599 lines
20 KiB
Python
599 lines
20 KiB
Python
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# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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# ==============================================================================
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"""Experimental public APIs for the HParams plugin.
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These are porcelain on top of `api_pb2` (`api.proto`) and `summary.py`.
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"""
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import abc
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import hashlib
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import json
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import random
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import time
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import numpy as np
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from tensorboard.compat import tf2 as tf
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from tensorboard.compat.proto import summary_pb2
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from tensorboard.plugins.hparams import api_pb2
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from tensorboard.plugins.hparams import metadata
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from tensorboard.plugins.hparams import plugin_data_pb2
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def hparams(hparams, trial_id=None, start_time_secs=None):
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# NOTE: Keep docs in sync with `hparams_pb` below.
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"""Write hyperparameter values for a single trial.
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Args:
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hparams: A `dict` mapping hyperparameters to the values used in this
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trial. Keys should be the names of `HParam` objects used in an
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experiment, or the `HParam` objects themselves. Values should be
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Python `bool`, `int`, `float`, or `string` values, depending on
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the type of the hyperparameter. The corresponding numpy types,
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like `np.float32`, are also permitted.
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trial_id: An optional `str` ID for the set of hyperparameter values
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used in this trial. Defaults to a hash of the hyperparameters.
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start_time_secs: The time that this trial started training, as
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seconds since epoch. Defaults to the current time.
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Returns:
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A tensor whose value is `True` on success, or `False` if no summary
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was written because no default summary writer was available.
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"""
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pb = hparams_pb(
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hparams=hparams,
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trial_id=trial_id,
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start_time_secs=start_time_secs,
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)
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return _write_summary("hparams", pb)
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def hparams_pb(hparams, trial_id=None, start_time_secs=None):
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# NOTE: Keep docs in sync with `hparams` above.
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"""Create a summary encoding hyperparameter values for a single trial.
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Args:
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hparams: A `dict` mapping hyperparameters to the values used in this
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trial. Keys should be the names of `HParam` objects used in an
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experiment, or the `HParam` objects themselves. Values should be
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Python `bool`, `int`, `float`, or `string` values, depending on
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the type of the hyperparameter.
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trial_id: An optional `str` ID for the set of hyperparameter values
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used in this trial. Defaults to a hash of the hyperparameters.
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start_time_secs: The time that this trial started training, as
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seconds since epoch. Defaults to the current time.
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Returns:
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A TensorBoard `summary_pb2.Summary` message.
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"""
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if start_time_secs is None:
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start_time_secs = time.time()
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hparams = _normalize_hparams(hparams)
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group_name = _derive_session_group_name(trial_id, hparams)
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session_start_info = plugin_data_pb2.SessionStartInfo(
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group_name=group_name,
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start_time_secs=start_time_secs,
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)
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for hp_name in sorted(hparams):
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hp_value = hparams[hp_name]
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if isinstance(hp_value, bool):
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session_start_info.hparams[hp_name].bool_value = hp_value
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elif isinstance(hp_value, (float, int)):
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session_start_info.hparams[hp_name].number_value = hp_value
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elif isinstance(hp_value, str):
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session_start_info.hparams[hp_name].string_value = hp_value
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else:
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raise TypeError(
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"hparams[%r] = %r, of unsupported type %r"
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% (hp_name, hp_value, type(hp_value))
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)
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return _summary_pb(
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metadata.SESSION_START_INFO_TAG,
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plugin_data_pb2.HParamsPluginData(
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session_start_info=session_start_info
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),
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)
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def hparams_config(hparams, metrics, time_created_secs=None):
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# NOTE: Keep docs in sync with `hparams_config_pb` below.
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"""Write a top-level experiment configuration.
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This configuration describes the hyperparameters and metrics that will
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be tracked in the experiment, but does not record any actual values of
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those hyperparameters and metrics. It can be created before any models
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are actually trained.
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Args:
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hparams: A list of `HParam` values.
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metrics: A list of `Metric` values.
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time_created_secs: The time that this experiment was created, as
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seconds since epoch. Defaults to the current time.
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Returns:
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A tensor whose value is `True` on success, or `False` if no summary
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was written because no default summary writer was available.
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"""
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pb = hparams_config_pb(
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hparams=hparams,
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metrics=metrics,
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time_created_secs=time_created_secs,
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)
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return _write_summary("hparams_config", pb)
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def hparams_config_pb(hparams, metrics, time_created_secs=None):
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# NOTE: Keep docs in sync with `hparams_config` above.
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"""Create a top-level experiment configuration.
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This configuration describes the hyperparameters and metrics that will
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be tracked in the experiment, but does not record any actual values of
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those hyperparameters and metrics. It can be created before any models
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are actually trained.
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Args:
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hparams: A list of `HParam` values.
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metrics: A list of `Metric` values.
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time_created_secs: The time that this experiment was created, as
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seconds since epoch. Defaults to the current time.
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Returns:
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A TensorBoard `summary_pb2.Summary` message.
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"""
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hparam_infos = []
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for hparam in hparams:
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info = api_pb2.HParamInfo(
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name=hparam.name,
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description=hparam.description,
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display_name=hparam.display_name,
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)
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domain = hparam.domain
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if domain is not None:
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domain.update_hparam_info(info)
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hparam_infos.append(info)
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metric_infos = [metric.as_proto() for metric in metrics]
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experiment = api_pb2.Experiment(
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hparam_infos=hparam_infos,
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metric_infos=metric_infos,
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time_created_secs=time_created_secs,
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)
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return _summary_pb(
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metadata.EXPERIMENT_TAG,
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plugin_data_pb2.HParamsPluginData(experiment=experiment),
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)
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def _normalize_hparams(hparams):
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"""Normalize a dict keyed by `HParam`s and/or raw strings.
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Args:
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hparams: A `dict` whose keys are `HParam` objects and/or strings
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representing hyperparameter names, and whose values are
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hyperparameter values. No two keys may have the same name.
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Returns:
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A `dict` whose keys are hyperparameter names (as strings) and whose
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values are the corresponding hyperparameter values, after numpy
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normalization (see `_normalize_numpy_value`).
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Raises:
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ValueError: If two entries in `hparams` share the same
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hyperparameter name.
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"""
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result = {}
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for (k, v) in hparams.items():
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if isinstance(k, HParam):
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k = k.name
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if k in result:
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raise ValueError("multiple values specified for hparam %r" % (k,))
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result[k] = _normalize_numpy_value(v)
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return result
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def _normalize_numpy_value(value):
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"""Convert a Python or Numpy scalar to a Python scalar.
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For instance, `3.0`, `np.float32(3.0)`, and `np.float64(3.0)` all
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map to `3.0`.
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Args:
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value: A Python scalar (`int`, `float`, `str`, or `bool`) or
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rank-0 `numpy` equivalent (e.g., `np.int64`, `np.float32`).
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Returns:
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A Python scalar equivalent to `value`.
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"""
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if isinstance(value, np.generic):
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return value.item()
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else:
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return value
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def _derive_session_group_name(trial_id, hparams):
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if trial_id is not None:
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if not isinstance(trial_id, str):
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raise TypeError(
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"`trial_id` should be a `str`, but got: %r" % (trial_id,)
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)
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return trial_id
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# Use `json.dumps` rather than `str` to ensure invariance under string
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# type (incl. across Python versions) and dict iteration order.
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jparams = json.dumps(hparams, sort_keys=True, separators=(",", ":"))
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return hashlib.sha256(jparams.encode("utf-8")).hexdigest()
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def _write_summary(name, pb):
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"""Write a summary, returning the writing op.
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Args:
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name: As passed to `summary_scope`.
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pb: A `summary_pb2.Summary` message.
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Returns:
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A tensor whose value is `True` on success, or `False` if no summary
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was written because no default summary writer was available.
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"""
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raw_pb = pb.SerializeToString()
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summary_scope = (
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getattr(tf.summary.experimental, "summary_scope", None)
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or tf.summary.summary_scope
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)
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with summary_scope(name):
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return tf.summary.experimental.write_raw_pb(raw_pb, step=0)
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def _summary_pb(tag, hparams_plugin_data):
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"""Create a summary holding the given `HParamsPluginData` message.
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Args:
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tag: The `str` tag to use.
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hparams_plugin_data: The `HParamsPluginData` message to use.
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Returns:
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A TensorBoard `summary_pb2.Summary` message.
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"""
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summary = summary_pb2.Summary()
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summary_metadata = metadata.create_summary_metadata(hparams_plugin_data)
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value = summary.value.add(
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tag=tag, metadata=summary_metadata, tensor=metadata.NULL_TENSOR
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)
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return summary
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class HParam:
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"""A hyperparameter in an experiment.
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This class describes a hyperparameter in the abstract. It ranges
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over a domain of values, but is not bound to any particular value.
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"""
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def __init__(self, name, domain=None, display_name=None, description=None):
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"""Create a hyperparameter object.
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Args:
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name: A string ID for this hyperparameter, which should be unique
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within an experiment.
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domain: An optional `Domain` object describing the values that
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this hyperparameter can take on.
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display_name: An optional human-readable display name (`str`).
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description: An optional Markdown string describing this
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hyperparameter.
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Raises:
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ValueError: If `domain` is not a `Domain`.
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"""
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self._name = name
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self._domain = domain
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self._display_name = display_name
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self._description = description
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if not isinstance(self._domain, (Domain, type(None))):
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raise ValueError("not a domain: %r" % (self._domain,))
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def __str__(self):
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return "<HParam %r: %s>" % (self._name, self._domain)
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def __repr__(self):
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fields = [
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("name", self._name),
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("domain", self._domain),
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("display_name", self._display_name),
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("description", self._description),
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]
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fields_string = ", ".join("%s=%r" % (k, v) for (k, v) in fields)
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return "HParam(%s)" % fields_string
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@property
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def name(self):
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return self._name
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@property
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def domain(self):
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return self._domain
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@property
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def display_name(self):
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return self._display_name
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@property
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def description(self):
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return self._description
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class Domain(metaclass=abc.ABCMeta):
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"""The domain of a hyperparameter.
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Domains are restricted to values of the simple types `float`, `int`,
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`str`, and `bool`.
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"""
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@abc.abstractproperty
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def dtype(self):
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"""Data type of this domain: `float`, `int`, `str`, or `bool`."""
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pass
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@abc.abstractmethod
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def sample_uniform(self, rng=random):
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"""Sample a value from this domain uniformly at random.
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Args:
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rng: A `random.Random` interface; defaults to the `random` module
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itself.
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Raises:
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IndexError: If the domain is empty.
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"""
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pass
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@abc.abstractmethod
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def update_hparam_info(self, hparam_info):
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"""Update an `HParamInfo` proto to include this domain.
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This should update the `type` field on the proto and exactly one of
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the `domain` variants on the proto.
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Args:
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hparam_info: An `api_pb2.HParamInfo` proto to modify.
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"""
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pass
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class IntInterval(Domain):
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"""A domain that takes on all integer values in a closed interval."""
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def __init__(self, min_value=None, max_value=None):
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"""Create an `IntInterval`.
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Args:
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||
|
|
min_value: The lower bound (inclusive) of the interval.
|
||
|
|
max_value: The upper bound (inclusive) of the interval.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
TypeError: If `min_value` or `max_value` is not an `int`.
|
||
|
|
ValueError: If `min_value > max_value`.
|
||
|
|
"""
|
||
|
|
if not isinstance(min_value, int):
|
||
|
|
raise TypeError("min_value must be an int: %r" % (min_value,))
|
||
|
|
if not isinstance(max_value, int):
|
||
|
|
raise TypeError("max_value must be an int: %r" % (max_value,))
|
||
|
|
if min_value > max_value:
|
||
|
|
raise ValueError("%r > %r" % (min_value, max_value))
|
||
|
|
self._min_value = min_value
|
||
|
|
self._max_value = max_value
|
||
|
|
|
||
|
|
def __str__(self):
|
||
|
|
return "[%s, %s]" % (self._min_value, self._max_value)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return "IntInterval(%r, %r)" % (self._min_value, self._max_value)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def dtype(self):
|
||
|
|
return int
|
||
|
|
|
||
|
|
@property
|
||
|
|
def min_value(self):
|
||
|
|
return self._min_value
|
||
|
|
|
||
|
|
@property
|
||
|
|
def max_value(self):
|
||
|
|
return self._max_value
|
||
|
|
|
||
|
|
def sample_uniform(self, rng=random):
|
||
|
|
return rng.randint(self._min_value, self._max_value)
|
||
|
|
|
||
|
|
def update_hparam_info(self, hparam_info):
|
||
|
|
hparam_info.type = (
|
||
|
|
api_pb2.DATA_TYPE_FLOAT64
|
||
|
|
) # TODO(#1998): Add int dtype.
|
||
|
|
hparam_info.domain_interval.min_value = self._min_value
|
||
|
|
hparam_info.domain_interval.max_value = self._max_value
|
||
|
|
|
||
|
|
|
||
|
|
class RealInterval(Domain):
|
||
|
|
"""A domain that takes on all real values in a closed interval."""
|
||
|
|
|
||
|
|
def __init__(self, min_value=None, max_value=None):
|
||
|
|
"""Create a `RealInterval`.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
min_value: The lower bound (inclusive) of the interval.
|
||
|
|
max_value: The upper bound (inclusive) of the interval.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
TypeError: If `min_value` or `max_value` is not an `float`.
|
||
|
|
ValueError: If `min_value > max_value`.
|
||
|
|
"""
|
||
|
|
if not isinstance(min_value, float):
|
||
|
|
raise TypeError("min_value must be a float: %r" % (min_value,))
|
||
|
|
if not isinstance(max_value, float):
|
||
|
|
raise TypeError("max_value must be a float: %r" % (max_value,))
|
||
|
|
if min_value > max_value:
|
||
|
|
raise ValueError("%r > %r" % (min_value, max_value))
|
||
|
|
self._min_value = min_value
|
||
|
|
self._max_value = max_value
|
||
|
|
|
||
|
|
def __str__(self):
|
||
|
|
return "[%s, %s]" % (self._min_value, self._max_value)
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return "RealInterval(%r, %r)" % (self._min_value, self._max_value)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def dtype(self):
|
||
|
|
return float
|
||
|
|
|
||
|
|
@property
|
||
|
|
def min_value(self):
|
||
|
|
return self._min_value
|
||
|
|
|
||
|
|
@property
|
||
|
|
def max_value(self):
|
||
|
|
return self._max_value
|
||
|
|
|
||
|
|
def sample_uniform(self, rng=random):
|
||
|
|
return rng.uniform(self._min_value, self._max_value)
|
||
|
|
|
||
|
|
def update_hparam_info(self, hparam_info):
|
||
|
|
hparam_info.type = api_pb2.DATA_TYPE_FLOAT64
|
||
|
|
hparam_info.domain_interval.min_value = self._min_value
|
||
|
|
hparam_info.domain_interval.max_value = self._max_value
|
||
|
|
|
||
|
|
|
||
|
|
class Discrete(Domain):
|
||
|
|
"""A domain that takes on a fixed set of values.
|
||
|
|
|
||
|
|
These values may be of any (single) domain type.
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, values, dtype=None):
|
||
|
|
"""Construct a discrete domain.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
values: A iterable of the values in this domain.
|
||
|
|
dtype: The Python data type of values in this domain: one of
|
||
|
|
`int`, `float`, `bool`, or `str`. If `values` is non-empty,
|
||
|
|
`dtype` may be `None`, in which case it will be inferred as the
|
||
|
|
type of the first element of `values`.
|
||
|
|
|
||
|
|
Raises:
|
||
|
|
ValueError: If `values` is empty but no `dtype` is specified.
|
||
|
|
ValueError: If `dtype` or its inferred value is not `int`,
|
||
|
|
`float`, `bool`, or `str`.
|
||
|
|
TypeError: If an element of `values` is not an instance of
|
||
|
|
`dtype`.
|
||
|
|
"""
|
||
|
|
self._values = list(values)
|
||
|
|
if dtype is None:
|
||
|
|
if self._values:
|
||
|
|
dtype = type(self._values[0])
|
||
|
|
else:
|
||
|
|
raise ValueError("Empty domain with no dtype specified")
|
||
|
|
if dtype not in (int, float, bool, str):
|
||
|
|
raise ValueError("Unknown dtype: %r" % (dtype,))
|
||
|
|
self._dtype = dtype
|
||
|
|
for value in self._values:
|
||
|
|
if not isinstance(value, self._dtype):
|
||
|
|
raise TypeError(
|
||
|
|
"dtype mismatch: not isinstance(%r, %s)"
|
||
|
|
% (value, self._dtype.__name__)
|
||
|
|
)
|
||
|
|
self._values.sort()
|
||
|
|
|
||
|
|
def __str__(self):
|
||
|
|
return "{%s}" % (", ".join(repr(x) for x in self._values))
|
||
|
|
|
||
|
|
def __repr__(self):
|
||
|
|
return "Discrete(%r)" % (self._values,)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def dtype(self):
|
||
|
|
return self._dtype
|
||
|
|
|
||
|
|
@property
|
||
|
|
def values(self):
|
||
|
|
return list(self._values)
|
||
|
|
|
||
|
|
def sample_uniform(self, rng=random):
|
||
|
|
return rng.choice(self._values)
|
||
|
|
|
||
|
|
def update_hparam_info(self, hparam_info):
|
||
|
|
hparam_info.type = {
|
||
|
|
int: api_pb2.DATA_TYPE_FLOAT64, # TODO(#1998): Add int dtype.
|
||
|
|
float: api_pb2.DATA_TYPE_FLOAT64,
|
||
|
|
bool: api_pb2.DATA_TYPE_BOOL,
|
||
|
|
str: api_pb2.DATA_TYPE_STRING,
|
||
|
|
}[self._dtype]
|
||
|
|
hparam_info.ClearField("domain_discrete")
|
||
|
|
hparam_info.domain_discrete.extend(self._values)
|
||
|
|
|
||
|
|
|
||
|
|
class Metric:
|
||
|
|
"""A metric in an experiment.
|
||
|
|
|
||
|
|
A metric is a real-valued function of a model. Each metric is
|
||
|
|
associated with a TensorBoard scalar summary, which logs the
|
||
|
|
metric's value as the model trains.
|
||
|
|
"""
|
||
|
|
|
||
|
|
TRAINING = api_pb2.DATASET_TRAINING
|
||
|
|
VALIDATION = api_pb2.DATASET_VALIDATION
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
tag,
|
||
|
|
group=None,
|
||
|
|
display_name=None,
|
||
|
|
description=None,
|
||
|
|
dataset_type=None,
|
||
|
|
):
|
||
|
|
"""
|
||
|
|
|
||
|
|
Args:
|
||
|
|
tag: The tag name of the scalar summary that corresponds to this
|
||
|
|
metric (as a `str`).
|
||
|
|
group: An optional string listing the subdirectory under the
|
||
|
|
session's log directory containing summaries for this metric.
|
||
|
|
For instance, if summaries for training runs are written to
|
||
|
|
events files in `ROOT_LOGDIR/SESSION_ID/train`, then `group`
|
||
|
|
should be `"train"`. Defaults to the empty string: i.e.,
|
||
|
|
summaries are expected to be written to the session logdir.
|
||
|
|
display_name: An optional human-readable display name.
|
||
|
|
description: An optional Markdown string with a human-readable
|
||
|
|
description of this metric, to appear in TensorBoard.
|
||
|
|
dataset_type: Either `Metric.TRAINING` or `Metric.VALIDATION`, or
|
||
|
|
`None`.
|
||
|
|
"""
|
||
|
|
self._tag = tag
|
||
|
|
self._group = group
|
||
|
|
self._display_name = display_name
|
||
|
|
self._description = description
|
||
|
|
self._dataset_type = dataset_type
|
||
|
|
if self._dataset_type not in (None, Metric.TRAINING, Metric.VALIDATION):
|
||
|
|
raise ValueError("invalid dataset type: %r" % (self._dataset_type,))
|
||
|
|
|
||
|
|
def as_proto(self):
|
||
|
|
return api_pb2.MetricInfo(
|
||
|
|
name=api_pb2.MetricName(
|
||
|
|
group=self._group,
|
||
|
|
tag=self._tag,
|
||
|
|
),
|
||
|
|
display_name=self._display_name,
|
||
|
|
description=self._description,
|
||
|
|
dataset_type=self._dataset_type,
|
||
|
|
)
|