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Glq

v0.7.1 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

✓ No known CVEs patched
Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

inference llm model-compression pytorch quantization

Summary

AI summary

Performance optimizations across Validation, wikitext-2, and exact modules increase B=1 decode throughput by 39% while maintaining bit‑exact PPL.

Full changelog

Perf-only release: deletes the B=1 RHT/elementwise launch bracket around the 3INST trellis decode. No API breaks (new ops and trailing optional args are additive), bit-exact end to end — wikitext-2 PPL is unchanged at 9.2299 for every stage.

  • RS1 — fold the ×wscale elementwise into the matvec/matmul store
  • RS2/RS3 — FUSE_IN: input RHT + fp16 cast computed inside every matvec block (single-block pow2 and block-diagonal shapes); standalone input-RHT launches drop from 21.4% of decode time to 0.1%
  • RS4a — warp-shuffle the 5 lowest FHT butterfly stages (__shfl_xor_sync) in all RHT kernels + shared glq_fht.cuh helpers
  • S4b — shard-batched output RHT: fused QKV/gate_up shards run ONE grid.y launch instead of per-shard launches (7 → 4 per layer), torch.cat eliminated

Measured (SmolLM3-3B 3inst-4bpw, RTX PRO 6000, vLLM 0.25.0, random 128/256 ignore-eos, n=3)

| | before | after |
|---|---|---|
| B=1 decode | 129 tok/s | 179 tok/s (+39%) |
| B=32 decode | ~2330 | 2423–2437 |
| PPL (wikitext-2) | 9.2299 | 9.2299 (exact) |
| output-RHT launches/layer | 7 | 4 |

B=1 is now at parity with bf16 serving (180 tok/s) on the same box while using 1.9 GB vs 5.8 GB of weights.

Validation

  • 163 trellis-suite + 21 vLLM tests green, incl. CPU bit-mirror anchors and fused-vs-sequential torch.equal gates at B=1 and B>1
  • nsys mechanism checks: input-RHT launch count ≈ 0, output-RHT 7→4/layer; ncu: fused matvec 64 regs, zero hot-path local traffic
  • Both fusions can be disabled at runtime if ever needed: GLQ_TRELLIS_FUSE_INPUT=0 and GLQ_TRELLIS_BATCH_OUT_RHT=0 restore the previous per-kernel paths

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Beta — feedback welcome: [email protected]