def _current_observation(self): observations = online_tune.history_to_observations( self._trainer.state.history, self._observation_metrics, self._observation_range, self._include_lr_in_observation, ) assert observations.shape[0] > 0, "No values in history for any metric." return observations[-1, :]
def test_clips_observations(self): history = trax_history.History() self._append_metrics(history, ("eval", "loss"), [-10, 10]) observations = online_tune.history_to_observations( history, metrics=(("eval", "loss"), ), observation_range=(-2, 2), include_lr=False, ) np.testing.assert_array_equal(observations, [[-2], [2]])
def test_clips_observations(self): history = trax_history.History() self._append_metrics(history, ("eval", "loss"), [-10, 10]) observations = online_tune.history_to_observations( history, metrics=(("eval", "loss"), ), observation_range=(-2, 2), control_configs=None, ) np.testing.assert_array_equal(observations, [[-1], [1]])
def _current_observation(self): observations = online_tune.history_to_observations( self._trainer.state.history, self._observation_metrics, self._metric_range, self._control_configs if self._include_controls_in_observation else None, ) assert observations.shape[0] > 0, "No values in history for any metric." return observations[-1, :]
def test_converts_history_to_observations_without_learning_rate(self): history = trax_history.History() self._append_metrics(history, ("train", "loss"), [3.0, 1.07]) self._append_metrics(history, ("eval", "accuracy"), [0.12, 0.68]) observations = online_tune.history_to_observations( history, metrics=(("eval", "accuracy"), ("train", "loss")), observation_range=(0, 5), include_lr=False, ) np.testing.assert_array_equal(observations, [[0.12, 3.0], [0.68, 1.07]])
def test_converts_history_to_observations_without_controls(self): history = trax_history.History() self._append_metrics(history, ("train", "loss"), [1.0, 0.07]) self._append_metrics(history, ("eval", "accuracy"), [0.12, 0.68]) observations = online_tune.history_to_observations( history, metrics=(("eval", "accuracy"), ("train", "loss")), observation_range=(-1, 1), control_configs=None, ) np.testing.assert_array_almost_equal(observations, [[0.12, 1.0], [0.68, 0.07]])
def test_converts_history_to_observations_with_learning_rate(self): history = trax_history.History() self._append_metrics(history, ("train", "training/learning_rate"), [1e-3, 1e-4]) observations = online_tune.history_to_observations( history, metrics=(), observation_range=(0, 5), include_lr=True, ) self.assertEqual(observations.shape, (2, 1)) ((log_lr_1, ), (log_lr_2, )) = observations self.assertGreater(log_lr_1, log_lr_2)
def test_converts_history_to_observations_with_controls(self): history = trax_history.History() self._append_metrics(history, ("train", "training/learning_rate"), [1e-3, 1e-4]) observations = online_tune.history_to_observations( history, metrics=(), observation_range=(0, 5), control_configs=(("learning_rate", None, (1e-9, 10.0), False), ), ) self.assertEqual(observations.shape, (2, 1)) ((log_lr_1, ), (log_lr_2, )) = observations self.assertGreater(log_lr_1, log_lr_2)
def PolicySchedule( history, observation_metrics=( ("train", "metrics/accuracy"), ("train", "metrics/loss"), ("eval", "metrics/accuracy"), ("eval", "metrics/loss"), ), include_lr_in_observation=False, observation_range=(0.0, 5.0), start_lr=0.001, max_lr=10.0, action_multipliers=(1.0 / 1.5, 1.0 / 1.25, 1.0, 1.25, 1.5), policy_and_value_model=trax_models.FrameStackMLP, policy_and_value_two_towers=False, policy_dir=gin.REQUIRED, ): """Learning rate schedule controlled by a learned policy. Args: history: the history of training and evaluation (History object). observation_metrics: list of pairs (mode, metric), as in the History object. include_lr_in_observation: bool, whether to include the learning rate in observations. observation_range: tuple (low, high), range to clip the observation to. start_lr: starting learning rate. max_lr: maximum value to clip the learning rate to. action_multipliers: sequence of LR multipliers that policy actions correspond to. policy_and_value_model: Trax model to use as the policy. policy_and_value_two_towers: bool, whether the action distribution and value prediction is computed by separate model towers. policy_dir: directory with the policy checkpoint. Returns: a function learning_rate(step): float -> float, the step-dependent lr. """ # Turn the history into observations for the policy. If we don't have any, # return the initial learning rate. start_time = time.time() observations = online_tune.history_to_observations( history, observation_metrics, observation_range, include_lr_in_observation) logging.vlog(1, "Building observations took %0.2f sec.", time.time() - start_time) if observations.shape[0] == 0: return lambda _: start_lr # Build the policy network and load its parameters. start_time = time.time() net = ppo.policy_and_value_net( n_actions=len(action_multipliers), bottom_layers_fn=policy_and_value_model, two_towers=policy_and_value_two_towers, ) logging.vlog(1, "Building the policy network took %0.2f sec.", time.time() - start_time) start_time = time.time() # (opt_state, state, epoch, opt_step) (opt_state, state, _, _) = ppo.maybe_restore_opt_state(policy_dir) assert opt_state is not None, "Policy checkpoint not found." (params, _) = opt_state logging.vlog(1, "Restoring the policy parameters took %0.2f sec.", time.time() - start_time) # Run the policy and sample an action. seed = random.randint(0, 2**31 - 1) rng = jax_random.get_prng(seed=seed) start_time = time.time() # ((log_probs, value_preds), state). We have no way to pass state to the next # step, but that should be fine. ((log_probs, _), _) = net(np.array([observations]), params, state, rng=rng) logging.vlog(1, "Running the policy took %0.2f sec.", time.time() - start_time) # Sample from the action distribution for the last timestep. action = utils.gumbel_sample(log_probs[0, -1, :]) # Get a new learning rate. new_lr = online_tune.new_learning_rate(action, history, action_multipliers, max_lr) return lambda _: new_lr
def PolicySchedule( history, observation_metrics=( ("train", "metrics/accuracy"), ("train", "metrics/loss"), ("eval", "metrics/accuracy"), ("eval", "metrics/loss"), ), include_controls_in_observation=False, control_configs=( # (name, start, (low, high), flip) ("learning_rate", 1e-3, (1e-9, 10.0), False), ), observation_range=(0.0, 10.0), action_multipliers=(1.0 / 1.5, 1.0 / 1.25, 1.0, 1.25, 1.5), policy_and_value_model=trax_models.FrameStackMLP, policy_and_value_two_towers=False, policy_and_value_vocab_size=None, policy_dir=gin.REQUIRED, temperature=1.0, ): """Learning rate schedule controlled by a learned policy. Args: history: the history of training and evaluation (History object). observation_metrics: list of pairs (mode, metric), as in the History object. include_controls_in_observation: bool, whether to include the controls in observations. control_configs: control configs, see trax.rl.envs.OnlineTuneEnv. observation_range: tuple (low, high), range to clip the metrics to. action_multipliers: sequence of LR multipliers that policy actions correspond to. policy_and_value_model: Trax model to use as the policy. policy_and_value_two_towers: bool, whether the action distribution and value prediction is computed by separate model towers. policy_and_value_vocab_size: vocabulary size of a policy and value network operating on serialized representation. If None, use raw continuous representation. policy_dir: directory with the policy checkpoint. temperature: temperature for sampling from the policy. Returns: a function nontrainable_params(step): float -> {"name": float}, the step-dependent schedule for nontrainable parameters. """ # Turn the history into observations for the policy. If we don't have any, # return the initial learning rate. start_time = time.time() observations = online_tune.history_to_observations( history, observation_metrics, observation_range, control_configs if include_controls_in_observation else None) logging.vlog(1, "Building observations took %0.2f sec.", time.time() - start_time) if observations.shape[0] == 0: controls = { name: start_value for (name, start_value, _, _) in control_configs } return lambda _: controls assert policy_and_value_vocab_size is None, ( "Serialized policies are not supported yet.") # Build the policy network and load its parameters. start_time = time.time() net = ppo.policy_and_value_net( n_controls=len(control_configs), n_actions=len(action_multipliers), vocab_size=policy_and_value_vocab_size, bottom_layers_fn=policy_and_value_model, two_towers=policy_and_value_two_towers, ) logging.vlog(1, "Building the policy network took %0.2f sec.", time.time() - start_time) start_time = time.time() # (opt_state, state, epoch, opt_step) (opt_state, state, _, _) = ppo.maybe_restore_opt_state(policy_dir) assert opt_state is not None, "Policy checkpoint not found." (params, _) = opt_state logging.vlog(1, "Restoring the policy parameters took %0.2f sec.", time.time() - start_time) # Run the policy and sample an action. seed = random.randint(0, 2**31 - 1) rng = jax_random.get_prng(seed=seed) start_time = time.time() # ((log_probs, value_preds), state). We have no way to pass state to the next # step, but that should be fine. (log_probs, _) = (net(np.array([observations]), params=params, state=state, rng=rng)) logging.vlog(1, "Running the policy took %0.2f sec.", time.time() - start_time) # Sample from the action distribution for the last timestep. assert log_probs.shape == (1, len(control_configs) * observations.shape[0], len(action_multipliers)) action = utils.gumbel_sample(log_probs[0, -len(control_configs):, :] / temperature) # Get new controls. controls = { # name: value control_config[0]: online_tune.update_control( # pylint: disable=g-complex-comprehension control_config, control_action, history, action_multipliers) for (control_action, control_config) in zip(action, control_configs) } return lambda _: controls