wehub-resource-sync
182 lines
6.5 KiB
Python
182 lines
6.5 KiB
Python
#!/usr/bin/env python
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import argparse
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import json
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import os
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import random
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import tempfile
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import numpy as np
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import ray
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from ray import tune
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from ray.tune import Checkpoint
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from ray.tune.schedulers import PopulationBasedTraining
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def pbt_function(config):
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"""Toy PBT problem for benchmarking adaptive learning rate.
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The goal is to optimize this trainable's accuracy. The accuracy increases
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fastest at the optimal lr, which is a function of the current accuracy.
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The optimal lr schedule for this problem is the triangle wave as follows.
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Note that many lr schedules for real models also follow this shape:
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best lr
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^
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| /\
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| / \
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| / \
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| / \
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------------> accuracy
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In this problem, using PBT with a population of 2-4 is sufficient to
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roughly approximate this lr schedule. Higher population sizes will yield
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faster convergence. Training will not converge without PBT.
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"""
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lr = config["lr"]
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checkpoint_interval = config.get("checkpoint_interval", 1)
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accuracy = 0.0 # end = 1000
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# NOTE: See below why step is initialized to 1
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step = 1
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checkpoint = tune.get_checkpoint()
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if checkpoint:
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with checkpoint.as_directory() as checkpoint_dir:
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with open(os.path.join(checkpoint_dir, "checkpoint.json"), "r") as f:
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checkpoint_dict = json.load(f)
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accuracy = checkpoint_dict["acc"]
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last_step = checkpoint_dict["step"]
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# Current step should be 1 more than the last checkpoint step
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step = last_step + 1
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# triangle wave:
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# - start at 0.001 @ t=0,
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# - peak at 0.01 @ t=midpoint,
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# - end at 0.001 @ t=midpoint * 2,
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midpoint = 100 # lr starts decreasing after acc > midpoint
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q_tolerance = 3 # penalize exceeding lr by more than this multiple
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noise_level = 2 # add gaussian noise to the acc increase
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# Let `stop={"done": True}` in the configs below handle trial stopping
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while True:
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if accuracy < midpoint:
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optimal_lr = 0.01 * accuracy / midpoint
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else:
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optimal_lr = 0.01 - 0.01 * (accuracy - midpoint) / midpoint
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optimal_lr = min(0.01, max(0.001, optimal_lr))
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# compute accuracy increase
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q_err = max(lr, optimal_lr) / min(lr, optimal_lr)
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if q_err < q_tolerance:
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accuracy += (1.0 / q_err) * random.random()
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elif lr > optimal_lr:
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accuracy -= (q_err - q_tolerance) * random.random()
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accuracy += noise_level * np.random.normal()
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accuracy = max(0, accuracy)
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metrics = {
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"mean_accuracy": accuracy,
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"cur_lr": lr,
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"optimal_lr": optimal_lr, # for debugging
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"q_err": q_err, # for debugging
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"done": accuracy > midpoint * 2, # this stops the training process
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}
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if step % checkpoint_interval == 0:
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# Checkpoint every `checkpoint_interval` steps
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# NOTE: if we initialized `step=0` above, our checkpointing and perturbing
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# would be out of sync by 1 step.
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# Ex: if `checkpoint_interval` = `perturbation_interval` = 3
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# step: 0 (checkpoint) 1 2 3 (checkpoint)
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# training_iteration: 1 2 3 (perturb) 4
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with tempfile.TemporaryDirectory() as tempdir:
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with open(os.path.join(tempdir, "checkpoint.json"), "w") as f:
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checkpoint_dict = {"acc": accuracy, "step": step}
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json.dump(checkpoint_dict, f)
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tune.report(metrics, checkpoint=Checkpoint.from_directory(tempdir))
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else:
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tune.report(metrics)
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step += 1
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def run_tune_pbt(smoke_test=False):
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perturbation_interval = 5
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pbt = PopulationBasedTraining(
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time_attr="training_iteration",
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perturbation_interval=perturbation_interval,
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hyperparam_mutations={
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# distribution for resampling
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"lr": tune.uniform(0.0001, 0.02),
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# allow perturbations within this set of categorical values
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"some_other_factor": [1, 2],
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},
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)
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tuner = tune.Tuner(
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pbt_function,
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run_config=tune.RunConfig(
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name="pbt_function_api_example",
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verbose=False,
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stop={
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# Stop when done = True or at some # of train steps
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# (whichever comes first)
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"done": True,
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"training_iteration": 10 if smoke_test else 1000,
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},
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failure_config=tune.FailureConfig(
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fail_fast=True,
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),
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checkpoint_config=tune.CheckpointConfig(
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checkpoint_score_attribute="mean_accuracy",
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num_to_keep=2,
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),
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),
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tune_config=tune.TuneConfig(
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scheduler=pbt,
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metric="mean_accuracy",
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mode="max",
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num_samples=8,
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reuse_actors=True,
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),
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param_space={
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"lr": 0.0001,
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# Note: `some_other_factor` is perturbed because it is specified under
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# the PBT scheduler's `hyperparam_mutations` argument, but has no effect on
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# the model training in this example
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"some_other_factor": 1,
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# Note: `checkpoint_interval` will not be perturbed (since it's not
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# included above), and it will be used to determine how many steps to take
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# between each checkpoint.
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# We recommend matching `perturbation_interval` and `checkpoint_interval`
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# (e.g. checkpoint every 4 steps, and perturb on those same steps)
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# or making `perturbation_interval` a multiple of `checkpoint_interval`
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# (e.g. checkpoint every 2 steps, and perturb every 4 steps).
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# This is to ensure that the lastest checkpoints are being used by PBT
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# when trials decide to exploit. If checkpointing and perturbing are not
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# aligned, then PBT may use a stale checkpoint to resume from.
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"checkpoint_interval": perturbation_interval,
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},
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)
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results = tuner.fit()
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print("Best hyperparameters found were: ", results.get_best_result().config)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--smoke-test",
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action="store_true",
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default=False,
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help="Finish quickly for testing",
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)
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args, _ = parser.parse_known_args()
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if args.smoke_test:
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ray.init(num_cpus=2) # force pausing to happen for test
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run_tune_pbt(smoke_test=args.smoke_test)
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