import copy
from functools import partial
import logging
import platform
import queue
import random
import time
from typing import List, Optional, Set, Tuple, Type, Union
import numpy as np
import tree # pip install dm_tree
import ray
from ray import ObjectRef
from ray.rllib import SampleBatch
from ray.rllib.algorithms.algorithm import Algorithm
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
from ray.rllib.core import (
COMPONENT_ENV_TO_MODULE_CONNECTOR,
COMPONENT_MODULE_TO_ENV_CONNECTOR,
)
from ray.rllib.core.rl_module.rl_module import RLModuleSpec
from ray.rllib.execution.buffers.mixin_replay_buffer import MixInMultiAgentReplayBuffer
from ray.rllib.execution.learner_thread import LearnerThread
from ray.rllib.execution.multi_gpu_learner_thread import MultiGPULearnerThread
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.sample_batch import concat_samples
from ray.rllib.utils.actor_manager import (
FaultAwareApply,
FaultTolerantActorManager,
RemoteCallResults,
)
from ray.rllib.utils.actors import create_colocated_actors
from ray.rllib.utils.annotations import OldAPIStack, override
from ray.rllib.utils.deprecation import DEPRECATED_VALUE, deprecation_warning
from ray.rllib.utils.metrics import (
ALL_MODULES,
ENV_RUNNER_RESULTS,
LEARNER_GROUP,
LEARNER_RESULTS,
LEARNER_UPDATE_TIMER,
MEAN_NUM_EPISODE_LISTS_RECEIVED,
MEAN_NUM_LEARNER_GROUP_RESULTS_RECEIVED,
MEAN_NUM_LEARNER_GROUP_UPDATE_CALLED,
NUM_AGENT_STEPS_SAMPLED,
NUM_AGENT_STEPS_TRAINED,
NUM_ENV_STEPS_SAMPLED,
NUM_ENV_STEPS_SAMPLED_LIFETIME,
NUM_ENV_STEPS_TRAINED,
NUM_ENV_STEPS_TRAINED_LIFETIME,
NUM_MODULE_STEPS_TRAINED,
NUM_SYNCH_WORKER_WEIGHTS,
NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS,
SYNCH_WORKER_WEIGHTS_TIMER,
SAMPLE_TIMER,
TIMERS,
)
from ray.rllib.utils.metrics.learner_info import LearnerInfoBuilder
from ray.rllib.utils.replay_buffers.multi_agent_replay_buffer import ReplayMode
from ray.rllib.utils.replay_buffers.replay_buffer import _ALL_POLICIES
from ray.rllib.utils.schedules.scheduler import Scheduler
from ray.rllib.utils.typing import (
LearningRateOrSchedule,
PartialAlgorithmConfigDict,
PolicyID,
ResultDict,
SampleBatchType,
)
from ray.tune.execution.placement_groups import PlacementGroupFactory
from ray.util.annotations import DeveloperAPI
logger = logging.getLogger(__name__)
LEARNER_RESULTS_CURR_ENTROPY_COEFF_KEY = "curr_entropy_coeff"
[docs]
class IMPALAConfig(AlgorithmConfig):
"""Defines a configuration class from which an Impala can be built.
.. testcode::
from ray.rllib.algorithms.impala import IMPALAConfig
config = (
IMPALAConfig()
.environment("CartPole-v1")
.env_runners(num_env_runners=1)
.training(lr=0.0003, train_batch_size_per_learner=512)
.learners(num_learners=1)
)
# Build a Algorithm object from the config and run 1 training iteration.
algo = config.build()
algo.train()
del algo
.. testcode::
from ray.rllib.algorithms.impala import IMPALAConfig
from ray import air
from ray import tune
config = (
IMPALAConfig()
.environment("CartPole-v1")
.env_runners(num_env_runners=1)
.training(lr=tune.grid_search([0.0001, 0.0002]), grad_clip=20.0)
.learners(num_learners=1)
)
# Run with tune.
tune.Tuner(
"IMPALA",
param_space=config,
run_config=air.RunConfig(stop={"training_iteration": 1}),
).fit()
"""
def __init__(self, algo_class=None):
"""Initializes a IMPALAConfig instance."""
self.exploration_config = { # @OldAPIstack
# The Exploration class to use. In the simplest case, this is the name
# (str) of any class present in the `rllib.utils.exploration` package.
# You can also provide the python class directly or the full location
# of your class (e.g. "ray.rllib.utils.exploration.epsilon_greedy.
# EpsilonGreedy").
"type": "StochasticSampling",
# Add constructor kwargs here (if any).
}
super().__init__(algo_class=algo_class or IMPALA)
# fmt: off
# __sphinx_doc_begin__
# IMPALA specific settings:
self.vtrace = True
self.vtrace_clip_rho_threshold = 1.0
self.vtrace_clip_pg_rho_threshold = 1.0
self.learner_queue_size = 3
self.max_requests_in_flight_per_env_runner = 1
self.max_requests_in_flight_per_aggregator_worker = 2
self.timeout_s_sampler_manager = 0.0
self.timeout_s_aggregator_manager = 0.0
self.broadcast_interval = 1
self.num_aggregation_workers = 0
self.num_gpu_loader_threads = 8
self.grad_clip = 40.0
# Note: Only when using enable_rl_module_and_learner=True can the clipping mode
# be configured by the user. On the old API stack, RLlib will always clip by
# global_norm, no matter the value of `grad_clip_by`.
self.grad_clip_by = "global_norm"
self.vf_loss_coeff = 0.5
self.entropy_coeff = 0.01
# Override some of AlgorithmConfig's default values with IMPALA-specific values.
self.num_learners = 1
self.rollout_fragment_length = 50
self.train_batch_size = 500 # @OldAPIstack
self.num_env_runners = 2
self.lr = 0.0005
self.min_time_s_per_iteration = 10
# __sphinx_doc_end__
# fmt: on
# IMPALA takes care of its own EnvRunner (weights, connector, metrics) synching.
self._dont_auto_sync_env_runner_states = True
self.lr_schedule = None # @OldAPIStack
self.entropy_coeff_schedule = None # @OldAPIStack
self.num_multi_gpu_tower_stacks = 1 # @OldAPIstack
self.minibatch_buffer_size = 1 # @OldAPIstack
self.replay_proportion = 0.0 # @OldAPIstack
self.replay_buffer_num_slots = 0 # @OldAPIstack
self.learner_queue_timeout = 300 # @OldAPIstack
self.opt_type = "adam" # @OldAPIstack
self.decay = 0.99 # @OldAPIstack
self.momentum = 0.0 # @OldAPIstack
self.epsilon = 0.1 # @OldAPIstack
self._separate_vf_optimizer = False # @OldAPIstack
self._lr_vf = 0.0005 # @OldAPIstack
self.num_gpus = 1 # @OldAPIstack
self._tf_policy_handles_more_than_one_loss = True # @OldAPIstack
[docs]
@override(AlgorithmConfig)
def training(
self,
*,
vtrace: Optional[bool] = NotProvided,
vtrace_clip_rho_threshold: Optional[float] = NotProvided,
vtrace_clip_pg_rho_threshold: Optional[float] = NotProvided,
gamma: Optional[float] = NotProvided,
num_gpu_loader_threads: Optional[int] = NotProvided,
num_multi_gpu_tower_stacks: Optional[int] = NotProvided,
minibatch_buffer_size: Optional[int] = NotProvided,
replay_proportion: Optional[float] = NotProvided,
replay_buffer_num_slots: Optional[int] = NotProvided,
learner_queue_size: Optional[int] = NotProvided,
learner_queue_timeout: Optional[float] = NotProvided,
max_requests_in_flight_per_aggregator_worker: Optional[int] = NotProvided,
timeout_s_sampler_manager: Optional[float] = NotProvided,
timeout_s_aggregator_manager: Optional[float] = NotProvided,
broadcast_interval: Optional[int] = NotProvided,
num_aggregation_workers: Optional[int] = NotProvided,
grad_clip: Optional[float] = NotProvided,
opt_type: Optional[str] = NotProvided,
lr_schedule: Optional[List[List[Union[int, float]]]] = NotProvided,
decay: Optional[float] = NotProvided,
momentum: Optional[float] = NotProvided,
epsilon: Optional[float] = NotProvided,
vf_loss_coeff: Optional[float] = NotProvided,
entropy_coeff: Optional[LearningRateOrSchedule] = NotProvided,
entropy_coeff_schedule: Optional[List[List[Union[int, float]]]] = NotProvided,
_separate_vf_optimizer: Optional[bool] = NotProvided,
_lr_vf: Optional[float] = NotProvided,
# Deprecated args.
after_train_step=DEPRECATED_VALUE,
**kwargs,
) -> "IMPALAConfig":
"""Sets the training related configuration.
Args:
vtrace: V-trace params (see vtrace_tf/torch.py).
vtrace_clip_rho_threshold:
vtrace_clip_pg_rho_threshold:
gamma: Float specifying the discount factor of the Markov Decision process.
num_gpu_loader_threads: The number of GPU-loader threads (per Learner
worker), used to load incoming (CPU) batches to the GPU, if applicable.
The incoming batches are produced by each Learner's LearnerConnector
pipeline. After loading the batches on the GPU, the threads place them
on yet another queue for the Learner thread (only one per Learner
worker) to pick up and perform `forward_train/loss` computations.
num_multi_gpu_tower_stacks: For each stack of multi-GPU towers, how many
slots should we reserve for parallel data loading? Set this to >1 to
load data into GPUs in parallel. This will increase GPU memory usage
proportionally with the number of stacks.
Example:
2 GPUs and `num_multi_gpu_tower_stacks=3`:
- One tower stack consists of 2 GPUs, each with a copy of the
model/graph.
- Each of the stacks will create 3 slots for batch data on each of its
GPUs, increasing memory requirements on each GPU by 3x.
- This enables us to preload data into these stacks while another stack
is performing gradient calculations.
minibatch_buffer_size: How many train batches should be retained for
minibatching. This conf only has an effect if `num_epochs > 1`.
replay_proportion: Set >0 to enable experience replay. Saved samples will
be replayed with a p:1 proportion to new data samples.
replay_buffer_num_slots: Number of sample batches to store for replay.
The number of transitions saved total will be
(replay_buffer_num_slots * rollout_fragment_length).
learner_queue_size: Max queue size for train batches feeding into the
learner.
learner_queue_timeout: Wait for train batches to be available in minibatch
buffer queue this many seconds. This may need to be increased e.g. when
training with a slow environment.
max_requests_in_flight_per_aggregator_worker: Level of queuing for replay
aggregator operations (if using aggregator workers).
timeout_s_sampler_manager: The timeout for waiting for sampling results
for workers -- typically if this is too low, the manager won't be able
to retrieve ready sampling results.
timeout_s_aggregator_manager: The timeout for waiting for replay worker
results -- typically if this is too low, the manager won't be able to
retrieve ready replay requests.
broadcast_interval: Number of training step calls before weights are
broadcasted to rollout workers that are sampled during any iteration.
num_aggregation_workers: Use n (`num_aggregation_workers`) extra Actors for
multi-level aggregation of the data produced by the m RolloutWorkers
(`num_env_runners`). Note that n should be much smaller than m.
This can make sense if ingesting >2GB/s of samples, or if
the data requires decompression.
grad_clip: If specified, clip the global norm of gradients by this amount.
opt_type: Either "adam" or "rmsprop".
lr_schedule: Learning rate schedule. In the format of
[[timestep, lr-value], [timestep, lr-value], ...]
Intermediary timesteps will be assigned to interpolated learning rate
values. A schedule should normally start from timestep 0.
decay: Decay setting for the RMSProp optimizer, in case `opt_type=rmsprop`.
momentum: Momentum setting for the RMSProp optimizer, in case
`opt_type=rmsprop`.
epsilon: Epsilon setting for the RMSProp optimizer, in case
`opt_type=rmsprop`.
vf_loss_coeff: Coefficient for the value function term in the loss function.
entropy_coeff: Coefficient for the entropy regularizer term in the loss
function.
entropy_coeff_schedule: Decay schedule for the entropy regularizer.
_separate_vf_optimizer: Set this to true to have two separate optimizers
optimize the policy-and value networks. Only supported for some
algorithms (APPO, IMPALA) on the old API stack.
_lr_vf: If _separate_vf_optimizer is True, define separate learning rate
for the value network.
Returns:
This updated AlgorithmConfig object.
"""
# Pass kwargs onto super's `training()` method.
super().training(**kwargs)
if after_train_step != DEPRECATED_VALUE:
deprecation_warning(old="config.training(after_train_step=...)", error=True)
if vtrace is not NotProvided:
self.vtrace = vtrace
if vtrace_clip_rho_threshold is not NotProvided:
self.vtrace_clip_rho_threshold = vtrace_clip_rho_threshold
if vtrace_clip_pg_rho_threshold is not NotProvided:
self.vtrace_clip_pg_rho_threshold = vtrace_clip_pg_rho_threshold
if gamma is not NotProvided:
self.gamma = gamma
if num_gpu_loader_threads is not NotProvided:
self.num_gpu_loader_threads = num_gpu_loader_threads
if num_multi_gpu_tower_stacks is not NotProvided:
self.num_multi_gpu_tower_stacks = num_multi_gpu_tower_stacks
if minibatch_buffer_size is not NotProvided:
self.minibatch_buffer_size = minibatch_buffer_size
if replay_proportion is not NotProvided:
self.replay_proportion = replay_proportion
if replay_buffer_num_slots is not NotProvided:
self.replay_buffer_num_slots = replay_buffer_num_slots
if learner_queue_size is not NotProvided:
self.learner_queue_size = learner_queue_size
if learner_queue_timeout is not NotProvided:
self.learner_queue_timeout = learner_queue_timeout
if broadcast_interval is not NotProvided:
self.broadcast_interval = broadcast_interval
if num_aggregation_workers is not NotProvided:
self.num_aggregation_workers = num_aggregation_workers
if max_requests_in_flight_per_aggregator_worker is not NotProvided:
self.max_requests_in_flight_per_aggregator_worker = (
max_requests_in_flight_per_aggregator_worker
)
if timeout_s_sampler_manager is not NotProvided:
self.timeout_s_sampler_manager = timeout_s_sampler_manager
if timeout_s_aggregator_manager is not NotProvided:
self.timeout_s_aggregator_manager = timeout_s_aggregator_manager
if grad_clip is not NotProvided:
self.grad_clip = grad_clip
if opt_type is not NotProvided:
self.opt_type = opt_type
if lr_schedule is not NotProvided:
self.lr_schedule = lr_schedule
if decay is not NotProvided:
self.decay = decay
if momentum is not NotProvided:
self.momentum = momentum
if epsilon is not NotProvided:
self.epsilon = epsilon
if vf_loss_coeff is not NotProvided:
self.vf_loss_coeff = vf_loss_coeff
if entropy_coeff is not NotProvided:
self.entropy_coeff = entropy_coeff
if entropy_coeff_schedule is not NotProvided:
self.entropy_coeff_schedule = entropy_coeff_schedule
if _separate_vf_optimizer is not NotProvided:
self._separate_vf_optimizer = _separate_vf_optimizer
if _lr_vf is not NotProvided:
self._lr_vf = _lr_vf
return self
@override(AlgorithmConfig)
def validate(self) -> None:
# Call the super class' validation method first.
super().validate()
# IMPALA and APPO need vtrace (A3C Policies no longer exist).
if not self.vtrace:
raise ValueError(
"IMPALA and APPO do NOT support vtrace=False anymore! Set "
"`config.training(vtrace=True)`."
)
# New API stack checks.
if self.enable_env_runner_and_connector_v2:
# Does NOT support aggregation workers yet or a mixin replay buffer.
if self.replay_ratio != 0.0:
raise ValueError(
"The new API stack in combination with the new EnvRunner API "
"does NOT support a mixin replay buffer yet for "
f"{self} (set `config.replay_proportion` to 0.0)!"
)
# Entropy coeff schedule checking.
if self.entropy_coeff_schedule is not None:
raise ValueError(
"`entropy_coeff_schedule` is deprecated and must be None! Use the "
"`entropy_coeff` setting to setup a schedule."
)
Scheduler.validate(
fixed_value_or_schedule=self.entropy_coeff,
setting_name="entropy_coeff",
description="entropy coefficient",
)
# Learner API specific checks.
if self.minibatch_size is not None and not (
(self.minibatch_size % self.rollout_fragment_length == 0)
and self.minibatch_size <= self.total_train_batch_size
):
raise ValueError(
f"`minibatch_size` ({self._minibatch_size}) must either be None "
"or a multiple of `rollout_fragment_length` "
f"({self.rollout_fragment_length}) while at the same time smaller "
"than or equal to `total_train_batch_size` "
f"({self.total_train_batch_size})!"
)
elif isinstance(self.entropy_coeff, float) and self.entropy_coeff < 0.0:
raise ValueError("`entropy_coeff` must be >= 0.0")
# Check whether worker to aggregation-worker ratio makes sense.
if self.num_aggregation_workers > self.num_env_runners:
raise ValueError(
"`num_aggregation_workers` must be smaller than or equal "
"`num_env_runners`! Aggregation makes no sense otherwise."
)
elif self.num_aggregation_workers > self.num_env_runners / 2:
logger.warning(
"`num_aggregation_workers` should be significantly smaller "
"than `num_env_runners`! Try setting it to 0.5*`num_env_runners`"
" or less."
)
# If two separate optimizers/loss terms used for tf, must also set
# `_tf_policy_handles_more_than_one_loss` to True.
if (
self.framework_str in ["tf", "tf2"]
and self._separate_vf_optimizer is True
and self._tf_policy_handles_more_than_one_loss is False
):
raise ValueError(
"`_tf_policy_handles_more_than_one_loss` must be set to True, for "
"TFPolicy to support more than one loss term/optimizer! Try setting "
"config.training(_tf_policy_handles_more_than_one_loss=True)."
)
@property
def replay_ratio(self) -> float:
"""Returns replay ratio (between 0.0 and 1.0) based off self.replay_proportion.
Formula: ratio = 1 / proportion
"""
return (1 / self.replay_proportion) if self.replay_proportion > 0 else 0.0
@override(AlgorithmConfig)
def get_default_learner_class(self):
if self.framework_str == "torch":
from ray.rllib.algorithms.impala.torch.impala_torch_learner import (
IMPALATorchLearner,
)
return IMPALATorchLearner
elif self.framework_str in ["tf2", "tf"]:
raise ValueError(
"TensorFlow is no longer supported on the new API stack! "
"Use `framework='torch'`."
)
else:
raise ValueError(
f"The framework {self.framework_str} is not supported. "
"Use `framework='torch'`."
)
@override(AlgorithmConfig)
def get_default_rl_module_spec(self) -> RLModuleSpec:
from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
if self.framework_str == "tf2":
from ray.rllib.algorithms.ppo.tf.ppo_tf_rl_module import PPOTfRLModule
return RLModuleSpec(module_class=PPOTfRLModule, catalog_class=PPOCatalog)
elif self.framework_str == "torch":
from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_module import (
PPOTorchRLModule,
)
return RLModuleSpec(module_class=PPOTorchRLModule, catalog_class=PPOCatalog)
else:
raise ValueError(
f"The framework {self.framework_str} is not supported. "
"Use either 'torch' or 'tf2'."
)
ImpalaConfig = IMPALAConfig
class IMPALA(Algorithm):
"""Importance weighted actor/learner architecture (IMPALA) Algorithm
== Overview of data flow in IMPALA ==
1. Policy evaluation in parallel across `num_env_runners` actors produces
batches of size `rollout_fragment_length * num_envs_per_env_runner`.
2. If enabled, the replay buffer stores and produces batches of size
`rollout_fragment_length * num_envs_per_env_runner`.
3. If enabled, the minibatch ring buffer stores and replays batches of
size `train_batch_size` up to `num_epochs` times per batch.
4. The learner thread executes data parallel SGD across `num_gpus` GPUs
on batches of size `train_batch_size`.
"""
@classmethod
@override(Algorithm)
def get_default_config(cls) -> AlgorithmConfig:
return IMPALAConfig()
@classmethod
@override(Algorithm)
def get_default_policy_class(
cls, config: AlgorithmConfig
) -> Optional[Type[Policy]]:
if config.framework_str == "torch":
from ray.rllib.algorithms.impala.impala_torch_policy import (
ImpalaTorchPolicy,
)
return ImpalaTorchPolicy
elif config.framework_str == "tf":
from ray.rllib.algorithms.impala.impala_tf_policy import (
ImpalaTF1Policy,
)
return ImpalaTF1Policy
else:
from ray.rllib.algorithms.impala.impala_tf_policy import (
ImpalaTF2Policy,
)
return ImpalaTF2Policy
@override(Algorithm)
def setup(self, config: AlgorithmConfig):
super().setup(config)
# Queue of data to be sent to the Learner.
self.data_to_place_on_learner = []
# The local mixin buffer (if required).
self.local_mixin_buffer = None
# Create extra aggregation workers and assign each rollout worker to
# one of them.
self.batch_being_built = []
if self.config.num_aggregation_workers > 0:
# This spawns `num_aggregation_workers` actors that aggregate
# experiences coming from RolloutWorkers in parallel. We force
# colocation on the same node (localhost) to maximize data bandwidth
# between them and the learner.
localhost = platform.node()
assert localhost != "", (
"ERROR: Cannot determine local node name! "
"`platform.node()` returned empty string."
)
all_co_located = create_colocated_actors(
actor_specs=[
# (class, args, kwargs={}, count=1)
(
AggregationWorker
if self.config.enable_env_runner_and_connector_v2
else AggregatorWorker_OldAPIStack,
[
self.config,
],
{},
self.config.num_aggregation_workers,
)
],
node=localhost,
)
aggregator_workers = [
actor for actor_groups in all_co_located for actor in actor_groups
]
self._aggregator_actor_manager = FaultTolerantActorManager(
aggregator_workers,
max_remote_requests_in_flight_per_actor=(
self.config.max_requests_in_flight_per_aggregator_worker
),
)
elif self.config.enable_rl_module_and_learner:
self._aggregator_actor_manager = None
else:
# Create our local mixin buffer if the num of aggregation workers is 0.
if self.config.replay_proportion > 0.0:
self.local_mixin_buffer = MixInMultiAgentReplayBuffer(
capacity=(
self.config.replay_buffer_num_slots
if self.config.replay_buffer_num_slots > 0
else 1
),
replay_ratio=self.config.replay_ratio,
replay_mode=ReplayMode.LOCKSTEP,
)
self._aggregator_actor_manager = None
# This variable is used to keep track of the statistics from the most recent
# update of the learner group
self._results = {}
if not self.config.enable_rl_module_and_learner:
# Create and start the learner thread.
self._learner_thread = make_learner_thread(self.env_runner, self.config)
self._learner_thread.start()
@override(Algorithm)
def training_step(self):
# Old API stack.
if not self.config.enable_rl_module_and_learner:
return self._training_step_old_api_stack()
do_async_updates = self.config.num_learners > 0
# Asynchronously request all EnvRunners to sample and return their current
# (e.g. ConnectorV2) states and sampling metrics/stats.
# Note that each item in `episode_refs` is a reference to a list of Episodes.
with self.metrics.log_time((TIMERS, SAMPLE_TIMER)):
(
episode_refs,
connector_states,
env_runner_metrics,
env_runner_indices_to_update,
) = self._sample_and_get_connector_states()
# Reduce EnvRunner metrics over the n EnvRunners.
self.metrics.merge_and_log_n_dicts(
env_runner_metrics,
key=ENV_RUNNER_RESULTS,
)
# Log the average number of sample results (list of episodes) received.
self.metrics.log_value(MEAN_NUM_EPISODE_LISTS_RECEIVED, len(episode_refs))
time.sleep(0.01)
# "Batch" collected episode refs into groups, such that exactly
# `total_train_batch_size` timesteps are sent to
# `LearnerGroup.update_from_episodes()`.
data_packages_for_learner_group = self._pre_queue_episode_refs(episode_refs)
time.sleep(0.01)
# If we do tree aggregation, we perform the LearnerConnector pass on the
# aggregation workers.
if self.config.num_aggregation_workers:
data_packages_for_learner_group = (
self._process_env_runner_data_via_aggregation(
data_packages_for_learner_group
)
)
# TODO (sven): When and how long to sleep best is an ongoing investigation.
# We observe
# Balance the backpressures: Sampling vs training through sleeping a small
# amount of time. The sleep time is adjusted automatically based on trying
# to reach a maximum training throughput.
# with self.metrics.log_time((TIMERS, "_balance_backpressure")):
# self.balance_backpressure()
# Call the LearnerGroup's `update_from_episodes` method.
with self.metrics.log_time((TIMERS, LEARNER_UPDATE_TIMER)):
self.metrics.log_value(
key=MEAN_NUM_LEARNER_GROUP_UPDATE_CALLED,
value=len(data_packages_for_learner_group),
)
rl_module_state = None
num_learner_group_results_received = 0
for batch_ref_or_episode_list_ref in data_packages_for_learner_group:
return_state = (
self.metrics.peek(
NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS,
default=0,
)
>= self.config.broadcast_interval
)
timesteps = {
NUM_ENV_STEPS_SAMPLED_LIFETIME: self.metrics.peek(
(ENV_RUNNER_RESULTS, NUM_ENV_STEPS_SAMPLED_LIFETIME), default=0
),
NUM_ENV_STEPS_TRAINED_LIFETIME: self.metrics.peek(
(LEARNER_RESULTS, ALL_MODULES, NUM_ENV_STEPS_TRAINED_LIFETIME),
default=0,
),
}
if self.config.num_aggregation_workers:
learner_results = self.learner_group.update_from_batch(
batch=batch_ref_or_episode_list_ref,
async_update=do_async_updates,
return_state=return_state,
timesteps=timesteps,
num_epochs=self.config.num_epochs,
minibatch_size=self.config.minibatch_size,
shuffle_batch_per_epoch=self.config.shuffle_batch_per_epoch,
)
else:
learner_results = self.learner_group.update_from_episodes(
episodes=batch_ref_or_episode_list_ref,
async_update=do_async_updates,
return_state=return_state,
timesteps=timesteps,
num_epochs=self.config.num_epochs,
minibatch_size=self.config.minibatch_size,
shuffle_batch_per_epoch=self.config.shuffle_batch_per_epoch,
)
# TODO (sven): Rename this metric into a more fitting name: ex.
# `NUM_LEARNER_UPDATED_SINCE_LAST_WEIGHTS_SYNC`
self.metrics.log_value(
NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS,
1,
reduce="sum",
)
if not do_async_updates:
learner_results = [learner_results]
for results_from_n_learners in learner_results:
if not results_from_n_learners[0]:
continue
num_learner_group_results_received += 1
for r in results_from_n_learners:
rl_module_state = r.pop(
"_rl_module_state_after_update", rl_module_state
)
self.metrics.merge_and_log_n_dicts(
stats_dicts=results_from_n_learners,
key=LEARNER_RESULTS,
)
self.metrics.log_value(
key=MEAN_NUM_LEARNER_GROUP_RESULTS_RECEIVED,
value=num_learner_group_results_received,
)
# Update LearnerGroup's own stats.
self.metrics.log_dict(self.learner_group.get_stats(), key=LEARNER_GROUP)
time.sleep(0.01)
# Figure out, whether we should sync/broadcast the (remote) EnvRunner states.
# Note: `learner_results` is a List of n (num async calls) Lists of m
# (num Learner workers) ResultDicts each.
if rl_module_state is not None:
self.metrics.set_value(
NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS, 0
)
self.metrics.log_value(NUM_SYNCH_WORKER_WEIGHTS, 1, reduce="sum")
with self.metrics.log_time((TIMERS, SYNCH_WORKER_WEIGHTS_TIMER)):
self.env_runner_group.sync_env_runner_states(
config=self.config,
connector_states=connector_states,
rl_module_state=rl_module_state,
)
time.sleep(0.01)
def _sample_and_get_connector_states(self):
def _remote_sample_get_state_and_metrics(_worker):
_episodes = _worker.sample()
# Get the EnvRunner's connector states.
_connector_states = _worker.get_state(
components=[
COMPONENT_ENV_TO_MODULE_CONNECTOR,
COMPONENT_MODULE_TO_ENV_CONNECTOR,
]
)
_metrics = _worker.get_metrics()
# Return episode lists by reference so we don't have to send them to the
# main algo process, but to the Learner workers directly.
return ray.put(_episodes), _connector_states, _metrics
env_runner_indices_to_update = set()
episode_refs = []
connector_states = []
env_runner_metrics = []
num_healthy_remote_workers = self.env_runner_group.num_healthy_remote_workers()
# Perform asynchronous sampling on all (healthy) remote rollout workers.
if num_healthy_remote_workers > 0:
async_results: List[
Tuple[int, ObjectRef]
] = self.env_runner_group.fetch_ready_async_reqs(
timeout_seconds=self.config.timeout_s_sampler_manager,
return_obj_refs=False,
)
self.env_runner_group.foreach_worker_async(
_remote_sample_get_state_and_metrics
)
# Get results from the n different async calls and store those EnvRunner
# indices we should update.
results = []
for r in async_results:
env_runner_indices_to_update.add(r[0])
results.append(r[1])
for (episodes, states, metrics) in results:
episode_refs.append(episodes)
connector_states.append(states)
env_runner_metrics.append(metrics)
# Sample from the local EnvRunner.
else:
episodes = self.env_runner.sample()
env_runner_metrics = [self.env_runner.get_metrics()]
episode_refs = [ray.put(episodes)]
connector_states = [
self.env_runner.get_state(
components=[
COMPONENT_ENV_TO_MODULE_CONNECTOR,
COMPONENT_MODULE_TO_ENV_CONNECTOR,
]
)
]
return (
episode_refs,
connector_states,
env_runner_metrics,
env_runner_indices_to_update,
)
def _pre_queue_episode_refs(
self, episode_refs: List[ObjectRef]
) -> List[List[ObjectRef]]:
# Each element in this list is itself a list of ObjRef[Episodes].
# Each ObjRef was returned by one EnvRunner from a single sample() call.
episode_refs_for_learner_group: List[List[ObjectRef]] = []
for ref in episode_refs:
self.batch_being_built.append(ref)
if (
len(self.batch_being_built)
* self.config.num_envs_per_env_runner
* self.config.get_rollout_fragment_length()
>= self.config.total_train_batch_size
):
episode_refs_for_learner_group.append(self.batch_being_built)
self.batch_being_built = []
return episode_refs_for_learner_group
def _process_env_runner_data_via_aggregation(
self,
learner_group_data_packages: List[List[ObjectRef]],
) -> List[ObjectRef]:
"""Process sample batches using tree aggregation workers.
Args:
learner_group_data_packages: List of (env_runner_id, ObjectRef of EnvRunner-
returned data)
NOTE: This will provide speedup when sample batches have been compressed,
and the decompression can happen on the aggregation workers in parallel to
the training.
Returns:
Batches that have been processed by the mixin buffers on the aggregation
workers.
"""
def _process_data(_actor, _episodes):
return _actor.process_episodes(ray.get(_episodes))
for data in learner_group_data_packages:
assert isinstance(data, ObjectRef), (
"For efficiency, process_experiences_tree_aggregation should "
f"be given ObjectRefs instead of {type(data)}."
)
# Randomly pick an aggregation worker to process this batch.
aggregator_id = random.choice(
self._aggregator_actor_manager.healthy_actor_ids()
)
calls_placed = self._aggregator_actor_manager.foreach_actor_async(
partial(_process_data, _episodes=data),
remote_actor_ids=[aggregator_id],
)
if calls_placed <= 0:
self.metrics.log_value(
"num_times_no_aggregation_worker_available", 1, reduce="sum"
)
waiting_processed_sample_batches: RemoteCallResults = (
self._aggregator_actor_manager.fetch_ready_async_reqs(
timeout_seconds=self.config.timeout_s_aggregator_manager,
)
)
FaultTolerantActorManager.handle_remote_call_result_errors(
waiting_processed_sample_batches,
ignore_ray_errors=(
self.config.ignore_env_runner_failures
or self.config.restart_failed_env_runners
),
)
return list(waiting_processed_sample_batches.ignore_errors())
# def balance_backpressure(self):
# # Sleep for n seconds to balance backpressure.
# time.sleep(0.2)#self._sleep_time_controller.current)
#
# # Adjust the sleep time once every iteration (on the first `training_step()`
# # call in each iteration).
# if self.metrics.peek(NUM_TRAINING_STEP_CALLS_PER_ITERATION, default=0) == 0:
# train_throughput = self.metrics.peek(
# (
# LEARNER_RESULTS,
# ALL_MODULES,
# NUM_ENV_STEPS_TRAINED_LIFETIME,
# ),
# default=0.0,
# throughput=True,
# )
# self.metrics.log_value(
# "_measured_train_throughput",
# train_throughput,
# window=1,
# )
# self._sleep_time_controller.log_result(train_throughput)
# self.metrics.log_value(
# "_current_sleep_time",
# self._sleep_time_controller.current,
# window=1,
# )
@classmethod
@override(Algorithm)
def default_resource_request(
cls,
config: Union[AlgorithmConfig, PartialAlgorithmConfigDict],
):
if isinstance(config, AlgorithmConfig):
cf: IMPALAConfig = config
else:
cf: IMPALAConfig = cls.get_default_config().update_from_dict(config)
eval_config = cf.get_evaluation_config_object()
bundles = (
[
{
# Driver + Aggregation Workers:
# Force to be on same node to maximize data bandwidth
# between aggregation workers and the learner (driver).
# Aggregation workers tree-aggregate experiences collected
# from RolloutWorkers (n rollout workers map to m
# aggregation workers, where m < n) and always use 1 CPU
# each.
"CPU": (
max(
cf.num_cpus_for_main_process,
cf.num_cpus_per_learner if cf.num_learners == 0 else 0,
)
+ cf.num_aggregation_workers
),
# Use n GPUs if we have a local Learner (num_learners=0).
"GPU": (
(cf.num_gpus_per_learner if cf.num_learners == 0 else 0)
if cf.enable_rl_module_and_learner
else (0 if cf._fake_gpus else cf.num_gpus)
),
}
]
+ [
{
# EnvRunners.
"CPU": cf.num_cpus_per_env_runner,
"GPU": cf.num_gpus_per_env_runner,
**cf.custom_resources_per_env_runner,
}
for _ in range(cf.num_env_runners)
]
+ (
[
{
# Evaluation (remote) workers.
# Note: The local eval worker is located on the driver
# CPU or not even created iff >0 eval workers.
"CPU": eval_config.num_cpus_per_env_runner,
"GPU": eval_config.num_gpus_per_env_runner,
**eval_config.custom_resources_per_env_runner,
}
for _ in range(cf.evaluation_num_env_runners)
]
if cf.evaluation_interval
else []
)
)
# TODO (avnishn): Remove this once we have a way to extend placement group
# factories.
# Only if we have actual (remote) learner workers. In case of a local learner,
# the resource has already been taken care of above.
if cf.enable_rl_module_and_learner and cf.num_learners > 0:
bundles += cls._get_learner_bundles(cf)
# Return PlacementGroupFactory containing all needed resources
# (already properly defined as device bundles).
return PlacementGroupFactory(
bundles=bundles,
strategy=cf.placement_strategy,
)
@OldAPIStack
def _training_step_old_api_stack(self):
# First, check, whether our learner thread is still healthy.
if not self._learner_thread.is_alive():
raise RuntimeError("The learner thread died while training!")
use_tree_aggregation = (
self._aggregator_actor_manager
and self._aggregator_actor_manager.num_healthy_actors() > 0
)
# Get sampled SampleBatches from our workers (by ray references if we use
# tree-aggregation).
unprocessed_sample_batches = self._get_samples_from_workers_old_api_stack(
return_object_refs=use_tree_aggregation,
)
# Tag workers that actually produced ready sample batches this iteration.
# Those workers will have to get updated at the end of the iteration.
workers_that_need_updates = {
worker_id for worker_id, _ in unprocessed_sample_batches
}
# Send the collected batches (still object refs) to our aggregation workers.
if use_tree_aggregation:
batches = self._process_experiences_tree_aggregation(
unprocessed_sample_batches
)
# Resolve collected batches here on local process (using the mixin buffer).
else:
batches = self._process_experiences_directly(unprocessed_sample_batches)
# Increase sampling counters now that we have the actual SampleBatches on
# the local process (and can measure their sizes).
for batch in batches:
self._counters[NUM_ENV_STEPS_SAMPLED] += batch.count
self._counters[NUM_AGENT_STEPS_SAMPLED] += batch.agent_steps()
# Concatenate single batches into batches of size `total_train_batch_size`.
self._concatenate_batches_and_pre_queue(batches)
# Move train batches (of size `total_train_batch_size`) onto learner queue.
self._place_processed_samples_on_learner_thread_queue()
# Extract most recent train results from learner thread.
train_results = self._process_trained_results()
# Sync worker weights (only those policies that were actually updated).
with self._timers[SYNCH_WORKER_WEIGHTS_TIMER]:
pids = list(train_results.keys())
self._update_workers_old_api_stack(
workers_that_need_updates=workers_that_need_updates,
policy_ids=pids,
)
# With a training step done, try to bring any aggregators back to life
# if necessary.
# Aggregation workers are stateless, so we do not need to restore any
# state here.
if self._aggregator_actor_manager:
self._aggregator_actor_manager.probe_unhealthy_actors(
timeout_seconds=self.config.env_runner_health_probe_timeout_s,
mark_healthy=True,
)
return train_results
@OldAPIStack
def _get_samples_from_workers_old_api_stack(
self,
return_object_refs: Optional[bool] = False,
) -> List[Tuple[int, Union[ObjectRef, SampleBatchType]]]:
"""Get samples from rollout workers for training.
Args:
return_object_refs: If True, return ObjectRefs instead of the samples
directly. This is useful when using aggregator workers so that data
collected on rollout workers is directly de referenced on the aggregator
workers instead of first in the driver and then on the aggregator
workers.
Returns:
a list of tuples of (worker_index, sample batch or ObjectRef to a sample
batch)
"""
with self._timers[SAMPLE_TIMER]:
# Sample from healthy remote workers by default. If there is no healthy
# worker (either because they have all died, or because there was none to
# begin) check if the local_worker exists. If the local worker has an
# env_instance (either because there are no remote workers or
# self.config.create_env_on_local_worker == True), then sample from the
# local worker. Otherwise just return an empty list.
if self.env_runner_group.num_healthy_remote_workers() > 0:
# Perform asynchronous sampling on all (remote) rollout workers.
self.env_runner_group.foreach_worker_async(
lambda worker: worker.sample()
)
sample_batches: List[
Tuple[int, ObjectRef]
] = self.env_runner_group.fetch_ready_async_reqs(
timeout_seconds=self.config.timeout_s_sampler_manager,
return_obj_refs=return_object_refs,
)
elif self.config.num_env_runners == 0 or (
self.env_runner and self.env_runner.async_env is not None
):
# Sampling from the local worker
sample_batch = self.env_runner.sample()
if return_object_refs:
sample_batch = ray.put(sample_batch)
sample_batches = [(0, sample_batch)]
else:
# Not much we can do. Return empty list and wait.
sample_batches = []
return sample_batches
@OldAPIStack
def _process_experiences_tree_aggregation(
self,
worker_to_sample_batches_refs: List[Tuple[int, ObjectRef]],
) -> List[SampleBatchType]:
"""Process sample batches using tree aggregation workers.
Args:
worker_to_sample_batches_refs: List of (worker_id, sample_batch_ref)
NOTE: This will provide speedup when sample batches have been compressed,
and the decompression can happen on the aggregation workers in parallel to
the training.
Returns:
Batches that have been processed by the mixin buffers on the aggregation
workers.
"""
def _process_episodes(actor, batch):
return actor.process_episodes(ray.get(batch))
for _, batch in worker_to_sample_batches_refs:
assert isinstance(batch, ObjectRef), (
"For efficiency, process_experiences_tree_aggregation should "
f"be given ObjectRefs instead of {type(batch)}."
)
# Randomly pick an aggregation worker to process this batch.
aggregator_id = random.choice(
self._aggregator_actor_manager.healthy_actor_ids()
)
calls_placed = self._aggregator_actor_manager.foreach_actor_async(
partial(_process_episodes, batch=batch),
remote_actor_ids=[aggregator_id],
)
if calls_placed <= 0:
self.metrics.log_value(
"num_times_no_aggregation_worker_available", 1, reduce="sum"
)
waiting_processed_sample_batches: RemoteCallResults = (
self._aggregator_actor_manager.fetch_ready_async_reqs(
timeout_seconds=self.config.timeout_s_aggregator_manager,
)
)
FaultTolerantActorManager.handle_remote_call_result_errors(
waiting_processed_sample_batches,
ignore_ray_errors=(
self.config.ignore_env_runner_failures
or self.config.restart_failed_env_runners
),
)
return [b.get() for b in waiting_processed_sample_batches.ignore_errors()]
@OldAPIStack
def _process_experiences_directly(
self,
worker_to_sample_batches: List[Tuple[int, SampleBatch]],
) -> List[SampleBatchType]:
"""Process sample batches directly on the driver, for training.
Args:
worker_to_sample_batches: List of (worker_id, sample_batch) tuples.
Returns:
Batches that have been processed by the mixin buffer.
"""
batches = [b for _, b in worker_to_sample_batches]
processed_batches = []
for batch in batches:
assert not isinstance(
batch, ObjectRef
), "_process_experiences_directly can not handle ObjectRefs. "
batch = batch.decompress_if_needed()
# Only make a pass through the buffer, if replay proportion is > 0.0 (and
# we actually have one).
if self.local_mixin_buffer:
self.local_mixin_buffer.add(batch)
batch = self.local_mixin_buffer.replay(_ALL_POLICIES)
if batch:
processed_batches.append(batch)
return processed_batches
@OldAPIStack
def _concatenate_batches_and_pre_queue(self, batches: List[SampleBatch]) -> None:
"""Concatenate batches that are being returned from rollout workers
Args:
batches: List of batches of experiences from EnvRunners.
"""
def aggregate_into_larger_batch():
if (
sum(b.count for b in self.batch_being_built)
>= self.config.total_train_batch_size
):
batch_to_add = concat_samples(self.batch_being_built)
self.data_to_place_on_learner.append(batch_to_add)
self.batch_being_built = []
for batch in batches:
# TODO (sven): Strange bug after a RolloutWorker crash and proper
# restart. The bug is related to (old, non-V2) connectors being used and
# seems to happen inside the AgentCollector's `add_action_reward_next_obs`
# method, at the end of which the number of vf_preds (and all other
# extra action outs) in the batch is one smaller than the number of obs/
# actions/rewards, which then leads to a malformed train batch.
# IMPALA/APPO crash inside the loss function (during v-trace operations)
# b/c of the resulting shape mismatch. The following if-block prevents
# this from happening and it can be removed once we are on the new API
# stack for good (and use the new connectors and also no longer
# AgentCollectors, RolloutWorkers, Policies, TrajectoryView API, etc..):
if (
self.config.batch_mode == "truncate_episodes"
and self.config.restart_failed_env_runners
):
if any(
SampleBatch.VF_PREDS in pb
and (
pb[SampleBatch.VF_PREDS].shape[0]
!= pb[SampleBatch.REWARDS].shape[0]
)
for pb in batch.policy_batches.values()
):
continue
self.batch_being_built.append(batch)
aggregate_into_larger_batch()
@OldAPIStack
def _learn_on_processed_samples(self) -> ResultDict:
"""Update the learner group with the latest batch of processed samples.
Returns:
Aggregated results from the learner group after an update is completed.
"""
# Nothing on the queue -> Don't send requests to learner group
# or no results ready (from previous `self.learner_group.update()` calls) for
# reducing.
if not self.data_to_place_on_learner:
return {}
# There are batches on the queue -> Send them all to the learner group.
batches = self.data_to_place_on_learner[:]
self.data_to_place_on_learner.clear()
# If there are no learner workers and learning is directly on the driver
# Then we can't do async updates, so we need to block.
async_update = self.config.num_learners > 0
results = []
for batch in batches:
results = self.learner_group.update_from_batch(
batch=batch,
timesteps={
NUM_ENV_STEPS_SAMPLED_LIFETIME: (
self.metrics.peek(NUM_ENV_STEPS_SAMPLED_LIFETIME)
),
},
async_update=async_update,
num_epochs=self.config.num_epochs,
minibatch_size=self.config.minibatch_size,
)
if not async_update:
results = [results]
for r in results:
self._counters[NUM_ENV_STEPS_TRAINED] += r[ALL_MODULES].pop(
NUM_ENV_STEPS_TRAINED
)
self._counters[NUM_AGENT_STEPS_TRAINED] += r[ALL_MODULES].pop(
NUM_MODULE_STEPS_TRAINED
)
self._counters.update(self.learner_group.get_stats())
# If there are results, reduce-mean over each individual value and return.
if results:
return tree.map_structure(lambda *x: np.mean(x), *results)
# Nothing on the queue -> Don't send requests to learner group
# or no results ready (from previous `self.learner_group.update_from_batch()`
# calls) for reducing.
return {}
@OldAPIStack
def _place_processed_samples_on_learner_thread_queue(self) -> None:
"""Place processed samples on the learner queue for training."""
for i, batch in enumerate(self.data_to_place_on_learner):
try:
self._learner_thread.inqueue.put(
batch,
# Setting block = True for the very last item in our list prevents
# the learner thread, this main thread, and the GPU loader threads
# from thrashing when there are more samples than the learner can
# reasonably process.
# see https://github.com/ray-project/ray/pull/26581#issuecomment-1187877674 # noqa
block=i == len(self.data_to_place_on_learner) - 1,
)
self._counters["num_samples_added_to_queue"] += (
batch.agent_steps()
if self.config.count_steps_by == "agent_steps"
else batch.count
)
except queue.Full:
self._counters["num_times_learner_queue_full"] += 1
self.data_to_place_on_learner.clear()
@OldAPIStack
def _process_trained_results(self) -> ResultDict:
"""Process training results that are outputed by the learner thread.
Returns:
Aggregated results from the learner thread after an update is completed.
"""
# Get learner outputs/stats from output queue.
num_env_steps_trained = 0
num_agent_steps_trained = 0
learner_infos = []
# Loop through output queue and update our counts.
for _ in range(self._learner_thread.outqueue.qsize()):
(
env_steps,
agent_steps,
learner_results,
) = self._learner_thread.outqueue.get(timeout=0.001)
num_env_steps_trained += env_steps
num_agent_steps_trained += agent_steps
if learner_results:
learner_infos.append(learner_results)
# Nothing new happened since last time, use the same learner stats.
if not learner_infos:
final_learner_info = copy.deepcopy(self._learner_thread.learner_info)
# Accumulate learner stats using the `LearnerInfoBuilder` utility.
else:
builder = LearnerInfoBuilder()
for info in learner_infos:
builder.add_learn_on_batch_results_multi_agent(info)
final_learner_info = builder.finalize()
# Update the steps trained counters.
self._counters[NUM_ENV_STEPS_TRAINED] += num_env_steps_trained
self._counters[NUM_AGENT_STEPS_TRAINED] += num_agent_steps_trained
return final_learner_info
@OldAPIStack
def _update_workers_old_api_stack(
self,
workers_that_need_updates: Set[int],
policy_ids: Optional[List[PolicyID]] = None,
) -> None:
"""Updates all RolloutWorkers that require updating.
Updates only if NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS has been
reached and the worker has sent samples in this iteration. Also only updates
those policies, whose IDs are given via `policies` (if None, update all
policies).
Args:
workers_that_need_updates: Set of worker IDs that need to be updated.
policy_ids: Optional list of Policy IDs to update. If None, will update all
policies on the to-be-updated workers.
"""
# Update global vars of the local worker.
if self.config.policy_states_are_swappable:
self.env_runner.lock()
global_vars = {
"timestep": self._counters[NUM_AGENT_STEPS_TRAINED],
"num_grad_updates_per_policy": {
pid: self.env_runner.policy_map[pid].num_grad_updates
for pid in policy_ids or []
},
}
self.env_runner.set_global_vars(global_vars, policy_ids=policy_ids)
if self.config.policy_states_are_swappable:
self.env_runner.unlock()
# Only need to update workers if there are remote workers.
self._counters[NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS] += 1
if (
self.env_runner_group.num_remote_workers() > 0
and self._counters[NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS]
>= self.config.broadcast_interval
and workers_that_need_updates
):
if self.config.policy_states_are_swappable:
self.env_runner.lock()
weights = self.env_runner.get_weights(policy_ids)
if self.config.policy_states_are_swappable:
self.env_runner.unlock()
weights_ref = ray.put(weights)
self._learner_thread.policy_ids_updated.clear()
self._counters[NUM_TRAINING_STEP_CALLS_SINCE_LAST_SYNCH_WORKER_WEIGHTS] = 0
self._counters[NUM_SYNCH_WORKER_WEIGHTS] += 1
self.env_runner_group.foreach_worker(
func=lambda w: w.set_weights(ray.get(weights_ref), global_vars),
local_env_runner=False,
remote_worker_ids=list(workers_that_need_updates),
timeout_seconds=0, # Don't wait for the workers to finish.
)
@override(Algorithm)
def _compile_iteration_results_old_api_stack(self, *args, **kwargs):
result = super()._compile_iteration_results_old_api_stack(*args, **kwargs)
if not self.config.enable_rl_module_and_learner:
result = self._learner_thread.add_learner_metrics(
result, overwrite_learner_info=False
)
return result
Impala = IMPALA
@DeveloperAPI
@ray.remote(num_cpus=0, max_restarts=-1)
class AggregationWorker(FaultAwareApply):
"""A worker performing LearnerConnector pass throughs of collected episodes."""
def __init__(self, config: AlgorithmConfig):
self.config = config
self._learner_connector = self.config.build_learner_connector(
input_observation_space=None,
input_action_space=None,
)
self._rl_module = None
def process_episodes(self, episodes):
batch = self._learner_connector(
batch={},
episodes=episodes,
rl_module=self._rl_module,
shared_data={},
)
return batch
def get_host(self) -> str:
return platform.node()
@OldAPIStack
@ray.remote(num_cpus=0, max_restarts=-1)
class AggregatorWorker_OldAPIStack(FaultAwareApply):
"""A worker for doing tree aggregation of collected episodes"""
def __init__(self, config: AlgorithmConfig):
self.config = config
self._mixin_buffer = MixInMultiAgentReplayBuffer(
capacity=(
self.config.replay_buffer_num_slots
if self.config.replay_buffer_num_slots > 0
else 1
),
replay_ratio=self.config.replay_ratio,
replay_mode=ReplayMode.LOCKSTEP,
)
def process_episodes(self, batch: SampleBatchType) -> SampleBatchType:
batch = batch.decompress_if_needed()
self._mixin_buffer.add(batch)
processed_batches = self._mixin_buffer.replay(_ALL_POLICIES)
return processed_batches
def get_host(self) -> str:
return platform.node()
@OldAPIStack
def make_learner_thread(local_worker, config):
if not config["simple_optimizer"]:
logger.info(
"Enabling multi-GPU mode, {} GPUs, {} parallel tower-stacks".format(
config["num_gpus"], config["num_multi_gpu_tower_stacks"]
)
)
num_stacks = config["num_multi_gpu_tower_stacks"]
buffer_size = config["minibatch_buffer_size"]
if num_stacks < buffer_size:
logger.warning(
"In multi-GPU mode you should have at least as many "
"multi-GPU tower stacks (to load data into on one device) as "
"you have stack-index slots in the buffer! You have "
f"configured {num_stacks} stacks and a buffer of size "
f"{buffer_size}. Setting "
f"`minibatch_buffer_size={num_stacks}`."
)
config["minibatch_buffer_size"] = num_stacks
learner_thread = MultiGPULearnerThread(
local_worker,
num_gpus=config["num_gpus"],
lr=config["lr"],
train_batch_size=config["train_batch_size"],
num_multi_gpu_tower_stacks=config["num_multi_gpu_tower_stacks"],
num_sgd_iter=config["num_epochs"],
learner_queue_size=config["learner_queue_size"],
learner_queue_timeout=config["learner_queue_timeout"],
num_data_load_threads=config["num_gpu_loader_threads"],
)
else:
learner_thread = LearnerThread(
local_worker,
minibatch_buffer_size=config["minibatch_buffer_size"],
num_sgd_iter=config["num_epochs"],
learner_queue_size=config["learner_queue_size"],
learner_queue_timeout=config["learner_queue_timeout"],
)
return learner_thread