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[RLlib] Fix AlphaStar for tf2+tracing; smaller cleanups around avoiding to wrap a TFPolicy as_eager() or with_tracing more than once. #24271

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Apr 28, 2022
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[RLlib] Fix AlphaStar for tf2+tracing; smaller cleanups around avoiding to wrap a TFPolicy as_eager() or with_tracing more than once. #24271

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Merge branch 'master' of https://github.com/ray-project/ray into chec…
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9 changes: 7 additions & 2 deletions rllib/policy/eager_tf_policy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -250,6 +250,11 @@ def compute_gradients(self, loss, var_list):
return list(zip(self.tape.gradient(loss, var_list), var_list))


class EagerTFPolicy(Policy):
"""Dummy class to recognize any eagerized TFPolicy by its inheritance."""
pass


def build_eager_tf_policy(
name,
loss_fn,
Expand Down Expand Up @@ -309,7 +314,7 @@ def __init__(self, observation_space, action_space, config):
if not tf1.executing_eagerly():
tf1.enable_eager_execution()
self.framework = config.get("framework", "tfe")
Policy.__init__(self, observation_space, action_space, config)
EagerTFPolicy.__init__(self, observation_space, action_space, config)

# Global timestep should be a tensor.
self.global_timestep = tf.Variable(0, trainable=False, dtype=tf.int64)
Expand Down Expand Up @@ -594,7 +599,7 @@ def postprocess_trajectory(
):
assert tf.executing_eagerly()
# Call super's postprocess_trajectory first.
sample_batch = Policy.postprocess_trajectory(self, sample_batch)
sample_batch = EagerTFPolicy.postprocess_trajectory(self, sample_batch)
if postprocess_fn:
return postprocess_fn(self, sample_batch, other_agent_batches, episode)
return sample_batch
Expand Down
6 changes: 4 additions & 2 deletions rllib/utils/tf_utils.py
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Expand Up @@ -240,9 +240,11 @@ class for.
assert tf1.executing_eagerly()

from ray.rllib.policy.tf_policy import TFPolicy
from ray.rllib.policy.eager_tf_policy import EagerTFPolicy

# Create eager-class.
if hasattr(orig_cls, "as_eager"):
# Create eager-class (if not already one).
if hasattr(orig_cls, "as_eager") and \
not issubclass(orig_cls, EagerTFPolicy):
cls = orig_cls.as_eager()
if config.get("eager_tracing"):
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per our offline message, I think the real fix should actually be to pull this

if config.get("eager_tracing"): cls = cls.with_tracing()

block out of as_eager block.
so we will try to enable tracing regardless if we as_eager() here, or somewhere else.
just FYI.

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You are right. Thanks for the catch.

cls = cls.with_tracing()
Expand Down