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[tune] Update ZOOpt to better support the latest Ray (#11462)

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Co-authored-by: Servon <zewen.li@polixir.ai>
This commit is contained in:
Servon
2020-10-20 00:28:11 +08:00
committed by GitHub
parent 798bd6a359
commit 202b1859ef
4 changed files with 56 additions and 23 deletions
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python/ray/tune/examples/zoopt_example.py
+8 -1
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@@ -51,14 +51,21 @@ if __name__ == "__main__":
# "height": (ValueType.CONTINUOUS, [-10, 10], 1e-2),
# # for discrete dimensions: (discrete, search_range, has_order)
# "width": (ValueType.DISCRETE, [0, 10], True)
# # for grid dimensions: (grid, grid_list)
# "layers": (ValueType.GRID, [4, 8, 16])
# }
zoopt_search_config = {
"parallel_num": 8,
}
zoopt_search = ZOOptSearch(
algo="Asracos", # only support ASRacos currently
budget=tune_kwargs["num_samples"],
# dim_dict=space, # If you want to set the space yourself
metric="mean_loss",
mode="min")
mode="min",
**zoopt_search_config)
scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")
python/ray/tune/suggest/zoopt.py
+33 -14
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@@ -2,6 +2,7 @@ import copy
import logging
from typing import Dict, Optional, Tuple
import ray
import ray.cloudpickle as pickle
from ray.tune.sample import Categorical, Domain, Float, Integer, Quantized, \
Uniform
@@ -27,7 +28,7 @@ class ZOOptSearch(Searcher):
Asynchronous Sequential RAndomized COordinate Shrinking (ASRacos)
is implemented in Tune.
To use ZOOptSearch, install zoopt (>=0.4.0): ``pip install -U zoopt``.
To use ZOOptSearch, install zoopt (>=0.4.1): ``pip install -U zoopt``.
Tune automatically converts search spaces to ZOOpt"s format:
@@ -67,19 +68,26 @@ class ZOOptSearch(Searcher):
dim_dict = {
"height": (ValueType.CONTINUOUS, [-10, 10], 1e-2),
"width": (ValueType.DISCRETE, [-10, 10], False)
"width": (ValueType.DISCRETE, [-10, 10], False),
"layers": (ValueType.GRID, [4, 8, 16])
}
"config": {
"iterations": 10, # evaluation times
}
zoopt_search_config = {
"parallel_num": 8, # how many workers to parallel
}
zoopt_search = ZOOptSearch(
algo="Asracos", # only support Asracos currently
budget=20, # must match `num_samples` in `tune.run()`.
dim_dict=dim_dict,
metric="mean_loss",
mode="min")
mode="min",
**zoopt_search_config
)
tune.run(my_objective,
config=config,
@@ -94,14 +102,17 @@ class ZOOptSearch(Searcher):
budget (int): Number of samples.
dim_dict (dict): Dimension dictionary.
For continuous dimensions: (continuous, search_range, precision);
For discrete dimensions: (discrete, search_range, has_order).
For discrete dimensions: (discrete, search_range, has_order);
For grid dimensions: (grid, grid_list).
More details can be found in zoopt package.
metric (str): The training result objective value attribute.
Defaults to "episode_reward_mean".
mode (str): One of {min, max}. Determines whether objective is
minimizing or maximizing the metric attribute.
Defaults to "min".
parallel_num (int): How many workers to parallel. Note that initial
phase may start less workers than this number. More details can
be found in zoopt package.
"""
optimizer = None
@@ -113,7 +124,8 @@ class ZOOptSearch(Searcher):
metric: Optional[str] = None,
mode: Optional[str] = None,
**kwargs):
assert zoopt is not None, "Zoopt not found - please install zoopt."
assert zoopt is not None, "ZOOpt not found - please install zoopt " \
"by `pip install -U zoopt`."
assert budget is not None, "`budget` should not be None!"
if mode:
assert mode in ["min", "max"], "`mode` must be 'min' or 'max'."
@@ -137,8 +149,9 @@ class ZOOptSearch(Searcher):
self.best_solution_list = []
self.optimizer = None
super(ZOOptSearch, self).__init__(
metric=self._metric, mode=mode, **kwargs)
self.kwargs = kwargs
super(ZOOptSearch, self).__init__(metric=self._metric, mode=mode)
if self._dim_dict:
self.setup_zoopt()
@@ -153,7 +166,8 @@ class ZOOptSearch(Searcher):
par = zoopt.Parameter(budget=self._budget)
if self._algo == "sracos" or self._algo == "asracos":
from zoopt.algos.opt_algorithms.racos.sracos import SRacosTune
self.optimizer = SRacosTune(dimension=dim, parameter=par)
self.optimizer = SRacosTune(
dimension=dim, parameter=par, **self.kwargs)
def set_search_properties(self, metric: Optional[str], mode: Optional[str],
config: Dict) -> bool:
@@ -184,6 +198,13 @@ class ZOOptSearch(Searcher):
"`tune.run()`.".format(self.__class__.__name__, "space"))
_solution = self.optimizer.suggest()
if _solution == "FINISHED":
if ray.__version__ >= "0.8.7":
return Searcher.FINISHED
else:
return None
if _solution:
self.solution_dict[str(trial_id)] = _solution
_x = _solution.get_x()
@@ -248,14 +269,12 @@ class ZOOptSearch(Searcher):
True)
elif isinstance(domain, Categorical):
# Categorical variables would use ValjeType.DISCRETE with
# Categorical variables would use ValueType.DISCRETE with
# has_partial_order=False, however, currently we do not
# keep track of category values and cannot automatically
# translate back and forth between them.
raise ValueError(
"ZOOpt does not support automatic conversion for "
"categorical variables. Please instantiate ZOOpt with "
"a manually defined search space.")
if isinstance(sampler, Uniform):
return (ValueType.GRID, domain.categories)
raise ValueError("ZOOpt does not support parameters of type "
"`{}` with samplers of type `{}`".format(
python/ray/tune/tests/test_sample.py
+14 -7
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@@ -533,7 +533,7 @@ class SearchSpaceTest(unittest.TestCase):
"a": tune.sample.Categorical([2, 3, 4]).uniform(),
"b": {
"x": tune.sample.Integer(0, 5).quantized(2),
"y": 4,
"y": tune.sample.Categorical([2, 4, 6, 8]).uniform(),
"z": tune.sample.Float(1e-4, 1e-2).loguniform()
}
}
@@ -544,7 +544,7 @@ class SearchSpaceTest(unittest.TestCase):
"a": 2,
"b": {
"x": tune.sample.Integer(0, 5).uniform(),
"y": 4,
"y": tune.sample.Categorical([2, 4, 6, 8]).uniform(),
"z": tune.sample.Float(-3, 7).uniform().quantized(1e-4)
}
}
@@ -552,11 +552,16 @@ class SearchSpaceTest(unittest.TestCase):
zoopt_config = {
"b/x": (ValueType.DISCRETE, [0, 5], True),
"b/z": (ValueType.CONTINUOUS, [-3, 7], 1e-4)
"b/y": (ValueType.GRID, [2, 4, 6, 8]),
"b/z": (ValueType.CONTINUOUS, [-3, 7], 1e-4),
}
searcher1 = ZOOptSearch(dim_dict=converted_config, budget=5)
searcher2 = ZOOptSearch(dim_dict=zoopt_config, budget=5)
zoopt_search_config = {"parallel_num": 4}
searcher1 = ZOOptSearch(
dim_dict=converted_config, budget=5, **zoopt_search_config)
searcher2 = ZOOptSearch(
dim_dict=zoopt_config, budget=5, **zoopt_search_config)
np.random.seed(1234)
config1 = searcher1.suggest("0")
@@ -565,14 +570,16 @@ class SearchSpaceTest(unittest.TestCase):
self.assertEqual(config1, config2)
self.assertIn(config1["b"]["x"], list(range(5)))
self.assertIn(config1["b"]["y"], [2, 4, 6, 8])
self.assertLess(-3, config1["b"]["z"])
self.assertLess(config1["b"]["z"], 7)
searcher = ZOOptSearch(budget=5, metric="a", mode="max")
searcher = ZOOptSearch(
budget=5, metric="a", mode="max", **zoopt_search_config)
analysis = tune.run(
_mock_objective, config=config, search_alg=searcher, num_samples=1)
trial = analysis.trials[0]
self.assertEqual(trial.config["b"]["y"], 4)
self.assertIn(trial.config["b"]["y"], [2, 4, 6, 8])
if __name__ == "__main__":
python/requirements_tune.txt
+1 -1
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@@ -30,4 +30,4 @@ git+git://github.com/huggingface/transformers.git@bdcc4b78a27775d1ec8f3fd297cb67
git+git://github.com/ray-project/tune-sklearn@master#tune-sklearn
wandb
xgboost
zoopt>=0.4.0
zoopt>=0.4.1