Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[Feature] RNG for RBs #2379

Merged
merged 4 commits into from
Aug 8, 2024
Merged

[Feature] RNG for RBs #2379

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
f28d76d
init
vmoens Aug 7, 2024
c96e75c
amend
vmoens Aug 8, 2024
aa06172
amend
vmoens Aug 8, 2024
1fe487e
amend
vmoens Aug 8, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
amend
  • Loading branch information
@vmoens
vmoens committed Aug 8, 2024
commit aa06172f9e378fd016faf762eb7c4aac905fbb48
29 changes: 29 additions & 0 deletions test/test_rb.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1232,6 +1232,35 @@ def test_slice_rng(self):
c = rb.sample(32)
assert (a != c).any()

def test_rng_state_dict(self):
state = torch.random.get_rng_state()
rb = ReplayBufferRNG(sampler=RandomSampler(), storage=LazyTensorStorage(100))
rb.extend(torch.arange(100))
rb._rng.set_state(state)
sd = rb.state_dict()
assert sd.get("_rng") is not None
a = rb.sample(32)

rb.load_state_dict(sd)
b = rb.sample(32)
assert (a == b).all()
c = rb.sample(32)
assert (a != c).any()

def test_rng_dumps(self, tmpdir):
state = torch.random.get_rng_state()
rb = ReplayBufferRNG(sampler=RandomSampler(), storage=LazyTensorStorage(100))
rb.extend(torch.arange(100))
rb._rng.set_state(state)
rb.dumps(tmpdir)
a = rb.sample(32)

rb.loads(tmpdir)
b = rb.sample(32)
assert (a == b).all()
c = rb.sample(32)
assert (a != c).any()


@pytest.mark.parametrize(
"rbtype,storage",
Expand Down
42 changes: 42 additions & 0 deletions torchrl/data/replay_buffers/replay_buffers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
is_tensorclass,
LazyStackedTensorDict,
NestedKey,
TensorDict,
TensorDictBase,
unravel_key,
)
Expand Down Expand Up @@ -269,7 +270,9 @@ def __init__(
raise ValueError("dim_extend must be a positive value.")
self.dim_extend = dim_extend
self._storage.checkpointer = checkpointer
self.set_rng(generator=generator)

def set_rng(self, generator):
self._rng = generator
self._storage._rng = generator
self._sampler._rng = generator
Expand Down Expand Up @@ -426,6 +429,9 @@ def state_dict(self) -> Dict[str, Any]:
"_writer": self._writer.state_dict(),
"_transforms": self._transform.state_dict(),
"_batch_size": self._batch_size,
"_rng": (self._rng.get_state().clone(), str(self._rng.device))
if self._rng is not None
else None,
}

def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
Expand All @@ -434,6 +440,12 @@ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
self._writer.load_state_dict(state_dict["_writer"])
self._transform.load_state_dict(state_dict["_transforms"])
self._batch_size = state_dict["_batch_size"]
rng = state_dict.get("_rng")
if rng is not None:
state, device = rng
rng = torch.Generator(device=device)
rng.set_state(state)
self.set_rng(generator=rng)

def dumps(self, path):
"""Saves the replay buffer on disk at the specified path.
Expand Down Expand Up @@ -477,6 +489,13 @@ def dumps(self, path):
self._storage.dumps(path / "storage")
self._sampler.dumps(path / "sampler")
self._writer.dumps(path / "writer")
if self._rng is not None:
rng_state = TensorDict(
rng_state=self._rng.get_state().clone(),
device=self._rng.device,
)
rng_state.memmap(path / "rng_state")

# fall back on state_dict for transforms
transform_sd = self._transform.state_dict()
if transform_sd:
Expand All @@ -499,6 +518,11 @@ def loads(self, path):
self._storage.loads(path / "storage")
self._sampler.loads(path / "sampler")
self._writer.loads(path / "writer")
if (path / "rng_state").exists():
rng_state = TensorDict.load_memmap(path / "rng_state")
rng = torch.Generator(device=rng_state.device)
rng.set_state(rng_state["rng_state"])
self.set_rng(rng)
# fall back on state_dict for transforms
if (path / "transform.t").exists():
self._transform.load_state_dict(torch.load(path / "transform.t"))
Expand Down Expand Up @@ -765,6 +789,12 @@ def __iter__(self):

def __getstate__(self) -> Dict[str, Any]:
state = self.__dict__.copy()
if self._rng is not None:
rng_state = TensorDict(
rng_state=self._rng.get_state().clone(),
device=self._rng.device,
)
state["_rng"] = rng_state
_replay_lock = state.pop("_replay_lock", None)
_futures_lock = state.pop("_futures_lock", None)
if _replay_lock is not None:
Expand All @@ -774,6 +804,12 @@ def __getstate__(self) -> Dict[str, Any]:
return state

def __setstate__(self, state: Dict[str, Any]):
if "_rng" in state:
rng = state["_rng"]
if rng is not None:
rng = torch.Generator(device=rng.device)
rng.set_state(rng["rng_state"])
state["_rng"] = rng
if "_replay_lock_placeholder" in state:
state.pop("_replay_lock_placeholder")
_replay_lock = threading.RLock()
Expand Down Expand Up @@ -1482,12 +1518,18 @@ def update_tensordict_priority(self, data: TensorDictBase) -> None:
class InPlaceSampler:
"""A sampler to write tennsordicts in-place.

.. warning:: This class is deprecated and will be removed in v0.7.

To be used cautiously as this may lead to unexpected behaviour (i.e. tensordicts
overwritten during execution).

"""

def __init__(self, device: DEVICE_TYPING | None = None):
warnings.warn(
"InPlaceSampler has been deprecated and will be removed in v0.7.",
category=DeprecationWarning,
)
self.out = None
if device is None:
device = "cpu"
Expand Down
Loading