ray/rllib/policy/eager_tf_policy.py

"""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