def __init__(
self,
networks: impala_networks.IMPALANetworks,
iterator: Iterator[reverb.ReplaySample],
optimizer: optax.GradientTransformation,
random_key: networks_lib.PRNGKey,
discount: float = 0.99,
entropy_cost: float = 0.,
baseline_cost: float = 1.,
max_abs_reward: float = np.inf,
counter: Optional[counting.Counter] = None,
logger: Optional[loggers.Logger] = None,
devices: Optional[Sequence[jax.xla.Device]] = None,
prefetch_size: int = 2,
num_prefetch_threads: Optional[int] = None,
):
local_devices = jax.local_devices()
process_id = jax.process_index()
logging.info('Learner process id: %s. Devices passed: %s', process_id,
devices)
logging.info('Learner process id: %s. Local devices from JAX API: %s',
process_id, local_devices)
self._devices = devices or local_devices
self._local_devices = [d for d in self._devices if d in local_devices]
loss_fn = losses.impala_loss(networks.unroll_fn,
discount=discount,
max_abs_reward=max_abs_reward,
baseline_cost=baseline_cost,
entropy_cost=entropy_cost)
@jax.jit
def sgd_step(
state: TrainingState, sample: reverb.ReplaySample
) -> Tuple[TrainingState, Dict[str, jnp.ndarray]]:
"""Computes an SGD step, returning new state and metrics for logging."""
# Compute gradients.
grad_fn = jax.value_and_grad(loss_fn)
loss_value, gradients = grad_fn(state.params, sample)
# Average gradients over pmap replicas before optimizer update.
gradients = jax.lax.pmean(gradients, _PMAP_AXIS_NAME)
# Apply updates.
updates, new_opt_state = optimizer.update(gradients,
state.opt_state)
new_params = optax.apply_updates(state.params, updates)
metrics = {
'loss': loss_value,
'param_norm': optax.global_norm(new_params),
'param_updates_norm': optax.global_norm(updates),
}
new_state = TrainingState(params=new_params,
opt_state=new_opt_state)
return new_state, metrics
def make_initial_state(key: jnp.ndarray) -> TrainingState:
"""Initialises the training state (parameters and optimiser state)."""
key, key_initial_state = jax.random.split(key)
# Note: parameters do not depend on the batch size, so initial_state below
# does not need a batch dimension.
params = networks.initial_state_init_fn(key_initial_state)
# TODO(jferret): as it stands, we do not yet support
# training the initial state params.
initial_state = networks.initial_state_fn(params)
initial_params = networks.unroll_init_fn(key, initial_state)
initial_opt_state = optimizer.init(initial_params)
return TrainingState(params=initial_params,
opt_state=initial_opt_state)
# Initialise training state (parameters and optimiser state).
state = make_initial_state(random_key)
self._state = utils.replicate_in_all_devices(state,
self._local_devices)
if num_prefetch_threads is None:
num_prefetch_threads = len(self._local_devices)
self._prefetched_iterator = utils.sharded_prefetch(
iterator,
buffer_size=prefetch_size,
devices=self._local_devices,
num_threads=num_prefetch_threads,
)
self._sgd_step = jax.pmap(sgd_step,
axis_name=_PMAP_AXIS_NAME,
devices=self._devices)
# Set up logging/counting.
self._counter = counter or counting.Counter()
self._logger = logger or loggers.make_default_logger('learner')
def __init__(
self,
obs_spec: specs.Array,
unroll_fn: networks_lib.PolicyValueRNN,
initial_state_fn: Callable[[], hk.LSTMState],
iterator: Iterator[reverb.ReplaySample],
optimizer: optax.GradientTransformation,
random_key: networks_lib.PRNGKey,
discount: float = 0.99,
entropy_cost: float = 0.,
baseline_cost: float = 1.,
max_abs_reward: float = np.inf,
counter: counting.Counter = None,
logger: loggers.Logger = None,
devices: Optional[Sequence[jax.xla.Device]] = None,
prefetch_size: int = 2,
num_prefetch_threads: Optional[int] = None,
):
self._devices = devices or jax.local_devices()
# Transform into pure functions.
unroll_fn = hk.without_apply_rng(hk.transform(unroll_fn, apply_rng=True))
initial_state_fn = hk.without_apply_rng(
hk.transform(initial_state_fn, apply_rng=True))
loss_fn = losses.impala_loss(
unroll_fn,
discount=discount,
max_abs_reward=max_abs_reward,
baseline_cost=baseline_cost,
entropy_cost=entropy_cost)
@jax.jit
def sgd_step(
state: TrainingState, sample: reverb.ReplaySample
) -> Tuple[TrainingState, Dict[str, jnp.ndarray]]:
"""Computes an SGD step, returning new state and metrics for logging."""
# Compute gradients.
grad_fn = jax.value_and_grad(loss_fn)
loss_value, gradients = grad_fn(state.params, sample)
# Average gradients over pmap replicas before optimizer update.
gradients = jax.lax.pmean(gradients, _PMAP_AXIS_NAME)
# Apply updates.
updates, new_opt_state = optimizer.update(gradients, state.opt_state)
new_params = optax.apply_updates(state.params, updates)
metrics = {
'loss': loss_value,
}
new_state = TrainingState(params=new_params, opt_state=new_opt_state)
return new_state, metrics
def make_initial_state(key: jnp.ndarray) -> TrainingState:
"""Initialises the training state (parameters and optimiser state)."""
dummy_obs = utils.zeros_like(obs_spec)
dummy_obs = utils.add_batch_dim(dummy_obs) # Dummy 'sequence' dim.
initial_state = initial_state_fn.apply(None)
initial_params = unroll_fn.init(key, dummy_obs, initial_state)
initial_opt_state = optimizer.init(initial_params)
return TrainingState(params=initial_params, opt_state=initial_opt_state)
# Initialise training state (parameters and optimiser state).
state = make_initial_state(random_key)
self._state = utils.replicate_in_all_devices(state, self._devices)
if num_prefetch_threads is None:
num_prefetch_threads = len(self._devices)
self._prefetched_iterator = utils.sharded_prefetch(
iterator,
buffer_size=prefetch_size,
devices=devices,
num_threads=num_prefetch_threads,
)
self._sgd_step = jax.pmap(
sgd_step, axis_name=_PMAP_AXIS_NAME, devices=self._devices)
# Set up logging/counting.
self._counter = counter or counting.Counter()
self._logger = logger or loggers.make_default_logger('learner')
def __init__(
self,
obs_spec: specs.Array,
unroll_fn: networks.PolicyValueRNN,
initial_state_fn: Callable[[], hk.LSTMState],
iterator: Iterator[reverb.ReplaySample],
optimizer: optax.GradientTransformation,
rng: hk.PRNGSequence,
discount: float = 0.99,
entropy_cost: float = 0.,
baseline_cost: float = 1.,
max_abs_reward: float = np.inf,
counter: counting.Counter = None,
logger: loggers.Logger = None,
):
# Transform into pure functions.
unroll_fn = hk.without_apply_rng(
hk.transform(unroll_fn, apply_rng=True))
initial_state_fn = hk.without_apply_rng(
hk.transform(initial_state_fn, apply_rng=True))
loss_fn = losses.impala_loss(unroll_fn,
discount=discount,
max_abs_reward=max_abs_reward,
baseline_cost=baseline_cost,
entropy_cost=entropy_cost)
@jax.jit
def sgd_step(
state: TrainingState, sample: reverb.ReplaySample
) -> Tuple[TrainingState, Dict[str, jnp.ndarray]]:
"""Computes an SGD step, returning new state and metrics for logging."""
# Compute gradients.
grad_fn = jax.value_and_grad(loss_fn)
loss_value, gradients = grad_fn(state.params, sample)
# Apply updates.
updates, new_opt_state = optimizer.update(gradients,
state.opt_state)
new_params = optax.apply_updates(state.params, updates)
metrics = {
'loss': loss_value,
}
new_state = TrainingState(params=new_params,
opt_state=new_opt_state)
return new_state, metrics
def make_initial_state(key: jnp.ndarray) -> TrainingState:
"""Initialises the training state (parameters and optimiser state)."""
dummy_obs = utils.zeros_like(obs_spec)
dummy_obs = utils.add_batch_dim(dummy_obs) # Dummy 'sequence' dim.
initial_state = initial_state_fn.apply(None)
initial_params = unroll_fn.init(key, dummy_obs, initial_state)
initial_opt_state = optimizer.init(initial_params)
return TrainingState(params=initial_params,
opt_state=initial_opt_state)
# Initialise training state (parameters and optimiser state).
self._state = make_initial_state(next(rng))
# Internalise iterator.
self._iterator = iterator
self._sgd_step = sgd_step
# Set up logging/counting.
self._counter = counter or counting.Counter()
self._logger = logger or loggers.make_default_logger('learner')
