[None][doc] GVR V2: Self-Sampling and Multi-Thresholding for Faster Exact Top-K

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Description

GVR V2: Self-Sampling and Multi-Thresholding for Faster Exact Top-K
A Unified Selection Core for Prefill and Decode in TensorRT-LLM

Sparse-attention Top-K must find exact winners while controlling full-row reads, candidate work, and integration state. This article explains how GVR V2 uses current-row self-sampling and dense multi-threshold verification to narrow exact refinement, and how removing the temporal Top-K prior lets prefill and decode share the streaming selection core.

Four main sections cover:

1. Motivation and design foundations: temporal-hint variability and prior-state maintenance, followed by the connection to Floyd–Rivest SELECT. Later V1 already uses multi-thresholding; V2 changes calibration and removes the prior lifecycle. Figure 2 defines overlap after temporal-hint index mapping: +1 for V3.2 and unchanged compressed-bin indices for V4 Pro. Its blue points are current selections predicted by the mapped hint; this distinguishes prediction accuracy from same-token retention. Classical randomized comparison bounds are distinguished from V2's deterministic sampling and GPU latency objective.
2. Self-sampling and multi-thresholding: an algorithm overview and exactness invariant precede candidate amplification and GPU implementation. Figure 5 uses a simplified tail-count staircase and explicit K/E/D population segments, with qualitative explanations instead of arbitrary numerical ratios. The article explains 8/16-score sample windows, vector loads, sector utilization, sample coverage, register pressure, histogram anchors, and execution families. Detailed addressing, HIC/TSH rules, counter ownership, and prefetch placement are documented in the companion.
3. Performance and roofline analysis: direct comparisons of GVR V2, GVR V1 (temporal hint), and TensorRT-LLM radix CUDA. Paired heatmaps show V2 speedups over radix CUDA and GVR V1 across the length–batch grid. Three-implementation latency plots and Pareto curves include average/peak reachable rates. The text distinguishes ideal-traffic efficiency from measured DRAM utilization and the repeated-score-row benchmark from heterogeneous serving concurrency.
4. TensorRT-LLM integration and takeaways: removed prior state, phase-specific row metadata, shared streaming selection, graph readiness, historical serving results with separate attribution, and a trtllm-bench throughput enablement example.

The performance reference is PR #19076's measured public revision. All three implementations cover the same 9,746 B200 FP32 cases, including 21 Flash layers, 30 Pro layers, and all 61 V3.2 layers. GVR V2 delivers geometric-mean gains of 5.05× over radix CUDA and 1.46× over GVR V1. Individual minima and win rates remain visible. GVR V1 denotes the temporal-hint implementation from PR #16877, the newer V1 implementation in the measured dataset. The comparison spans benchmark runs and does not isolate calibration alone.

The radix heatmap aggregates 275 shape cells across all eleven batch sizes and reaches 20.18× on V4 Pro at 4,099 scores and batch size 1,024. Cell values are geometric means across layers, distinct from individual-case extrema. The color scale covers all observed values without clipping. The GVR V1 heatmap covers the same 275 shapes with a separate 1–3× scale; its averages range from 1.05× to 2.87×. Orange corners mark 15 shapes containing individual-layer regressions, preserving the distinction between a 1.05× Pro shape average and the 0.689× individual minimum. The discussion explains that informative temporal hints can still improve local admission, while their variability motivates V2’s robustness goal.

Eleven SVGs show the comparison, temporal variability, algorithm evolution, selection/recovery, candidate work, GPU sampling, radix and V1 speedup distributions, latency, roofline behavior, and shared integration. Public timing exports, provenance, summaries, and plotting code contain only the retained implementations. Existing retained timing observations are unchanged; the single V1 reference, V2, and radix use complete common coverage.

Validation

• Applicable pre-commit checks, DCO, and whitespace checks.
• Strict focused Sphinx/MyST build passed (-n -W --keep-going).
• Source fidelity checks and independent recomputation passed for 9,746 cases, temporal P5/minimum/win statistics, 550 heatmap cells across the two comparisons, 150 latency points, and 75 roofline points.
• Exact retained-field comparison with the archived prior version and deterministic data/figure regeneration.
• Eleven figures, 79 article formulas, twelve companion formulas, four main sections, and fifteen contents entries checked in local and GitHub rendering. Desktop/mobile layouts, all nine centered tables, and figure references were inspected.
• Publication review of Markdown, SVG metadata, JSON, Python, decompressed CSVs, previews, and this description.

Documentation only. No model or GPU benchmark was rerun. The focused build covers the article and companion.

Dev Engineer Review

• Changes only docs/source/conf.py.
• Reformats the helper import block and adds spacing.
• Changes static runtime_summary and executor_summary values from f-strings to plain strings.
• No runtime, API, benchmark, documentation-content, or test behavior changes are evident.

QA Engineer Review

No test changes.

Per-File QA Perspective

• docs/source/conf.py: Verify that Sphinx configuration imports successfully and that generated Runtime and Executor documentation remains unchanged. Confirm that focused documentation builds pass.

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Walkthrough

This PR adds GVR V2 benchmark methodology, provenance, published results, a regeneration script for statistics and ten figures, a technical blog, and Sphinx handling that excludes JSON files from documentation sources.

Changes

GVR V2 benchmark publication

Layer / File(s)	Summary
Benchmark methodology and published results docs/source/blogs/media/gvr_v2/README.md, docs/source/blogs/media/gvr_v2/provenance.json, docs/source/blogs/media/gvr_v2/summary.json, docs/source/conf.py	Defines benchmark scope, data formats, aggregation rules, correctness boundaries, roofline calculations, provenance, published comparison results, and excludes .json files from Sphinx sources.
Statistics and figure regeneration docs/source/blogs/media/gvr_v2/plot_results.py	Validates timing data, computes comparison and roofline statistics, and regenerates ten deterministic SVG figures, including GVR V1 speedup maps and roofline views.
GVR V2 technical documentation docs/source/blogs/tech_blog/blog29_GVR_V2_Self_Sampling_Exact_TopK_for_Sparse_Attention.md	Documents self-sampling, exact multi-thresholding, kernel families, TensorRT-LLM integration, configuration, and benchmark results.

Priority: ⬇️ Low

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

Change: Other

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 21.05% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 19 functions across 2 files.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title follows the required [None][doc] format and clearly describes the main documentation change: GVR V2 self-sampling and multi-thresholding for exact Top-K.
Description check	✅ Passed	The description is detailed and directly covers the change, benchmark scope, validation, and documentation impact. It does not include the template's explicit Test Coverage or PR Checklist sections, b…
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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In `@docs/source/blogs/media/gvr_v2/plot_results.py`:
- Around line 68-71: Update the temporal join loop using TEMPORAL and lookup so
blank _us values are converted to None instead of passed to float(); retain
numeric conversion for present values, matching _stats missing-value handling.

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In
`@docs/source/blogs/tech_blog/blog29_GVR_V2_Self_Sampling_Exact_TopK_for_Sparse_Attention.md`:
- Around line 95-108: Update the V2 documentation to state that correctness no
longer depends on a Top-K prior, while `tiered_topk` still requires `pre_idx`
for supported heuristic routes and may use it for threshold seeding or temporal
shifting. Revise the “What state crosses decode steps?” table entry and the
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