import copy
import logging
from collections.abc import Mapping
from typing import Any, Dict, Generator, List, Optional, Tuple
import numpy
import random
from ray.tune import TuneError
from ray.tune.sample import Categorical, Domain, Function
logger = logging.getLogger(__name__)
def generate_variants(
unresolved_spec: Dict) -> Generator[Tuple[Dict, Dict], None, None]:
"""Generates variants from a spec (dict) with unresolved values.
There are two types of unresolved values:
Grid search: These define a grid search over values. For example, the
following grid search values in a spec will produce six distinct
variants in combination:
"activation": grid_search(["relu", "tanh"])
"learning_rate": grid_search([1e-3, 1e-4, 1e-5])
Lambda functions: These are evaluated to produce a concrete value, and
can express dependencies or conditional distributions between values.
They can also be used to express random search (e.g., by calling
into the `random` or `np` module).
"cpu": lambda spec: spec.config.num_workers
"batch_size": lambda spec: random.uniform(1, 1000)
Finally, to support defining specs in plain JSON / YAML, grid search
and lambda functions can also be defined alternatively as follows:
"activation": {"grid_search": ["relu", "tanh"]}
"cpu": {"eval": "spec.config.num_workers"}
Use `format_vars` to format the returned dict of hyperparameters.
Yields:
(Dict of resolved variables, Spec object)
"""
for resolved_vars, spec in _generate_variants(unresolved_spec):
assert not _unresolved_values(spec)
yield resolved_vars, spec
[docs]def grid_search(values: List) -> Dict[str, List]:
"""Convenience method for specifying grid search over a value.
Arguments:
values: An iterable whose parameters will be gridded.
"""
return {"grid_search": values}
_STANDARD_IMPORTS = {
"random": random,
"np": numpy,
}
_MAX_RESOLUTION_PASSES = 20
def resolve_nested_dict(nested_dict: Dict) -> Dict[Tuple, Any]:
"""Flattens a nested dict by joining keys into tuple of paths.
Can then be passed into `format_vars`.
"""
res = {}
for k, v in nested_dict.items():
if isinstance(v, dict):
for k_, v_ in resolve_nested_dict(v).items():
res[(k, ) + k_] = v_
else:
res[(k, )] = v
return res
def format_vars(resolved_vars: Dict) -> str:
"""Formats the resolved variable dict into a single string."""
out = []
for path, value in sorted(resolved_vars.items()):
if path[0] in ["run", "env", "resources_per_trial"]:
continue # TrialRunner already has these in the experiment_tag
pieces = []
last_string = True
for k in path[::-1]:
if isinstance(k, int):
pieces.append(str(k))
elif last_string:
last_string = False
pieces.append(k)
pieces.reverse()
out.append(_clean_value("_".join(pieces)) + "=" + _clean_value(value))
return ",".join(out)
def flatten_resolved_vars(resolved_vars: Dict) -> Dict:
"""Formats the resolved variable dict into a mapping of (str -> value)."""
flattened_resolved_vars_dict = {}
for pieces, value in resolved_vars.items():
if pieces[0] == "config":
pieces = pieces[1:]
pieces = [str(piece) for piece in pieces]
flattened_resolved_vars_dict["/".join(pieces)] = value
return flattened_resolved_vars_dict
def _clean_value(value: Any) -> str:
if isinstance(value, float):
return "{:.5}".format(value)
else:
return str(value).replace("/", "_")
def parse_spec_vars(spec: Dict) -> Tuple[List[Tuple[Tuple, Any]], List[Tuple[
Tuple, Any]], List[Tuple[Tuple, Any]]]:
resolved, unresolved = _split_resolved_unresolved_values(spec)
resolved_vars = list(resolved.items())
if not unresolved:
return resolved_vars, [], []
grid_vars = []
domain_vars = []
for path, value in unresolved.items():
if value.is_grid():
grid_vars.append((path, value))
else:
domain_vars.append((path, value))
grid_vars.sort()
return resolved_vars, domain_vars, grid_vars
def count_spec_samples(spec: Dict, num_samples=1) -> int:
"""Count samples for a specific spec"""
_, domain_vars, grid_vars = parse_spec_vars(spec)
grid_count = 1
for path, domain in grid_vars:
grid_count *= len(domain.categories)
return num_samples * grid_count
def count_variants(spec: Dict, presets: Optional[List[Dict]] = None) -> int:
# Helper function: Deep update dictionary
def deep_update(d, u):
for k, v in u.items():
if isinstance(v, Mapping):
d[k] = deep_update(d.get(k, {}), v)
else:
d[k] = v
return d
total_samples = 0
total_num_samples = spec.get("num_samples", 1)
# For each preset, overwrite the spec and count the samples generated
# for this preset
for preset in presets:
preset_spec = copy.deepcopy(spec)
deep_update(preset_spec["config"], preset)
total_samples += count_spec_samples(preset_spec, 1)
total_num_samples -= 1
# Add the remaining samples
if total_num_samples > 0:
total_samples += count_spec_samples(spec, total_num_samples)
return total_samples
def _generate_variants(spec: Dict) -> Tuple[Dict, Dict]:
spec = copy.deepcopy(spec)
_, domain_vars, grid_vars = parse_spec_vars(spec)
if not domain_vars and not grid_vars:
yield {}, spec
return
grid_search = _grid_search_generator(spec, grid_vars)
for resolved_spec in grid_search:
resolved_vars = _resolve_domain_vars(resolved_spec, domain_vars)
for resolved, spec in _generate_variants(resolved_spec):
for path, value in grid_vars:
resolved_vars[path] = _get_value(spec, path)
for k, v in resolved.items():
if (k in resolved_vars and v != resolved_vars[k]
and _is_resolved(resolved_vars[k])):
raise ValueError(
"The variable `{}` could not be unambiguously "
"resolved to a single value. Consider simplifying "
"your configuration.".format(k))
resolved_vars[k] = v
yield resolved_vars, spec
def get_preset_variants(spec: Dict, config: Dict):
"""Get variants according to a spec, initialized with a config.
Variables from the spec are overwritten by the variables in the config.
Thus, we may end up with less sampled parameters.
This function also checks if values used to overwrite search space
parameters are valid, and logs a warning if not.
"""
spec = copy.deepcopy(spec)
resolved, _, _ = parse_spec_vars(config)
for path, val in resolved:
try:
domain = _get_value(spec["config"], path)
if isinstance(domain, dict):
if "grid_search" in domain:
domain = Categorical(domain["grid_search"])
else:
# If users want to overwrite an entire subdict,
# let them do it.
domain = None
except IndexError as exc:
raise ValueError(
f"Pre-set config key `{'/'.join(path)}` does not correspond "
f"to a valid key in the search space definition. Please add "
f"this path to the `config` variable passed to `tune.run()`."
) from exc
if domain and not domain.is_valid(val):
logger.warning(
f"Pre-set value `{val}` is not within valid values of "
f"parameter `{'/'.join(path)}`: {domain.domain_str}")
assign_value(spec["config"], path, val)
return _generate_variants(spec)
def assign_value(spec: Dict, path: Tuple, value: Any):
for k in path[:-1]:
spec = spec[k]
spec[path[-1]] = value
def _get_value(spec: Dict, path: Tuple) -> Any:
for k in path:
spec = spec[k]
return spec
def _resolve_domain_vars(spec: Dict,
domain_vars: List[Tuple[Tuple, Domain]]) -> Dict:
resolved = {}
error = True
num_passes = 0
while error and num_passes < _MAX_RESOLUTION_PASSES:
num_passes += 1
error = False
for path, domain in domain_vars:
if path in resolved:
continue
try:
value = domain.sample(_UnresolvedAccessGuard(spec))
except RecursiveDependencyError as e:
error = e
except Exception:
raise ValueError(
"Failed to evaluate expression: {}: {}".format(
path, domain))
else:
assign_value(spec, path, value)
resolved[path] = value
if error:
raise error
return resolved
def _grid_search_generator(unresolved_spec: Dict,
grid_vars: List) -> Generator[Dict, None, None]:
value_indices = [0] * len(grid_vars)
def increment(i):
value_indices[i] += 1
if value_indices[i] >= len(grid_vars[i][1]):
value_indices[i] = 0
if i + 1 < len(value_indices):
return increment(i + 1)
else:
return True
return False
if not grid_vars:
yield unresolved_spec
return
while value_indices[-1] < len(grid_vars[-1][1]):
spec = copy.deepcopy(unresolved_spec)
for i, (path, values) in enumerate(grid_vars):
assign_value(spec, path, values[value_indices[i]])
yield spec
if grid_vars:
done = increment(0)
if done:
break
def _is_resolved(v) -> bool:
resolved, _ = _try_resolve(v)
return resolved
def _try_resolve(v) -> Tuple[bool, Any]:
if isinstance(v, Domain):
# Domain to sample from
return False, v
elif isinstance(v, dict) and len(v) == 1 and "eval" in v:
# Lambda function in eval syntax
return False, Function(
lambda spec: eval(v["eval"], _STANDARD_IMPORTS, {"spec": spec}))
elif isinstance(v, dict) and len(v) == 1 and "grid_search" in v:
# Grid search values
grid_values = v["grid_search"]
if not isinstance(grid_values, list):
raise TuneError(
"Grid search expected list of values, got: {}".format(
grid_values))
return False, Categorical(grid_values).grid()
return True, v
def _split_resolved_unresolved_values(
spec: Dict) -> Tuple[Dict[Tuple, Any], Dict[Tuple, Any]]:
resolved_vars = {}
unresolved_vars = {}
for k, v in spec.items():
resolved, v = _try_resolve(v)
if not resolved:
unresolved_vars[(k, )] = v
elif isinstance(v, dict):
# Recurse into a dict
_resolved_children, _unresolved_children = \
_split_resolved_unresolved_values(v)
for (path, value) in _resolved_children.items():
resolved_vars[(k, ) + path] = value
for (path, value) in _unresolved_children.items():
unresolved_vars[(k, ) + path] = value
elif isinstance(v, list):
# Recurse into a list
for i, elem in enumerate(v):
_resolved_children, _unresolved_children = \
_split_resolved_unresolved_values({i: elem})
for (path, value) in _resolved_children.items():
resolved_vars[(k, ) + path] = value
for (path, value) in _unresolved_children.items():
unresolved_vars[(k, ) + path] = value
else:
resolved_vars[(k, )] = v
return resolved_vars, unresolved_vars
def _unresolved_values(spec: Dict) -> Dict[Tuple, Any]:
return _split_resolved_unresolved_values(spec)[1]
def has_unresolved_values(spec: Dict) -> bool:
return True if _unresolved_values(spec) else False
class _UnresolvedAccessGuard(dict):
def __init__(self, *args, **kwds):
super(_UnresolvedAccessGuard, self).__init__(*args, **kwds)
self.__dict__ = self
def __getattribute__(self, item):
value = dict.__getattribute__(self, item)
if not _is_resolved(value):
raise RecursiveDependencyError(
"`{}` recursively depends on {}".format(item, value))
elif isinstance(value, dict):
return _UnresolvedAccessGuard(value)
else:
return value
class RecursiveDependencyError(Exception):
def __init__(self, msg: str):
Exception.__init__(self, msg)
