def test_beta_op():
set_global_seed(1024)
_alpha, _beta = 2, 0.8
_expected_mean = _alpha / (_alpha + _beta)
_expected_std = np.sqrt(_alpha * _beta / ((_alpha + _beta)**2 *
(_alpha + _beta + 1)))
alpha = F.full([8, 9, 11, 12], value=_alpha, dtype="float32")
beta = F.full([8, 9, 11, 12], value=_beta, dtype="float32")
op = BetaRNG(seed=get_global_rng_seed())
(output, ) = apply(op, alpha, beta)
assert np.fabs(output.numpy().mean() - _expected_mean) < 1e-1
assert np.fabs(np.sqrt(output.numpy().var()) - _expected_std) < 1e-1
assert str(output.device) == str(CompNode("xpux"))
cn = CompNode("xpu2")
seed = 233333
h = new_rng_handle(cn, seed)
alpha = F.full([8, 9, 11, 12], value=_alpha, dtype="float32", device=cn)
beta = F.full([8, 9, 11, 12], value=_beta, dtype="float32", device=cn)
op = BetaRNG(seed=seed, handle=h)
(output, ) = apply(op, alpha, beta)
delete_rng_handle(h)
assert np.fabs(output.numpy().mean() - _expected_mean) < 1e-1
assert np.fabs(np.sqrt(output.numpy().var()) - _expected_std) < 1e-1
assert str(output.device) == str(cn)
def test_gaussian_op():
# FIXME: remove this sync
mge.core.set_option("async_level", 0)
set_global_seed(1024)
shape = (
8,
9,
11,
12,
)
shape = Tensor(shape, dtype="int32")
op = GaussianRNG(seed=get_global_rng_seed(),
mean=1.0,
std=3.0,
dtype="float32")
(output, ) = apply(op, shape)
assert np.fabs(output.numpy().mean() - 1.0) < 1e-1
assert np.fabs(np.sqrt(output.numpy().var()) - 3.0) < 1e-1
assert str(output.device) == str(CompNode("xpux"))
assert output.dtype == np.float32
cn = CompNode("xpu2")
seed = 233333
h = new_rng_handle(cn, seed)
op = GaussianRNG(seed=seed, mean=3.0, std=1.0, dtype="float32", handle=h)
(output, ) = apply(op, shape)
delete_rng_handle(h)
assert np.fabs(output.numpy().mean() - 3.0) < 1e-1
assert np.fabs(np.sqrt(output.numpy().var()) - 1.0) < 1e-1
assert str(output.device) == str(cn)
assert output.dtype == np.float32
def test_gamma_op():
set_global_seed(1024)
_shape, _scale = 2, 0.8
_expected_mean, _expected_std = _shape * _scale, np.sqrt(_shape) * _scale
shape = F.full([8, 9, 11, 12], value=_shape, dtype="float32")
scale = F.full([8, 9, 11, 12], value=_scale, dtype="float32")
op = GammaRNG(seed=get_global_rng_seed(), handle=0)
(output, ) = apply(op, shape, scale)
assert np.fabs(output.numpy().mean() - _expected_mean) < 1e-1
assert np.fabs(np.sqrt(output.numpy().var()) - _expected_std) < 1e-1
assert str(output.device) == str(CompNode("xpux"))
cn = CompNode("xpu2")
seed = 233333
h = new_rng_handle(cn, seed)
shape = F.full([8, 9, 11, 12],
value=_shape,
dtype="float32",
device="xpu2")
scale = F.full([8, 9, 11, 12],
value=_scale,
dtype="float32",
device="xpu2")
op = GammaRNG(seed=seed, handle=h)
(output, ) = apply(op, shape, scale)
delete_rng_handle(h)
assert np.fabs(output.numpy().mean() - _expected_mean) < 1e-1
assert np.fabs(np.sqrt(output.numpy().var()) - _expected_std) < 1e-1
assert str(output.device) == str(cn)
def test_poisson_op():
set_global_seed(1024)
lam = F.full([8, 9, 11, 12], value=2, dtype="float32")
op = PoissonRNG(seed=get_global_rng_seed())
(output, ) = apply(op, lam)
assert np.fabs(output.numpy().mean() - 2.0) < 1e-1
assert np.fabs(np.sqrt(output.numpy().var()) - np.sqrt(2.0)) < 1e-1
assert str(output.device) == str(CompNode("xpux"))
cn = CompNode("xpu2")
seed = 233333
h = new_rng_handle(cn, seed)
lam = F.full([8, 9, 11, 12], value=2, dtype="float32", device=cn)
op = PoissonRNG(seed=seed, handle=h)
(output, ) = apply(op, lam)
delete_rng_handle(h)
assert np.fabs(output.numpy().mean() - 2.0) < 1e-1
assert np.fabs(np.sqrt(output.numpy().var()) - np.sqrt(2.0)) < 1e-1
assert str(output.device) == str(cn)
def test_uniform_op():
shape = (
8,
9,
11,
12,
)
shape = tensor(shape, dtype="int32")
op = UniformRNG(seed=get_global_rng_seed())
(output, ) = apply(op, shape)
assert np.fabs(output.numpy().mean() - 0.5) < 1e-1
assert str(output.device) == str(CompNode("xpux"))
cn = CompNode("xpu2")
seed = 233333
h = new_rng_handle(cn, seed)
op = UniformRNG(seed=seed, handle=h)
(output, ) = apply(op, shape)
delete_rng_handle(h)
assert np.fabs(output.numpy().mean() - 0.5) < 1e-1
assert str(output.device) == str(cn)
def test_permutation_op_dtype(dtype):
def sum_result(res, fun):
return sum(
[1 if i == v else 0 for i, v in enumerate(fun(res.numpy()))])
shape = Tensor((n, ), dtype="int32")
op = PermutationRNG(seed=get_global_rng_seed(), dtype=dtype)
(output, ) = apply(op, shape)
assert sum_result(output, lambda x: x) < 500
assert sum_result(output, np.sort) == n
assert str(output.device) == str(CompNode("xpux"))
assert output.dtype == dtype
cn = CompNode("xpu2")
seed = 233333
h = new_rng_handle(cn, seed)
op = PermutationRNG(seed=seed, handle=h, dtype=dtype)
(output, ) = apply(op, shape)
delete_rng_handle(h)
assert sum_result(output, lambda x: x) < 500
assert sum_result(output, np.sort) == n
assert str(output.device) == str(cn)
assert output.dtype == dtype
def test_gaussian_op():
shape = (
8,
9,
11,
12,
)
shape = tensor(shape, dtype="int32")
op = GaussianRNG(seed=get_global_rng_seed(), mean=1.0, std=3.0)
(output, ) = apply(op, shape)
assert np.fabs(output.numpy().mean() - 1.0) < 1e-1
assert np.sqrt(output.numpy().var()) - 3.0 < 1e-1
assert str(output.device) == str(CompNode("xpux"))
cn = CompNode("xpu2")
seed = 233333
h = new_rng_handle(cn, seed)
op = GaussianRNG(seed=seed, mean=3.0, std=1.0, handle=h)
(output, ) = apply(op, shape)
delete_rng_handle(h)
assert np.fabs(output.numpy().mean() - 3.0) < 1e-1
assert np.sqrt(output.numpy().var()) - 1.0 < 1e-1
assert str(output.device) == str(cn)