# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import copy
import logging
import os
import random
import numpy as np
from schema import Schema, Optional
import nni
from nni import ClassArgsValidator
import nni.parameter_expressions
from nni.tuner import Tuner
from nni.utils import OptimizeMode, extract_scalar_reward, split_index, json2parameter, json2space
logger = logging.getLogger('pbt_tuner_AutoML')
def perturbation(hyperparameter_type, value, resample_probablity, uv, ub, lv, lb, random_state):
"""
Perturbation for hyperparameters
Parameters
----------
hyperparameter_type : str
type of hyperparameter
value : list
parameters for sampling hyperparameter
resample_probability : float
probability for resampling
uv : float/int
upper value after perturbation
ub : float/int
upper bound
lv : float/int
lower value after perturbation
lb : float/int
lower bound
random_state : RandomState
random state
"""
if random.random() < resample_probablity:
if hyperparameter_type == "choice":
return value.index(nni.parameter_expressions.choice(value, random_state))
else:
return getattr(nni.parameter_expressions, hyperparameter_type)(*(value + [random_state]))
else:
if random.random() > 0.5:
return min(uv, ub)
else:
return max(lv, lb)
def exploit_and_explore(bot_trial_info, top_trial_info, factor, resample_probability, epoch, search_space):
"""
Replace checkpoint of bot_trial with top, and perturb hyperparameters
Parameters
----------
bot_trial_info : TrialInfo
bottom model whose parameters should be replaced
top_trial_info : TrialInfo
better model
factor : float
factor for perturbation
resample_probability : float
probability for resampling
epoch : int
step of PBTTuner
search_space : dict
search_space to keep perturbed hyperparameters in range
"""
bot_checkpoint_dir = bot_trial_info.checkpoint_dir
top_hyper_parameters = top_trial_info.hyper_parameters
hyper_parameters = copy.deepcopy(top_hyper_parameters)
random_state = np.random.RandomState()
hyper_parameters['load_checkpoint_dir'] = hyper_parameters['save_checkpoint_dir']
hyper_parameters['save_checkpoint_dir'] = os.path.join(bot_checkpoint_dir, str(epoch))
for key in hyper_parameters.keys():
hyper_parameter = hyper_parameters[key]
if key == 'load_checkpoint_dir' or key == 'save_checkpoint_dir':
continue
elif search_space[key]["_type"] == "choice":
choices = search_space[key]["_value"]
ub, uv = len(choices) - 1, choices.index(hyper_parameter) + 1
lb, lv = 0, choices.index(hyper_parameter) - 1
elif search_space[key]["_type"] == "randint":
lb, ub = search_space[key]["_value"][:2]
ub -= 1
uv = hyper_parameter + 1
lv = hyper_parameter - 1
elif search_space[key]["_type"] == "uniform":
lb, ub = search_space[key]["_value"][:2]
perturb = (ub - lb) * factor
uv = hyper_parameter + perturb
lv = hyper_parameter - perturb
elif search_space[key]["_type"] == "quniform":
lb, ub, q = search_space[key]["_value"][:3]
multi = round(hyper_parameter / q)
uv = (multi + 1) * q
lv = (multi - 1) * q
elif search_space[key]["_type"] == "loguniform":
lb, ub = search_space[key]["_value"][:2]
perturb = (np.log(ub) - np.log(lb)) * factor
uv = np.exp(min(np.log(hyper_parameter) + perturb, np.log(ub)))
lv = np.exp(max(np.log(hyper_parameter) - perturb, np.log(lb)))
elif search_space[key]["_type"] == "qloguniform":
lb, ub, q = search_space[key]["_value"][:3]
multi = round(hyper_parameter / q)
uv = (multi + 1) * q
lv = (multi - 1) * q
elif search_space[key]["_type"] == "normal":
sigma = search_space[key]["_value"][1]
perturb = sigma * factor
uv = ub = hyper_parameter + perturb
lv = lb = hyper_parameter - perturb
elif search_space[key]["_type"] == "qnormal":
q = search_space[key]["_value"][2]
uv = ub = hyper_parameter + q
lv = lb = hyper_parameter - q
elif search_space[key]["_type"] == "lognormal":
sigma = search_space[key]["_value"][1]
perturb = sigma * factor
uv = ub = np.exp(np.log(hyper_parameter) + perturb)
lv = lb = np.exp(np.log(hyper_parameter) - perturb)
elif search_space[key]["_type"] == "qlognormal":
q = search_space[key]["_value"][2]
uv = ub = hyper_parameter + q
lv, lb = hyper_parameter - q, 1E-10
else:
logger.warning("Illegal type to perturb: %s", search_space[key]["_type"])
continue
if search_space[key]["_type"] == "choice":
idx = perturbation(search_space[key]["_type"], search_space[key]["_value"],
resample_probability, uv, ub, lv, lb, random_state)
hyper_parameters[key] = choices[idx]
else:
hyper_parameters[key] = perturbation(search_space[key]["_type"], search_space[key]["_value"],
resample_probability, uv, ub, lv, lb, random_state)
bot_trial_info.hyper_parameters = hyper_parameters
bot_trial_info.clean_id()
class TrialInfo:
"""
Information of each trial, refresh for each epoch
"""
def __init__(self, checkpoint_dir=None, hyper_parameters=None, parameter_id=None, score=None):
self.checkpoint_dir = checkpoint_dir
self.hyper_parameters = hyper_parameters
self.parameter_id = parameter_id
self.score = score
def clean_id(self):
self.parameter_id = None
class PBTClassArgsValidator(ClassArgsValidator):
def validate_class_args(self, **kwargs):
Schema({
'optimize_mode': self.choices('optimize_mode', 'maximize', 'minimize'),
Optional('all_checkpoint_dir'): str,
Optional('population_size'): self.range('population_size', int, 0, 99999),
Optional('factors'): float,
Optional('fraction'): float,
}).validate(kwargs)
[docs]
class PBTTuner(Tuner):
"""
Population Based Training (PBT) comes from `Population Based Training of Neural Networks <https://arxiv.org/abs/1711.09846v1>`__.
It's a simple asynchronous optimization algorithm which effectively utilizes a fixed computational budget to jointly optimize
a population of models and their hyperparameters to maximize performance.
Importantly, PBT discovers a schedule of hyperparameter settings rather than following the generally sub-optimal strategy of
trying to find a single fixed set to use for the whole course of training.
.. image:: ../../img/pbt.jpg
PBT tuner initializes a population with several trials (i.e., ``population_size``).
There are four steps in the above figure, each trial only runs by one step. How long is one step is controlled by trial code,
e.g., one epoch. When a trial starts, it loads a checkpoint specified by PBT tuner and continues to run one step,
then saves checkpoint to a directory specified by PBT tuner and exits.
The trials in a population run steps synchronously, that is, after all the trials finish the ``i``-th step,
the ``(i+1)``-th step can be started. Exploitation and exploration of PBT are executed between two consecutive steps.
Two important steps to follow if you are trying to use PBT tuner:
1. **Provide checkpoint directory**. Since some trials need to load other trial's checkpoint,
users should provide a directory (i.e., ``all_checkpoint_dir``) which is accessible by every trial.
It is easy for local mode, users could directly use the default directory or specify any directory on the local machine.
For other training services, users should follow
:doc:`the document of those training services </experiment/training_service/shared_storage>`
to provide a directory in a shared storage, such as NFS, Azure storage.
2. **Modify your trial code**. Before running a step, a trial needs to load a checkpoint,
the checkpoint directory is specified in hyper-parameter configuration generated by PBT tuner,
i.e., ``params['load_checkpoint_dir']``. Similarly, the directory for saving checkpoint is also included in the configuration,
i.e., ``params['save_checkpoint_dir']``. Here, ``all_checkpoint_dir`` is base folder of ``load_checkpoint_dir``
and ``save_checkpoint_dir`` whose format is ``all_checkpoint_dir/<population-id>/<step>``.
.. code-block:: python
params = nni.get_next_parameter()
# the path of the checkpoint to load
load_path = os.path.join(params['load_checkpoint_dir'], 'model.pth')
# load checkpoint from `load_path`
...
# run one step
...
# the path for saving a checkpoint
save_path = os.path.join(params['save_checkpoint_dir'], 'model.pth')
# save checkpoint to `save_path`
...
The complete example code can be found :githublink:`here <examples/trials/mnist-pbt-tuner-pytorch>`.
Parameters
----------
optimize_mode : ``maximize`` or ``minimize``, default: ``maximize``
If ``maximize``, the tuner will target to maximize metrics. If ``minimize``, the tuner will target to minimize metrics.
all_checkpoint_dir : str
Directory for trials to load and save checkpoint.
If not specified, the directory would be ``~/nni/checkpoint/``.
Note that if the experiment is not local mode,
users should provide a path in a shared storage which can be accessed by all the trials.
population_size : int, default = 10
Number of trials in a population. Each step has this number of trials.
In our implementation, one step is running each trial by specific training epochs set by users.
factor : float, default = (1.2, 0.8)
Factors for perturbation of hyperparameters.
resample_probability : float, default = 0.25
Probability for resampling.
fraction : float, default = 0.2
Fraction for selecting bottom and top trials.
Examples
--------
Below is an example of PBT tuner configuration in experiment config file.
.. code-block:: yaml
tuner:
name: PBT
classArgs:
optimize_mode: maximize
all_checkpoint_dir: /the/path/to/store/checkpoints
population_size: 10
Notes
-----
Assessor is not allowed if PBT tuner is used.
"""
def __init__(self, optimize_mode="maximize", all_checkpoint_dir=None, population_size=10, factor=0.2,
resample_probability=0.25, fraction=0.2):
self.optimize_mode = OptimizeMode(optimize_mode)
if all_checkpoint_dir is None:
all_checkpoint_dir = os.getenv('NNI_CHECKPOINT_DIRECTORY')
logger.info("Checkpoint dir is set to %s by default.", all_checkpoint_dir)
self.all_checkpoint_dir = all_checkpoint_dir
self.population_size = population_size
self.factor = factor
self.resample_probability = resample_probability
self.fraction = fraction
# defined in trial code
#self.perturbation_interval = perturbation_interval
self.population = None
self.pos = -1
self.param_ids = []
self.running = {}
self.finished = []
self.credit = 0
self.finished_trials = 0
self.epoch = 0
self.searchspace_json = None
self.space = None
self.send_trial_callback = None
logger.info('PBT tuner initialization')
def update_search_space(self, search_space):
"""
Get search space
Parameters
----------
search_space : dict
Search space
"""
logger.info('Update search space %s', search_space)
self.searchspace_json = search_space
self.space = json2space(self.searchspace_json)
self.random_state = np.random.RandomState()
self.population = []
is_rand = dict()
for item in self.space:
is_rand[item] = True
for i in range(self.population_size):
hyper_parameters = json2parameter(
self.searchspace_json, is_rand, self.random_state)
hyper_parameters = split_index(hyper_parameters)
checkpoint_dir = os.path.join(self.all_checkpoint_dir, str(i))
hyper_parameters['load_checkpoint_dir'] = os.path.join(checkpoint_dir, str(self.epoch))
hyper_parameters['save_checkpoint_dir'] = os.path.join(checkpoint_dir, str(self.epoch))
self.population.append(TrialInfo(checkpoint_dir=checkpoint_dir, hyper_parameters=hyper_parameters))
def generate_multiple_parameters(self, parameter_id_list, **kwargs):
"""
Returns multiple sets of trial (hyper-)parameters, as iterable of serializable objects.
Parameters
----------
parameter_id_list : list of int
Unique identifiers for each set of requested hyper-parameters.
These will later be used in :meth:`receive_trial_result`.
**kwargs
Used for send_trial_callback.
Returns
-------
list
A list of newly generated configurations
"""
result = []
self.send_trial_callback = kwargs['st_callback']
for parameter_id in parameter_id_list:
had_exception = False
try:
logger.debug("generating param for %s", parameter_id)
res = self.generate_parameters(parameter_id, **kwargs)
except nni.NoMoreTrialError:
had_exception = True
if not had_exception:
result.append(res)
return result
def generate_parameters(self, parameter_id, **kwargs):
"""
Generate parameters, if no trial configration for now, self.credit plus 1 to send the config later
Parameters
----------
parameter_id : int
Unique identifier for requested hyper-parameters.
This will later be used in :meth:`receive_trial_result`.
**kwargs
Not used
Returns
-------
dict
One newly generated configuration
"""
if self.pos == self.population_size - 1:
logger.debug('Credit added by one in parameters request')
self.credit += 1
self.param_ids.append(parameter_id)
raise nni.NoMoreTrialError('No more parameters now.')
self.pos += 1
trial_info = self.population[self.pos]
trial_info.parameter_id = parameter_id
self.running[parameter_id] = trial_info
logger.info('Generate parameter : %s', trial_info.hyper_parameters)
return trial_info.hyper_parameters
def _proceed_next_epoch(self):
"""
"""
logger.info('Proceeding to next epoch')
self.epoch += 1
self.population = []
self.pos = -1
self.running = {}
#exploit and explore
reverse = True if self.optimize_mode == OptimizeMode.Maximize else False
self.finished = sorted(self.finished, key=lambda x: x.score, reverse=reverse)
cutoff = int(np.ceil(self.fraction * len(self.finished)))
tops = self.finished[:cutoff]
bottoms = self.finished[self.finished_trials - cutoff:]
for bottom in bottoms:
top = np.random.choice(tops)
exploit_and_explore(bottom, top, self.factor, self.resample_probability, self.epoch, self.searchspace_json)
for trial in self.finished:
if trial not in bottoms:
trial.clean_id()
trial.hyper_parameters['load_checkpoint_dir'] = trial.hyper_parameters['save_checkpoint_dir']
trial.hyper_parameters['save_checkpoint_dir'] = os.path.join(trial.checkpoint_dir, str(self.epoch))
self.finished_trials = 0
for _ in range(self.population_size):
trial_info = self.finished.pop()
self.population.append(trial_info)
while self.credit > 0 and self.pos + 1 < len(self.population):
self.credit -= 1
self.pos += 1
parameter_id = self.param_ids.pop()
trial_info = self.population[self.pos]
trial_info.parameter_id = parameter_id
self.running[parameter_id] = trial_info
self.send_trial_callback(parameter_id, trial_info.hyper_parameters)
def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
"""
Receive trial's result. if the number of finished trials equals ``self.population_size``, start the next epoch to
train the model.
Parameters
----------
parameter_id : int
Unique identifier of used hyper-parameters, same with :meth:`generate_parameters`.
parameters : dict
Hyper-parameters generated by :meth:`generate_parameters`.
value : dict
Result from trial (the return value of :func:`nni.report_final_result`).
"""
logger.info('Get one trial result, id = %d, value = %s', parameter_id, value)
value = extract_scalar_reward(value)
trial_info = self.running.pop(parameter_id, None)
trial_info.score = value
self.finished.append(trial_info)
self.finished_trials += 1
if self.finished_trials == self.population_size:
self._proceed_next_epoch()
def trial_end(self, parameter_id, success, **kwargs):
"""
Deal with trial failure
Parameters
----------
parameter_id : int
Unique identifier for hyper-parameters used by this trial.
success : bool
True if the trial successfully completed; False if failed or terminated.
**kwargs
Unstable parameters which should be ignored by normal users.
"""
if success:
return
if self.optimize_mode == OptimizeMode.Minimize:
value = float('inf')
else:
value = float('-inf')
trial_info = self.running.pop(parameter_id, None)
trial_info.score = value
self.finished.append(trial_info)
self.finished_trials += 1
if self.finished_trials == self.population_size:
self._proceed_next_epoch()
def import_data(self, data):
"""
Parameters
----------
data : json obj
imported data records
Returns
-------
int
the start epoch number after data imported, only used for unittest
"""
if self.running:
logger.warning("Do not support importing data in the middle of experiment")
return
# the following is for experiment resume
_completed_num = 0
epoch_data_dict = {}
for trial_info in data:
logger.info("Process data record %s / %s", _completed_num, len(data))
_completed_num += 1
# simply validate data format
_params = trial_info["parameter"]
_value = trial_info['value']
# assign fake value for failed trials
if not _value:
logger.info("Useless trial data, value is %s, skip this trial data.", _value)
_value = float('inf') if self.optimize_mode == OptimizeMode.Minimize else float('-inf')
_value = extract_scalar_reward(_value)
if 'save_checkpoint_dir' not in _params:
logger.warning("Invalid data record: save_checkpoint_dir is missing, abandon data import.")
return
epoch_num = int(os.path.basename(_params['save_checkpoint_dir']))
if epoch_num not in epoch_data_dict:
epoch_data_dict[epoch_num] = []
epoch_data_dict[epoch_num].append((_params, _value))
if not epoch_data_dict:
logger.warning("No valid epochs, abandon data import.")
return
# figure out start epoch for resume
max_epoch_num = max(epoch_data_dict, key=int)
if len(epoch_data_dict[max_epoch_num]) < self.population_size:
max_epoch_num -= 1
# If there is no a single complete round, no data to import, start from scratch
if max_epoch_num < 0:
logger.warning("No completed epoch, abandon data import.")
return
assert len(epoch_data_dict[max_epoch_num]) == self.population_size
# check existence of trial save checkpoint dir
for params, _ in epoch_data_dict[max_epoch_num]:
if not os.path.isdir(params['save_checkpoint_dir']):
logger.warning("save_checkpoint_dir %s does not exist, data will not be resumed", params['save_checkpoint_dir'])
return
# resume data
self.epoch = max_epoch_num
self.finished_trials = self.population_size
for params, value in epoch_data_dict[max_epoch_num]:
checkpoint_dir = os.path.dirname(params['save_checkpoint_dir'])
self.finished.append(TrialInfo(checkpoint_dir=checkpoint_dir, hyper_parameters=params, score=value))
self._proceed_next_epoch()
logger.info("Successfully import data to PBT tuner, total data: %d, imported data: %d.", len(data), self.population_size)
logger.info("Start from epoch %d ...", self.epoch)
return self.epoch # return for test