Source code for torch.quasirandom
import torch
from typing import Optional
[docs]class SobolEngine:
r"""
The :class:`torch.quasirandom.SobolEngine` is an engine for generating
(scrambled) Sobol sequences. Sobol sequences are an example of low
discrepancy quasi-random sequences.
This implementation of an engine for Sobol sequences is capable of
sampling sequences up to a maximum dimension of 21201. It uses direction
numbers from https://web.maths.unsw.edu.au/~fkuo/sobol/ obtained using the
search criterion D(6) up to the dimension 21201. This is the recommended
choice by the authors.
References:
- Art B. Owen. Scrambling Sobol and Niederreiter-Xing points.
Journal of Complexity, 14(4):466-489, December 1998.
- I. M. Sobol. The distribution of points in a cube and the accurate
evaluation of integrals.
Zh. Vychisl. Mat. i Mat. Phys., 7:784-802, 1967.
Args:
dimension (Int): The dimensionality of the sequence to be drawn
scramble (bool, optional): Setting this to ``True`` will produce
scrambled Sobol sequences. Scrambling is
capable of producing better Sobol
sequences. Default: ``False``.
seed (Int, optional): This is the seed for the scrambling. The seed
of the random number generator is set to this,
if specified. Otherwise, it uses a random seed.
Default: ``None``
Examples::
>>> # xdoctest: +SKIP("unseeded random state")
>>> soboleng = torch.quasirandom.SobolEngine(dimension=5)
>>> soboleng.draw(3)
tensor([[0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
[0.5000, 0.5000, 0.5000, 0.5000, 0.5000],
[0.7500, 0.2500, 0.2500, 0.2500, 0.7500]])
"""
MAXBIT = 30
MAXDIM = 21201
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 not self.scramble:
self.shift = torch.zeros(self.dimension, device=cpu, dtype=torch.long)
else:
self._scramble()
self.quasi = self.shift.clone(memory_format=torch.contiguous_format)
self._first_point = (self.quasi / 2 ** self.MAXBIT).reshape(1, -1)
self.num_generated = 0
[docs] def draw(self, n: int = 1, out: Optional[torch.Tensor] = None,
dtype: torch.dtype = torch.float32) -> torch.Tensor:
r"""
Function to draw a sequence of :attr:`n` points from a Sobol sequence.
Note that the samples are dependent on the previous samples. The size
of the result is :math:`(n, dimension)`.
Args:
n (Int, optional): The length of sequence of points to draw.
Default: 1
out (Tensor, optional): The output tensor
dtype (:class:`torch.dtype`, optional): the desired data type of the
returned tensor.
Default: ``torch.float32``
"""
if self.num_generated == 0:
if n == 1:
result = self._first_point.to(dtype)
else:
result, self.quasi = torch._sobol_engine_draw(
self.quasi, n - 1, self.sobolstate, self.dimension, self.num_generated, dtype=dtype,
)
result = torch.cat((self._first_point, result), dim=-2)
else:
result, self.quasi = torch._sobol_engine_draw(
self.quasi, n, self.sobolstate, self.dimension, self.num_generated - 1, dtype=dtype,
)
self.num_generated += n
if out is not None:
out.resize_as_(result).copy_(result)
return out
return result
[docs] def draw_base2(self, m: int, out: Optional[torch.Tensor] = None,
dtype: torch.dtype = torch.float32) -> torch.Tensor:
r"""
Function to draw a sequence of :attr:`2**m` points from a Sobol sequence.
Note that the samples are dependent on the previous samples. The size
of the result is :math:`(2**m, dimension)`.
Args:
m (Int): The (base2) exponent of the number of points to draw.
out (Tensor, optional): The output tensor
dtype (:class:`torch.dtype`, optional): the desired data type of the
returned tensor.
Default: ``torch.float32``
"""
n = 2 ** m
total_n = self.num_generated + n
if not (total_n & (total_n - 1) == 0):
raise ValueError("The balance properties of Sobol' points require "
f"n to be a power of 2. {self.num_generated} points have been "
f"previously generated, then: n={self.num_generated}+2**{m}={total_n}. "
"If you still want to do this, please use "
"'SobolEngine.draw()' instead."
)
return self.draw(n=n, out=out, dtype=dtype)
[docs] def reset(self):
r"""
Function to reset the ``SobolEngine`` to base state.
"""
self.quasi.copy_(self.shift)
self.num_generated = 0
return self
[docs] def fast_forward(self, n):
r"""
Function to fast-forward the state of the ``SobolEngine`` by
:attr:`n` steps. This is equivalent to drawing :attr:`n` samples
without using the samples.
Args:
n (Int): The number of steps to fast-forward by.
"""
if self.num_generated == 0:
torch._sobol_engine_ff_(self.quasi, n - 1, self.sobolstate, self.dimension, self.num_generated)
else:
torch._sobol_engine_ff_(self.quasi, n, self.sobolstate, self.dimension, self.num_generated - 1)
self.num_generated += n
return self
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 __repr__(self):
fmt_string = [f'dimension={self.dimension}']
if self.scramble:
fmt_string += ['scramble=True']
if self.seed is not None:
fmt_string += [f'seed={self.seed}']
return self.__class__.__name__ + '(' + ', '.join(fmt_string) + ')'
