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@ -22,34 +22,57 @@ from miplearn.instance.base import Instance
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from miplearn.solvers import _RedirectOutput
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from miplearn.solvers.internal import InternalSolver
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from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
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from miplearn.types import LearningSolveStats
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from miplearn.types import LearningSolveStats, ConstraintName
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import gzip
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import pickle
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import miplearn
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import json
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from os.path import exists
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from os import remove
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import pyomo.environ as pe
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logger = logging.getLogger(__name__)
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class PyomoFindLazyCutCallbackHandler:
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def __init__(self):
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pass
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def value(self, var):
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return var.value
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class PyomoEnforceLazyCutsCallbackHandler:
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def __init__(self, opt, model):
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self.model = model
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self.opt = opt
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if not hasattr(model, "miplearn_lazy_cb"):
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model.miplearn_lazy_cb = pe.ConstraintList()
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def enforce(self, expr):
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constr = self.model.miplearn_lazy_cb.add(expr=expr)
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self.opt.add_constraint(constr)
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class FileInstanceWrapper(Instance):
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def __init__(
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self,
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data_filename: Any,
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build_model: Callable,
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self, data_filename: Any, build_model: Callable, mode: Optional[str] = None
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):
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super().__init__()
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assert data_filename.endswith(".pkl.gz")
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self.filename = data_filename
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self.sample_filename = data_filename.replace(".pkl.gz", ".h5")
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self.sample = Hdf5Sample(
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self.sample_filename,
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mode="r+" if exists(self.sample_filename) else "w",
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)
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self.build_model = build_model
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self.mode = mode
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self.sample = None
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self.model = None
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@overrides
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def to_model(self) -> Any:
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return miplearn.load(self.filename, self.build_model)
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if self.model is None:
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self.model = miplearn.load(self.filename, self.build_model)
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return self.model
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@overrides
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def create_sample(self) -> Sample:
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@ -59,6 +82,44 @@ class FileInstanceWrapper(Instance):
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def get_samples(self) -> List[Sample]:
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return [self.sample]
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@overrides
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def free(self) -> None:
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self.sample.file.close()
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@overrides
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def load(self) -> None:
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if self.mode is None:
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self.mode = "r+" if exists(self.sample_filename) else "w"
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self.sample = Hdf5Sample(self.sample_filename, mode=self.mode)
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@overrides
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def has_dynamic_lazy_constraints(self) -> bool:
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assert hasattr(self, "model")
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return hasattr(self.model, "_miplearn_find_lazy_cuts")
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@overrides
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def find_violated_lazy_constraints(
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self,
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solver: "InternalSolver",
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model: Any,
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) -> Dict[ConstraintName, Any]:
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if not hasattr(self.model, "_miplearn_find_lazy_cuts"):
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return {}
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cb = PyomoFindLazyCutCallbackHandler()
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violations = model._miplearn_find_lazy_cuts(cb)
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return {json.dumps(v).encode(): v for v in violations}
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@overrides
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def enforce_lazy_constraint(
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self,
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solver: "InternalSolver",
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model: Any,
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violation: Any,
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) -> None:
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assert isinstance(solver, GurobiPyomoSolver)
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cb = PyomoEnforceLazyCutsCallbackHandler(solver._pyomo_solver, model)
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model._miplearn_enforce_lazy_cuts(cb, violation)
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class MemoryInstanceWrapper(Instance):
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def __init__(self, model: Any) -> None:
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@ -70,12 +131,39 @@ class MemoryInstanceWrapper(Instance):
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def to_model(self) -> Any:
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return self.model
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@overrides
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def has_dynamic_lazy_constraints(self) -> bool:
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assert hasattr(self, "model")
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return hasattr(self.model, "_miplearn_find_lazy_cuts")
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@overrides
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def find_violated_lazy_constraints(
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self,
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solver: "InternalSolver",
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model: Any,
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) -> Dict[ConstraintName, Any]:
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cb = PyomoFindLazyCutCallbackHandler()
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violations = model._miplearn_find_lazy_cuts(cb)
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return {json.dumps(v).encode(): v for v in violations}
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@overrides
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def enforce_lazy_constraint(
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self,
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solver: "InternalSolver",
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model: Any,
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violation: Any,
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) -> None:
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assert isinstance(solver, GurobiPyomoSolver)
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cb = PyomoEnforceLazyCutsCallbackHandler(solver._pyomo_solver, model)
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model._miplearn_enforce_lazy_cuts(cb, violation)
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class _GlobalVariables:
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def __init__(self) -> None:
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self.solver: Optional[LearningSolver] = None
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self.instances: Optional[List[Instance]] = None
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self.discard_outputs: bool = False
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self.build_model: Optional[Callable] = None
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self.filenames: Optional[List[str]] = None
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self.skip = False
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# Global variables used for multiprocessing. Global variables are copied by the
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@ -86,23 +174,19 @@ _GLOBAL = [_GlobalVariables()]
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def _parallel_solve(
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idx: int,
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) -> Tuple[Optional[int], Optional[LearningSolveStats], Optional[Instance]]:
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) -> Tuple[Optional[int], Optional[LearningSolveStats]]:
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solver = _GLOBAL[0].solver
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instances = _GLOBAL[0].instances
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discard_outputs = _GLOBAL[0].discard_outputs
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filenames = _GLOBAL[0].filenames
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build_model = _GLOBAL[0].build_model
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skip = _GLOBAL[0].skip
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assert solver is not None
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assert instances is not None
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try:
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stats = solver._solve(
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instances[idx],
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discard_output=discard_outputs,
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)
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instances[idx].free()
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return idx, stats, instances[idx]
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stats = solver.solve([filenames[idx]], build_model, skip=skip)
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return idx, stats[0]
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except Exception as e:
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traceback.print_exc()
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logger.exception(f"Exception while solving {instances[idx]}. Ignoring.")
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return None, None, None
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logger.exception(f"Exception while solving {filenames[idx]}. Ignoring.")
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return idx, None
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class LearningSolver:
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@ -380,85 +464,84 @@ class LearningSolver:
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build_model: Optional[Callable] = None,
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tee: bool = False,
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progress: bool = False,
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skip: bool = False,
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) -> Union[LearningSolveStats, List[LearningSolveStats]]:
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if isinstance(arg, list):
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assert build_model is not None
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stats = []
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for i in tqdm(arg, disable=not progress):
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s = self._solve(FileInstanceWrapper(i, build_model), tee=tee)
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instance = FileInstanceWrapper(i, build_model)
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solved = False
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if exists(instance.sample_filename):
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try:
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with Hdf5Sample(instance.sample_filename, mode="r") as sample:
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if sample.get_scalar("mip_lower_bound"):
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solved = True
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except OSError:
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# File exists but it is unreadable/corrupted. Delete it.
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remove(instance.sample_filename)
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if solved and skip:
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stats.append({})
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else:
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s = self._solve(instance, tee=tee)
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# Export to gzipped MPS file
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mps_filename = instance.sample_filename.replace(".h5", ".mps")
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instance.model.write(
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filename=mps_filename,
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io_options={
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"labeler": pe.NameLabeler(),
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"skip_objective_sense": True,
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},
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)
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with open(mps_filename, "rb") as original:
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with gzip.open(f"{mps_filename}.gz", "wb") as compressed:
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compressed.writelines(original)
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remove(mps_filename)
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stats.append(s)
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return stats
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else:
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return self._solve(MemoryInstanceWrapper(arg), tee=tee)
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def fit(self, filenames: List[str], build_model: Callable) -> None:
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def fit(
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self,
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filenames: List[str],
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build_model: Callable,
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progress: bool = False,
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n_jobs: int = 1,
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) -> None:
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instances: List[Instance] = [
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FileInstanceWrapper(f, build_model) for f in filenames
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FileInstanceWrapper(f, build_model, mode="r") for f in filenames
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]
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self._fit(instances)
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self._fit(instances, progress=progress, n_jobs=n_jobs)
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def parallel_solve(
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self,
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instances: List[Instance],
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filenames: List[str],
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build_model: Optional[Callable] = None,
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n_jobs: int = 4,
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label: str = "solve",
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discard_outputs: bool = False,
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progress: bool = False,
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label: str = "solve",
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skip: bool = False,
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) -> List[LearningSolveStats]:
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"""
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Solves multiple instances in parallel.
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This method is equivalent to calling `solve` for each item on the list,
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but it processes multiple instances at the same time. Like `solve`, this
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method modifies each instance in place. Also like `solve`, a list of
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filenames may be provided.
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Parameters
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----------
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discard_outputs: bool
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If True, do not write the modified instances anywhere; simply discard
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them instead. Useful during benchmarking.
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label: str
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Label to show in the progress bar.
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instances: List[Instance]
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The instances to be solved.
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n_jobs: int
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Number of instances to solve in parallel at a time.
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Returns
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-------
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List[LearningSolveStats]
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List of solver statistics, with one entry for each provided instance.
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The list is the same you would obtain by calling
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`[solver.solve(p) for p in instances]`
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"""
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if n_jobs == 1:
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return [
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self._solve(p)
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for p in tqdm(
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instances,
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disable=not progress,
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desc=label,
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)
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]
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else:
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self.internal_solver = None
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self._silence_miplearn_logger()
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_GLOBAL[0].solver = self
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_GLOBAL[0].instances = instances
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_GLOBAL[0].discard_outputs = discard_outputs
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_GLOBAL[0].build_model = build_model
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_GLOBAL[0].filenames = filenames
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_GLOBAL[0].skip = skip
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results = p_umap(
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_parallel_solve,
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list(range(len(instances))),
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list(range(len(filenames))),
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num_cpus=n_jobs,
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desc=label,
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disable=not progress,
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desc=label,
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)
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results = [r for r in results if r[1]]
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stats: List[LearningSolveStats] = [{} for _ in range(len(results))]
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for (idx, s, instance) in results:
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stats: List[LearningSolveStats] = [{} for _ in range(len(filenames))]
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for (idx, s) in results:
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if s:
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stats[idx] = s
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instances[idx] = instance
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self._restore_miplearn_logger()
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return stats
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