def test_cat_empty_tensor(self):
t = _gen_tensor(2, 5, 3)
empty_tensor = torch.Tensor()
x = t.to(xm.xla_device())
empty_tensor_xla = empty_tensor.to(xm.xla_device())
t_cat = torch.cat([t, empty_tensor], 0)
x_cat = torch.cat([x, empty_tensor_xla], 0)
self.assertEqual(t_cat.data, x_cat.data.cpu())
def test_masked_fill_with_tensor(self):
input = _gen_tensor(2, 5, 4, 3)
mask = torch.randint(0, 2, input.size(), dtype=torch.bool)
value = torch.tensor(42)
xla_input = input.to(xm.xla_device())
xla_mask = mask.to(xm.xla_device())
xla_value = value.to(xm.xla_device())
result = torch.masked_fill(input, mask, value)
xla_result = torch.masked_fill(xla_input, xla_mask, xla_value)
self.assertEqual(input.data, xla_input.data.cpu())
self.assertEqual(result.data, xla_result.data.cpu())
def test_empty_strided(self):
xla_device = xm.xla_device()
m = nn.Conv1d(4, 6, kernel_size=3, groups=2)
a = torch.rand(2, 4, 6, requires_grad=True)
xla_m = copy.deepcopy(m).to(xla_device)
xla_a = a.clone().to(xla_device).detach()
xla_a.requires_grad = True
output = m(a)
grad_input = torch.autograd.grad(output, (a, ) + tuple(m.parameters()),
output,
create_graph=True)
grad_grad_input = torch.autograd.grad(
output.sum() + sum(map(lambda x: x.sum(), grad_input)),
(a, output) + tuple(m.parameters()),
retain_graph=True)
xla_output = xla_m(xla_a)
xla_grad_input = torch.autograd.grad(xla_output, (xla_a, ) +
tuple(xla_m.parameters()),
xla_output,
create_graph=True)
xla_grad_grad_input = torch.autograd.grad(
xla_output.sum() + sum(map(lambda x: x.sum(), xla_grad_input)),
(xla_a, xla_output) + tuple(xla_m.parameters()),
retain_graph=True)
self.assertEqual(grad_grad_input, xla_grad_grad_input)
def test_empty_advanced_indexing(self):
xla_device = xm.xla_device()
base = torch.randn(2, 3, 4, 5)
xla_base = base.to(device=xla_device)
result = base[:, torch.empty(0, 6, dtype=torch.int64)]
xla_result = xla_base[:, torch.empty(0, 6, dtype=torch.int64)]
self.assertEqual(result, xla_result)
def test_slice_stepped_other_assign(self):
a = torch.ones((10, 4))
xla_device = xm.xla_device()
xla_a = a.to(xla_device)
a[:, 1::4] = 2
xla_a[:, 1::4] = 2
self.assertEqual(a.data, xla_a.data.cpu())
def test_frac_negative(self):
xla_device = xm.xla_device()
a = torch.tensor(-3.2)
b = a.frac()
xla_a = a.to(xla_device)
xla_b = xla_a.frac()
self.assertEqual(b, xla_b)
def test(self):
xla_device = xm.xla_device()
kdata = [_gen_tensor(2, 3), _gen_tensor(3, 4)]
kdata.append([_gen_tensor(2, 5), _gen_tensor(3, 6)])
data = dict()
data[_gen_tensor(2, 2)] = tuple(kdata)
data[_gen_tensor(2, 4)] = set([12.0, _gen_tensor(3, 7)])
data['ABC'] = _gen_tensor(4, 3)
def select_fn(v):
return type(v) == torch.Tensor
def convert_fn(tensors):
devices = [str(xla_device)] * len(tensors)
return torch_xla._XLAC._xla_tensors_from_aten(tensors, devices)
def check_fn(v):
if select_fn(v):
return xm.is_xla_tensor(v)
elif isinstance(v, (list, tuple, set)):
for x in v:
if not check_fn(x):
return False
elif isinstance(v, dict):
for k, x in v.items():
if not check_fn(k) or not check_fn(x):
return False
return True
xla_data = xm.ToXlaTensorArena(convert_fn, select_fn).transform(data)
self.assertTrue(check_fn(xla_data))
def test_index_put(self):
xla_device = xm.xla_device()
a = torch.tensor([1, 1, 1, 1]).to(xla_device).to(dtype=torch.float32)
b = torch.rand(4) > 0.1
a[b] = 10
vset = b.sum().item()
self.assertEqual(a.sum().item(), 10.0 * vset + (4.0 - vset))
def test_norm_p0(self):
# p = 0 is equivalent to nonzero
xla_device = xm.xla_device()
a = torch.randn(3, 2)
xla_a = a.to(xla_device)
norm = a.norm(p=0)
xla_norm = xla_a.norm(p=0)
self.assertEqual(norm, xla_norm)
def test_ailing_slice(self):
xla_device = xm.xla_device()
a = torch.ones((1000, 324)).to(xla_device)
xla_a = a.to(xla_device)
w = a[:, 2::4]
xla_w = a[:, 2::4]
dw = torch.clamp(w, max=3.1)
xla_dw = torch.clamp(xla_w, max=3.1)
self.assertEqual(w.data, xla_w.data.cpu())
def test_max_broadcast(self):
xla_device = xm.xla_device()
a = torch.rand(3, 1, 2)
b = torch.rand(4, 2)
c = torch.max(a, b)
xla_a = a.to(xla_device)
xla_b = b.to(xla_device)
xla_c = torch.max(xla_a, xla_b)
self.assertEqual(c.data, xla_c.data.cpu())
def test_slice_rnd_stepped_assign(self):
xla_device = xm.xla_device()
size = 10
for s in range(0, size - 1):
for e in range(1, size - s):
a = torch.ones((3, size))
xla_a = a.to(xla_device)
a[:, s::e] = 2
xla_a[:, s::e] = 2
self.assertEqual(a.data, xla_a.data.cpu())
def test_slice_assign(self):
a = torch.rand(3, 3, 3)
xla_device = xm.xla_device()
xla_a = a.to(xla_device)
shape = (4, 4, 4)
b = a.new(*shape).zero_()
xla_b = xla_a.new(*shape).zero_()
b[0, :, :] = 1
xla_b[0, :, :] = 1
self.assertEqual(b.data, xla_b.data.cpu())
def test_slice_copy(self):
a = torch.rand(3, 3, 3)
xla_device = xm.xla_device()
xla_a = a.to(xla_device)
shape = (4, 4, 4)
b = a.new(*shape).zero_()
xla_b = xla_a.new(*shape).zero_()
b[:a.shape[0], :a.shape[1], :a.shape[2]].copy_(a)
xla_b[:a.shape[0], :a.shape[1], :a.shape[2]].copy_(xla_a)
self.assertEqual(b.data, xla_b.data.cpu())
def runAtenTest(self, tensors, fn, device=None, rel_err=1e-2, abs_err=1e-5):
if device is None:
device = xm.xla_device()
tensors = xu.as_list(tensors)
xla_tensors = [x.to(device) for x in tensors]
results = xu.as_list(fn(*tensors))
xla_results = xu.as_list(fn(*xla_tensors))
for at, xt in zip(results, xla_results):
self.assertEqualRel(
self.makeComparable(xt), at, rel_err=rel_err, abs_err=abs_err)
def test_save(self):
xla_device = xm.xla_device()
x = torch.randn(5, device=xla_device)
x_file = tempfile.mktemp()
try:
torch.save(x, x_file)
x_loaded = torch.load(x_file)
self.assertEqual(x, x_loaded)
finally:
os.remove(x_file)
def test_bitwise_type(self):
xla_device = xm.xla_device()
a = torch.randint(255, (4, ), dtype=torch.long)
xla_a = a.to(xla_device)
self.assertRaises(RuntimeError, lambda: a & a.byte())
self.assertRaises(RuntimeError, lambda: xla_a & xla_a.byte())
def test_fn(a):
return a & (~a)
self.runAtenTest(a, test_fn)
def test_scatter_add_bool(self):
xla_device = xm.xla_device()
a = torch.tensor([[True, True, True, True, True],
[True, True, True, True, True]])
b = torch.zeros(3, 5, dtype=torch.bool)
index = torch.tensor([[0, 1, 2, 0, 0], [2, 0, 0, 1, 2]])
b.scatter_add_(0, index, a)
xla_a = a.to(xla_device)
xla_b = b.to(xla_device)
xla_index = index.to(xla_device)
xla_b.scatter_add_(0, xla_index, xla_a)
self.assertEqual(b, xla_b)
def test(self):
device = xm.xla_device()
orig_x = torch.Tensor([[1, 2], [3, 4]])
orig_y = torch.Tensor([[0.1, 0.2], [0.3, 0.4]])
x = orig_x
y = orig_y
xla_x = orig_x.to(device)
xla_y = orig_y.to(device)
for i in range(0, 2000):
x = x + 2 * y
xla_x = xla_x + 2 * xla_y
self.assertEqualRel(x, xla_x.cpu(), rel_err=1e-3, abs_err=5)
def test_save_view_alias_check(self):
class Nested(object):
def __init__(self, x, y):
self.x = x
self.y = y
a = torch.rand(16, device=xm.xla_device())
b = a[:10]
c = a[6:]
self.assertRaises(RuntimeError, lambda: xm.check_view_sharing([b, c]))
nested = Nested(b, c)
self.assertRaises(RuntimeError, lambda: xm.check_view_sharing(nested))
def _mp_fn(index):
device = xm.xla_device()
real_device = xm.xla_real_devices([str(device)])[0]
if real_device.startswith('TPU:'):
ones = torch.ones((2, 3))
xones = ones.to(device)
torch_xla._XLAC._xla_cross_replica_sum([xones], 1.0, [])
if not xones.cpu().allclose(ones * float(xm.xrt_world_size())):
print('CrossReplicaSum produced wrong reductions')
print(xones, file=sys.stderr)
sys.exit(1)
else:
print('Default device {} is not a TPU device'.format(real_device),
file=sys.stderr)
def test_pred_type(self):
xla_device = xm.xla_device()
a = torch.rand(4)
b = torch.rand(4)
xla_a = a.to(xla_device)
xla_b = b.to(xla_device)
c = (a >= 0.25)
d = (b >= 0.5)
xla_c = (xla_a >= 0.25)
xla_d = (xla_b >= 0.5)
e = torch.cat([a, b], dim=0)
xla_e = torch.cat([xla_a, xla_b], dim=0)
f = e.sum().item()
xla_f = xla_e.sum().item()
self.assertEqual(f, xla_f)
def test_rrelu_module(self):
xla_device = xm.xla_device()
a = torch.rand(1, 2, 2, requires_grad=True)
xla_a = a.to(xla_device).detach()
xla_a.requires_grad = True
m = nn.RReLU()
xla_m = m.to(xla_device)
output = m(a)
xla_output = xla_m(xla_a)
self.assertEqual(output, xla_output.cpu())
output.sum().backward()
xla_output.sum().backward()
self.assertEqual(a.grad, xla_a.grad.cpu())
def test_reduction_0dim(self):
self.runAtenTest(torch.rand(2, 0, 4).bool(), lambda x: torch.all(x))
self.runAtenTest(torch.rand(2, 0, 4).bool(), lambda x: torch.any(x))
self.runAtenTest(torch.rand(2, 0, 4), lambda x: torch.sum(x))
self.runAtenTest(torch.rand(2, 0, 4), lambda x: torch.mean(x))
self.runAtenTest(torch.rand(2, 0, 4), lambda x: torch.prod(x))
# min & max throws
xla_device = xm.xla_device()
a = torch.rand(2, 0, 4)
xla_a = a.to(xla_device)
self.assertRaises(RuntimeError, lambda: torch.max(a, dim=1))
self.assertRaises(RuntimeError, lambda: torch.max(a))
self.assertRaises(RuntimeError, lambda: torch.min(a, dim=1))
self.assertRaises(RuntimeError, lambda: torch.min(a))
self.assertRaises(RuntimeError, lambda: torch.max(xla_a, dim=1))
self.assertRaises(RuntimeError, lambda: torch.max(xla_a))
self.assertRaises(RuntimeError, lambda: torch.min(xla_a, dim=1))
self.assertRaises(RuntimeError, lambda: torch.min(xla_a))
def test(self):
xla_device = xm.xla_device()
x = _gen_tensor(8, 1, 28, 28)
torch.manual_seed(42)
model = MNISTComparator()
save_dir1 = xu.TmpFolder()
mc.configure(save_dir1.name)
model(x)
save_dir2 = xu.TmpFolder()
mc.configure(save_dir2.name)
torch.manual_seed(42)
xla_model = MNISTComparator().to(xla_device)
xla_x = x.to(xla_device)
xla_model(xla_x)
report = mc.compare(save_dir1.name, save_dir2.name, rtol=1e-03, atol=1e-04)
if report:
print(report)
self.assertEqual(len(report), 0)
def test_pred_type(self):
xla_device = xm.xla_device()
a = torch.rand(4)
b = torch.rand(4)
xla_a = a.to(xla_device)
xla_b = b.to(xla_device)
c = (a >= 0.25)
d = (b >= 0.5)
xla_c = (xla_a >= 0.25)
xla_d = (xla_b >= 0.5)
e = torch.cat([a, b], dim=0)
xla_e = torch.cat([xla_a, xla_b], dim=0)
f = e.sum().item()
xla_f = xla_e.sum().item()
self.assertEqual(f, xla_f)
# PRED can be automatically promoted in arithmetic ops.
self.runAtenTest(c, lambda x: x + x.byte())
# PRED cannot be automatically promoted to other dtypes in bitwise ops.
# This is not aligned with numpy behavior which means it might change
# in the future.
self.assertRaises(RuntimeError, lambda: c & c.byte())
self.assertRaises(RuntimeError, lambda: xla_c & xla_c.byte())
def test_print(self):
xla_device = xm.xla_device()
x = torch.tensor([5], device=xla_device)
expected_str = 'tensor([5], device=\'' + str(xla_device) + '\')'
self.assertExpectedInline(str(x), expected_str)
def test_copy(self):
xla_device = xm.xla_device()
x = torch.rand(5, device=xla_device)
y = copy.copy(x)
self.assertEqual(x, y)
def test_byte_dtype(self):
xla_device = xm.xla_device()
x = torch.ByteTensor([0, 1]).to(xla_device)
y = torch.ByteTensor([0, 1]).to(xla_device)
z = x + y
self.assertEqual(z.dtype, torch.uint8)
def test_slice_zero_sized_dim(self):
xla_device = xm.xla_device()
v = torch.randn(2, 3, 4, 5).to(xla_device)
y = v[:, :, :, 1]
z = y[:, 1:1, :]
self.assertEqual(z.size()[1], 0)
