Heuristic:Gretelai Gretel synthetics Gumbel Softmax NaN Retry
| Knowledge Sources | |
|---|---|
| Domains | Debugging, Tabular_Data, Optimization |
| Last Updated | 2026-02-14 19:00 GMT |
Overview
Stability workaround for NaN values in PyTorch Gumbel-Softmax sampling, using retry logic (up to 10 attempts) for older PyTorch versions and automatic fallback based on version detection.
Description
The Gumbel-Softmax trick is used in ACTGAN to sample from categorical distributions in a differentiable way, with a temperature parameter (tau=0.2) controlling the sharpness. Older PyTorch versions (< 1.2.0) have a known instability in `torch.nn.functional.gumbel_softmax` that can produce NaN values. The codebase includes a stabilized version that retries the operation up to 10 times, discarding NaN results. For PyTorch >= 1.2.0, the native implementation is used directly.
Usage
This heuristic applies automatically based on the detected PyTorch version. If you observe `ValueError: gumbel_softmax returning NaN` during ACTGAN training, it means the stabilized version exhausted all 10 retries. This is a sign of deeper numerical issues, possibly from extreme logit values or very low temperature.
The Insight (Rule of Thumb)
- Action: Upgrade PyTorch to >= 1.2.0 for native stable Gumbel-Softmax. If stuck on older versions, the retry mechanism is automatic.
- Value: tau=0.2 (Gumbel-Softmax temperature) is used for discrete column activation. Up to 10 retries on NaN.
- Trade-off: The retry mechanism adds negligible overhead (retries are rare on modern PyTorch). On older versions, up to 10x slower in worst case for individual softmax calls.
Reasoning
The Gumbel-Softmax distribution involves sampling from the Gumbel distribution and then applying a softmax. The Gumbel samples can occasionally produce extreme values that, after exponentiation in the softmax, lead to numerical overflow/underflow resulting in NaN. PyTorch fixed this in version 1.2.0 by clamping intermediate values. The retry approach is a pragmatic workaround: since NaN results are rare and independent, simply re-sampling almost always produces a valid result within a few attempts.
Code Evidence
Version-based dispatch from `actgan/actgan.py:363-367`:
_gumbel_softmax = staticmethod(
functional.gumbel_softmax
if version.parse(torch.__version__) >= version.parse("1.2.0")
else _gumbel_softmax_stabilized
)
Stabilized retry implementation from `actgan/actgan.py:143-172`:
def _gumbel_softmax_stabilized(
logits: torch.Tensor, tau: float = 1, hard: bool = False,
eps: float = 1e-10, dim: int = -1,
):
"""Deals with the instability of the gumbel_softmax for older versions of torch."""
for i in range(10):
transformed = functional.gumbel_softmax(
logits, tau=tau, hard=hard, eps=eps, dim=dim
)
if not torch.isnan(transformed).any():
return transformed
raise ValueError("gumbel_softmax returning NaN.")
Usage with tau=0.2 from `actgan/actgan.py:671-672`:
elif isinstance(enc, OneHotColumnEncoding):
self._activation_fns.append(
(st, ed, lambda data: self._gumbel_softmax(data, tau=0.2))
)