def create_eviction_policy(model_prob):
"""Creates the appropriate eviction policy for collecting data.
Args:
model_prob (float): the returned policy is a mixture of the learned
eviction policy and the oracle policy, where the learned eviction policy
is played model_prob of the time.
Returns:
EvictionPolicy
"""
# Need to update the eviction policy and keep the cache state around
oracle_scorer = {
"lru": eviction_policy.LRUScorer(),
"belady": eviction_policy.BeladyScorer(trace),
}[FLAGS.oracle_eviction_policy]
learned_scorer = model_eviction_policy.LearnedScorer(
eviction_model)
# Use scoring_policy_index = 0 to always get scores from the oracle scorer
scoring_policy_index = 0 if use_oracle_scores else None
return eviction_policy.MixturePolicy(
[
eviction_policy.GreedyEvictionPolicy(oracle_scorer),
eviction_policy.GreedyEvictionPolicy(learned_scorer)
],
[1 - model_prob, model_prob],
scoring_policy_index=scoring_policy_index,
)
def scorer_from_config(config, trace):
"""Creates an eviction_policy.CacheLineScorer from the config.
Args:
config (Config): config for the cache line scorer.
trace (MemoryTrace): see get_eviction_policy.
Returns:
CacheLineScorer
"""
scorer_type = config.get("type")
if scorer_type == "lru":
return eviction_policy_mod.LRUScorer()
elif scorer_type == "belady":
return eviction_policy_mod.BeladyScorer(trace)
elif scorer_type == "learned":
with open(config.get("config_path"), "r") as model_config:
return (model_eviction_policy_mod.LearnedScorer.
from_model_checkpoint(
cfg.Config.from_file(model_config),
config.get("checkpoint")))
else:
raise ValueError(
"Invalid scorer type: {}".format(scorer_type))