Rsa proxy#1
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Replace the per-request Python loop in the CCA prefill path with a single batched conv over a flat segmented layout. The loop ran one sequential conv_qk call per prefill request and forced GPU->CPU syncs per request per layer via query_start_loc_p slicing; the batched form uses only device-side index math (no syncs) and one conv kernel. Each request occupies a contiguous [cached-state | tokens] segment in the flat conv input, so every valid causal output window stays inside its own segment. Verified numerically equivalent to the loop for fresh/continuation requests, mixed batches, and chunks shorter than the conv window. Co-authored-by: Claude Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: Pat Carter <patcarter@agripath.com.au>
Standalone OpenAI-compatible proxy implementing Recursive Self-Aggregation (Venkatraman et al., arXiv:2509.26626) with configurable tail truncation, reproducing the Markovian RSA scheme from the ZAYA1-8B technical report (arXiv:2605.05365). Sits in front of a vLLM server; requests with tools, n>1, or "rsa": false pass through unchanged. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: Pat Carter <patcarter@agripath.com.au>
- Tail truncation now prefers a locally loaded AutoTokenizer (auto-resolved from the backend's model root, e.g. ZAYA1's GemmaTokenizerFast), falling back to /tokenize+/detokenize, then char approximation. - All tail paths advance the cut to a paragraph/line boundary within the leading 10% so aggregation prompts never start mid-thought. - Optional verifier (--rsa-verifier math|code|auto) excludes provably broken candidates (unparseable boxed answers, crashing python blocks) from the aggregation sampling pool and final vote, with fallback to the full population to preserve RSA's diversity requirement. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: Pat Carter <patcarter@agripath.com.au>
rsa/compare.py benchmarks RSA against single-sample and self-consistency baselines extracted from the same run (round-0 population doubles as the baseline samples). On 5 AIME problems with ZAYA1-8B (N=8, K=3, T=2, tail=1536, beta=5000): single-sample 27/40, self-consistency 5/5, RSA 5/5, per-trace accuracy 67.5% -> 82.5% after one aggregation round. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: Pat Carter <patcarter@agripath.com.au>
bench/: client-side latency/accuracy measurement for RSA queries (wall-clock percentiles, token usage, and Prometheus-sourced peak concurrency and decode throughput as the capacity check). quant/: offline, GPU-free W8A8 INT8 quantization of ZAYA1-8B's MoE expert weights, rewriting safetensors directly into a compressed-tensors checkpoint (ZAYA1 ships no transformers modeling code, so the standard llm-compressor flow cannot load it). Router, attention/CCA, norms, and embeddings stay bf16. Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Signed-off-by: Pat Carter <patcarter@agripath.com.au>
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