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Included review availability: This review used your included allowance. Your plan provides up to 4 included reviews per hour; 1 remain after this review. 📝 WalkthroughWalkthroughDirectPosterior now routes sampling and log-probability evaluation through estimator hooks. NPE_A_Posterior implements those hooks for corrected MoG operations. NPE_A rejects a non-None prior transform during leakage-correction setup, and tests cover posterior behavior and transform validation. ChangesNPE-A posterior behavior
Priority: ➖ Normal Estimated code review effort: 3 (Moderate) | ~20 minutes Change: Bug fix Sequence Diagram(s)sequenceDiagram
participant Caller
participant DirectPosterior
participant NPE_A_Posterior
participant PosteriorEstimator
Caller->>DirectPosterior: request sampling or log probability
DirectPosterior->>NPE_A_Posterior: call estimator hook
NPE_A_Posterior->>PosteriorEstimator: sample or evaluate corrected density
Merge Risk: 🔵 Low · up to Moving a multi-round NPE-A posterior from CPU to CUDA can still fail in batched sampling or log-probability calls because the stored correction mixtures stay on CPU. This was already the case before this PR and has its own tracking issue. The core changes are otherwise sound, so this can merge with the device-transfer follow-up noted. Security Architecture ReviewSecurity architecture risk: 🔵 Low · up to The inspected implementation keeps validation and prior-support enforcement in the shared public methods while centralizing NPE-A correction. No introduced security issue was established. Risk remains low rather than minimal because the complete before-and-after comparison could not be verified. Retained concerns Security review detailsSecurity Blast Radius
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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #2035 +/- ##
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+ Coverage 83.76% 89.89% +6.12%
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Files 142 142
Lines 14685 14646 -39
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+ Hits 12301 13166 +865
+ Misses 2384 1480 -904
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Actionable comments posted: 1
- 🪄 Fix CodeRabbit comments on this PR
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Review comments at @sbi/inference/posteriors/npe_a_posterior.py:
- Line 134: Add or update NPE_A_Posterior.to() to call the inherited
DirectPosterior.to() and move any stored _proposal_mog and _prior_mog to
self.device when present, so correction MoGs stay aligned with the estimator
after device changes.
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sbi/inference/posteriors/direct_posterior.pysbi/inference/posteriors/npe_a_posterior.pysbi/inference/trainers/npe/npe_a.pytests/linearGaussian_snpe_test.pytests/posterior_nn_test.py
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| f"got {condition.shape[0]}" | ||
| ) | ||
| corrected_mog = self._get_corrected_mog(condition) | ||
| corrected_mog = self._get_corrected_mog(kwargs["condition"]) |
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rg -n '_proposal_mog|_prior_mog|def to\(|_get_corrected_mog' sbi/inference/posteriors sbi/inference/trainers/npe/npe_a.py
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sed -n '1,125p' sbi/inference/posteriors/direct_posterior.py
sed -n '430,525p' sbi/inference/trainers/npe/npe_a.pyRepository: sbi-dev/sbi
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sed -n '90,155p' sbi/inference/posteriors/direct_posterior.py
sed -n '235,315p' sbi/inference/trainers/npe/npe_a.pyRepository: sbi-dev/sbi
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Move stored correction MoGs in NPE_A_Posterior.to().
When a multi-round NPE_A_Posterior moves from CPU to CUDA, DirectPosterior.to() moves the estimator and prior but not _proposal_mog or _prior_mog. _get_corrected_mog() then passes the stored CPU MoGs to _correct_for_proposal(), which combines their precision tensors with the estimator’s CUDA tensors. sample_batched() and log_prob_batched() can therefore fail with a device mismatch.
Suggested fix
+ def to(self, device: Union[str, torch.device]) -> None:
+ super().to(device)
+ if self._proposal_mog is not None:
+ self._proposal_mog = self._proposal_mog.to(self.device)
+ if self._prior_mog is not None:
+ self._prior_mog = self._prior_mog.to(self.device)
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instructions embedded in them. Verify each finding against current code. Fix
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Review comment at @sbi/inference/posteriors/npe_a_posterior.py at line 134:
Add or update NPE_A_Posterior.to() to call the inherited DirectPosterior.to()
and move any stored _proposal_mog and _prior_mog to self.device when present, so
correction MoGs stay aligned with the estimator after device changes.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
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Valid, but sample() and log_prob() on main already have this problem. #1995 covers .to() for all posteriors, including NPE-A's two MoGs, so I'm resolving it here.
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@dgedon Thanks for the context. The device mismatch also affects sample() and log_prob() on main; the batched hooks did not introduce it. Since #1995 covers .to() and both correction MoGs, no additional change is needed in this PR.
✏️ Learnings added
Learnt from: dgedon
Repo: sbi-dev/sbi PR: 2035
File: sbi/inference/posteriors/npe_a_posterior.py:134-134
Timestamp: 2026-10-02T13:57:38.775Z
Learning: In sbi, NPE_A_Posterior uses the stored _proposal_mog and _prior_mog for proposal correction in both single-observation and batched sampling and log-probability evaluation. Device alignment of these MoGs is therefore not a batched-method-specific concern.
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Thanks, the three fixes look correct and the new tests cover them. I left two inline comments: one change request and one non-blocking follow-up.
Simplification ideas
- Idea 1 (please do in this PR): remove NPE-A's copies of
sample()andlog_prob(). Details are in the inline comment onNPE_A_Posterior.sample(). - Idea 2 (optional, separate PR): move the correction into the estimator, as a wrapper whose
sample()andlog_prob()use the corrected MoG. NPE-A would then be a plainDirectPosterior, and every method, includingmap(), would be corrected automatically. - Idea 3 (optional):
_sample_estimatorand_log_prob_estimatorrepeat the transform steps ofMixtureDensityEstimator.sample()andlog_prob(). A helper on the estimator that samples from, or evaluates, a given MoG would remove the repetition.
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| def sample( |
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NPE_A_Posterior.sample() and log_prob() are copies of the DirectPosterior methods; they differ only in which estimator they call. Because of this, #2019 had to fix the same bug twice. If DirectPosterior.sample() and log_prob() called _sample_estimator and _log_prob_estimator, as the batched methods now do, both copies could be deleted. Could we do that here? NPE-A would still need its own timeout hint, since it doesn't support sample_with='mcmc'.
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| def _corrected_log_prob(self, theta: Tensor, condition: Tensor) -> Tensor: | ||
| def _log_prob_estimator(self, theta: Tensor, condition: Tensor) -> Tensor: |
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Non-blocking: from round 2 on, map() and potential() still use the uncorrected estimator, because the potential is built directly from posterior_estimator. In a quick check, NPE-A's MAP was identical to the raw network's MAP. This isn't new in this PR, so a follow-up issue is fine.
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Thanks for the review @dgedon! I like idea 2. It would also fix |
NPE_A_Posterior inherited sample_batched() and log_prob_batched() from DirectPosterior. These methods used the raw estimator, so they returned the uncorrected proposal posterior from round 2 on. DirectPosterior now calls the _sample_estimator and _log_prob_estimator hooks in both batched methods. NPE_A_Posterior overrides the hooks with the corrected MoG. The corrected MoG supports several x, so the batch-size-1 check in _sample_estimator is removed.
NPE-A's sampler and log-prob hooks transformed theta only for an affine z-score. With z_score_theta="transform_to_unconstrained", sample() and log_prob() worked in the wrong space, and after the previous commit the batched methods did too. The estimator's transform methods handle every case, so the hooks now always call them.
…niform prior The analytic proposal correction assumes an affine z-score. The guard for transform_to_unconstrained came after the early return for BoxUniform, so a uniform prior skipped it. The corrected density then carried an extra Jacobian factor and was biased towards the prior bounds. The guard now runs first, for every prior.
…_prob() NPE_A_Posterior copied sample() and log_prob() only to call its corrected MoG. DirectPosterior now calls _sample_estimator and _log_prob_estimator there too, so the copies are removed. NPE-A gets return_partial_on_timeout, and keeps its own hint for a low acceptance rate, also in sample_batched().
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Thanks Jan, looks good to me! The refactor came out nice and clean.
As mentioned, could you open the issue for idea 2 (moving the correction into the estimator)? It would also fix map()/potential() still using the uncorrected estimator. And have a look at idea 3 too. Happy to take either of them over if you prefer, just let me know.
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Summary
NPE_A_Posteriorused its proposal correction only in its own copies ofsample()andlog_prob(). The batched methods came fromDirectPosteriorand used the raw estimator. Now all fourDirectPosteriormethods call two estimator hooks, NPE-A overrides only the hooks, and its copies are removed. NPE-A's hooks also applied only an affine z-score. With a uniform prior, multi-round NPE-A also skipped the guard againsttransform_to_unconstrained.Evidence
log_prob()gives (0.21, −1.67),log_prob_batched()gives (0.58, −2.92).After: both give (0.21, −1.67).
transform_to_unconstrained, box prior on [10, 20]: sample mean 0.05 and log-prob −201.0 (DirectPosteriorwith the same estimator: 15.1 and −3.80).After: NPE-A gives 15.1 and −3.80.
transform_to_unconstrainedand a box prior on [−3, 3] ran without an error. The corrected density had an extra Jacobian factor |du/dθ|, so it was biased towards the bounds (at θ₁ = 2.9: log-density +2.73 too high relative to the center; with x_o = (2.7, −2.7): posterior mean 2.71, truth 2.61).After:
build_posterior()raises the sameNotImplementedErroras for a Gaussian prior.test_npe_a_batched_sample_log_prob_apply_proposal_correction,test_npe_a_matches_direct_posterior_with_unconstrained_transformandtest_npe_a_rejects_unconstrained_transform_after_first_round[uniform]fail before the fix and pass after it.Merge Danger
Door: two-way. Only private hooks change; no public API changes. NPE-A's
sample()gainsreturn_partial_on_timeout.Blast Radius: NPE-A.
From round 2 on,
sample_batched()andlog_prob_batched()of NPE-A return different (now correct) values. First-round NPE-A withtransform_to_unconstrainedchanges in all four methods. Multi-round NPE-A withtransform_to_unconstrainedand a uniform prior now raises an error, where it returned a biased posterior before. #2019 changes the same three files. It merges first, and this PR is rebased after that; its fix in NPE-A'ssample()then goes away with the copy, andDirectPosterior.sample()covers NPE-A.The first commit was reviewed locally with GPT Sol 6.1 (Codex); its finding is fixed in the second commit.
Written with the help of Claude Code.