def test_from_file(self): params = np.array([[0.0, 0.1], [0.2, 0.3], [0.4, 0.5]]) filename = self.create_tempfile('params.npy').full_path with open(filename, 'wb') as f: np.save(f, params) initializer = initializers.InitializerFromFile(filename) input_shape = (3, 2) init_value = initializer(input_shape, random.get_prng(0)) self.assertEqual('%s' % init_value, '%s' % params)
def init_random_number_generators(seed=None): """Initializes random generators for Python, NumPy, TensorFlow, and JAX.""" # Seed Python random (None as seed is okay), then use it to seed the others. random.seed(seed) if seed is None: seed = random.randint(0, 2**31 - 1) numpy.random.seed(seed) tf.random.set_seed(seed) return jax_random.get_prng(seed)
def get_random_number_generator_and_set_seed(seed=None): """Get a JAX random number generator and set random seed everywhere.""" random.seed(seed) # While python random accepts None as seed and uses time/os seed then, # some other functions expect integers so we create one here. if seed is None: seed = random.randint(0, 2**31 - 1) tf.random.set_seed(seed) numpy.random.seed(seed) return jax_random.get_prng(seed)
def test_computes(self): rng_key = jax_random.get_prng(0) hidden_size = (4, 4) output_size = 6 model = atari_cnn.FrameStackMLP(hidden_sizes=hidden_size, output_size=output_size) B, T, OBS = 2, 2, 3 # pylint: disable=invalid-name rng_key, key = jax_random.split(rng_key) _, _ = model.initialize_once((1, 1, OBS), onp.float32, key) x = onp.arange(B * (T + 1) * OBS).reshape(B, T + 1, OBS) y = model(x) self.assertEqual((B, T + 1, output_size), y.shape)
def _make_schedule( self, history, control_configs, observation_metrics=(('eval', 'metrics/accuracy'), ), action_multipliers=(1.0, ), vocab_size=None, ): policy_and_value_model = functools.partial( transformer.TransformerDecoder, d_model=2, d_ff=2, n_layers=0, vocab_size=vocab_size, ) net = ppo.policy_and_value_net( n_actions=len(action_multipliers), n_controls=len(control_configs), vocab_size=None, bottom_layers_fn=policy_and_value_model, two_towers=False, ) rng = jax_random.get_prng(seed=0) obs_dim = len(observation_metrics) if vocab_size is None: shape = (1, 1, obs_dim) dtype = np.float32 else: shape = (1, 1) dtype = np.int32 (params, state) = net.initialize_once(shape, dtype, rng) policy_dir = self.get_temp_dir() # Optimizer slots should not be used for anything. slots = None opt_state = (params, slots) ppo.save_opt_state(policy_dir, opt_state, state, epoch=0, total_opt_step=0, history=history) return learning_rate.PolicySchedule( history, observation_metrics=observation_metrics, include_controls_in_observation=False, action_multipliers=action_multipliers, control_configs=control_configs, policy_and_value_model=policy_and_value_model, policy_and_value_two_towers=False, policy_and_value_vocab_size=vocab_size, policy_dir=policy_dir, )
def test_computes(self): rng_key = jax_random.get_prng(0) hidden_size = (4, 4) output_size = 6 model = atari_cnn.AtariCnn(hidden_sizes=hidden_size, output_size=output_size) B, T, OBS = 2, 2, (28, 28, 3) # pylint: disable=invalid-name rng_key, key = jax_random.split(rng_key) _, _ = model.initialize_once((1, 1) + OBS, onp.float32, key) x = onp.arange(B * (T + 1) * functools.reduce(op.mul, OBS)).reshape( B, T + 1, *OBS) y = model(x) self.assertEqual((B, T + 1, output_size), y.shape)
def _make_schedule( self, history, control_configs, observation_metrics=(("eval", "metrics/accuracy"), ), action_multipliers=(1.0, ), ): policy_and_value_model = atari_cnn.FrameStackMLP net = ppo.policy_and_value_net( n_actions=len(action_multipliers), n_controls=len(control_configs), vocab_size=None, bottom_layers_fn=policy_and_value_model, two_towers=False, ) rng = jax_random.get_prng(seed=0) obs_dim = len(observation_metrics) (params, state) = net.initialize_once((1, 1, obs_dim), np.float32, rng) policy_dir = self.get_temp_dir() # Optimizer slots should not be used for anything. slots = None opt_state = (params, slots) ppo.save_opt_state(policy_dir, opt_state, state, epoch=0, total_opt_step=0) return learning_rate.PolicySchedule( history, observation_metrics=observation_metrics, include_controls_in_observation=False, action_multipliers=action_multipliers, control_configs=control_configs, policy_and_value_model=policy_and_value_model, policy_and_value_two_towers=False, policy_dir=policy_dir, )
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 # 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, history) (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() (low, high) = observation_range observation_space = gym.spaces.Box(shape=observations.shape[1:], low=low, high=high) action_space = gym.spaces.MultiDiscrete(nvec=(len(action_multipliers), ) * len(control_configs)) n_timesteps = observations.shape[0] rewards_to_actions = ppo.init_rewards_to_actions( policy_and_value_vocab_size, observation_space, action_space, n_timesteps) # (log_probs, value_preds, state, rng) (log_probs, _, _, _) = ppo.run_policy( policy_and_value_net_apply=net, observations=np.array([observations]), lengths=np.array([n_timesteps]), weights=params, state=state, rng=rng, vocab_size=policy_and_value_vocab_size, observation_space=observation_space, action_space=action_space, rewards_to_actions=rewards_to_actions, ) 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), len(action_multipliers)) action = utils.gumbel_sample(log_probs[0] / 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
def seed(self, seed=None): if seed is None: seed = random.randint(0, 2**31 - 1) self._rng = jax_random.get_prng(seed) return super(SimulatedEnvProblem, self).seed(seed=seed)
def test_orthogonal(self): initializer = initializers.OrthogonalInitializer() input_shape = (29, 5, 7, 20) init_value = initializer(input_shape, random.get_prng(0)) self.assertEqual(tuple(init_value.shape), input_shape)
def test_kaiming_uniform(self): initializer = initializers.KaimingUniformInitializer() input_shape = (29, 5, 7, 20) init_value = initializer(input_shape, random.get_prng(0)) self.assertEqual(tuple(init_value.shape), input_shape)
def test_lecun_normal(self): initializer = initializers.LeCunNormalInitializer() input_shape = (29, 5, 7, 20) init_value = initializer(input_shape, random.get_prng(0)) self.assertEqual(tuple(init_value.shape), input_shape)
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