[RLLib] Make movement of tensors to device only happen once

rllib/core/learner/learner.py

@@ -765,14 +765,14 @@ def get_parameters(self, module: RLModule) -> Sequence[Param]:
  """
 
  @abc.abstractmethod
- def _convert_batch_type(self, batch: MultiAgentBatch) -> NestedDict[TensorType]:
- """Converts a MultiAgentBatch to a NestedDict of Tensors on the correct device.
+ def _convert_batch_type(self, batch: MultiAgentBatch) -> MultiAgentBatch:
+ """Converts the elements of a MultiAgentBatch to Tensors on the correct device.
 
  Args:
  batch: The MultiAgentBatch object to convert.
 
  Returns:
- The resulting NestedDict with framework-specific tensor values placed
+ The resulting MultiAgentBatch with framework-specific tensor values placed
  on the correct device.
  """
 
@@ -1143,19 +1143,22 @@ def update(
  batch_iter = MiniBatchDummyIterator
 
  results = []
- for minibatch in batch_iter(batch, minibatch_size, num_iters):
- # Convert minibatch into a tensor batch (NestedDict).
- tensor_minibatch = self._convert_batch_type(minibatch)
+ # Convert minibatch into a tensor batch (NestedDict) on the correct device
+ # (e.g. GPU). We mov the batch to device here to avoid moving ever minibatch
+ # that is created in the `batch_iter` below.
+ batch = self._convert_batch_type(batch)
+ for tensor_minibatch in batch_iter(batch, minibatch_size, num_iters):
  # Make the actual in-graph/traced `_update` call. This should return
  # all tensor values (no numpy).
+ nested_tensor_minibatch = NestedDict(tensor_minibatch.policy_batches)
  (
  fwd_out,
  loss_per_module,
  metrics_per_module,
- ) = self._update(tensor_minibatch)
+ ) = self._update(nested_tensor_minibatch)
 
  result = self.compile_results(
- batch=minibatch,
+ batch=tensor_minibatch,
  fwd_out=fwd_out,
  loss_per_module=loss_per_module,
  metrics_per_module=defaultdict(dict, **metrics_per_module),

rllib/core/learner/tf/tf_learner.py

@@ -316,9 +316,10 @@ def _check_registered_optimizer(
  )
 
  @override(Learner)
- def _convert_batch_type(self, batch: MultiAgentBatch) -> NestedDict[TensorType]:
+ def _convert_batch_type(self, batch: MultiAgentBatch) -> MultiAgentBatch:
  batch = _convert_to_tf(batch.policy_batches)
- batch = NestedDict(batch)
+ length = max(len(b) for b in batch.values())
+ batch = MultiAgentBatch(batch, env_steps=length)
  return batch
 
  @override(Learner)

rllib/core/learner/torch/torch_learner.py

@@ -185,9 +185,10 @@ def get_parameters(self, module: RLModule) -> Sequence[Param]:
  return list(module.parameters())
 
  @override(Learner)
- def _convert_batch_type(self, batch: MultiAgentBatch):
+ def _convert_batch_type(self, batch: MultiAgentBatch) -> MultiAgentBatch:
  batch = convert_to_torch_tensor(batch.policy_batches, device=self._device)
- batch = NestedDict(batch)
+ length = max(len(b) for b in batch.values())
+ batch = MultiAgentBatch(batch, env_steps=length)
  return batch
 
  @override(Learner)

rllib/examples/learner/ppo_tuner.py

@@ -41,9 +41,7 @@ def _parse_args():
 
  config = (
  PPOConfig()
- .framework(args.framework)
- .training(_enable_learner_api=True)
- .rl_module(_enable_rl_module_api=True)
+ .framework(args.framework, eager_tracing=True)
  .environment("CartPole-v1")
  .resources(**RESOURCE_CONFIG[args.config])
  )

rllib/policy/sample_batch.py

@@ -1,4 +1,5 @@
 import collections
+from functools import partial
 import numpy as np
 import sys
 import itertools
@@ -1532,11 +1533,14 @@ def concat_samples(samples: List[SampleBatchType]) -> SampleBatchType:
  try:
  if k == "infos":
  concatd_data[k] = _concat_values(
- *[s[k] for s in concated_samples], time_major=time_major
+ *[s[k] for s in concated_samples],
+ time_major=time_major,
  )
  else:
+ values_to_concat = [c[k] for c in concated_samples]
+ _concat_values_w_time = partial(_concat_values, time_major=time_major)
  concatd_data[k] = tree.map_structure(
- _concat_values, *[c[k] for c in concated_samples]
+ _concat_values_w_time, *values_to_concat
  )
  except RuntimeError as e:
  # This should catch torch errors that occur when concatenating
@@ -1631,7 +1635,22 @@ def _concat_values(*values, time_major=None) -> TensorType:
  time_major: Whether to concatenate along the first axis
  (time_major=False) or the second axis (time_major=True).
  """
- return np.concatenate(list(values), axis=1 if time_major else 0)
+ if torch and torch.is_tensor(values[0]):
+ return torch.cat(values, dim=1 if time_major else 0)
+ elif isinstance(values[0], np.ndarray):
+ return np.concatenate(values, axis=1 if time_major else 0)
+ elif tf and tf.is_tensor(values[0]):
+ return tf.concat(values, axis=1 if time_major else 0)
+ elif isinstance(values[0], list):
+ concatenated_list = []
+ for sublist in values:
+ concatenated_list.extend(sublist)
+ return concatenated_list
+ else:
+ raise ValueError(
+ f"Unsupported type for concatenation: {type(values[0])} "
+ f"first element: {values[0]}"
+ )
 
 
 @DeveloperAPI

rllib/utils/minibatch_utils.py

@@ -54,8 +54,8 @@ def __init__(
  self._num_covered_epochs = {mid: 0 for mid in batch.policy_batches.keys()}
 
  def __iter__(self):
-
  while min(self._num_covered_epochs.values()) < self._num_iters:
+
  minibatch = {}
  for module_id, module_batch in self._batch.policy_batches.items():
 
@@ -90,7 +90,8 @@ def __iter__(self):
  # TODO (Kourosh): len(batch) is not correct here. However it's also not
  # clear what the correct value should be. Since training does not depend on
  # this it will be fine for now.
- minibatch = MultiAgentBatch(minibatch, len(self._batch))
+ length = max(len(b) for b in minibatch.values())
+ minibatch = MultiAgentBatch(minibatch, length)
  yield minibatch