ray/rllib/execution/rollout_ops.py

import logging
import time
from typing import Container, List, Optional, Tuple, TYPE_CHECKING, Union

import ray
from ray.rllib.evaluation.rollout_worker import get_global_worker
from ray.rllib.evaluation.worker_set import WorkerSet
from ray.rllib.execution.common import (
    AGENT_STEPS_SAMPLED_COUNTER,
    STEPS_SAMPLED_COUNTER,
    SAMPLE_TIMER,
    GRAD_WAIT_TIMER,
    _check_sample_batch_type,
    _get_shared_metrics,
)
from ray.rllib.policy.sample_batch import (
    SampleBatch,
    DEFAULT_POLICY_ID,
    MultiAgentBatch,
)
from ray.rllib.utils.annotations import ExperimentalAPI
from ray.rllib.utils.metrics.learner_info import LEARNER_INFO, LEARNER_STATS_KEY
from ray.rllib.utils.sgd import standardized
from ray.rllib.utils.typing import PolicyID, SampleBatchType, ModelGradients
from ray.util.iter import from_actors, LocalIterator
from ray.util.iter_metrics import SharedMetrics

if TYPE_CHECKING:
    from ray.rllib.evaluation.rollout_worker import RolloutWorker

logger = logging.getLogger(__name__)


@ExperimentalAPI
def synchronous_parallel_sample(
    *,
    worker_set: WorkerSet,
    max_agent_steps: Optional[int] = None,
    max_env_steps: Optional[int] = None,
    concat: bool = True,
) -> Union[List[SampleBatchType], SampleBatchType]:
    """Runs parallel and synchronous rollouts on all remote workers.

    Waits for all workers to return from the remote calls.

    If no remote workers exist (num_workers == 0), use the local worker
    for sampling.

    Alternatively to calling `worker.sample.remote()`, the user can provide a
    `remote_fn()`, which will be applied to the worker(s) instead.

    Args:
        worker_set: The WorkerSet to use for sampling.
        remote_fn: If provided, use `worker.apply.remote(remote_fn)` instead
            of `worker.sample.remote()` to generate the requests.
        max_agent_steps: Optional number of agent steps to be included in the
            final batch.
        max_env_steps: Optional number of environment steps to be included in the
            final batch.
        concat: Whether to concat all resulting batches at the end and return the
            concat'd batch.

    Returns:
        The list of collected sample batch types (one for each parallel
        rollout worker in the given `worker_set`).

    Examples:
        >>> # Define an RLlib Algorithm.
        >>> algorithm = ... # doctest: +SKIP
        >>> # 2 remote workers (num_workers=2):
        >>> batches = synchronous_parallel_sample(algorithm.workers) # doctest: +SKIP
        >>> print(len(batches)) # doctest: +SKIP
        2
        >>> print(batches[0]) # doctest: +SKIP
        SampleBatch(16: ['obs', 'actions', 'rewards', 'dones'])
        >>> # 0 remote workers (num_workers=0): Using the local worker.
        >>> batches = synchronous_parallel_sample(algorithm.workers) # doctest: +SKIP
        >>> print(len(batches)) # doctest: +SKIP
        1
    """
    # Only allow one of `max_agent_steps` or `max_env_steps` to be defined.
    assert not (max_agent_steps is not None and max_env_steps is not None)

    agent_or_env_steps = 0
    max_agent_or_env_steps = max_agent_steps or max_env_steps or None
    all_sample_batches = []

    # Stop collecting batches as soon as one criterium is met.
    while (max_agent_or_env_steps is None and agent_or_env_steps == 0) or (
        max_agent_or_env_steps is not None
        and agent_or_env_steps < max_agent_or_env_steps
    ):
        # No remote workers in the set -> Use local worker for collecting
        # samples.
        if not worker_set.remote_workers():
            sample_batches = [worker_set.local_worker().sample()]
        # Loop over remote workers' `sample()` method in parallel.
        else:
            sample_batches = ray.get(
                [worker.sample.remote() for worker in worker_set.remote_workers()]
            )
        # Update our counters for the stopping criterion of the while loop.
        for b in sample_batches:
            if max_agent_steps:
                agent_or_env_steps += b.agent_steps()
            else:
                agent_or_env_steps += b.env_steps()
        all_sample_batches.extend(sample_batches)

    if concat is True:
        full_batch = SampleBatch.concat_samples(all_sample_batches)
        # Discard collected incomplete episodes in episode mode.
        # if max_episodes is not None and episodes >= max_episodes:
        #    last_complete_ep_idx = len(full_batch) - full_batch[
        #        SampleBatch.DONES
        #    ].reverse().index(1)
        #    full_batch = full_batch.slice(0, last_complete_ep_idx)
        return full_batch
    else:
        return all_sample_batches


def ParallelRollouts(
    workers: WorkerSet, *, mode="bulk_sync", num_async=1
) -> LocalIterator[SampleBatch]:
    """Operator to collect experiences in parallel from rollout workers.

    If there are no remote workers, experiences will be collected serially from
    the local worker instance instead.

    Args:
        workers: set of rollout workers to use.
        mode: One of 'async', 'bulk_sync', 'raw'. In 'async' mode,
            batches are returned as soon as they are computed by rollout
            workers with no order guarantees. In 'bulk_sync' mode, we collect
            one batch from each worker and concatenate them together into a
            large batch to return. In 'raw' mode, the ParallelIterator object
            is returned directly and the caller is responsible for implementing
            gather and updating the timesteps counter.
        num_async: In async mode, the max number of async
            requests in flight per actor.

    Returns:
        A local iterator over experiences collected in parallel.

    Examples:
        >>> from ray.rllib.execution import ParallelRollouts
        >>> workers = ... # doctest: +SKIP
        >>> rollouts = ParallelRollouts(workers, mode="async") # doctest: +SKIP
        >>> batch = next(rollouts) # doctest: +SKIP
        >>> print(batch.count) # doctest: +SKIP
        50  # config.rollout_fragment_length
        >>> rollouts = ParallelRollouts(workers, mode="bulk_sync") # doctest: +SKIP
        >>> batch = next(rollouts) # doctest: +SKIP
        >>> print(batch.count) # doctest: +SKIP
        200  # config.rollout_fragment_length * config.num_workers

    Updates the STEPS_SAMPLED_COUNTER counter in the local iterator context.
    """

    # Ensure workers are initially in sync.
    workers.sync_weights()

    def report_timesteps(batch):
        metrics = _get_shared_metrics()
        metrics.counters[STEPS_SAMPLED_COUNTER] += batch.count
        if isinstance(batch, MultiAgentBatch):
            metrics.counters[AGENT_STEPS_SAMPLED_COUNTER] += batch.agent_steps()
        else:
            metrics.counters[AGENT_STEPS_SAMPLED_COUNTER] += batch.count
        return batch

    if not workers.remote_workers():
        # Handle the `num_workers=0` case, in which the local worker
        # has to do sampling as well.
        return LocalIterator(
            lambda timeout: workers.local_worker().item_generator, SharedMetrics()
        ).for_each(report_timesteps)

    # Create a parallel iterator over generated experiences.
    rollouts = from_actors(workers.remote_workers())

    if mode == "bulk_sync":
        return (
            rollouts.batch_across_shards()
            .for_each(lambda batches: SampleBatch.concat_samples(batches))
            .for_each(report_timesteps)
        )
    elif mode == "async":
        return rollouts.gather_async(num_async=num_async).for_each(report_timesteps)
    elif mode == "raw":
        return rollouts
    else:
        raise ValueError(
            "mode must be one of 'bulk_sync', 'async', 'raw', got '{}'".format(mode)
        )


def AsyncGradients(workers: WorkerSet) -> LocalIterator[Tuple[ModelGradients, int]]:
    """Operator to compute gradients in parallel from rollout workers.

    Args:
        workers: set of rollout workers to use.

    Returns:
        A local iterator over policy gradients computed on rollout workers.

    Examples:
        >>> from ray.rllib.execution.rollout_ops import AsyncGradients
        >>> workers = ... # doctest: +SKIP
        >>> grads_op = AsyncGradients(workers) # doctest: +SKIP
        >>> print(next(grads_op)) # doctest: +SKIP
        {"var_0": ..., ...}, 50  # grads, batch count

    Updates the STEPS_SAMPLED_COUNTER counter and LEARNER_INFO field in the
    local iterator context.
    """

    # Ensure workers are initially in sync.
    workers.sync_weights()

    # This function will be applied remotely on the workers.
    def samples_to_grads(samples):
        return get_global_worker().compute_gradients(samples), samples.count

    # Record learner metrics and pass through (grads, count).
    class record_metrics:
        def _on_fetch_start(self):
            self.fetch_start_time = time.perf_counter()

        def __call__(self, item):
            (grads, info), count = item
            metrics = _get_shared_metrics()
            metrics.counters[STEPS_SAMPLED_COUNTER] += count
            metrics.info[LEARNER_INFO] = (
                {DEFAULT_POLICY_ID: info} if LEARNER_STATS_KEY in info else info
            )
            metrics.timers[GRAD_WAIT_TIMER].push(
                time.perf_counter() - self.fetch_start_time
            )
            return grads, count

    rollouts = from_actors(workers.remote_workers())
    grads = rollouts.for_each(samples_to_grads)
    return grads.gather_async().for_each(record_metrics())


class ConcatBatches:
    """Callable used to merge batches into larger batches for training.

    This should be used with the .combine() operator if using_iterators=True.

    Examples:
        >>> from ray.rllib.execution import ParallelRollouts
        >>> rollouts = ParallelRollouts(...) # doctest: +SKIP
        >>> rollouts = rollouts.combine(ConcatBatches( # doctest: +SKIP
        ...    min_batch_size=10000, count_steps_by="env_steps")) # doctest: +SKIP
        >>> print(next(rollouts).count) # doctest: +SKIP
        10000
    """

    def __init__(
        self,
        min_batch_size: int,
        count_steps_by: str = "env_steps",
        using_iterators=True,
    ):
        self.min_batch_size = min_batch_size
        self.count_steps_by = count_steps_by
        self.buffer = []
        self.count = 0
        self.last_batch_time = time.perf_counter()
        self.using_iterators = using_iterators

    def __call__(self, batch: SampleBatchType) -> List[SampleBatchType]:
        if not batch:
            return []
        _check_sample_batch_type(batch)

        if self.count_steps_by == "env_steps":
            size = batch.count
        else:
            assert isinstance(batch, MultiAgentBatch), (
                "`count_steps_by=agent_steps` only allowed in multi-agent "
                "environments!"
            )
            size = batch.agent_steps()

        # Incoming batch is an empty dummy batch -> Ignore.
        # Possibly produced automatically by a PolicyServer to unblock
        # an external env waiting for inputs from unresponsive/disconnected
        # client(s).
        if size == 0:
            return []

        self.count += size
        self.buffer.append(batch)

        if self.count >= self.min_batch_size:
            if self.count > self.min_batch_size * 2:
                logger.info(
                    "Collected more training samples than expected "
                    "(actual={}, expected={}). ".format(self.count, self.min_batch_size)
                    + "This may be because you have many workers or "
                    "long episodes in 'complete_episodes' batch mode."
                )
            out = SampleBatch.concat_samples(self.buffer)

            perf_counter = time.perf_counter()
            if self.using_iterators:
                timer = _get_shared_metrics().timers[SAMPLE_TIMER]
                timer.push(perf_counter - self.last_batch_time)
                timer.push_units_processed(self.count)

            self.last_batch_time = perf_counter
            self.buffer = []
            self.count = 0
            return [out]
        return []


class SelectExperiences:
    """Callable used to select experiences from a MultiAgentBatch.

    This should be used with the .for_each() operator.

    Examples:
        >>> from ray.rllib.execution import ParallelRollouts
        >>> from ray.rllib.execution.rollout_ops import SelectExperiences
        >>> rollouts = ParallelRollouts(...) # doctest: +SKIP
        >>> rollouts = rollouts.for_each( # doctest: +SKIP
        ...     SelectExperiences(["pol1", "pol2"]))
        >>> print(next(rollouts).policy_batches.keys()) # doctest: +SKIP
        {"pol1", "pol2"}
    """

    def __init__(
        self,
        policy_ids: Optional[Container[PolicyID]] = None,
        local_worker: Optional["RolloutWorker"] = None,
    ):
        """Initializes a SelectExperiences instance.

        Args:
            policy_ids: Container of PolicyID to select from passing through
                batches. If not provided, must provide the `local_worker` arg.
            local_worker: The local worker to use to determine, which policy
                IDs are trainable. If not provided, must provide the
                `policy_ids` arg.
        """
        assert (
            policy_ids is not None or local_worker is not None
        ), "ERROR: Must provide either one of `policy_ids` or `local_worker` args!"

        self.local_worker = self.policy_ids = None
        if local_worker:
            self.local_worker = local_worker
        else:
            assert isinstance(policy_ids, Container), policy_ids
            self.policy_ids = set(policy_ids)

    def __call__(self, samples: SampleBatchType) -> SampleBatchType:
        _check_sample_batch_type(samples)

        if isinstance(samples, MultiAgentBatch):
            if self.local_worker:
                samples = MultiAgentBatch(
                    {
                        pid: batch
                        for pid, batch in samples.policy_batches.items()
                        if self.local_worker.is_policy_to_train(pid, batch)
                    },
                    samples.count,
                )
            else:
                samples = MultiAgentBatch(
                    {
                        k: v
                        for k, v in samples.policy_batches.items()
                        if k in self.policy_ids
                    },
                    samples.count,
                )

        return samples


def standardize_fields(samples: SampleBatchType, fields: List[str]) -> SampleBatchType:
    """Standardize fields of the given SampleBatch"""
    _check_sample_batch_type(samples)
    wrapped = False

    if isinstance(samples, SampleBatch):
        samples = samples.as_multi_agent()
        wrapped = True

    for policy_id in samples.policy_batches:
        batch = samples.policy_batches[policy_id]
        for field in fields:
            if field in batch:
                batch[field] = standardized(batch[field])

    if wrapped:
        samples = samples.policy_batches[DEFAULT_POLICY_ID]

    return samples


class StandardizeFields:
    """Callable used to standardize fields of batches.

    This should be used with the .for_each() operator. Note that the input
    may be mutated by this operator for efficiency.

    Examples:
        >>> from ray.rllib.execution import ParallelRollouts
        >>> from ray.rllib.execution.rollout_ops import StandardizeFields
        >>> import numpy as np
        >>> rollouts = ParallelRollouts(...) # doctest: +SKIP
        >>> rollouts = rollouts.for_each( # doctest: +SKIP
        ...     StandardizeFields(["advantages"]))
        >>> print(np.std(next(rollouts)["advantages"])) # doctest: +SKIP
        1.0
    """

    def __init__(self, fields: List[str]):
        self.fields = fields

    def __call__(self, samples: SampleBatchType) -> SampleBatchType:
        return standardize_fields(samples, self.fields)