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mark metricmodule methods as experimental #2983

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3 changes: 3 additions & 0 deletions torchrec/metrics/metric_module.py
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Original file line number Diff line number Diff line change
Expand Up @@ -68,6 +68,7 @@
from torchrec.metrics.unweighted_ne import UnweightedNEMetric
from torchrec.metrics.weighted_avg import WeightedAvgMetric
from torchrec.metrics.xauc import XAUCMetric
from torchrec.utils.experimental import experimental


logger: logging.Logger = logging.getLogger(__name__)
Expand Down Expand Up @@ -394,6 +395,7 @@ def _get_metric_states(

return state_aggregated

@experimental
def get_pre_compute_states(
self, pg: Optional[Union[dist.ProcessGroup, DeviceMesh]] = None
) -> Dict[str, Dict[str, Dict[str, Union[torch.Tensor, List[torch.Tensor]]]]]:
Expand Down Expand Up @@ -442,6 +444,7 @@ def get_pre_compute_states(

return aggregated_states

@experimental
def load_pre_compute_states(
self,
source: Dict[
Expand Down
2 changes: 1 addition & 1 deletion torchrec/metrics/tests/test_metric_module.py
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Expand Up @@ -643,7 +643,7 @@ def metric_module_gather_state(
metric_module.update(test_batch)

computed_value = metric_module.compute()
states = metric_module.get_pre_compute_states(pg=ctx.pg) # pyre-ignore[6]
states = metric_module.get_pre_compute_states(pg=ctx.pg)

torch.distributed.barrier(ctx.pg)
# Compare to computing metrics on metric module that loads from pre_compute_states
Expand Down
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