# Copyright 2021 Jij Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import openjij
import openjij as oj
import openjij.cxxjij as cxxjij
from openjij.sampler.chimera_gpu.base_gpu_chimera import BaseGPUChimeraSampler
from openjij.sampler.sa_sampler import SASampler
[docs]class GPUChimeraSASampler(SASampler, BaseGPUChimeraSampler):
"""Sampler with Simulated Annealing (SA) on GPU.
Inherits from :class:`openjij.sampler.sampler.BaseSampler`.
Args:
beta_min (float): Minimum inverse temperature.
beta_max (float): Maximum inverse temperature.
num_sweeps (int): Length of Monte Carlo step.
schedule_info (dict): Information about a annealing schedule.
num_reads (int): Number of iterations.
unit_num_L (int): Length of one side of two-dimensional lattice in which chimera unit cells are arranged.
Raises:
ValueError: If variables violate as below.
- trotter number is odd.
- no input "unit_num_L" to an argument or this constructor.
- given problem graph is incompatible with chimera graph.
AttributeError: If GPU doesn't work.
"""
def __init__(
self,
beta_min=None,
beta_max=None,
num_sweeps=1000,
schedule=None,
num_reads=1,
unit_num_L=None,
):
super().__init__(
beta_min=beta_min,
beta_max=beta_max,
num_reads=num_reads,
num_sweeps=num_sweeps,
schedule=schedule,
)
self.unit_num_L = unit_num_L
self._make_system = {"singlespinflip": cxxjij.system.make_chimera_classical_gpu}
self._algorithm = {"singlespinflip": cxxjij.algorithm.Algorithm_GPU_run}
[docs] def sample_ising(
self,
h,
J,
beta_min=None,
beta_max=None,
num_sweeps=None,
num_reads=1,
schedule=None,
initial_state=None,
updater=None,
reinitialize_state=True,
seed=None,
unit_num_L=None,
):
"""Sample with Ising model.
Args:
h (dict): linear biases
J (dict): quadratic biases
beta_min (float): minimal value of inverse temperature
beta_max (float): maximum value of inverse temperature
num_sweeps (int): number of sweeps
num_reads (int): number of reads
schedule (list): list of inverse temperature
initial_state (dict): initial state
updater(str): updater algorithm
reinitialize_state (bool): if true reinitialize state for each run
seed (int): seed for Monte Carlo algorithm
unit_num_L (int): number of chimera units
Returns:
:class:`openjij.sampler.response.Response`: results
Examples::
>>> sampler = openjij.sampler.chimera_gpu.gpu_sa_sampler.GPUChimeraSASampler(unit_num_L=2)
>>> h = {0: -1, 1: -1, 2: 1, 3: 1},
>>> J = {(0, 4): -1, (2, 5): -1}
>>> res = sampler.sample_ising(h, J)
"""
if updater is None:
updater = "single spin flip"
self.unit_num_L = unit_num_L if unit_num_L else self.unit_num_L
model = oj.model.chimera_model.ChimeraModel(
linear=h, quadratic=J, vartype="SPIN", unit_num_L=self.unit_num_L, gpu=True
)
# define Chimera structure
structure = {}
structure["size"] = 8 * self.unit_num_L * self.unit_num_L
structure["dict"] = {}
if isinstance(model.indices[0], int):
# identity dict
for ind in model.indices:
structure["dict"][ind] = ind
elif isinstance(model.indices[0], tuple):
# map chimera coordinate to index
for ind in model.indices:
structure["dict"][ind] = model.to_index(*ind, self.unit_num_L)
return self._sampling(
model,
beta_min,
beta_max,
num_sweeps,
num_reads,
schedule,
initial_state,
updater,
reinitialize_state,
seed,
structure,
)
