"""Eager mode TF policy built using build_tf_policy(). It supports both traced and non-traced eager execution modes.""" import logging import functools import numpy as np import tree from ray.util.debug import log_once from ray.rllib.evaluation.episode import _flatten_action from ray.rllib.models.catalog import ModelCatalog from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY from ray.rllib.policy.sample_batch import SampleBatch from ray.rllib.policy.policy import ACTION_PROB, ACTION_LOGP from ray.rllib.utils import add_mixins from ray.rllib.utils.annotations import override from ray.rllib.utils.framework import try_import_tf tf = try_import_tf() logger = logging.getLogger(__name__) def _convert_to_tf(x): if isinstance(x, SampleBatch): x = {k: v for k, v in x.items() if k != SampleBatch.INFOS} return tree.map_structure(_convert_to_tf, x) if isinstance(x, Policy): return x if x is not None: x = tree.map_structure( lambda f: tf.convert_to_tensor(f) if f is not None else None, x) return x def _convert_to_numpy(x): if x is None: return None try: return tree.map_structure(lambda component: component.numpy(), x) except AttributeError: raise TypeError( ("Object of type {} has no method to convert to numpy.").format( type(x))) def convert_eager_inputs(func): @functools.wraps(func) def _func(*args, **kwargs): if tf.executing_eagerly(): args = [_convert_to_tf(x) for x in args] # TODO(gehring): find a way to remove specific hacks kwargs = { k: _convert_to_tf(v) for k, v in kwargs.items() if k not in {"info_batch", "episodes"} } return func(*args, **kwargs) return _func def convert_eager_outputs(func): @functools.wraps(func) def _func(*args, **kwargs): out = func(*args, **kwargs) if tf.executing_eagerly(): out = tree.map_structure(_convert_to_numpy, out) return out return _func def _disallow_var_creation(next_creator, **kw): v = next_creator(**kw) raise ValueError("Detected a variable being created during an eager " "forward pass. Variables should only be created during " "model initialization: {}".format(v.name)) def traced_eager_policy(eager_policy_cls): """Wrapper that enables tracing for all eager policy methods. This is enabled by the --trace / "eager_tracing" config.""" class TracedEagerPolicy(eager_policy_cls): def __init__(self, *args, **kwargs): self._traced_learn_on_batch = None self._traced_compute_actions = None self._traced_compute_gradients = None self._traced_apply_gradients = None super(TracedEagerPolicy, self).__init__(*args, **kwargs) @override(Policy) @convert_eager_inputs @convert_eager_outputs def learn_on_batch(self, samples): if self._traced_learn_on_batch is None: self._traced_learn_on_batch = tf.function( super(TracedEagerPolicy, self).learn_on_batch, autograph=False) return self._traced_learn_on_batch(samples) @override(Policy) @convert_eager_inputs @convert_eager_outputs def compute_actions(self, obs_batch, state_batches, prev_action_batch=None, prev_reward_batch=None, info_batch=None, episodes=None, explore=None, timestep=None, **kwargs): obs_batch = tf.convert_to_tensor(obs_batch) state_batches = _convert_to_tf(state_batches) prev_action_batch = _convert_to_tf(prev_action_batch) prev_reward_batch = _convert_to_tf(prev_reward_batch) if self._traced_compute_actions is None: self._traced_compute_actions = tf.function( super(TracedEagerPolicy, self).compute_actions, autograph=False) return self._traced_compute_actions( obs_batch, state_batches, prev_action_batch, prev_reward_batch, info_batch, episodes, explore, timestep, **kwargs) @override(Policy) @convert_eager_inputs @convert_eager_outputs def compute_gradients(self, samples): if self._traced_compute_gradients is None: self._traced_compute_gradients = tf.function( super(TracedEagerPolicy, self).compute_gradients, autograph=False) return self._traced_compute_gradients(samples) @override(Policy) @convert_eager_inputs @convert_eager_outputs def apply_gradients(self, grads): if self._traced_apply_gradients is None: self._traced_apply_gradients = tf.function( super(TracedEagerPolicy, self).apply_gradients, autograph=False) return self._traced_apply_gradients(grads) TracedEagerPolicy.__name__ = eager_policy_cls.__name__ TracedEagerPolicy.__qualname__ = eager_policy_cls.__qualname__ return TracedEagerPolicy def build_eager_tf_policy(name, loss_fn, get_default_config=None, postprocess_fn=None, stats_fn=None, optimizer_fn=None, gradients_fn=None, apply_gradients_fn=None, grad_stats_fn=None, extra_learn_fetches_fn=None, extra_action_fetches_fn=None, before_init=None, before_loss_init=None, after_init=None, make_model=None, action_sampler_fn=None, log_likelihood_fn=None, mixins=None, obs_include_prev_action_reward=True, get_batch_divisibility_req=None): """Build an eager TF policy. An eager policy runs all operations in eager mode, which makes debugging much simpler, but has lower performance. You shouldn't need to call this directly. Rather, prefer to build a TF graph policy and use set {"eager": true} in the trainer config to have it automatically be converted to an eager policy. This has the same signature as build_tf_policy().""" base = add_mixins(Policy, mixins) class eager_policy_cls(base): def __init__(self, observation_space, action_space, config): assert tf.executing_eagerly() self.framework = "tf" Policy.__init__(self, observation_space, action_space, config) self._is_training = False self._loss_initialized = False self._sess = None if get_default_config: config = dict(get_default_config(), **config) if before_init: before_init(self, observation_space, action_space, config) self.config = config self.dist_class = None if action_sampler_fn: if not make_model: raise ValueError("`make_model` is required if " "`action_sampler_fn` is given") else: self.dist_class, logit_dim = ModelCatalog.get_action_dist( action_space, self.config["model"]) if make_model: self.model = make_model(self, observation_space, action_space, config) else: self.model = ModelCatalog.get_model_v2( observation_space, action_space, logit_dim, config["model"], framework="tf", ) self._state_in = [ tf.convert_to_tensor(np.array([s])) for s in self.model.get_initial_state() ] input_dict = { SampleBatch.CUR_OBS: tf.convert_to_tensor( np.array([observation_space.sample()])), SampleBatch.PREV_ACTIONS: tf.convert_to_tensor( [_flatten_action(action_space.sample())]), SampleBatch.PREV_REWARDS: tf.convert_to_tensor([0.]), } self.model(input_dict, self._state_in, tf.convert_to_tensor([1])) if before_loss_init: before_loss_init(self, observation_space, action_space, config) self._initialize_loss_with_dummy_batch() self._loss_initialized = True if optimizer_fn: self._optimizer = optimizer_fn(self, config) else: self._optimizer = tf.train.AdamOptimizer(config["lr"]) if after_init: after_init(self, observation_space, action_space, config) @override(Policy) def postprocess_trajectory(self, samples, other_agent_batches=None, episode=None): assert tf.executing_eagerly() if postprocess_fn: return postprocess_fn(self, samples, other_agent_batches, episode) else: return samples @override(Policy) @convert_eager_inputs @convert_eager_outputs def learn_on_batch(self, samples): with tf.variable_creator_scope(_disallow_var_creation): grads_and_vars, stats = self._compute_gradients(samples) self._apply_gradients(grads_and_vars) return stats @override(Policy) @convert_eager_inputs @convert_eager_outputs def compute_gradients(self, samples): with tf.variable_creator_scope(_disallow_var_creation): grads_and_vars, stats = self._compute_gradients(samples) grads = [g for g, v in grads_and_vars] return grads, stats @override(Policy) @convert_eager_inputs @convert_eager_outputs def compute_actions(self, obs_batch, state_batches, prev_action_batch=None, prev_reward_batch=None, info_batch=None, episodes=None, explore=None, timestep=None, **kwargs): explore = explore if explore is not None else \ self.config["explore"] # TODO: remove python side effect to cull sources of bugs. self._is_training = False self._state_in = state_batches if tf.executing_eagerly(): n = len(obs_batch) else: n = obs_batch.shape[0] seq_lens = tf.ones(n, dtype=tf.int32) input_dict = { SampleBatch.CUR_OBS: tf.convert_to_tensor(obs_batch), "is_training": tf.constant(False), } if obs_include_prev_action_reward: input_dict.update({ SampleBatch.PREV_ACTIONS: tf.convert_to_tensor( prev_action_batch), SampleBatch.PREV_REWARDS: tf.convert_to_tensor( prev_reward_batch), }) # Custom sampler fn given (which may handle self.exploration). if action_sampler_fn is not None: state_out = [] action, logp = action_sampler_fn( self, self.model, input_dict, self.observation_space, self.action_space, explore, self.config, timestep=timestep if timestep is not None else self.global_timestep) # Use Exploration object. else: with tf.variable_creator_scope(_disallow_var_creation): model_out, state_out = self.model(input_dict, state_batches, seq_lens) action, logp = self.exploration.get_exploration_action( model_out, self.dist_class, self.model, timestep=timestep if timestep is not None else self.global_timestep, explore=explore) extra_fetches = {} if logp is not None: extra_fetches.update({ ACTION_PROB: tf.exp(logp), ACTION_LOGP: logp, }) if extra_action_fetches_fn: extra_fetches.update(extra_action_fetches_fn(self)) # Increase our global sampling timestep counter by 1. self.global_timestep += 1 return action, state_out, extra_fetches @override(Policy) def compute_log_likelihoods(self, actions, obs_batch, state_batches=None, prev_action_batch=None, prev_reward_batch=None): seq_lens = tf.ones(len(obs_batch), dtype=tf.int32) input_dict = { SampleBatch.CUR_OBS: tf.convert_to_tensor(obs_batch), "is_training": tf.constant(False), } if obs_include_prev_action_reward: input_dict.update({ SampleBatch.PREV_ACTIONS: tf.convert_to_tensor( prev_action_batch), SampleBatch.PREV_REWARDS: tf.convert_to_tensor( prev_reward_batch), }) # Custom log_likelihood function given. if log_likelihood_fn: log_likelihoods = log_likelihood_fn( self, self.model, actions, input_dict, self.observation_space, self.action_space, self.config) # Default log-likelihood calculation. else: dist_inputs, _ = self.model(input_dict, state_batches, seq_lens) action_dist = self.dist_class(dist_inputs, self.model) log_likelihoods = action_dist.logp(actions) return log_likelihoods @override(Policy) def apply_gradients(self, gradients): self._apply_gradients( zip([(tf.convert_to_tensor(g) if g is not None else None) for g in gradients], self.model.trainable_variables())) @override(Policy) def get_exploration_info(self): return _convert_to_numpy(self.exploration.get_info()) @override(Policy) def get_weights(self): variables = self.variables() return [v.numpy() for v in variables] @override(Policy) def set_weights(self, weights): variables = self.variables() assert len(weights) == len(variables), (len(weights), len(variables)) for v, w in zip(variables, weights): v.assign(w) def variables(self): """Return the list of all savable variables for this policy.""" return self.model.variables() @override(Policy) def is_recurrent(self): return len(self._state_in) > 0 @override(Policy) def num_state_tensors(self): return len(self._state_in) @override(Policy) def get_initial_state(self): return self.model.get_initial_state() def get_session(self): return None # None implies eager def get_placeholder(self, ph): raise ValueError( "get_placeholder() is not allowed in eager mode. Try using " "rllib.utils.tf_ops.make_tf_callable() to write " "functions that work in both graph and eager mode.") def loss_initialized(self): return self._loss_initialized @override(Policy) def export_model(self, export_dir): pass @override(Policy) def export_checkpoint(self, export_dir): pass def _get_is_training_placeholder(self): return tf.convert_to_tensor(self._is_training) def _apply_gradients(self, grads_and_vars): if apply_gradients_fn: apply_gradients_fn(self, self._optimizer, grads_and_vars) else: self._optimizer.apply_gradients(grads_and_vars) def _compute_gradients(self, samples): """Computes and returns grads as eager tensors.""" self._is_training = True with tf.GradientTape(persistent=gradients_fn is not None) as tape: # TODO: set seq len and state-in properly state_in = [] for i in range(self.num_state_tensors()): state_in.append(samples["state_in_{}".format(i)]) self._state_in = state_in self._seq_lens = None if len(state_in) > 0: self._seq_lens = tf.ones( samples[SampleBatch.CUR_OBS].shape[0], dtype=tf.int32) samples["seq_lens"] = self._seq_lens model_out, _ = self.model(samples, self._state_in, self._seq_lens) loss = loss_fn(self, self.model, self.dist_class, samples) variables = self.model.trainable_variables() if gradients_fn: class OptimizerWrapper: def __init__(self, tape): self.tape = tape def compute_gradients(self, loss, var_list): return list( zip(self.tape.gradient(loss, var_list), var_list)) grads_and_vars = gradients_fn(self, OptimizerWrapper(tape), loss) else: grads_and_vars = list( zip(tape.gradient(loss, variables), variables)) if log_once("grad_vars"): for _, v in grads_and_vars: logger.info("Optimizing variable {}".format(v.name)) grads = [g for g, v in grads_and_vars] stats = self._stats(self, samples, grads) return grads_and_vars, stats def _stats(self, outputs, samples, grads): fetches = {} if stats_fn: fetches[LEARNER_STATS_KEY] = { k: v for k, v in stats_fn(outputs, samples).items() } else: fetches[LEARNER_STATS_KEY] = {} if extra_learn_fetches_fn: fetches.update( {k: v for k, v in extra_learn_fetches_fn(self).items()}) if grad_stats_fn: fetches.update({ k: v for k, v in grad_stats_fn(self, samples, grads).items() }) return fetches def _initialize_loss_with_dummy_batch(self): # Dummy forward pass to initialize any policy attributes, etc. action_dtype, action_shape = ModelCatalog.get_action_shape( self.action_space) dummy_batch = { SampleBatch.CUR_OBS: np.array( [self.observation_space.sample()]), SampleBatch.NEXT_OBS: np.array( [self.observation_space.sample()]), SampleBatch.DONES: np.array([False], dtype=np.bool), SampleBatch.ACTIONS: tree.map_structure( lambda c: np.array([c]), self.action_space.sample()), SampleBatch.REWARDS: np.array([0], dtype=np.float32), } if obs_include_prev_action_reward: dummy_batch.update({ SampleBatch.PREV_ACTIONS: dummy_batch[SampleBatch.ACTIONS], SampleBatch.PREV_REWARDS: dummy_batch[SampleBatch.REWARDS], }) for i, h in enumerate(self._state_in): dummy_batch["state_in_{}".format(i)] = h dummy_batch["state_out_{}".format(i)] = h if self._state_in: dummy_batch["seq_lens"] = np.array([1], dtype=np.int32) # Convert everything to tensors. dummy_batch = tree.map_structure(tf.convert_to_tensor, dummy_batch) # for IMPALA which expects a certain sample batch size. def tile_to(tensor, n): return tf.tile(tensor, [n] + [1 for _ in tensor.shape.as_list()[1:]]) if get_batch_divisibility_req: dummy_batch = tree.map_structure( lambda c: tile_to(c, get_batch_divisibility_req(self)), dummy_batch) # Execute a forward pass to get self.action_dist etc initialized, # and also obtain the extra action fetches _, _, fetches = self.compute_actions( dummy_batch[SampleBatch.CUR_OBS], self._state_in, dummy_batch.get(SampleBatch.PREV_ACTIONS), dummy_batch.get(SampleBatch.PREV_REWARDS)) dummy_batch.update(fetches) postprocessed_batch = self.postprocess_trajectory( SampleBatch(dummy_batch)) # model forward pass for the loss (needed after postprocess to # overwrite any tensor state from that call) self.model.from_batch(dummy_batch) postprocessed_batch = tree.map_structure( lambda c: tf.convert_to_tensor(c), postprocessed_batch.data) loss_fn(self, self.model, self.dist_class, postprocessed_batch) if stats_fn: stats_fn(self, postprocessed_batch) @classmethod def with_tracing(cls): return traced_eager_policy(cls) eager_policy_cls.__name__ = name + "_eager" eager_policy_cls.__qualname__ = name + "_eager" return eager_policy_cls