Source code for openjij.sampler.chimera_gpu.gpu_sa_sampler

# 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


# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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, )