diff options
Diffstat (limited to 'resources/libraries/python/MLRsearch/strategy/base.py')
-rw-r--r-- | resources/libraries/python/MLRsearch/strategy/base.py | 132 |
1 files changed, 132 insertions, 0 deletions
diff --git a/resources/libraries/python/MLRsearch/strategy/base.py b/resources/libraries/python/MLRsearch/strategy/base.py new file mode 100644 index 0000000000..0724f882bf --- /dev/null +++ b/resources/libraries/python/MLRsearch/strategy/base.py @@ -0,0 +1,132 @@ +# Copyright (c) 2023 Cisco and/or its affiliates. +# 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. + +"""Module defining StrategyBase class.""" + + +from abc import ABC, abstractmethod +from dataclasses import dataclass, field +from typing import Callable, Optional, Tuple + +from ..discrete_interval import DiscreteInterval +from ..discrete_load import DiscreteLoad +from ..discrete_width import DiscreteWidth +from ..expander import TargetedExpander +from ..limit_handler import LimitHandler +from ..relevant_bounds import RelevantBounds +from ..target_spec import TargetSpec + + +@dataclass +class StrategyBase(ABC): + """Abstract class encompassing data common to most strategies. + + A strategy is one piece of logic a selector may use + when nominating a candidate according to its current target. + + The two initial bound arguments may not be bounds at all. + For initial targets, the two values are usually mrr and mrr2. + For subsequent targets, the initial values are usually + the relevant bounds of the preceding target, + but one of them may be None if hitting min or max load. + + The initial values are mainly used as stable alternatives + to relevant bounds of preceding target, + because those bounds may have been unpredictably altered + by nominations from unrelated search goals. + This greatly simplifies reasoning about strategies making progress. + """ + + target: TargetSpec + """The target this strategy is focusing on.""" + expander: TargetedExpander + """Instance to track width expansion during search (if applicable).""" + initial_lower_load: Optional[DiscreteLoad] + """Smaller of the two loads distinguished at instance creation. + Can be None if upper bound is the min load.""" + initial_upper_load: Optional[DiscreteLoad] + """Larger of the two loads distinguished at instance creation. + Can be None if lower bound is the max load.""" + handler: LimitHandler = field(repr=False) + """Reference to the limit handler instance.""" + debug: Callable[[str], None] = field(repr=False) + """Injectable function for debug logging.""" + + @abstractmethod + def nominate( + self, bounds: RelevantBounds + ) -> Tuple[Optional[DiscreteLoad], Optional[DiscreteWidth]]: + """Nominate a load candidate if the conditions activate this strategy. + + A complete candidate refers also to the nominating selector. + To prevent circular dependence (selector refers to nominating strategy), + this function returns only duration and width. + + Width should only be non-None if global current width should be updated + when the candidate based on this becomes winner. + But currently all strategies return non-None width + if they return non-None load. + + :param bounds: Freshly updated bounds relevant for current target. + :type bounds: RelevantBounds + :returns: Two nones or candidate intended load and duration. + :rtype: Tuple[Optional[DiscreteLoad], Optional[DiscreteWidth]] + """ + return None, None + + def won(self, bounds: RelevantBounds, load: DiscreteLoad) -> None: + """Notify the strategy its candidate became the winner. + + Most strategies have no use for this information, + but some strategies may need to update their private information. + + :param bounds: Freshly updated bounds relevant for current target. + :param load: The current load, so strategy does not need to remember. + :type bounds: RelevantBounds + :type load: DiscreteLoad + """ + return + + def not_worth(self, bounds: RelevantBounds, load: DiscreteLoad) -> bool: + """A check on bounds common for multiple strategies. + + The load is worth measuring only if it can create or improve + either relevant bound. + + Each strategy is designed to create a relevant bound for current target, + which is only needed if that (or better) bound does not exist yet. + Conversely, if a strategy does not nominate, it is because + the load it would nominate (if any) is found not worth by this method. + + :param bounds: Current relevant bounds. + :param load: Load of a possible candidate. + :type bounds: RelevantBounds + :type load: DiscreteLoad + :returns: True if the load should NOT be nominated. + :rtype: bool + """ + if bounds.clo and bounds.clo >= load: + return True + if bounds.chi and bounds.chi <= load: + return True + if bounds.clo and bounds.chi: + # We are not hitting min nor max load. + # Measuring at this load will create or improve clo or chi. + # The only reason not to nominate is if interval is narrow already. + wig = DiscreteInterval( + lower_bound=bounds.clo, + upper_bound=bounds.chi, + ).width_in_goals(self.target.discrete_width) + if wig <= 1.0: + return True + return False |