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Let AOTAutogradCache cache graphs with custom autograd.Functions #2162

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Let AOTAutogradCache cache graphs with custom autograd.Functions #2162

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6 changes: 6 additions & 0 deletions src/fairchem/core/units/mlip_unit/predict.py
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Expand Up @@ -539,6 +539,12 @@ def _lazy_init(self, data: AtomicData) -> None:
# The model's scalars are fixed at inference, so this skips dynamo's
# TensorifyScalarRestartAnalysis retrace during compile.
torch._dynamo.config.specialize_float = True
# The model uses custom autograd.Functions, which AOTAutogradCache
# refuses to cache unless this is set, so without it the largest
# graph is re-compiled from scratch on every process start.
import torch._functorch.config as _functorch_config

_functorch_config.autograd_cache_allow_custom_autograd_functions = True
self.model = torch.compile(self.model, dynamic=True)

self.lazy_model_intialized = True
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