d812bfe96dbfc5e99bdb5ee614be25f572f3cdb5
3
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 vh
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a91cc3fb38 |
docs(quant): consolidate quantization lessons into a durable playbook
Quants are hard-fought and we keep re-paying for the same lessons. A survey found quant knowledge scattered across 18 files in four trees, with three documents having independently discovered and recorded overlapping "landmines" sections — and one of them now actively misleading. Adds docs/pfi/model-quantization-playbook.md as the single home for the TRANSFERABLE lessons, with per-model artifacts demoted to worked examples that link up to it. Contents: - scheme decision table, incl. that a literal "W4A8" NVFP4 checkpoint is unservable on vLLM (two legal activation settings, FP8 is not one) - the reference mixed-precision recipe and the three parts of it that are load-bearing and easy to drop - the recurring landmines, ordered by cost: the loader-class trap (rediscovered THREE times), the three separate ways to lose the MTP head, toolchain deadlocks, vision configs, memory/device placement - pipeline shape: prove targets before spending GPU time; mandatory post-steps that verify rather than assume - the acceptance gate, and the three ways measurement has lied to us — prefix caching faking both speed metrics, prompt_logprobs going uniform under speculative decoding, and a 0600 .env making compose silently no-op - hardware/co-residency, including that a SMALLER model can starve its neighbour because gpu-memory-utilization is a fraction of the whole card - a superseded-claims table, and measured negatives not to re-chase The superseded table earns its place immediately: the heretic2 runbook tells readers to use modelopt because "compressed-tensors can't load the BF16 MTP head, 0% acceptance". That symptom was real but the cause was not the format -- it was the missing re:^mtp.* ignore entry. compressed-tensors gives 47.7-83.2% acceptance in production. A fresh session following that doc would be sent down the modelopt path that current memory calls dependency hell, so the runbook now carries a stale-warning header pointing here. Wires discovery: an orientation.md "Where to look for what" row, pointers from the gen-seat / heretic2 / mistral artifacts, and a CLAUDE.md maintenance rule so the playbook gets fed instead of going stale -- model-agnostic lessons land in the playbook, model-specific ones stay put, and a wrong claim earns a dated superseded row rather than a silent edit. Motivated by Qwen3.8 having just released: the next model swap will need a requant, and this is what that session should read first. |
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vh
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4a5c3fcccf |
perf(gen-seat): record prefill measurements — roughly doubled
Closes the one axis of the original premise left unverified. Measured cold (cache-busted) on both builds under matching serve configs: ~6.7k-token prompt 3,206 -> 6,334 tok/s prefill (+98%) ~27k-token prompt 2,862 -> 5,085 tok/s prefill (+78%) TTFT on a ~27k doc 9.43 -> 5.31 s (-44%) Prefill gains far exceed the +18% decode gain, and that ordering is the expected one: decode at bs=1 is memory-bandwidth-bound and the weights are 4-bit under either scheme, so little changes; prefill is compute-bound, which is where native Blackwell FP4 tensor cores replace the Marlin dequant-to-BF16 path. The summarizer aliases are the consumers that feel this. Adds bench/prefill_bench.py plus the raw JSON. The harness deliberately uses SystemRandom: a seeded nonce regenerates the previous run's prompts verbatim, prefix caching then serves them, and the first attempt read ~41k tok/s of cache-hit rather than ~5k of actual prefill. |
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vh
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74f596b1d3 |
feat(gen-seat): mixed NVFP4+FP8 requant — +18% decode at equal MTP acceptance
Re-quantizes the fleet `gen` seat from weight-only NVFP4A16 to a mixed-precision build: NVFP4 W4A4 for layers 0-55 MLPs, FP8 W8A8 for the attention projections / linear_attn / lm_head / layers 56-63 MLPs, FP8 KV cache. Replicates the scheme of unsloth/Qwen3.8-27B-NVFP4 on the abliterated weights. The queued task named this "W4A8" (NVFP4 weights + FP8 activations). That checkpoint cannot be served: vLLM 0.24's compressed-tensors dispatcher (compressed_tensors.py:704-713) accepts NVFP4 weights with either no input quantization (W4A16, which forces the Marlin kernel) or NVFP4 input quantization (W4A4) -- anything else, FP8 included, raises ValueError at load. CompressedTensorsW4A8Fp8 is INT4 weights gated on an exact-sm90 check, so it is closed on Blackwell twice over. The ~20% intuition was correct; the scheme name was not. Getting FP8 into the mix has to be done per-layer-group. Established the gain before spending GPU time: unsloth's build was already on-box, so serving it as a probe measured +19.1% over our seat at identical MTP acceptance -- a kernel-level result, no requant needed to learn it. Measured, cache-busted, bs=1: decode 80.12 -> 94.53 tok/s (+18.0%) MTP acceptance 47.8% -> 47.7% (unchanged) perplexity (n=6) 6.941 -> 7.059 (+1.7%) abliteration 4/4 -> 4/4 (preserved) weights on disk 27.7 -> 22.5 GB (-19%) Surface test green on the live seat: plain chat, vision, tool calling, thinking split, 36K-token needle retrieval, streaming. All 7 LiteLLM aliases verified routing. GEN_GPU_MEM_UTIL 0.45 -> 0.43: the new weights are 5.2 GB smaller, and at 0.45 the seat absorbed that slack as KV, leaving meromero-charrp 0.18 GiB short of its budget on the shared GPU0 -- it crash-looped. Handing the space back leaves gen 422K tokens of KV (1.6x its 262K context) and both seats co-resident at 89.8/97.9 GB. Also records two measured negatives so they are not re-chased: GEN_SPEC_TOKENS is already optimal at 3 (swept 2/3/4/5 -> 77.1/80.1/78.7/ 75.9 tok/s), and vLLM's prompt_logprobs are ~uniform while speculative decoding is on, so perplexity must be measured with spec off. Pipeline, acceptance harness and raw measurements land in services/gen-seat-mixed-quant/. Rollback is one .env line; the previous build is untouched at /tank/aimodels/qwen38-27b-uncensored-nvfp4. |