(type(self_variable.data) == type(self_variable.grad.data))
test_case.assertTrue(self_variable.size() == self_variable.grad.size())
def _test_barrier_helper(self, group, group_id, rank):
WAIT_TIME = 0.3 # seconds
for dest in group:
expected_time = torch.DoubleTensor(1).fill_(0.0)
if dest == rank:
expected_time.fill_(time.time() + WAIT_TIME)
dist.broadcast(expected_time, dest, group_id)
time.sleep(WAIT_TIME + 0.1) # sleep a little bit longer
dist.barrier(group_id)
else:
dist.broadcast(expected_time, dest, group_id)
dist.barrier(group_id)
self.assertGreaterEqual(time.time(), expected_time[0])
self._barrier()
def test_serialization_array_with_storage(self):
x = torch.randn(5, 5).cuda()
y = torch.IntTensor(2, 5).fill_(0).cuda()
q = [x, y, x, y.storage()]
with tempfile.NamedTemporaryFile() as f:
torch.save(q, f)
f.seek(0)
q_copy = torch.load(f)
self.assertEqual(q_copy, q, 0)
q_copy[0].fill_(5)
self.assertEqual(q_copy[0], q_copy[2], 0)
self.assertTrue(isinstance(q_copy[0], torch.cuda.DoubleTensor))
self.assertTrue(isinstance(q_copy[1], torch.cuda.IntTensor))
self.assertTrue(isinstance(q_copy[2], torch.cuda.DoubleTensor))
self.assertTrue(isinstance(q_copy[3], torch.cuda.IntStorage))
q_copy[1].fill_(10)
self.assertTrue(q_copy[3], torch.cuda.IntStorage(10).fill_(10))
def test_cuda(self, test_case):
if not TEST_CUDA or not self.should_test_cuda:
raise unittest.SkipTest('Excluded from CUDA tests')
try:
cpu_input = self._get_input()
type_map = {
torch.DoubleTensor: torch.cuda.FloatTensor,
}
gpu_input = to_gpu(cpu_input, type_map=type_map)
cpu_target = self.target
gpu_target = to_gpu(self.target, type_map=type_map)
cpu_module = self.constructor(*self.constructor_args)
gpu_module = self.constructor(*self.constructor_args).float().cuda()
cpu_output = test_case._forward_criterion(cpu_module, cpu_input, cpu_target)
gpu_output = test_case._forward_criterion(gpu_module, gpu_input, gpu_target)
test_case.assertEqual(cpu_output, gpu_output, 4e-4)
cpu_gradInput = test_case._backward_criterion(cpu_module, cpu_input, cpu_target)
gpu_gradInput = test_case._backward_criterion(gpu_module, gpu_input, gpu_target)
test_case.assertEqual(cpu_gradInput, gpu_gradInput, 4e-4)
except NotImplementedError:
pass
def test_Copy(self):
input = torch.randn(3, 4).double()
c = nn.Copy(torch.DoubleTensor, torch.FloatTensor)
output = c.forward(input)
self.assertEqual(torch.typename(output), 'torch.FloatTensor')
self.assertEqual(output, input.float(), 1e-6)
gradInput = c.backward(input, output.fill_(1))
self.assertEqual(torch.typename(gradInput), 'torch.DoubleTensor')
self.assertEqual(gradInput, output.double(), 1e-6)
c.dontCast = True
c.double()
self.assertEqual(torch.typename(output), 'torch.FloatTensor')
# Check that these don't raise errors
c.__repr__()
str(c)
def _test_neg(self, cast):
float_types = ['torch.DoubleTensor', 'torch.FloatTensor', 'torch.LongTensor']
int_types = ['torch.IntTensor', 'torch.ShortTensor']
for t in float_types + int_types:
if t in float_types:
a = cast(torch.randn(100, 90).type(t))
else:
a = cast(torch.Tensor(100, 90).type(t).random_())
zeros = cast(torch.Tensor().type(t)).resize_as_(a).zero_()
res_add = torch.add(zeros, -1, a)
res_neg = a.clone()
res_neg.neg_()
self.assertEqual(res_neg, res_add)
# test out of place as well
res_neg_out_place = a.clone().neg()
self.assertEqual(res_neg_out_place, res_add)
# test via __neg__ operator
res_neg_op = -a.clone()
self.assertEqual(res_neg_op, res_add)
def test_abs(self):
size = 1000
max_val = 1000
original = torch.rand(size).mul(max_val)
# Tensor filled with values from {-1, 1}
switch = torch.rand(size).mul(2).floor().mul(2).add(-1)
types = ['torch.DoubleTensor', 'torch.FloatTensor', 'torch.LongTensor', 'torch.IntTensor']
for t in types:
data = original.type(t)
switch = switch.type(t)
res = torch.mul(data, switch)
# abs is used in assertEqual so we use the slow version instead
self.assertTensorsSlowEqual(res.abs(), data, 1e-16)
# Checking that the right abs function is called for LongTensor
bignumber = 2 ^ 31 + 1
res = torch.LongTensor((-bignumber,))
self.assertGreater(res.abs()[0], 0)