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[Refactor] Add WEIGHT_SYNC_TIMEOUT constant for collector weight synchronization #3294

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Merged
vmoens merged 1 commit into from
Jan 8, 2026
Merged

[Refactor] Add WEIGHT_SYNC_TIMEOUT constant for collector weight synchronization #3294

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4 changes: 4 additions & 0 deletions torchrl/collectors/_constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,7 @@ def cudagraph_mark_step_begin():
"INSTANTIATE_TIMEOUT",
"_MIN_TIMEOUT",
"_MAX_IDLE_COUNT",
"WEIGHT_SYNC_TIMEOUT",
"DEFAULT_EXPLORATION_TYPE",
"_is_osx",
"_Interruptor",
Expand All @@ -38,6 +39,9 @@ def cudagraph_mark_step_begin():
_TIMEOUT = 1.0
INSTANTIATE_TIMEOUT = 20
_MIN_TIMEOUT = 1e-3 # should be several orders of magnitude inferior wrt time spent collecting a trajectory
# Timeout for weight synchronization during collector init.
# Increase this when using many collectors across different CUDA devices.
WEIGHT_SYNC_TIMEOUT = float(os.environ.get("TORCHRL_WEIGHT_SYNC_TIMEOUT", 120.0))
# MAX_IDLE_COUNT is the maximum number of times a Dataloader worker can timeout with his queue.
_MAX_IDLE_COUNT = int(os.environ.get("MAX_IDLE_COUNT", torch.iinfo(torch.int64).max))

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2 changes: 2 additions & 0 deletions torchrl/collectors/collectors.py
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Expand Up @@ -18,6 +18,7 @@
cudagraph_mark_step_begin,
DEFAULT_EXPLORATION_TYPE,
INSTANTIATE_TIMEOUT,
WEIGHT_SYNC_TIMEOUT,
)

from torchrl.collectors._multi_async import MultiAsyncCollector, MultiaSyncDataCollector
Expand Down Expand Up @@ -50,6 +51,7 @@
# Constants
"_TIMEOUT",
"INSTANTIATE_TIMEOUT",
"WEIGHT_SYNC_TIMEOUT",
"_MIN_TIMEOUT",
"_MAX_IDLE_COUNT",
"DEFAULT_EXPLORATION_TYPE",
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3 changes: 2 additions & 1 deletion torchrl/weight_update/_shared.py
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Expand Up @@ -11,6 +11,7 @@
from torch import multiprocessing as mp, nn

from torchrl._utils import logger as torchrl_logger
from torchrl.collectors._constants import WEIGHT_SYNC_TIMEOUT

from torchrl.weight_update.utils import _resolve_model
from torchrl.weight_update.weight_sync_schemes import (
Expand Down Expand Up @@ -97,7 +98,7 @@ def setup_connection_and_weights_on_receiver(
weights: Any = None,
model: Any = None,
strategy: Any = None,
timeout: float = 10.0,
timeout: float = WEIGHT_SYNC_TIMEOUT,
) -> TensorDictBase:
"""Receive shared memory buffer reference from sender via their per-worker queues.

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