restore CDBTune
This commit is contained in:
yangdsh
Dana Van Aken
parent
1e17c78956
commit
5579d8d94f
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@ -3,7 +3,7 @@
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#
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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#
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# from: https://github.com/KqSMea8/CDBTune
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# from: https://github.com/KqSMea8/use_default
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# Zhang, Ji, et al. "An end-to-end automatic cloud database tuning system using
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# deep reinforcement learning." Proceedings of the 2019 International Conference
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# on Management of Data. ACM, 2019
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@ -23,22 +23,36 @@ LOG = get_analysis_logger(__name__)
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class Actor(nn.Module):
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def __init__(self, n_states, n_actions, hidden_sizes):
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def __init__(self, n_states, n_actions, hidden_sizes, use_default):
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super(Actor, self).__init__()
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self.layers = nn.Sequential(
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nn.Linear(n_states, hidden_sizes[0]),
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nn.LeakyReLU(negative_slope=0.2),
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nn.BatchNorm1d(hidden_sizes[0]),
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nn.Linear(hidden_sizes[0], hidden_sizes[1]),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[1]),
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nn.Linear(hidden_sizes[1], hidden_sizes[2]),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[2]),
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nn.Linear(hidden_sizes[2], n_actions)
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)
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if use_default:
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self.layers = nn.Sequential(
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nn.Linear(n_states, 128),
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nn.LeakyReLU(negative_slope=0.2),
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nn.BatchNorm1d(hidden_sizes[0]),
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nn.Linear(128, 128),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.Linear(128, 128),
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nn.Tanh(),
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nn.Linear(128, 64),
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nn.Linear(64, n_actions)
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)
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else:
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self.layers = nn.Sequential(
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nn.Linear(n_states, hidden_sizes[0]),
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nn.LeakyReLU(negative_slope=0.2),
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nn.BatchNorm1d(hidden_sizes[0]),
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nn.Linear(hidden_sizes[0], hidden_sizes[1]),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[1]),
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nn.Linear(hidden_sizes[1], hidden_sizes[2]),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[2]),
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nn.Linear(hidden_sizes[2], n_actions)
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)
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# This act layer maps the output to (0, 1)
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self.act = nn.Sigmoid()
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self._init_weights()
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@ -58,22 +72,37 @@ class Actor(nn.Module):
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class Critic(nn.Module):
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def __init__(self, n_states, n_actions, hidden_sizes):
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def __init__(self, n_states, n_actions, hidden_sizes, use_default):
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super(Critic, self).__init__()
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self.state_input = nn.Linear(n_states, hidden_sizes[0])
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self.action_input = nn.Linear(n_actions, hidden_sizes[0])
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self.act = nn.Tanh()
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self.layers = nn.Sequential(
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nn.Linear(hidden_sizes[0] * 2, hidden_sizes[1]),
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nn.LeakyReLU(negative_slope=0.2),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[1]),
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nn.Linear(hidden_sizes[1], hidden_sizes[2]),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[2]),
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nn.Linear(hidden_sizes[2], 1),
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)
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if use_default:
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self.state_input = nn.Linear(n_states, 128)
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self.action_input = nn.Linear(n_actions, 128)
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self.layers = nn.Sequential(
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nn.Linear(256, 256),
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nn.LeakyReLU(negative_slope=0.2),
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nn.BatchNorm1d(256),
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nn.Linear(256, 256),
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nn.Linear(256, 64),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(64),
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nn.Linear(64, 1)
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)
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else:
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self.state_input = nn.Linear(n_states, hidden_sizes[0])
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self.action_input = nn.Linear(n_actions, hidden_sizes[0])
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self.layers = nn.Sequential(
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nn.Linear(hidden_sizes[0] * 2, hidden_sizes[1]),
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nn.LeakyReLU(negative_slope=0.2),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[1]),
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nn.Linear(hidden_sizes[1], hidden_sizes[2]),
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nn.Tanh(),
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nn.Dropout(0.3),
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nn.BatchNorm1d(hidden_sizes[2]),
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nn.Linear(hidden_sizes[2], 1)
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)
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self._init_weights()
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def _init_weights(self):
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@ -101,7 +130,8 @@ class DDPG(object):
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def __init__(self, n_states, n_actions, model_name='', alr=0.001, clr=0.001,
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gamma=0.9, batch_size=32, tau=0.002, shift=0, memory_size=100000,
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a_hidden_sizes=[128, 128, 64], c_hidden_sizes=[128, 256, 64]):
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a_hidden_sizes=[128, 128, 64], c_hidden_sizes=[128, 256, 64],
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use_default=False):
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self.n_states = n_states
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self.n_actions = n_actions
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self.alr = alr
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@ -113,6 +143,7 @@ class DDPG(object):
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self.a_hidden_sizes = a_hidden_sizes
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self.c_hidden_sizes = c_hidden_sizes
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self.shift = shift
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self.use_default = use_default
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self._build_network()
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@ -124,10 +155,12 @@ class DDPG(object):
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return Variable(torch.FloatTensor(x))
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def _build_network(self):
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self.actor = Actor(self.n_states, self.n_actions, self.a_hidden_sizes)
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self.target_actor = Actor(self.n_states, self.n_actions, self.a_hidden_sizes)
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self.critic = Critic(self.n_states, self.n_actions, self.c_hidden_sizes)
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self.target_critic = Critic(self.n_states, self.n_actions, self.c_hidden_sizes)
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self.actor = Actor(self.n_states, self.n_actions, self.a_hidden_sizes, self.use_default)
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self.target_actor = Actor(self.n_states, self.n_actions, self.a_hidden_sizes,
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self.use_default)
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self.critic = Critic(self.n_states, self.n_actions, self.c_hidden_sizes, self.use_default)
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self.target_critic = Critic(self.n_states, self.n_actions, self.c_hidden_sizes,
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self.use_default)
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# Copy actor's parameters
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self._update_target(self.target_actor, self.actor, tau=1.0)
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@ -52,11 +52,11 @@ DEFAULT_LEARNING_RATE = 0.01
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# a small bias when using training data points as starting points.
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GPR_EPS = 0.001
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DEFAULT_RIDGE = 1.0
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DEFAULT_RIDGE = 0.01
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DEFAULT_EPSILON = 1e-6
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DEFAULT_SIGMA_MULTIPLIER = 1.0
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DEFAULT_SIGMA_MULTIPLIER = 3.0
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DEFAULT_MU_MULTIPLIER = 1.0
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@ -84,6 +84,13 @@ DNN_DEBUG = True
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DNN_DEBUG_INTERVAL = 100
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# ---DDPG CONSTRAINTS CONSTANTS---
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# Use a simple reward
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DDPG_SIMPLE_REWARD = True
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# The weight of future rewards in Q value
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DDPG_GAMMA = 0.0
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# Batch size in DDPG model
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DDPG_BATCH_SIZE = 32
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@ -101,3 +108,14 @@ ACTOR_HIDDEN_SIZES = [128, 128, 64]
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# The number of hidden units in each layer of the critic MLP
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CRITIC_HIDDEN_SIZES = [64, 128, 64]
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# Use the same setting from the CDBTune paper
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USE_DEFAULT = True
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# Overwrite the DDPG settings if using CDBTune
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if USE_DEFAULT:
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DDPG_SIMPLE_REWARD = False
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DDPG_GAMMA = 0.99
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DDPG_BATCH_SIZE = 32
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ACTOR_LEARNING_RATE = 0.001
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CRITIC_LEARNING_RATE = 0.001
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UPDATE_EPOCHS = 1
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@ -36,6 +36,7 @@ from website.settings import (USE_GPFLOW, DEFAULT_LENGTH_SCALE, DEFAULT_MAGNITUD
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DEFAULT_EPSILON, MAX_ITER, GPR_EPS,
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DEFAULT_SIGMA_MULTIPLIER, DEFAULT_MU_MULTIPLIER,
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DEFAULT_UCB_SCALE, HP_LEARNING_RATE, HP_MAX_ITER,
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DDPG_SIMPLE_REWARD, DDPG_GAMMA, USE_DEFAULT,
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DDPG_BATCH_SIZE, ACTOR_LEARNING_RATE,
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CRITIC_LEARNING_RATE, UPDATE_EPOCHS,
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ACTOR_HIDDEN_SIZES, CRITIC_HIDDEN_SIZES,
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@ -285,18 +286,25 @@ def train_ddpg(result_id):
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result = Result.objects.get(pk=result_id)
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session = Result.objects.get(pk=result_id).session
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session_results = Result.objects.filter(session=session,
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creation_time__lte=result.creation_time)
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creation_time__lt=result.creation_time)
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result_info = {}
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result_info['newest_result_id'] = result_id
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# Extract data from result
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# Extract data from result and previous results
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result = Result.objects.filter(pk=result_id)
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base_result_id = session_results[0].pk
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if len(session_results) == 0:
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base_result_id = result_id
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prev_result_id = result_id
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else:
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base_result_id = session_results[0].pk
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prev_result_id = session_results[len(session_results)-1].pk
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base_result = Result.objects.filter(pk=base_result_id)
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prev_result = Result.objects.filter(pk=prev_result_id)
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agg_data = DataUtil.aggregate_data(result)
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metric_data = agg_data['y_matrix'].flatten()
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base_metric_data = (DataUtil.aggregate_data(base_result))['y_matrix'].flatten()
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prev_metric_data = (DataUtil.aggregate_data(prev_result))['y_matrix'].flatten()
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metric_scalar = MinMaxScaler().fit(metric_data.reshape(1, -1))
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normalized_metric_data = metric_scalar.transform(metric_data.reshape(1, -1))[0]
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@ -323,21 +331,37 @@ def train_ddpg(result_id):
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target_objective))
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objective = metric_data[target_obj_idx]
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base_objective = base_metric_data[target_obj_idx]
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prev_objective = prev_metric_data[target_obj_idx]
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metric_meta = db.target_objectives.get_metric_metadata(
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result.session.dbms.pk, result.session.target_objective)
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# Calculate the reward
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objective = objective / base_objective
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if metric_meta[target_objective].improvement == '(less is better)':
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reward = -objective
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if DDPG_SIMPLE_REWARD:
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objective = objective / base_objective
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if metric_meta[target_objective].improvement == '(less is better)':
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reward = -objective
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else:
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reward = objective
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else:
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reward = objective
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if metric_meta[target_objective].improvement == '(less is better)':
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if objective - base_objective <= 0: # positive reward
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reward = (np.square((2 * base_objective - objective) / base_objective) - 1)\
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* abs(2 * prev_objective - objective) / prev_objective
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else: # negative reward
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reward = -(np.square(objective / base_objective) - 1) * objective / prev_objective
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else:
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if objective - base_objective > 0: # positive reward
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reward = (np.square(objective / base_objective) - 1) * objective / prev_objective
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else: # negative reward
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reward = -(np.square((2 * base_objective - objective) / base_objective) - 1)\
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* abs(2 * prev_objective - objective) / prev_objective
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LOG.info('reward: %f', reward)
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# Update ddpg
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ddpg = DDPG(n_actions=knob_num, n_states=metric_num, alr=ACTOR_LEARNING_RATE,
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clr=CRITIC_LEARNING_RATE, gamma=0, batch_size=DDPG_BATCH_SIZE,
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a_hidden_sizes=ACTOR_HIDDEN_SIZES, c_hidden_sizes=CRITIC_HIDDEN_SIZES)
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clr=CRITIC_LEARNING_RATE, gamma=DDPG_GAMMA, batch_size=DDPG_BATCH_SIZE,
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a_hidden_sizes=ACTOR_HIDDEN_SIZES, c_hidden_sizes=CRITIC_HIDDEN_SIZES,
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use_default=USE_DEFAULT)
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if session.ddpg_actor_model and session.ddpg_critic_model:
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ddpg.set_model(session.ddpg_actor_model, session.ddpg_critic_model)
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if session.ddpg_reply_memory:
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@ -368,7 +392,7 @@ def configuration_recommendation_ddpg(result_info): # pylint: disable=invalid-n
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metric_num = len(metric_data)
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ddpg = DDPG(n_actions=knob_num, n_states=metric_num, a_hidden_sizes=ACTOR_HIDDEN_SIZES,
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c_hidden_sizes=CRITIC_HIDDEN_SIZES)
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c_hidden_sizes=CRITIC_HIDDEN_SIZES, use_default=USE_DEFAULT)
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if session.ddpg_actor_model is not None and session.ddpg_critic_model is not None:
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ddpg.set_model(session.ddpg_actor_model, session.ddpg_critic_model)
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if session.ddpg_reply_memory is not None:
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@ -646,9 +670,8 @@ def configuration_recommendation(recommendation_input):
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epsilon=DEFAULT_EPSILON,
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max_iter=MAX_ITER,
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sigma_multiplier=DEFAULT_SIGMA_MULTIPLIER,
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mu_multiplier=DEFAULT_MU_MULTIPLIER,
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ridge=DEFAULT_RIDGE)
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model.fit(X_scaled, y_scaled, X_min, X_max)
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mu_multiplier=DEFAULT_MU_MULTIPLIER)
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model.fit(X_scaled, y_scaled, X_min, X_max, ridge=DEFAULT_RIDGE)
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res = model.predict(X_samples, constraint_helper=constraint_helper)
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best_config_idx = np.argmin(res.minl.ravel())
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