Perf/cuda compact top p replay

[inaniloquentee]

Summary

• preserve strict VIME/vLLM top-p semantics by replaying the native finite nucleus
• add an SM90 kernel that scores compact replay IDs directly instead of materializing a full-vocabulary keep mask
• preserve rank-ordered reductions and handle TP shards with no replay candidates
• retain main's temperature-scaling compatibility contract from fix(vime): avoid double temperature scaling for reused logits #423

Performance

H100 TP4/CP2, Qwen3-8B, temperature 0.7, top_p 0.95, rollout batch 1 x 8, with --use-rollout-logprobs disabled:

• rollout: 58.52 s -> 48.49 s (-17.1%)
• rollout throughput: 87.48 -> 106.02 tok/GPU/s (+21.2%)
• end-to-end step: 93.88 s -> 84.14 s (-10.4%)
• train/rollout mismatch count and max |delta logp| remain exactly zero

The three old/new G11 steps produced identical response lengths, so the rollout comparison is workload-matched.

Validation

• �pytest tests/test_vime_linear_logp_provider.py -q�: 10 passed
• �pytest tests/test_framework_runtime_adapters.py::test_vllm_logp_replaces_every_duplicate_sampled_token_column -q�: passed
• randomized dense-mask vs compact-support CUDA checks: bitwise equal across TP offsets, including empty local shards
• 3-step Qwen3-8B strict R/R run: zero mismatches and zero max |delta logp| on every step
• CUDA 12.8 / SM90 build and 200-step validation run are in progress

Summary by CodeRabbit

• New Features

  • Added deterministic scoring for compact top-p replay data, including temperature support and tensor-parallel execution.
  • Added a public interface for calculating target logits and log-sum-exp values from sparse top-p results.
  • Strict VIME log-probability processing now supports replay masks when reusable local logits are available.

• Bug Fixes

  • Empty vocabulary shards now produce stable zero, negative-infinity, and log-probability results instead of NaNs.

[coderabbitai]

📝 Walkthrough

Walkthrough

The change adds deterministic compact top-p replay scoring for SM90 CUDA tensor-parallel execution. It handles empty vocabulary shards without NaNs and updates strict framework and VIME paths to use replayed logits.

Changes

Top-p replay scoring

Layer / File(s)	Summary
CUDA kernel and empty-shard handling csrc/cuda/fused_linear_logp_sm90.cu	Empty vocabulary rows now produce zero probabilities, -inf LSE, zero target logits, and -inf log probabilities. A compact top-p kernel validates replay inputs, sorts and deduplicates up to 64 IDs, applies temperature, and returns local reductions.
SM90 operator export csrc/ops.cpp	The new CUDA operator is declared and registered as linear_logp_top_p_local_bf16_forward.
Tensor-parallel top-p integration rl_engine/kernels/ops/cuda/loss/linear_logp.py, rl_engine/integrations/linear_logp.py, rl_engine/integrations/framework_operators.py	The Python path validates replay data, invokes the sparse CUDA operator, merges tensor-parallel results, and records top_p_replay provenance.
VIME masked replay behavior rl_engine/integrations/vime/linear_logp_provider.py, tests/test_vime_linear_logp_provider.py	Strict VIME execution reuses local logits for keep masks, applies -inf to excluded entries, disables fast metric entropy, and tests the replay path.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant VllmLogpOperator
  participant LinearLogpWrapper
  participant TPHelper
  participant CUDAOperator
  VllmLogpOperator->>LinearLogpWrapper: pass local logits and top-p replay data
  LinearLogpWrapper->>TPHelper: validate tensor-parallel inputs
  TPHelper->>CUDAOperator: score compact replay entries
  CUDAOperator-->>TPHelper: return local target logit and LSE
  TPHelper-->>VllmLogpOperator: return merged log probabilities

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Merge Risk: 🟡 Moderate · up to f00ee

Raw-mode top-p requests can return incorrect sampled-token log probabilities, so mode enforcement should be fixed before merge.

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 31.82% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 22 functions across 7 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Description Check	✅ Passed	Check skipped - CodeRabbit’s high-level summary is enabled.
Title check	✅ Passed	The title clearly identifies the main change: CUDA performance work for compact top-p replay. It is concise and directly related to the pull request objectives.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Actionable comments posted: 1

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🤖 Prompt to fix review comments

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Inline comments:
In `@rl_engine/integrations/framework_operators.py`:
- Around line 2364-2380: Guard the top-p replay setup in the sampling metadata
branch with the effective vLLM logprobs mode, ensuring replay is enabled only
for processed logprobs. Reject or convert raw mode before assigning
top_p_replay, while preserving the existing shape and row-alignment validation.

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📥 Commits

Reviewing files that changed from the base of the PR and between 78a0ab3 and f00ee72.

📒 Files selected for processing (7)

• csrc/cuda/fused_linear_logp_sm90.cu
• csrc/ops.cpp
• rl_engine/integrations/framework_operators.py
• rl_engine/integrations/linear_logp.py
• rl_engine/integrations/vime/linear_logp_provider.py
• rl_engine/kernels/ops/cuda/loss/linear_logp.py
• tests/test_vime_linear_logp_provider.py

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[Flink-ddd]

LGTM

[maxiaosong1124]

LGTM!