tsnpe_hpr_truncated_proposal
Compute TSNPE high-posterior-density truncation thresholds and rejection samples from a bounded prior.
- License 许可
- Apache-2.0
- 最近更新
- 2026-09-03
- Stars
- ★ 351
- Forks
- 27
01 前言 Front Matter
- name
- tsnpe_hpr_truncated_proposal
- description
- Compute TSNPE high-posterior-density truncation thresholds and rejection samples from a bounded prior.
02 资源文件 2
- 脚本scripts/hpr.py1.4 KB
- 脚本tests/test_hpr.py895 B
03 SKILL.md 正文
Tsnpe Hpr Truncated Proposal
Use this skill when implementing or checking the TSNPE proposal update: estimate the epsilon HPR threshold from posterior log probabilities at the observation, then accept only prior samples whose posterior log probability is above that threshold. Do not use it for APT/SNPE-C atomic-loss corrections.
Inputs: a JSON file with posterior_log_probs, prior_samples, prior_log_probs_for_samples, epsilon, and optional min_acceptance_rate. Outputs: JSON containing threshold, accepted_indices, accepted_samples, acceptance_rate, and mechanism_checks.
Workflow: validate finite numeric arrays; compute the epsilon quantile threshold; build the support mask; fail if no samples are accepted; record whether the proposal is prior-proportional inside support. Validation: run python tests/test_hpr.py.
04 同仓库更多 Skills
passnet-triton-opt
Make a single PassNet Triton kernel fast AND numerically faithful: performance model (when a replacement can win at all), block/grid/warp tuning, autotune policy, launch overhead, and per-op numeric recipes to pass the dtype baseline tolerances. Use when a pass matches and is correct but speedup below expected, or when correctness fails by small numeric margins.
passnet-solve
END-TO-END playbook for solving one PassNet sample: analyze the computation graphs, decide the optimization strategy (what to fuse, what to replace, what to leave alone), drive the iteration loop, and maximize the sample score. This is the ENTRY skill — invoke it first for any PassNet optimization task; it tells you when to use passnet-pattern-fusion, passnet-triton-opt and passnet-feedback.
passnet-skill
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Author PassNet pass files: write patterns that actually MATCH the FX graph, pick fusion regions (which ops to absorb into one Triton kernel), and structure multi-pass submissions with the mandatory shared-dispatch architecture. Use when creating or fixing pattern()/replacement_args()/replacement_func() files, when a pass "failed to match", or when deciding how to fuse multiple kernels/ops.
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passnet-feedback
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