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manager.py
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manager.py
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import jax
import jax.numpy as jnp
import flax.core.scope
import flax.struct
import qdax.core.emitters.emitter
import qdax.core.emitters.multi_emitter
import qdax.core.emitters.mutation_operators
import qdax.environments
from qdax.types import Genotype, Observation, Action, Fitness, Descriptor, Metrics
import numpy as np
import gym.spaces
import gymnasium.spaces
import hydra.utils
from omegaconf import OmegaConf
import logging
import wandb
import math
import time
import os
import tqdm
import tqdm.contrib.logging
from functools import partial
from collections.abc import Sequence, Callable
from contextlib import AbstractContextManager
from typing import Optional, TypeVar, Any, cast, assert_never
from overrides import override
from .manager_base import ManagerStateBase, ManagerConstantBase, ManagerBase
from .extended_me import ExtendedMAPElites
from .containers import (
compute_cvt_centroids, ExtendedMapElitesRepertoire, CPUMapElitesRepertoire,
ExtendedDepthRepertoire, CPUDepthRepertoire, get_repertoire_type,
)
from .containers import extended_me_repertoire, extended_depth_repertoire
from .emitters import RefEmitterState, PGAMEEmitter, RefPGAMEEmitter, DQNMEEmitter, RefDQNMEEmitter
from .neuroevolution import FakeRepresentation, CNN, activation, buffers
from .tasks import Task, RLTask, GymTask, QDaxBraxTask, EnvPoolTask, AtariTask
from .tasks import gym_task, envpool_task
from .metrics import qd_metrics
from .config.root import RootConfig
from .config.task import RLTaskConfig, QDaxBraxConfig, AtariConfig
from .config.framework import MEConfig
from .config.emitter import (
PGAMEGEmitterConfig, VRefPGAMEGEmitterConfig, DQNMEGEmitterConfig, VRefDQNMEGEmitterConfig
)
from .config.network import CNNPolicyNetConfig, SharedCNNPolicyNetConfig
from .treax import functional as F
from .treax import numpy as tjnp
from .treax import chain as tchain
from .utils import (
rich_highlight, fake_wrap, jax_jit, jax_compiled, lax_scan,
transpose_dict_of_list, uninterrupted, PeriodicEMA, CSVLogger,
)
_log = logging.getLogger(__name__)
class ManagerState(ManagerStateBase):
extra_keys: jax.Array
repertoire: ExtendedMapElitesRepertoire
tmp_repertoire: Optional[ExtendedMapElitesRepertoire]
emitter_state: Optional[qdax.core.emitters.emitter.EmitterState]
loop_completed: jax.Array
total_time: jax.Array
class ManagerConstant(ManagerConstantBase):
task_constant: Any
reeval_task_constant: Any
_CallableT = TypeVar('_CallableT', bound=Callable)
class Manager(ManagerBase[RootConfig, ManagerState, ManagerConstant]):
def __init__(self, cfg: RootConfig) -> None:
super().__init__(cfg)
if self._cfg.n_profile != 0:
_log.warning('The profiler is enabled. The runtime may be longer.')
self._task = self._create_task(self._cfg.emitter.env_batch_size)
self._scoring_fn = self._task.get_scoring_fn()
if self._cfg.emitter.reeval_env_batch_size is None:
self._reeval_task = self._task
else:
self._reeval_task = self._create_task(self._cfg.emitter.reeval_env_batch_size)
reeval_env_batch_size = (
self._cfg.emitter.reeval_env_batch_size or self._cfg.emitter.env_batch_size
)
match self._cfg.network.task_type:
case 'RL':
qdax_brax_net_config = cast(CNNPolicyNetConfig, self._cfg.network)
assert isinstance(self._task, RLTask)
assert isinstance(self._reeval_task, RLTask)
obs_space = self._task.obs_space
match obs_space:
case gym.spaces.Box() | gymnasium.spaces.Box():
cnn_input_shape = obs_space.shape
case _:
raise NotImplementedError(type(obs_space))
action_space = self._task.action_space
match action_space:
case gym.spaces.Box() | gymnasium.spaces.Box():
output_size = self._task.action_size
case gym.spaces.Discrete() | gymnasium.spaces.Discrete():
output_size = int(action_space.n)
case _:
raise NotImplementedError(type(action_space))
assert qdax_brax_net_config.conv_padding in ('SAME', 'VALID')
if self._cfg.network.emitter_type in ('Normal', 'Share'):
if self._cfg.network.emitter_type == 'Normal':
self._representation_net = FakeRepresentation()
decision_cnn_input_shape = cnn_input_shape
elif self._cfg.network.emitter_type == 'Share':
qdax_brax_net_config = cast(
SharedCNNPolicyNetConfig, qdax_brax_net_config
)
assert qdax_brax_net_config.conv_padding in ('SAME', 'VALID')
self._representation_net = CNN(
conv_features=qdax_brax_net_config.representation_conv_features,
conv_kernel_sizes=qdax_brax_net_config.representation_conv_kernel_sizes,
conv_activation=activation(qdax_brax_net_config.conv_activation),
conv_strides=qdax_brax_net_config.representation_conv_strides,
conv_padding=qdax_brax_net_config.conv_padding,
mlp_layer_sizes=qdax_brax_net_config.representation_mlp_hidden_layer_sizes, # noqa: E501
mlp_activation=activation(qdax_brax_net_config.mlp_activation),
mlp_final_activation=activation(qdax_brax_net_config.mlp_final_activation), # noqa: E501
cnn_input_shape=cnn_input_shape,
)
decision_cnn_input_shape = qdax_brax_net_config.decision_cnn_input_shape
else:
assert_never(self._cfg.network.emitter_type)
self._net = CNN(
conv_features=qdax_brax_net_config.conv_features,
conv_kernel_sizes=qdax_brax_net_config.conv_kernel_sizes,
conv_activation=activation(qdax_brax_net_config.conv_activation),
conv_strides=qdax_brax_net_config.conv_strides,
conv_padding=qdax_brax_net_config.conv_padding,
mlp_layer_sizes=(*qdax_brax_net_config.mlp_hidden_layer_sizes, output_size),
mlp_activation=activation(qdax_brax_net_config.mlp_activation),
mlp_final_activation=activation(qdax_brax_net_config.mlp_final_activation),
cnn_input_shape=decision_cnn_input_shape,
)
match (obs_space, action_space):
case (
gym.spaces.Box() | gymnasium.spaces.Box(),
gym.spaces.Box() | gymnasium.spaces.Box(),
):
def select_action_fn(
representation_genotypes: Genotype,
genotypes: Genotype,
obs: Observation,
) -> Action:
representation = self._representation_net.apply(
cast(flax.core.scope.VariableDict, representation_genotypes),
obs,
)
assert isinstance(representation, jax.Array)
action = self._net.apply(
cast(flax.core.scope.VariableDict, genotypes),
representation,
)
assert isinstance(action, Action)
return action
case (
gym.spaces.Box() | gymnasium.spaces.Box(),
gym.spaces.Discrete() | gymnasium.spaces.Discrete(),
):
def select_action_fn(
representation_genotypes: Genotype,
genotypes: Genotype,
obs: Observation,
) -> Action:
representation = self._representation_net.apply(
cast(flax.core.scope.VariableDict, representation_genotypes),
obs,
)
assert isinstance(representation, Action)
action = self._net.apply(
cast(flax.core.scope.VariableDict, genotypes),
representation,
)
assert isinstance(action, Action)
action = jnp.argmax(action, axis=-1, keepdims=True)
return action
case _:
raise NotImplementedError((type(obs_space), type(action_space)))
else:
raise NotImplementedError(self._cfg.network.emitter_type)
self._task.set_select_action_fn(select_action_fn)
if self._reeval_task is not self._task:
self._reeval_task.set_select_action_fn(select_action_fn)
case _:
raise NotImplementedError(self._cfg.task.typ)
match self._cfg.emitter.subtype:
case 'PGA-ME':
pga_me_config = cast(PGAMEGEmitterConfig, self._cfg.emitter)
self._emitter = PGAMEEmitter(
config=pga_me_config,
policy_network=self._net,
env=self._task,
variation_fn=partial(
qdax.core.emitters.mutation_operators.isoline_variation,
iso_sigma=pga_me_config.iso_sigma,
line_sigma=pga_me_config.line_sigma,
),
)
if self._cfg.task.typ == 'RL':
rl_task_config = cast(RLTaskConfig, self._cfg.task)
self._step_multiplier = rl_task_config.episode_len
else:
self._step_multiplier = 1
case 'Ref-PGA-ME':
refpga_me_config = cast(VRefPGAMEGEmitterConfig, self._cfg.emitter)
assert isinstance(self._task, RLTask)
assert not isinstance(self._representation_net, FakeRepresentation)
self._emitter = RefPGAMEEmitter(
config=refpga_me_config,
representation_net=self._representation_net,
decision_net=self._net,
task=self._task,
variation_fn=partial(
qdax.core.emitters.mutation_operators.isoline_variation,
iso_sigma=refpga_me_config.iso_sigma,
line_sigma=refpga_me_config.line_sigma,
),
)
if self._cfg.task.typ == 'RL':
rl_task_config = cast(RLTaskConfig, self._cfg.task)
self._step_multiplier = rl_task_config.episode_len
else:
self._step_multiplier = 1
case 'DQN-ME':
dqn_me_config = cast(DQNMEGEmitterConfig, self._cfg.emitter)
assert isinstance(self._task, RLTask)
self._emitter = DQNMEEmitter(
config=dqn_me_config,
policy_network=self._net,
task=self._task,
variation_fn=partial(
qdax.core.emitters.mutation_operators.isoline_variation,
iso_sigma=dqn_me_config.iso_sigma,
line_sigma=dqn_me_config.line_sigma,
),
)
if self._cfg.task.typ == 'RL':
rl_task_config = cast(RLTaskConfig, self._cfg.task)
self._step_multiplier = rl_task_config.episode_len
else:
self._step_multiplier = 1
case 'Ref-DQN-ME':
refdqn_me_config = cast(VRefDQNMEGEmitterConfig, self._cfg.emitter)
assert isinstance(self._task, RLTask)
assert not isinstance(self._representation_net, FakeRepresentation)
self._emitter = RefDQNMEEmitter(
config=refdqn_me_config,
representation_net=self._representation_net,
decision_net=self._net,
task=self._task,
variation_fn=partial(
qdax.core.emitters.mutation_operators.isoline_variation,
iso_sigma=refdqn_me_config.iso_sigma,
line_sigma=refdqn_me_config.line_sigma,
),
)
if self._cfg.task.typ == 'RL':
rl_task_config = cast(RLTaskConfig, self._cfg.task)
self._step_multiplier = rl_task_config.episode_len
else:
self._step_multiplier = 1
case _:
raise NotImplementedError(self._cfg.emitter.name)
match self._cfg.framework.name:
case 'ME':
self._metrics_fn = partial(
qd_metrics,
qd_offset=self._task.qd_offset,
)
self._qd = ExtendedMAPElites(
scoring_function=self._scoring_fn,
reeval_scoring_fn=self._reeval_task.get_scoring_fn(),
reeval_env_batch_size=reeval_env_batch_size,
emitter=self._emitter,
metrics_function=self._metrics_fn,
refresh_depth=self._cfg.emitter.refresh_depth,
)
case _:
raise NotImplementedError(self._cfg.framework.name)
self._seed_array = jnp.asarray(self._cfg.seed, dtype=jnp.int32)
if self._cfg.multiseed is None:
assert self._seed_array.shape[0] == 1
self._seed_array = self._seed_array[0]
def _create_task(self, batch_size: int) -> Task:
batch_shape = (batch_size,)
if self._cfg.multiseed:
batch_shape = (len(self._cfg.seed), *batch_shape)
match self._cfg.task.subtype:
case 'QDaxBrax':
qdax_task_config = cast(QDaxBraxConfig, self._cfg.task)
task = QDaxBraxTask(qdax_task_config, batch_shape)
case 'Atari':
atari_task_config = cast(AtariConfig, self._cfg.task)
task = AtariTask(atari_task_config, batch_shape)
case _:
raise NotImplementedError(self._cfg.task.typ)
return task
def _wandb_init(self) -> None:
resume = self._wandb_resume_arg
original_dir = os.path.realpath(hydra.utils.get_original_cwd())
id_dict = {
'task': self._cfg.task.name,
'framework': self._cfg.framework.name,
'emitter': self._cfg.emitter.name,
'network': self._cfg.network.name,
'code': self._cfg.code,
'run': self._cfg.run,
'seed': '-'.join(map(str, self._cfg.seed)),
}
cfg_dict = OmegaConf.to_container(self._cfg)
assert isinstance(cfg_dict, dict)
wandb.init(
project='RefQD',
id='{emitter}.{network}.{framework}.{task}.{code}.{run}.{seed}'.format(**id_dict),
resume=resume,
name='{emitter} {network} {framework} {task} {code} {run} {seed}'.format(**id_dict),
config=cfg_dict,
tags=[
self._cfg.task.name,
self._cfg.framework.name,
self._cfg.emitter.name,
self._cfg.network.name,
],
job_type=self._cfg.typ,
dir=original_dir,
)
assert wandb.run is not None
with open('wandb-run-dir', 'a') as f:
f.write(f'{wandb.run.dir}\n')
@property
def _saved_keys(self) -> Sequence[str]:
match self._cfg.emitter.subtype:
case 'DQN-ME':
return (
*super()._saved_keys,
'history',
)
case _:
return (
*super()._saved_keys,
'$extended_me_repertoire.global_genotypes',
'$buffers.global_buffer_data',
'history',
)
@property
def globals(self) -> dict[str, Any]:
assert type(self) is Manager
return globals()
def _reduce(self, saved_dict: dict[str, Any]) -> dict[str, Any]:
saved_dict['state'] = saved_dict['state'].replace(
repertoire=saved_dict['state'].repertoire.replace(genotypes=None)
)
if hasattr(saved_dict['state'].repertoire, 'genotypes_depth'):
saved_dict['state'] = saved_dict['state'].replace(
repertoire=saved_dict['state'].repertoire.replace(genotypes_depth=None)
)
try:
del saved_dict['$extended_me_repertoire.global_genotypes']
del saved_dict['$buffers.global_buffer_data']
except KeyError:
pass
return saved_dict
def _do_init(self, seed: jax.Array) -> tuple[ManagerState, ManagerConstant, Metrics]:
random_key = jax.random.key(seed)
_log.info('Initializing the task...')
random_key, task_key = jax.random.split(random_key)
task_key, subkey = jax.random.split(task_key)
task_constant = self._task.get_constant(subkey)
if self._reeval_task is self._task:
reeval_task_constant = task_constant
else:
reeval_task_constant = self._reeval_task.get_constant(subkey)
del subkey
del task_key
_log.info('Initializing the network...')
random_key, network_key = jax.random.split(random_key)
match self._cfg.network.task_type:
case 'RL':
qdax_brax_net_config = cast(CNNPolicyNetConfig, self._cfg.network)
assert isinstance(self._task, RLTask)
fake_obs = jnp.zeros((self._task.observation_size,))
assert qdax_brax_net_config.conv_padding in ('SAME', 'VALID')
match self._cfg.network.emitter_type:
case 'Normal' | 'Share':
if not self._dry_run:
_log.info(
'Representation network:\n%s', self._representation_net.tabulate(
jax.random.key(0),
fake_obs,
compute_flops=True,
compute_vjp_flops=True,
),
)
network_key, subkey = jax.random.split(network_key)
(
representation, initial_representation_genotypes
) = self._representation_net.init_with_output(subkey, fake_obs)
del subkey
if not self._dry_run:
_log.info('Decision network:\n%s', self._net.tabulate(
jax.random.key(0),
representation,
compute_flops=True,
compute_vjp_flops=True,
))
network_key, subkey = jax.random.split(network_key)
keys = jax.random.split(subkey, self._cfg.emitter.env_batch_size)
del subkey
initial_genotypes = jax.vmap(self._net.init, in_axes=(0, None))(
keys, representation
)
del keys
case _:
raise NotImplementedError(self._cfg.network.emitter_type)
case _:
raise NotImplementedError(self._cfg.task.typ)
del network_key
_log.info('Initializing the emitter...')
random_key, emitter_key = jax.random.split(random_key)
match self._cfg.emitter.subtype:
case 'PGA-ME' | 'Ref-PGA-ME' | 'DQN-ME' | 'Ref-DQN-ME':
pass
case _:
raise NotImplementedError(self._cfg.emitter.name)
del emitter_key
_log.info('Initializing the QD framework...')
random_key, framework_key = jax.random.split(random_key)
match self._cfg.framework.name:
case 'ME':
me_config = cast(MEConfig, self._cfg.framework)
minbd, maxbd = self._task.behavior_descriptor_limits
centroids, framework_key = compute_cvt_centroids(
self._task.behavior_descriptor_length,
me_config.n_init_cvt_samples,
me_config.n_centroids,
minbd,
maxbd,
framework_key,
)
framework_key, subkey = jax.random.split(framework_key)
(
repertoire,
tmp_repertoire,
emitter_state,
metrics,
_,
) = self._qd.init(
init_representation_genotypes=initial_representation_genotypes,
init_decision_genotypes=initial_genotypes,
centroids=centroids,
random_key=subkey,
task_constant=task_constant,
using_representation=self._cfg.emitter.typ == 'Share',
repertoire_type=get_repertoire_type(self._cfg.emitter.repertoire_type),
repertoire_kwargs=self._cfg.emitter.repertoire_kwargs,
)
del subkey
case _:
raise NotImplementedError(self._cfg.framework.name)
del framework_key
metrics |= {
'seed': seed,
'n_loops': jnp.asarray(0, dtype=jnp.int32),
'n_iters': jnp.asarray(0, dtype=jnp.int32),
'n_evals': jnp.asarray(0, dtype=jnp.int32),
'n_steps': jnp.asarray(0, dtype=jnp.int32),
}
random_key, extra_key = jax.random.split(random_key)
extra_keys = jax.random.split(extra_key, 4)
return (
ManagerState(
random_key=random_key,
extra_keys=extra_keys,
repertoire=repertoire,
tmp_repertoire=tmp_repertoire,
emitter_state=emitter_state,
loop_completed=jnp.asarray(0, dtype=jnp.int32),
total_time=jnp.asarray(0.0, dtype=jnp.float32),
),
ManagerConstant(task_constant=task_constant, reeval_task_constant=reeval_task_constant),
metrics,
)
def _log(self, metrics: Metrics) -> None:
if len(metrics['n_loops'].shape) - (self._cfg.multiseed is not None) == 0:
metrics = tjnp.expand_dims(metrics, axis=0)
csv_list, last_dict = self._get_log_metrics(metrics)
if self._dry_run:
return
self._csv_logger.log(csv_list)
log_strs: list[str] = []
has_value = True
for key in self._csv_header:
if key in last_dict.keys():
assert has_value
else:
has_value = False
continue
metric = last_dict[key].item()
if metric.is_integer() or 1e4 <= metric < 1e9:
metric = round(metric)
prefix = ''.join(map(lambda s: s[0], key.split('_')))
style = '{}'
if 'qs' in prefix:
style = '[bright_red]{}[/bright_red]'
elif 't' in prefix:
style = '[dim]{}[/dim]'
if isinstance(metric, float) and metric < 1e4:
log_strs.append(style.format(f'{prefix} {metric:.5}'))
else:
log_strs.append(style.format(f'{prefix} {metric}'))
log_str = ' '.join(log_strs)
_log.info('%s', log_str)
loop_completed = self.state.loop_completed.flatten()[0]
if wandb.run is not None and loop_completed % self._reeval_period == 0:
try:
wandb.run.log(last_dict)
wandb.run.summary.update(last_dict)
except Exception:
pass
def _get_time_metrics(
self,
time_reeval: float,
shape: tuple[int, ...],
prefix: str,
) -> Metrics:
time_metrics: Metrics = {f'{prefix}_time': jnp.full(shape, time_reeval, dtype=jnp.float32)}
match self.state.repertoire:
case CPUMapElitesRepertoire():
time_metrics[f'{prefix}_time_repertoire_empty'] = jnp.full(
shape, extended_me_repertoire.global_time_empty, dtype=jnp.float32
)
time_metrics[f'{prefix}_time_repertoire_add'] = jnp.full(
shape, extended_me_repertoire.global_time_add, dtype=jnp.float32
)
time_metrics[f'{prefix}_time_repertoire_sample'] = jnp.full(
shape, extended_me_repertoire.global_time_sample, dtype=jnp.float32
)
extended_me_repertoire.global_time_empty = 0.0
extended_me_repertoire.global_time_add = 0.0
extended_me_repertoire.global_time_sample = 0.0
case CPUDepthRepertoire():
time_metrics[f'{prefix}_time_repertoire_empty'] = jnp.full(
shape, extended_depth_repertoire.global_time_empty, dtype=jnp.float32
)
time_metrics[f'{prefix}_time_repertoire_add'] = jnp.full(
shape, extended_depth_repertoire.global_time_add, dtype=jnp.float32
)
time_metrics[f'{prefix}_time_repertoire_sample'] = jnp.full(
shape, extended_depth_repertoire.global_time_sample, dtype=jnp.float32
)
time_metrics[f'{prefix}_time_repertoire_copy_from'] = jnp.full(
shape, extended_depth_repertoire.global_time_copy_from, dtype=jnp.float32
)
extended_depth_repertoire.global_time_empty = 0.0
extended_depth_repertoire.global_time_add = 0.0
extended_depth_repertoire.global_time_sample = 0.0
extended_depth_repertoire.global_time_copy_from = 0.0
case ExtendedMapElitesRepertoire() | ExtendedDepthRepertoire():
pass
case _:
raise NotImplementedError(type(self.state.repertoire))
match self._cfg.emitter.subtype:
case 'PGA-ME' | 'Ref-PGA-ME' | 'DQN-ME' | 'Ref-DQN-ME':
time_metrics[f'{prefix}_time_buffer_restore'] = jnp.full(
shape, buffers.global_time_restore, dtype=jnp.float32
)
time_metrics[f'{prefix}_time_buffer_insert'] = jnp.full(
shape, buffers.global_time_insert, dtype=jnp.float32
)
time_metrics[f'{prefix}_time_buffer_sample'] = jnp.full(
shape, buffers.global_time_sample, dtype=jnp.float32
)
buffers.global_time_restore = 0.0
buffers.global_time_insert = 0.0
buffers.global_time_sample = 0.0
case _:
raise NotImplementedError(self._cfg.emitter.name)
match self._task:
case QDaxBraxTask():
pass
case GymTask():
time_metrics[f'{prefix}_time_env_reset'] = jnp.full(
shape, gym_task.global_time_reset, dtype=jnp.float32
)
time_metrics[f'{prefix}_time_env_step'] = jnp.full(
shape, gym_task.global_time_step, dtype=jnp.float32
)
gym_task.global_time_reset = 0.0
gym_task.global_time_step = 0.0
case EnvPoolTask():
time_metrics[f'{prefix}_time_env_reset'] = jnp.full(
shape, envpool_task.global_time_reset, dtype=jnp.float32
)
time_metrics[f'{prefix}_time_env_step'] = jnp.full(
shape, envpool_task.global_time_step, dtype=jnp.float32
)
envpool_task.global_time_reset = 0.0
envpool_task.global_time_step = 0.0
case _:
raise NotImplementedError(self._cfg.task.subtype)
return time_metrics
def _get_total_time_metrics(
self,
time_per_loop: float | jax.Array,
total_time: float | jax.Array,
shape: tuple[int, ...],
) -> Metrics:
time_metrics: Metrics = {
'time_per_loop': jnp.full(shape, time_per_loop, dtype=jnp.float32),
'total_time': jnp.full(shape, total_time, dtype=jnp.float32),
}
return time_metrics
def _reeval(
self, state: ManagerState
) -> tuple[ManagerState, tuple[Metrics, Fitness, Descriptor]]:
match self._cfg.emitter.typ:
case 'Normal':
representation_params = {}
case 'Share':
emitter_state = state.emitter_state
while isinstance(
emitter_state, qdax.core.emitters.multi_emitter.MultiEmitterState
):
emitter_state = emitter_state.emitter_states[0]
assert isinstance(emitter_state, RefEmitterState)
if emitter_state.emitted_representation_params is not None:
representation_params = emitter_state.emitted_representation_params
else:
representation_params = emitter_state.representation_params
case _:
raise NotImplementedError(self._cfg.emitter.typ)
_, emitter_state, reeval_key, metrics, fitness, descriptor = self._map(self._qd.reeval)(
None,
state.emitter_state,
state.extra_keys[..., 0],
representation_params=representation_params,
repertoire=state.repertoire,
task_constant=self.constant.task_constant,
)
state = state.replace(
emitter_state=emitter_state, extra_keys=state.extra_keys.at[..., 0].set(reeval_key)
)
return state, (metrics, fitness, descriptor)
def reeval(
self, wrapper: Callable[[str], AbstractContextManager[Any]], shape: tuple[int, ...]
) -> tuple[Metrics, Fitness, Descriptor]:
if hasattr(self, '_reeval_fn'):
reeval_fn = self._reeval_fn
else:
if self._dry_run:
reeval_fn = jax_jit(self._reeval, donate_argnums=0)
else:
_log.info('Compiling reeval_fn...')
reeval_fn = jax_compiled(self._reeval, donate_argnums=0)(
self.state
)
cost_stats = reeval_fn.cost_analysis()
if (
isinstance(cost_stats, list) and len(cost_stats) == 1
and isinstance(cost_stats[0], dict)
):
cost_stats = [{
key: value
for key, value in sorted(cost_stats[0].items())
if ' {' not in key
}]
_log.info('Cost analysis of reeval_fn: %s', cost_stats)
_log.info(
'Memory analysis of reeval_fn: %s',
reeval_fn.memory_analysis(),
)
self._reeval_fn = reeval_fn
with wrapper('tensorboard'):
time_reeval = time.monotonic()
(
self.state, (metrics, reeval_fitness, reeval_descriptor)
) = reeval_fn(self.state)
time_reeval = time.monotonic() - time_reeval
metrics = {key: jnp.full(shape, value) for key, value in metrics.items()}
metrics |= self._get_time_metrics(
time_reeval,
shape,
'reeval',
)
reeval_fitness = reeval_fitness[jnp.newaxis]
reeval_descriptor = reeval_descriptor[jnp.newaxis]
return metrics, reeval_fitness, reeval_descriptor
@override
def init(self) -> None:
if self._cfg.n_profile < 0:
wrapper = jax.profiler.trace
else:
wrapper = fake_wrap
do_init_fn = jax_jit(self._map(self._do_init))
with wrapper('tensorboard'):
time_do_init = time.monotonic()
self.state, self.constant, metrics = do_init_fn(self._seed_array)
time_do_init = time.monotonic() - time_do_init
time_metrics = self._get_time_metrics(time_do_init, metrics['n_loops'].shape, 'opt')
fitnesses = self.state.repertoire.fitnesses[jnp.newaxis]
descriptors = self.state.repertoire.descriptors[jnp.newaxis]
_log.info('Reevaluating...')
(
reeval_metrics, reeval_fitness, reeval_descriptor
) = self.reeval(wrapper, metrics['n_loops'].shape)
_log.info('Logging...')
metrics |= self._get_total_time_metrics(
time_do_init, self.state.total_time, metrics['n_loops'].shape,
)
metrics |= time_metrics | reeval_metrics
time_metrics_keys = tuple(key[4:] for key in time_metrics.keys())
self._csv_header = (
'seed',
'n_loops',
'n_iters',
'n_evals',
'n_steps',
'qd_score',
'max_fitness',
'coverage',
'min_fitness',
'mean_fitness',
'time_per_loop',
'total_time',
*(f'opt_{key}' for key in time_metrics_keys),
'reeval_qd_score',
'reeval_max_fitness',
'reeval_coverage',
'reeval_min_fitness',
'reeval_mean_fitness',
*(f'reeval_{key}' for key in time_metrics_keys),
)
assert all(key in self._csv_header for key in metrics.keys())
assert all(key in metrics.keys() for key in self._csv_header)
def get_log_metrics(
metrics: Metrics
) -> tuple[list[Metrics], Metrics]:
sorted_metrics: Metrics = {}
for key in self._csv_header:
if key in metrics.keys():
sorted_metrics[key] = metrics[key]
csv_list = transpose_dict_of_list(tjnp.reshape(sorted_metrics, newshape=(-1,)))
last_dict = tchain(tjnp.getitem, tjnp.mean)(sorted_metrics, -1)
return csv_list, last_dict
self._get_log_metrics = jax_jit(get_log_metrics)
if self._dry_run:
self.history = {}
else:
self.history = {
'fitnesses': [np.asarray(fitnesses)],
'descriptors': [np.asarray(descriptors)],
'reeval_fitnesses': [np.asarray(reeval_fitness)],
'reeval_descriptors': [np.asarray(reeval_descriptor)],
}
self._reeval_period = self._cfg.task.reeval_period * self._cfg.emitter.reeval_factor
match self._cfg.task.typ:
case 'RL':
rl_task_config = cast(RLTaskConfig, self._cfg.task)
if self._cfg.emitter.refresh_depth != 0:
reeval_factor = self._qd.calc_evals_per_reeval(self.state.repertoire)
else:
reeval_factor = 0
evals_per_reeval = (
self._cfg.emitter.env_batch_size
* rl_task_config.log_period
* self._reeval_period
+ reeval_factor
)
total_reevals = (
rl_task_config.total_steps / evals_per_reeval / rl_task_config.episode_len
)
case _:
raise NotImplementedError(self._cfg.task.typ)
if not total_reevals.is_integer():
_log.warning(f'total_reevals ({total_reevals}) is not an integer.')
total_reevals = math.ceil(total_reevals)
self._total_loops = total_reevals * self._reeval_period
self._total_evals = total_reevals * evals_per_reeval
if self._dry_run:
return
self._csv_logger = CSVLogger(self._cfg.metrics_filename, header=self._csv_header)
_log.info('%s', ' '.join(self._csv_header))
with open(self._cfg.metrics_filename, 'r') as f:
n_lines = len(f.readlines())
writing_initial_log = n_lines <= 1
if writing_initial_log:
self._log(metrics)
if self._resumed:
self.load()
def _map(self, func: _CallableT, in_axes: int | None | Sequence[Any] = 0) -> _CallableT:
match self._cfg.multiseed:
case None:
return func
case 'vmap':
return jax.vmap(func, in_axes=in_axes)
case _:
raise NotImplementedError(self._cfg.multiseed)
def _calc_n(self, state: ManagerState) -> tuple[jax.Array, jax.Array, jax.Array]:
loop_completed = state.loop_completed.flatten()[0]
if isinstance(loop_completed, jax.Array):
xnp = jnp
else:
xnp = np
n_loops = xnp.full((self._cfg.task.log_period,), loop_completed, dtype=xnp.int32)
stop_n_iter = loop_completed * self._cfg.task.log_period + 1
n_iter = xnp.arange(-self._cfg.task.log_period, 0) + stop_n_iter
n_evals = n_iter * self._cfg.emitter.env_batch_size
if self._cfg.emitter.refresh_depth != 0:
reeval_factor = self._qd.calc_evals_per_reeval(state.repertoire)
n_previous_reevals = (
xnp.floor((loop_completed - 1) / self._reeval_period).astype(xnp.int32)
* reeval_factor
)
n_reevals = (
xnp.floor((loop_completed) / self._reeval_period).astype(xnp.int32)
* reeval_factor
)
n_evals += n_previous_reevals
if isinstance(n_evals, jax.Array):
n_evals = n_evals.at[-1].add(n_reevals - n_previous_reevals)
else:
n_evals[-1] += n_reevals - n_previous_reevals
return n_loops, n_iter, n_evals # type: ignore
def _optimize(
self, state: ManagerState, refresh_repertoire: jax.Array
) -> tuple[ManagerState, tuple[Metrics, Metrics, Fitness, Descriptor, Fitness, Descriptor]]:
update_fn = self._map(self._qd.scan_update, in_axes=(0, None))
(
(repertoire, tmp_repertoire, emitter_state, random_key, _),
(metrics, reeval_metrics, fitnesses, descriptors, reeval_fitness, reeval_descriptor),
) = lax_scan(
update_fn,
(
state.repertoire,
state.tmp_repertoire,
state.emitter_state,
state.random_key,
self.constant.task_constant,
),
jnp.full(
(self._cfg.task.log_period,), False, dtype=bool
).at[-1].set(refresh_repertoire),
length=self._cfg.task.log_period,
)
state = state.replace(
repertoire=repertoire,
tmp_repertoire=tmp_repertoire,
emitter_state=emitter_state,
random_key=random_key,
loop_completed=state.loop_completed + 1,
)
n_loops, n_iter, n_evals = self._calc_n(state)
d = {
'n_loops': n_loops,
'n_iters': n_iter,
'n_evals': n_evals,
'n_steps': n_evals * self._step_multiplier,
}
if len(self._seed_array.shape) > 0:
d = tjnp.duplicate(d, repeats=self._seed_array.shape[-1], axis=-1)
metrics.update(d)
metrics['seed'] = F.duplicate(
self._seed_array, repeats=self._cfg.task.log_period, axis=0
)
reeval_fitness = reeval_fitness[:1]
reeval_descriptor = reeval_descriptor[:1]
return (
state,
(metrics, reeval_metrics, fitnesses, descriptors, reeval_fitness, reeval_descriptor),
)
@override
def run(self) -> None:
if self._dry_run:
optimize_fn = jax_jit(self._optimize, donate_argnums=0)
self.state = self.state.replace(loop_completed=np.full(
self.state.loop_completed.shape,
self._total_loops - 1,
self.state.loop_completed.dtype,
))
else:
_log.info('Compiling optimize_fn...')
optimize_fn = jax_compiled(self._optimize, donate_argnums=0)(
self.state, jnp.asarray(False)
)
cost_stats = optimize_fn.cost_analysis()
if (
isinstance(cost_stats, list) and len(cost_stats) == 1
and isinstance(cost_stats[0], dict)
):
cost_stats = [
{key: value for key, value in sorted(cost_stats[0].items()) if ' {' not in key}
]
_log.info('Cost analysis of optimize_fn: %s', cost_stats)
_log.info('Memory analysis of optimize_fn: %s', optimize_fn.memory_analysis())
desc_dict = {
'task': self._cfg.task.name,
'framework': self._cfg.framework.name,
'emitter': self._cfg.emitter.name,
'run': self._cfg.run,
}
desc = rich_highlight(
'\\[[purple]{task}/{framework}/{emitter}[/purple]|[purple]{run}[/purple]]'
.format(**desc_dict)
)
time_per_loop_ema = PeriodicEMA(period=self._reeval_period)
setattr(tqdm.std, 'EMA', partial(PeriodicEMA, period=self._reeval_period))
wrapper = jax.profiler.trace
n_profile = int(self._cfg.n_profile - self.state.loop_completed.flatten()[0])
with tqdm.tqdm(
initial=int(self._calc_n(self.state)[2].flatten()[-1]),
total=self._total_evals,
dynamic_ncols=True,