def __init__(self, dimension, scramble=False, seed=None):
if dimension > self.MAXDIM or dimension < 1:
raise ValueError("Supported range of dimensionality "
"for SobolEngine is [1, {}]".format(self.MAXDIM))
self.seed = seed
self.scramble = scramble
self.dimension = dimension
self.sobolstate = torch.zeros(dimension, self.MAXBIT, dtype=torch.long)
torch._sobol_engine_initialize_state_(self.sobolstate, self.dimension)
if self.scramble:
g = torch.Generator()
if self.seed is not None:
g.manual_seed(self.seed)
else:
g.seed()
self.shift = torch.mv(
torch.randint(2, (self.dimension, self.MAXBIT), generator=g),
torch.pow(2, torch.arange(0, self.MAXBIT)))
ltm = torch.randint(2, (self.dimension, self.MAXBIT, self.MAXBIT),
generator=g).tril()
torch._sobol_engine_scramble_(self.sobolstate, ltm, self.dimension)
else:
self.shift = torch.zeros(self.dimension, dtype=torch.long)
self.quasi = self.shift.clone(memory_format=torch.contiguous_format)
self.num_generated = 0
def _scramble(self):
g: Optional[torch.Generator] = None
if self.seed is not None:
g = torch.Generator()
g.manual_seed(self.seed)
cpu = torch.device("cpu")
# Generate shift vector
shift_ints = torch.randint(2, (self.dimension, self.MAXBIT), device=cpu, generator=g)
self.shift = torch.mv(shift_ints, torch.pow(2, torch.arange(0, self.MAXBIT, device=cpu)))
# Generate lower triangular matrices (stacked across dimensions)
ltm_dims = (self.dimension, self.MAXBIT, self.MAXBIT)
ltm = torch.randint(2, ltm_dims, device=cpu, generator=g).tril()
torch._sobol_engine_scramble_(self.sobolstate, ltm, self.dimension)
def __init__(self, dimension, scramble=False, seed=None):
if dimension > self.MAXDIM or dimension < 1:
raise ValueError("Supported range of dimensionality "
f"for SobolEngine is [1, {self.MAXDIM}]")
self.seed = seed
self.scramble = scramble
self.dimension = dimension
cpu = torch.device("cpu")
self.sobolstate = torch.zeros(dimension,
self.MAXBIT,
device=cpu,
dtype=torch.long)
torch._sobol_engine_initialize_state_(self.sobolstate, self.dimension)
if self.scramble:
g: Optional[torch.Generator] = None
if self.seed is not None:
g = torch.Generator()
g.manual_seed(self.seed)
shift_ints = torch.randint(2, (self.dimension, self.MAXBIT),
device=cpu,
generator=g)
self.shift = torch.mv(
shift_ints,
torch.pow(2, torch.arange(0, self.MAXBIT, device=cpu)))
ltm_dims = (self.dimension, self.MAXBIT, self.MAXBIT)
ltm = torch.randint(2, ltm_dims, device=cpu, generator=g).tril()
torch._sobol_engine_scramble_(self.sobolstate, ltm, self.dimension)
else:
self.shift = torch.zeros(self.dimension,
device=cpu,
dtype=torch.long)
self.quasi = self.shift.clone(memory_format=torch.contiguous_format)
self.num_generated = 0
