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323 lines
11 KiB
Python
323 lines
11 KiB
Python
# Copyright 2020 The Ray Authors.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This source file is adapted here because ray does not fully support Windows.
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# Copyright (c) Microsoft Corporation.
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import copy
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import logging
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from typing import Any, Dict, Generator, List, Tuple
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import numpy
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import random
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from ..tune.sample import Categorical, Domain, RandomState
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logger = logging.getLogger(__name__)
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class TuneError(Exception):
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"""General error class raised by ray.tune."""
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pass
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def generate_variants(
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unresolved_spec: Dict,
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constant_grid_search: bool = False,
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random_state: "RandomState" = None,
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) -> Generator[Tuple[Dict, Dict], None, None]:
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"""Generates variants from a spec (dict) with unresolved values.
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There are two types of unresolved values:
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Grid search: These define a grid search over values. For example, the
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following grid search values in a spec will produce six distinct
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variants in combination:
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"activation": grid_search(["relu", "tanh"])
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"learning_rate": grid_search([1e-3, 1e-4, 1e-5])
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Lambda functions: These are evaluated to produce a concrete value, and
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can express dependencies or conditional distributions between values.
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They can also be used to express random search (e.g., by calling
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into the `random` or `np` module).
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"cpu": lambda spec: spec.config.num_workers
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"batch_size": lambda spec: random.uniform(1, 1000)
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Finally, to support defining specs in plain JSON / YAML, grid search
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and lambda functions can also be defined alternatively as follows:
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"activation": {"grid_search": ["relu", "tanh"]}
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"cpu": {"eval": "spec.config.num_workers"}
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Use `format_vars` to format the returned dict of hyperparameters.
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Yields:
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(Dict of resolved variables, Spec object)
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"""
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for resolved_vars, spec in _generate_variants(
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unresolved_spec,
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constant_grid_search=constant_grid_search,
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random_state=random_state,
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):
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assert not _unresolved_values(spec)
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yield resolved_vars, spec
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def grid_search(values: List) -> Dict[str, List]:
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"""Convenience method for specifying grid search over a value.
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Arguments:
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values: An iterable whose parameters will be gridded.
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"""
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return {"grid_search": values}
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_STANDARD_IMPORTS = {
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"random": random,
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"np": numpy,
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}
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_MAX_RESOLUTION_PASSES = 20
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def parse_spec_vars(
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spec: Dict,
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) -> Tuple[List[Tuple[Tuple, Any]], List[Tuple[Tuple, Any]], List[Tuple[Tuple, Any]]]:
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resolved, unresolved = _split_resolved_unresolved_values(spec)
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resolved_vars = list(resolved.items())
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if not unresolved:
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return resolved_vars, [], []
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grid_vars = []
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domain_vars = []
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for path, value in unresolved.items():
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if value.is_grid():
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grid_vars.append((path, value))
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else:
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domain_vars.append((path, value))
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grid_vars.sort()
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return resolved_vars, domain_vars, grid_vars
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def _generate_variants(
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spec: Dict, constant_grid_search: bool = False, random_state: "RandomState" = None
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) -> Tuple[Dict, Dict]:
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spec = copy.deepcopy(spec)
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_, domain_vars, grid_vars = parse_spec_vars(spec)
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if not domain_vars and not grid_vars:
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yield {}, spec
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return
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# Variables to resolve
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to_resolve = domain_vars
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all_resolved = True
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if constant_grid_search:
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# In this path, we first sample random variables and keep them constant
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# for grid search.
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# `_resolve_domain_vars` will alter `spec` directly
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all_resolved, resolved_vars = _resolve_domain_vars(
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spec, domain_vars, allow_fail=True, random_state=random_state
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)
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if not all_resolved:
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# Not all variables have been resolved, but remove those that have
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# from the `to_resolve` list.
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to_resolve = [(r, d) for r, d in to_resolve if r not in resolved_vars]
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grid_search = _grid_search_generator(spec, grid_vars)
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for resolved_spec in grid_search:
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if not constant_grid_search or not all_resolved:
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# In this path, we sample the remaining random variables
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_, resolved_vars = _resolve_domain_vars(
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resolved_spec, to_resolve, random_state=random_state
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)
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for resolved, spec in _generate_variants(
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resolved_spec,
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constant_grid_search=constant_grid_search,
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random_state=random_state,
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):
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for path, value in grid_vars:
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resolved_vars[path] = _get_value(spec, path)
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for k, v in resolved.items():
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if (
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k in resolved_vars
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and v != resolved_vars[k]
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and _is_resolved(resolved_vars[k])
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):
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raise ValueError(
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"The variable `{}` could not be unambiguously "
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"resolved to a single value. Consider simplifying "
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"your configuration.".format(k)
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)
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resolved_vars[k] = v
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yield resolved_vars, spec
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def assign_value(spec: Dict, path: Tuple, value: Any):
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for k in path[:-1]:
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spec = spec[k]
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spec[path[-1]] = value
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def _get_value(spec: Dict, path: Tuple) -> Any:
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for k in path:
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spec = spec[k]
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return spec
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def _resolve_domain_vars(
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spec: Dict,
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domain_vars: List[Tuple[Tuple, Domain]],
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allow_fail: bool = False,
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random_state: "RandomState" = None,
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) -> Tuple[bool, Dict]:
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resolved = {}
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error = True
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num_passes = 0
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while error and num_passes < _MAX_RESOLUTION_PASSES:
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num_passes += 1
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error = False
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for path, domain in domain_vars:
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if path in resolved:
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continue
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try:
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value = domain.sample(
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_UnresolvedAccessGuard(spec), random_state=random_state
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)
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except RecursiveDependencyError as e:
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error = e
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except Exception:
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raise ValueError(
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"Failed to evaluate expression: {}: {}".format(path, domain)
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)
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else:
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assign_value(spec, path, value)
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resolved[path] = value
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if error:
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if not allow_fail:
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raise error
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else:
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return False, resolved
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return True, resolved
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def _grid_search_generator(
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unresolved_spec: Dict, grid_vars: List
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) -> Generator[Dict, None, None]:
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value_indices = [0] * len(grid_vars)
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def increment(i):
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value_indices[i] += 1
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if value_indices[i] >= len(grid_vars[i][1]):
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value_indices[i] = 0
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if i + 1 < len(value_indices):
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return increment(i + 1)
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else:
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return True
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return False
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if not grid_vars:
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yield unresolved_spec
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return
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while value_indices[-1] < len(grid_vars[-1][1]):
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spec = copy.deepcopy(unresolved_spec)
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for i, (path, values) in enumerate(grid_vars):
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assign_value(spec, path, values[value_indices[i]])
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yield spec
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if grid_vars:
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done = increment(0)
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if done:
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break
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def _is_resolved(v) -> bool:
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resolved, _ = _try_resolve(v)
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return resolved
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def _try_resolve(v) -> Tuple[bool, Any]:
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if isinstance(v, Domain):
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# Domain to sample from
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return False, v
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elif isinstance(v, dict) and len(v) == 1 and "grid_search" in v:
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# Grid search values
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grid_values = v["grid_search"]
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if not isinstance(grid_values, list):
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raise TuneError(
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"Grid search expected list of values, got: {}".format(grid_values)
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)
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return False, Categorical(grid_values).grid()
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return True, v
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def _split_resolved_unresolved_values(
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spec: Dict,
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) -> Tuple[Dict[Tuple, Any], Dict[Tuple, Any]]:
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resolved_vars = {}
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unresolved_vars = {}
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for k, v in spec.items():
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resolved, v = _try_resolve(v)
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if not resolved:
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unresolved_vars[(k,)] = v
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elif isinstance(v, dict):
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# Recurse into a dict
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(
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_resolved_children,
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_unresolved_children,
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) = _split_resolved_unresolved_values(v)
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for (path, value) in _resolved_children.items():
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resolved_vars[(k,) + path] = value
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for (path, value) in _unresolved_children.items():
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unresolved_vars[(k,) + path] = value
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elif isinstance(v, list):
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# Recurse into a list
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for i, elem in enumerate(v):
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(
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_resolved_children,
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_unresolved_children,
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) = _split_resolved_unresolved_values({i: elem})
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for (path, value) in _resolved_children.items():
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resolved_vars[(k,) + path] = value
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for (path, value) in _unresolved_children.items():
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unresolved_vars[(k,) + path] = value
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else:
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resolved_vars[(k,)] = v
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return resolved_vars, unresolved_vars
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def _unresolved_values(spec: Dict) -> Dict[Tuple, Any]:
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return _split_resolved_unresolved_values(spec)[1]
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def has_unresolved_values(spec: Dict) -> bool:
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return True if _unresolved_values(spec) else False
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class _UnresolvedAccessGuard(dict):
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def __init__(self, *args, **kwds):
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super(_UnresolvedAccessGuard, self).__init__(*args, **kwds)
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self.__dict__ = self
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def __getattribute__(self, item):
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value = dict.__getattribute__(self, item)
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if not _is_resolved(value):
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raise RecursiveDependencyError(
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"`{}` recursively depends on {}".format(item, value)
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)
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elif isinstance(value, dict):
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return _UnresolvedAccessGuard(value)
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else:
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return value
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class RecursiveDependencyError(Exception):
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def __init__(self, msg: str):
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Exception.__init__(self, msg)
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