"""Utilities for hyperparameter optimization.
This wrapper has an optional dependency on
Oríon
https://orion.readthedocs.io/en/stable/
https://github.com/Epistimio/orion
Authors
* Artem Ploujnikov 2021
"""
import importlib
import logging
import json
import os
import speechbrain as sb
import sys
from datetime import datetime
from hyperpyyaml import load_hyperpyyaml
logger = logging.getLogger(__name__)
MODULE_ORION = "orion.client"
FORMAT_TIMESTAMP = "%Y%m%d%H%M%S%f"
DEFAULT_TRIAL_ID = "hpopt"
DEFAULT_REPORTER = "generic"
ORION_TRIAL_ID_ENV = [
"ORION_EXPERIMENT_NAME",
"ORION_EXPERIMENT_VERSION",
"ORION_TRIAL_ID",
]
KEY_HPOPT = "hpopt"
KEY_HPOPT_MODE = "hpopt_mode"
KEY_TRIAL_ID = "trial_id"
HPOPT_KEYS = [KEY_HPOPT, KEY_HPOPT_MODE]
_hpopt_modes = {}
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def hpopt_mode(mode):
"""A decorator to register a reporter implementation for
a hyperparameter optimization mode
Arguments
---------
mode: str
the mode to register
Returns
-------
f: callable
a callable function that registers and returns the
reporter class
Example
-------
>>> @hpopt_mode("raw")
... class RawHyperparameterOptimizationReporter(HyperparameterOptimizationReporter):
... def __init__(self, *args, **kwargs):
... super().__init__( *args, **kwargs)
... def report_objective(self, result):
... objective = result[self.objective_key]
... print(f"Objective: {objective}")
>>> reporter = get_reporter("raw", objective_key="error")
>>> result = {"error": 1.2, "train_loss": 7.2}
>>> reporter.report_objective(result)
Objective: 1.2
"""
def f(cls):
""" "Call the function that registers and returns the reporter class"""
_hpopt_modes[mode] = cls
return cls
return f
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class HyperparameterOptimizationReporter:
"""A base class for hyperparameter fit reporters
Arguments
---------
objective_key: str
the key from the result dictionary to be used as the objective
"""
def __init__(self, objective_key):
self.objective_key = objective_key
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def report_objective(self, result):
"""Reports the objective for hyperparameter optimization.
Arguments
---------
result: dict
a dictionary with the run result.
"""
return NotImplemented
@property
def is_available(self):
"""Determines whether this reporter is available"""
return True
@property
def trial_id(self):
"""The unique ID of this trial (used for folder naming)"""
return DEFAULT_TRIAL_ID
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@hpopt_mode("generic")
class GenericHyperparameterOptimizationReporter(
HyperparameterOptimizationReporter
):
"""
A generic hyperparameter fit reporter that outputs the result as
JSON to an arbitrary data stream, which may be read as a third-party
tool
Arguments
---------
objective_key: str
the key from the result dictionary to be used as the objective
"""
def __init__(self, reference_date=None, output=None, *args, **kwargs):
super().__init__(*args, **kwargs)
self.output = output or sys.stdout
self.reference_date = reference_date
self._trial_id = None
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def report_objective(self, result):
"""Reports the objective for hyperparameter optimization.
Arguments
---------
result: dict
a dictionary with the run result.
Example
-------
>>> reporter = GenericHyperparameterOptimizationReporter(
... objective_key="error"
... )
>>> result = {"error": 1.2, "train_loss": 7.2}
>>> reporter.report_objective(result)
{"error": 1.2, "train_loss": 7.2, "objective": 1.2}
"""
json.dump(
dict(result, objective=result[self.objective_key]), self.output
)
@property
def trial_id(self):
"""The unique ID of this trial (used mainly for folder naming)
Example
-------
>>> import datetime
>>> reporter = GenericHyperparameterOptimizationReporter(
... objective_key="error",
... reference_date=datetime.datetime(2021, 1, 3)
... )
>>> print(reporter.trial_id)
20210103000000000000
"""
if self._trial_id is None:
date = self.reference_date or datetime.now()
self._trial_id = date.strftime(FORMAT_TIMESTAMP)
return self._trial_id
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@hpopt_mode("orion")
class OrionHyperparameterOptimizationReporter(
HyperparameterOptimizationReporter
):
"""A result reporter implementation based on Orion
Arguments
---------
orion_client: module
the Python module for Orion
"""
def __init__(self, objective_key):
super().__init__(objective_key=objective_key)
self.orion_client = None
self._trial_id = None
self._check_client()
def _check_client(self):
try:
self.orion_client = importlib.import_module(MODULE_ORION)
except ImportError:
logger.warning("Orion is not available")
self.orion_client = None
def _format_message(self, result):
"""Formats the log message for output
Arguments
---------
result: dict
the result dictionary
Returns
-------
message: str
a formatted message"""
return ", ".join(f"{key} = {value}" for key, value in result.items())
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def report_objective(self, result):
"""Reports the objective for hyperparameter optimization.
Arguments
---------
result: dict
a dictionary with the run result.
"""
message = self._format_message(result)
logger.info(f"Hyperparameter fit: {message}")
if self.orion_client is not None:
objective_value = result[self.objective_key]
self.orion_client.report_objective(objective_value)
@property
def trial_id(self):
"""The unique ID of this trial (used mainly for folder naming)"""
if self._trial_id is None:
self._trial_id = "-".join(
os.getenv(name) or "" for name in ORION_TRIAL_ID_ENV
)
return self._trial_id
@property
def is_available(self):
"""Determines if Orion is available. In order for it to
be available, the library needs to be installed, and at
least one of ORION_EXPERIMENT_NAME, ORION_EXPERIMENT_VERSION,
ORION_TRIAL_ID needs to be set"""
return self.orion_client is not None and any(
os.getenv(name) for name in ORION_TRIAL_ID_ENV
)
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def get_reporter(mode, *args, **kwargs):
"""Attempts to get the reporter specified by the mode
and reverts to a generic one if it is not available
Arguments
---------
mode: str
a string identifier for a registered hyperparametr
optimization mode, corresponding to a specific reporter
instance
Returns
-------
reporter: HyperparameterOptimizationReporter
a reporter instance
Example
-------
>>> reporter = get_reporter("generic", objective_key="error")
>>> result = {"error": 3.4, "train_loss": 1.2}
>>> reporter.report_objective(result)
{"error": 3.4, "train_loss": 1.2, "objective": 3.4}
"""
reporter_cls = _hpopt_modes.get(mode)
if reporter_cls is None:
logger.warning(
f"hpopt_mode {mode} is not supported, reverting to generic"
)
reporter_cls = _hpopt_modes[DEFAULT_REPORTER]
reporter = reporter_cls(*args, **kwargs)
if not reporter.is_available:
logger.warning("Reverting to a generic reporter")
reporter_cls = _hpopt_modes[DEFAULT_REPORTER]
reporter = reporter_cls(*args, **kwargs)
return reporter
_context = {"current": None}
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class HyperparameterOptimizationContext:
"""
A convenience context manager that makes it possible to conditionally
enable hyperparameter optimization for a recipe.
Arguments
---------
reporter_args: list
arguments to the reporter class
reporter_kwargs: dict
keyword arguments to the reporter class
Example
-------
>>> ctx = HyperparameterOptimizationContext(
... reporter_args=[],
... reporter_kwargs={"objective_key": "error"}
... )
"""
def __init__(self, reporter_args=None, reporter_kwargs=None):
self.reporter_args = reporter_args or []
self.reporter_kwargs = reporter_kwargs or {}
self.reporter = None
self.enabled = False
self.result = {"objective": 0.0}
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def parse_arguments(
self, arg_list, pass_hpopt_args=None, pass_trial_id=True
):
"""A version of speechbrain.parse_arguments enhanced for hyperparameter optimization.
If a parameter named 'hpopt' is provided, hyperparameter
optimization and reporting will be enabled.
If the parameter value corresponds to a filename, it will
be read as a hyperpyaml file, and the contents will be added
to "overrides". This is useful for cases where the values of
certain hyperparameters are different during hyperparameter
optimization vs during full training (e.g. number of epochs, saving
files, etc)
Arguments
---------
arg_list: list
a list of arguments
pass_hpopt_args: enumerable
forces arguments that are normally suppressed and only used
for hyperparameter optimization to be passed into overrides
pass_trial_id: bool
whether the "trial_id" argument is passed through (enabled by default)
Returns
-------
param_file : str
The location of the parameters file.
run_opts : dict
Run options, such as distributed, device, etc.
overrides : dict
The overrides to pass to ``load_hyperpyyaml``.
Example
-------
>>> ctx = HyperparameterOptimizationContext()
>>> arg_list = ["hparams.yaml", "--x", "1", "--y", "2"]
>>> hparams_file, run_opts, overrides = ctx.parse_arguments(arg_list)
>>> print(f"File: {hparams_file}, Overrides: {overrides}")
File: hparams.yaml, Overrides: {'x': 1, 'y': 2}
"""
if pass_hpopt_args is None:
pass_hpopt_args = []
pass_hpopt_args = set(pass_hpopt_args)
hparams_file, run_opts, overrides_yaml = sb.parse_arguments(arg_list)
overrides = load_hyperpyyaml(overrides_yaml) if overrides_yaml else {}
hpopt = overrides.get(KEY_HPOPT, False)
hpopt_mode = overrides.get(KEY_HPOPT_MODE) or DEFAULT_REPORTER
if hpopt:
self.enabled = True
self.reporter = get_reporter(
hpopt_mode, *self.reporter_args, **self.reporter_kwargs
)
if isinstance(hpopt, str) and os.path.exists(hpopt):
with open(hpopt) as hpopt_file:
trial_id = get_trial_id()
hpopt_overrides = load_hyperpyyaml(
hpopt_file,
overrides={"trial_id": trial_id},
overrides_must_match=False,
)
overrides = dict(hpopt_overrides, **overrides)
keys = list(HPOPT_KEYS)
if not pass_trial_id:
keys.append(KEY_TRIAL_ID)
for key in keys:
if key in overrides and key not in pass_hpopt_args:
del overrides[key]
return hparams_file, run_opts, overrides
def __enter__(self):
_context["current"] = self
return self
def __exit__(self, exc_type, exc_value, traceback):
if exc_type is None and self.result is not None:
reporter = self.reporter
if not reporter:
reporter = get_reporter(
DEFAULT_REPORTER,
*self.reporter_args,
**self.reporter_kwargs,
)
reporter.report_objective(self.result)
_context["current"] = None
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def hyperparameter_optimization(*args, **kwargs):
"""Initializes the hyperparameter optimization context
Example
-------
>>> import sys
>>> with hyperparameter_optimization(objective_key="error", output=sys.stdout) as hp_ctx:
... result = {"error": 3.5, "train_loss": 2.1}
... report_result(result)
...
{"error": 3.5, "train_loss": 2.1, "objective": 3.5}
"""
hpfit = HyperparameterOptimizationContext(args, kwargs)
return hpfit
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def report_result(result):
"""Reports the result using the current reporter, if available.
When not in hyperparameter optimization mode, this function does nothing.
Arguments
---------
result: dict
A dictionary of stats to be reported
Example
-------
>>> result = {"error": 3.5, "train_loss": 2.1}
>>> report_result(result["error"])
"""
ctx = _context["current"]
if ctx:
ctx.result = result
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def get_trial_id():
"""
Returns the ID of the current hyperparameter optimization trial,
used primarily for the name of experiment folders.
When using a context, the convention for identifying the trial ID
will depend on the reporter being used. The default implementation
returns a fixed value ("hpopt")
Returns
-------
trial_id: str
the trial identifier
Example
-------
>>> trial_id = get_trial_id()
>>> trial_id
'hpopt'
"""
ctx = _context["current"]
trial_id = ctx.reporter.trial_id if ctx else DEFAULT_TRIAL_ID
return trial_id