mirror of
https://github.com/wassname/ray.git
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289 lines
8.8 KiB
Python
289 lines
8.8 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import copy
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import logging
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import numpy
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import random
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import types
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from ray.tune import TuneError
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logger = logging.getLogger(__name__)
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def generate_variants(unresolved_spec):
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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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"""
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for resolved_vars, spec in _generate_variants(unresolved_spec):
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assert not _unresolved_values(spec)
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yield format_vars(resolved_vars), spec
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def grid_search(values):
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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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class sample_from(object):
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"""Specify that tune should sample configuration values from this function.
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The use of function arguments in tune configs must be disambiguated by
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either wrapped the function in tune.eval() or tune.function().
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Arguments:
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func: An callable function to draw a sample from.
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"""
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def __init__(self, func):
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self.func = func
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class function(object):
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"""Wraps `func` to make sure it is not expanded during resolution.
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The use of function arguments in tune configs must be disambiguated by
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either wrapped the function in tune.eval() or tune.function().
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Arguments:
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func: A function literal.
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"""
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def __init__(self, func):
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self.func = func
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def __call__(self, *args, **kwargs):
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return self.func(*args, **kwargs)
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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 format_vars(resolved_vars):
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out = []
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for path, value in sorted(resolved_vars.items()):
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if path[0] in ["run", "env", "resources_per_trial"]:
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continue # TrialRunner already has these in the experiment_tag
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pieces = []
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last_string = True
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for k in path[::-1]:
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if isinstance(k, int):
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pieces.append(str(k))
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elif last_string:
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last_string = False
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pieces.append(k)
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pieces.reverse()
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out.append(_clean_value("_".join(pieces)) + "=" + _clean_value(value))
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return ",".join(out)
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def _clean_value(value):
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if isinstance(value, float):
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return "{:.5}".format(value)
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else:
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return str(value).replace("/", "_")
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def _generate_variants(spec):
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spec = copy.deepcopy(spec)
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unresolved = _unresolved_values(spec)
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if not unresolved:
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yield {}, spec
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return
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grid_vars = []
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lambda_vars = []
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for path, value in unresolved.items():
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if isinstance(value, types.FunctionType):
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lambda_vars.append((path, value))
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else:
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grid_vars.append((path, value))
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grid_vars.sort()
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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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resolved_vars = _resolve_lambda_vars(resolved_spec, lambda_vars)
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for resolved, spec in _generate_variants(resolved_spec):
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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 (k in resolved_vars and v != resolved_vars[k]
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and _is_resolved(resolved_vars[k])):
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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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resolved_vars[k] = v
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yield resolved_vars, spec
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def _assign_value(spec, path, value):
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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, path):
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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_lambda_vars(spec, lambda_vars):
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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, fn in lambda_vars:
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try:
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value = fn(_UnresolvedAccessGuard(spec))
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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, fn) +
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". If you meant to pass this as a function literal, use "
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"tune.function() to escape it.")
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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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raise error
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return resolved
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def _grid_search_generator(unresolved_spec, grid_vars):
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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):
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resolved, _ = _try_resolve(v)
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return resolved
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def _try_resolve(v):
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if isinstance(v, types.FunctionType):
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logger.warning(
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"Deprecation warning: Function values are ambiguous in Tune "
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"configuations. Either wrap the function with "
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"`tune.function(func)` to specify a function literal, or "
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"`tune.sample_from(func)` to tell Tune to "
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"sample values from the function during variant generation: "
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"{}".format(v))
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return False, v
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elif isinstance(v, sample_from):
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# Function to sample from
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return False, v.func
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elif isinstance(v, dict) and len(v) == 1 and "eval" in v:
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# Lambda function in eval syntax
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return False, lambda spec: eval(
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v["eval"], _STANDARD_IMPORTS, {"spec": spec})
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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(
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grid_values))
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return False, grid_values
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return True, v
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def _unresolved_values(spec):
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found = {}
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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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found[(k, )] = v
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elif isinstance(v, dict):
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# Recurse into a dict
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for (path, value) in _unresolved_values(v).items():
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found[(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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for (path, value) in _unresolved_values({i: elem}).items():
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found[(k, ) + path] = value
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return found
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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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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):
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Exception.__init__(self, msg)
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