[RLlib] Issue #9437 (PyTorch converts to CPU tensor, even if on GPU).

rllib/agents/dqn/dqn_tf_policy.py

@@ -12,6 +12,7 @@
 from ray.rllib.policy.tf_policy_template import build_tf_policy
 from ray.rllib.utils.error import UnsupportedSpaceException
 from ray.rllib.utils.exploration import ParameterNoise
+from ray.rllib.utils.numpy import convert_to_numpy
 from ray.rllib.utils.framework import try_import_tf
 from ray.rllib.utils.tf_ops import huber_loss, reduce_mean_ignore_inf, \
     minimize_and_clip
@@ -378,7 +379,8 @@ def postprocess_nstep_and_prio(policy, batch, other_agent=None, episode=None):
             batch[SampleBatch.REWARDS], batch[SampleBatch.NEXT_OBS],
             batch[SampleBatch.DONES], batch[PRIO_WEIGHTS])
         new_priorities = (
-            np.abs(td_errors) + policy.config["prioritized_replay_eps"])
+            np.abs(convert_to_numpy(td_errors)) +
+            policy.config["prioritized_replay_eps"])
         batch.data[PRIO_WEIGHTS] = new_priorities
 
     return batch

rllib/agents/es/es_torch_policy.py

@@ -65,7 +65,7 @@ def _compute_actions(policy,
         observation = policy.observation_filter(
             observation[None], update=update)
 
-        observation = convert_to_torch_tensor(observation)
+        observation = convert_to_torch_tensor(observation, policy.device)
         dist_inputs, _ = policy.model({
             SampleBatch.CUR_OBS: observation
         }, [], None)

rllib/agents/ppo/ppo_torch_policy.py

@@ -188,15 +188,16 @@ def __init__(self, obs_space, action_space, config):
             def value(ob, prev_action, prev_reward, *state):
                 model_out, _ = self.model({
                     SampleBatch.CUR_OBS: convert_to_torch_tensor(
-                        np.asarray([ob])),
+                        np.asarray([ob]), self.device),
                     SampleBatch.PREV_ACTIONS: convert_to_torch_tensor(
-                        np.asarray([prev_action])),
+                        np.asarray([prev_action]), self.device),
                     SampleBatch.PREV_REWARDS: convert_to_torch_tensor(
-                        np.asarray([prev_reward])),
+                        np.asarray([prev_reward]), self.device),
                     "is_training": False,
-                }, [convert_to_torch_tensor(np.asarray([s])) for s in state],
-                                          convert_to_torch_tensor(
-                                              np.asarray([1])))
+                }, [convert_to_torch_tensor(np.asarray([s]), self.device) for
+                    s in state],
+                    convert_to_torch_tensor(
+                        np.asarray([1]), self.device))
                 return self.model.value_function()[0]
 
         else:

rllib/examples/centralized_critic.py

@@ -83,10 +83,13 @@ def centralized_critic_postprocessing(policy,
         # overwrite default VF prediction with the central VF
         if args.torch:
             sample_batch[SampleBatch.VF_PREDS] = policy.compute_central_vf(
-                convert_to_torch_tensor(sample_batch[SampleBatch.CUR_OBS]),
-                convert_to_torch_tensor(sample_batch[OPPONENT_OBS]),
-                convert_to_torch_tensor(sample_batch[OPPONENT_ACTION])). \
-                detach().numpy()
+                convert_to_torch_tensor(
+                    sample_batch[SampleBatch.CUR_OBS], policy.device),
+                convert_to_torch_tensor(
+                    sample_batch[OPPONENT_OBS], policy.device),
+                convert_to_torch_tensor(
+                    sample_batch[OPPONENT_ACTION], policy.device)) \
+                .detach().numpy()
         else:
             sample_batch[SampleBatch.VF_PREDS] = policy.compute_central_vf(
                 sample_batch[SampleBatch.CUR_OBS], sample_batch[OPPONENT_OBS],

rllib/models/tf/tf_action_dist.py

@@ -178,12 +178,13 @@ def __init__(self, inputs, model=None, temperature=1.0):
         assert temperature >= 0.0
         self.dist = tfp.distributions.RelaxedOneHotCategorical(
             temperature=temperature, logits=inputs)
+        self.probs = tf.nn.softmax(self.dist._distribution.logits)
         super().__init__(inputs, model)
 
     @override(ActionDistribution)
     def deterministic_sample(self):
         # Return the dist object's prob values.
-        return self.dist._distribution.probs
+        return self.probs
 
     @override(ActionDistribution)
     def logp(self, x):

rllib/policy/torch_policy.py

@@ -149,7 +149,8 @@ def compute_actions(
                 input_dict[SampleBatch.PREV_REWARDS] = \
                     np.asarray(prev_reward_batch)
             state_batches = [
-                convert_to_torch_tensor(s) for s in (state_batches or [])
+                convert_to_torch_tensor(s, self.device)
+                for s in (state_batches or [])
             ]
             actions, state_out, extra_fetches, logp = \
                 self._compute_action_helper(
@@ -556,7 +557,8 @@ def import_model_from_h5(self, import_file: str) -> None:
 
     def _lazy_tensor_dict(self, postprocessed_batch):
         train_batch = UsageTrackingDict(postprocessed_batch)
-        train_batch.set_get_interceptor(convert_to_torch_tensor)
+        train_batch.set_get_interceptor(functools.partial(
+            convert_to_torch_tensor, device=self.device))
         return train_batch
 
 

rllib/utils/exploration/per_worker_epsilon_greedy.py

@@ -28,8 +28,10 @@ def __init__(self, action_space, *, framework, num_workers, worker_index,
             if worker_index > 0:
                 # From page 5 of https://arxiv.org/pdf/1803.00933.pdf
                 alpha, eps, i = 7, 0.4, worker_index - 1
+                num_workers_minus_1 = float(num_workers - 1) \
+                    if num_workers > 1 else 1.0
                 epsilon_schedule = ConstantSchedule(
-                    eps**(1 + i / float(num_workers - 1) * alpha),
+                    eps**(1 + (i / num_workers_minus_1) * alpha),
                     framework=framework)
             # Local worker should have zero exploration so that eval
             # rollouts run properly.

rllib/utils/exploration/per_worker_gaussian_noise.py

@@ -24,7 +24,9 @@ def __init__(self, action_space, *, framework, num_workers, worker_index,
         # Use a fixed, different epsilon per worker. See: Ape-X paper.
         if num_workers > 0:
             if worker_index > 0:
-                exponent = (1 + worker_index / float(num_workers - 1) * 7)
+                num_workers_minus_1 = float(num_workers - 1) \
+                    if num_workers > 1 else 1.0
+                exponent = (1 + (worker_index / num_workers_minus_1) * 7)
                 scale_schedule = ConstantSchedule(
                     0.4**exponent, framework=framework)
             # Local worker should have zero exploration so that eval

rllib/utils/exploration/per_worker_ornstein_uhlenbeck_noise.py

@@ -25,7 +25,9 @@ def __init__(self, action_space, *, framework, num_workers, worker_index,
         # Use a fixed, different epsilon per worker. See: Ape-X paper.
         if num_workers > 0:
             if worker_index > 0:
-                exponent = (1 + worker_index / float(num_workers - 1) * 7)
+                num_workers_minus_1 = float(num_workers - 1) \
+                    if num_workers > 1 else 1.0
+                exponent = (1 + (worker_index / num_workers_minus_1) * 7)
                 scale_schedule = ConstantSchedule(
                     0.4**exponent, framework=framework)
             # Local worker should have zero exploration so that eval

rllib/utils/torch_ops.py

@@ -110,10 +110,10 @@ def mapping(item):
     return tree.map_structure(mapping, stats)
 
 
-def convert_to_torch_tensor(stats, device=None):
+def convert_to_torch_tensor(x, device=None):
     """Converts any struct to torch.Tensors.
 
-    stats (any): Any (possibly nested) struct, the values in which will be
+    x (any): Any (possibly nested) struct, the values in which will be
         converted and returned as a new struct with all leaves converted
         to torch tensors.
 
@@ -137,7 +137,7 @@ def mapping(item):
             tensor = tensor.float()
         return tensor if device is None else tensor.to(device)
 
-    return tree.map_structure(mapping, stats)
+    return tree.map_structure(mapping, x)
 
 
 def atanh(x):