Pipeline CPU expert work without a batch-wide phase barrier by moorwu · Pull Request #733 · Niko1221/Strata

moorwu · 2026-10-04T07:46:05Z

Pipeline CPU expert work without a batch-wide phase barrier

Why this helps

CPU expert verification currently finishes Gate/Up for the whole batch, quantizes
intermediate activations, then starts Down. Experts that are ready early must
wait for the rest of the batch.

This opt-in change quantizes each expert as soon as its last Gate/Up tile finishes
and publishes that expert's Down work. All producer tiles are claimed before
dependent tiles, so dependent waits cannot strand unclaimed producers.
The existing row partitions and arithmetic kernels are retained.

Use

Set STRATA_POOL_PIPELINE=1 before constructing the expert pool.
It is off by default and applies to the native multi-token path.

Validation environment

Two modified RTX 3080 20 GB cards; CUDA SM86, CUDA 13.1, Linux.
Intel Xeon E5-2686 v4 (18 cores / 36 threads), about 94 GiB usable RAM.
PCIe 3.0, no GPU peer-to-peer access; 17 expert-pool workers.
Qwen3.8-Flash-Next IQ3_S, 131,072-token context, INT8 KV,
32,768 resident KV cells.
Medium thinking: 2,048-token reasoning cap, 8,192-token output cap.
Physical GPU power limits stayed at 250 W and 280 W. No power increase.
Historical performance tests used an isolated engine based on official
v0.1.38 plus reviewed architectds/Strata changes (best, 05c0f36).
This PR contains only our change, ported to official main (99f3dbd).
The historical timings are NOT a measured speedup of this pure-upstream port.

Historical benefit

This is a small scheduling improvement, not a large tokens/s claim:

With disjoint cache and helper 9500	Pipeline off	Pipeline on
32K complete structured request	11.128 s	11.020 s
Natural-language generation	76.5 tokens/s	77.2 tokens/s

The extra gain was about 1%. The natural-language outputs differed in length;
their wall-time difference must not be described as equal-work acceleration.
Both variants passed complete-answer and tool-call checks. Broader cache gains
are not attributed to this PR.

Validation

The historical real-model test passed 810/810 bitwise comparisons across
three layer formats, 1/4/17 workers, host participation on/off, 1/2/4 tokens,
1/2/7/18/97 jobs, and three repeats. An independent serial row reference
also passed. This is not a ThreadSanitizer or general model-quality result.

The included real-model test can be run as:

pool_pipeline_parity /path/to/native/pack /path/to/corresponding/model-shard.gguf

Without model arguments it exits 77 (CTest skip), not a false pass.
It reads the first three layers from the supplied shard; use the matching
Flash-Next pack and shard.

The independent pure-upstream port also passed the 810/810 real-model bitwise
comparisons on the stated Linux host. All known expert-pool header consumers
were recompiled, and the independent engine and parity test linked successfully.
The older upstream VNNI pool selftest was skipped because this CPU lacks the
required AVX-512 features; it was not counted as passing. No model server was
started or restarted, and no production settings were changed.

Related work and remaining work

PR #500 parallelizes intermediate quantization as a separate phase. This proposal
instead pipelines per-expert readiness across phases. They touch the same code
and should be coordinated rather than merged blindly.
Keep draft until the independent upstream port completes end-to-end performance
and concurrency validation. No ThreadSanitizer run has been performed.
Other CPU architectures are unvalidated.

0.1.39 follow-up

The branch now includes official 0.1.39 (6f32ec0). Its refactored row and
quantization helpers retain upstream q2_native_kernels(...) ISA dispatch
instead of choosing kernels from the format number alone. The parity reference
uses the same dispatch. The test is registered on Windows and Unix, with Windows
environment-variable and 64-bit file-offset equivalents.

A contributor reported 810/810 comparisons passing on Ryzen 9 7940HS with
Windows/MSVC, but no convincing speedup: pipeline-on decode was 31.8 tokens/s
against off controls of 33.4 and 30.6. That was a combined build, not this exact
updated branch. The report is linked in PR #733; credit belongs to @midhatn.

Our combined custom 0.1.39 build passed a five-arm, 240-request HTTP suite. Its
whole-build gains cannot be attributed to this small scheduling change. Neither
that suite nor bitwise parity is a ThreadSanitizer result. Keep the PR draft
until isolated performance and concurrency validation is convincing.

The exact revised branch compiled and linked independently against official
0.1.39 on our Linux host. All known expert-pool header consumers were rebuilt.
Using the matching SC117 abliterated IQ3_S pack and shard, the updated parity
test passed 810/810 comparisons, including the independent serial reference,
1/4/17 workers, host participation on/off, repeated calls and 97-job batches.
We did not run the revised branch on Windows or launch a model server for this
check. The contributor's Windows result is separate evidence, not our own run.

…s falls back to the wheels (Niko1221#446) rocm_root took /opt/rocm when bin/hipcc and lib/libhipblas.so* were there and the version was 7.0 or newer. A runtime-only ROCm 7 (no -dev packages) has both, and the engine's cmake configure then failed on the missing hip-lang package. The system ROCm now also needs lib/cmake/hip-lang/hip-lang-config.cmake (lib, lib64 or the multiarch lib folder, where CMake looks) and include/hip/hip_runtime.h; without them setup warns, naming what is missing, and takes the TheRock wheels path, as it already does for a ROCm older than 7.0. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

… a failed tuning says why (Niko1221#447) tools/calibrate.py read any "gpu" list as a layer split and added --layer-split auto, so a one-card config with a helper card for the expert tier ("gpu": [0] + --expert-cache-device1) was refused by the engine before it read a weight, a "0,2" string never got its split, and split_skip_if_fits was ignored. The tuner now builds the arguments with serve.server.engine_args, the same as a normal start. When the tuning fails, setup prints the engine's own last error line from the log (only lines written during the tuning), and says the failure again where it is not lost: before --calibrate starts the model, and in the "All set." summary of an install that tuned. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…etup recommends the big card alone (Niko1221#448) - engine (layer splits only): when a stage's cache cannot fund the chunk CUDA0 alone would choose, a WARNING names it (CUDA index, name, cache slots, the slots the chunk borrows) and points at the helper cache (--expert-cache-device1, docs/SECOND_GPU.md). The chunk choice itself is unchanged: fits() is split into fits_one() with the same tests; one GPU never reaches the new log. - setup: a pair whose second card is under 12 GB beside a first card of 12 GB or more is still offered, but the first card alone is recommended (the default with --yes and Enter; offer_together asks "n" by default), with a note. A named --gpus pair is kept. Equal pairs (2x 8/16/24 GB) are unchanged. - The automatic drop of the small card from an auto split is not done: stages load their weights before the boundary search and the exact prompt-buffer bytes need the sessions made after it. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…the totals (Niko1221#457) The server already read drafts_offered / drafts_accepted from the engine's DONE line but only returned them in a response's timings (draft_n / draft_n_accepted). Each /metrics request entry now carries both (null when the engine did not report them) and the totals keep their running sums since the server started, so a dashboard such as llm-visuals can show the acceptance. Additive: no existing field changes. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…he --family/--model to use (Niko1221#444) With Unsloth's UD-IQ3_XXS or UD-Q2_K_XL in the folder, setup either said only "has no IQ3_XXS model file" (--family unsloth --model IQ3_XXS) or took the UD-IQ3_XXS file for the GSQ-RCO IQ3_XXS by the size in its name. Now: - a chosen shard whose name is a quantization setup has no size for (UD-IQ3_XXS, UD-Q2_K_XL, a K-quant) stops with "<file> is UD-IQ3_XXS, a GGUF Strata cannot run" and what Strata runs: ISTA-DASLab's GSQ-RCO files (and Swift 1.5's and the Coder's) and Unsloth's UD-Q4_K_XL only; - a folder without the chosen file but with other GGUFs lists the ones Strata cannot use and the --family/--model of the ones it can (an empty folder keeps check_shards' "missing"); - a size the family does not have names the families that do (--family qwen --model IQ3_XXS), and with --gguf-dir also what Strata runs. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…e unpruned sizes for that (Niko1221#438) README and docs/MODELS.md said only "Weaker outside coding". Niko1221#438 measured Chinese answers wrong or looping on the Coder where English was fine; both now say plainly that this includes Chinese and other CJK text and point to the sizes that keep every expert (Q2_0, IQ2_XS, IQ3_S). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…iko1221#461), the low-RAM resident mode on Windows 32 GB PCs (Niko1221#467), fixes Niko1221#460 Niko1221#459 Niko1221#446 Niko1221#447 Niko1221#457 Niko1221#448 Niko1221#444; setup requires it Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

The fill from the GGUF (--stream-experts) read each expert's three slices, copied the blob and waited for the copy before the next read: ~12,000-18,500 experts with neither the SSD nor PCIe kept busy. Now the slots are admitted in profile order first (same placement), reads go in file order by up to 8 threads into page-locked batches of 64, and a batch's copies overlap the next batch's reads. STRATA_FILL_SERIAL=1 keeps the serial fill. Tested on the B70: cold page cache, Coder fill 23.4 GiB 76.2 s -> 18.8 s; launch to first token Coder 82 s -> 26 s, IQ2_XS 120 s -> 41 s; outputs identical to the serial fill. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Rb4SRxFuLpffQAAXBf4HxA

… read (Niko1221#471) The engine's `strata serve: prompt ...` line now says "= 0 reused + 12288 of 98179 read in ... (cancelled)" with the rate from the tokens read, and DONE gets a 15th field: the prompt tokens actually read. The server's /metrics history and totals count reused + read for a cancelled read (prompt_total keeps the whole prompt); an older engine's DONE line (no such field) is read as before. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

Engine: when the MTP draft head's subset does not fit, it prints what the head needs, the VRAM free, and the smaller shipped subsets (--draft-vocab cyrillic / en, their MiB from the head's real row size) with how to pick one (START-HERE.bat / ./setup.sh --draft-vocab en, kept in the model's config). Text only, on the failure path. Server: a start that ended that way carries the engine's hint (or the same advice for an older engine) in its "the engine exited before it was ready" error. Setup: a card under 14 GB with the default subset gets a note recommending --draft-vocab en; nothing is changed, an explicit or saved choice gets no note, and the golden configs are unchanged. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…o1221#475) In a git clone they run `git pull --ff-only` (a zip copy is told to download the new zip), then `setup.py --update`: what a plain start does to an install before it starts the model - the Python packages, the ready-made engine when MIN_ENGINE is newer (a compiled one when its source changed), each model's config upgrades and its draft subset, the Windows AMD runtime - and nothing else: no question, no model files, no start. Both scripts run from one parsed block, since the pull can rewrite the running script. Documented in docs/INSTALL.md (Updating) and the README. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…profile-save, Niko1221#477) `--expert-profile-save P` (--serve) writes the adaptive tier's learning as a profile in tools/make_profile.py's format, byte for byte (read_expert_profile reads it back): every pair ranked - the experts resident in VRAM now first, then the routing counted since the start (the decayed usage summed at each adapt step, before its decay), then the profile the engine started from. On QUIT / stdin EOF and every --expert-profile-save-every minutes (default 10, 0 = exit only) between requests, through <P>.tmp and a rename. Without the flag nothing is counted or written: `heat` stays empty and every added loop is over it. Server: "expert_profile_save" (and "expert_profile_save_every") in the config pass the flags, and the next start begins from the saved file instead of the config's --expert-profile when its header matches that profile's layers x experts. tests/core/expert_profile_save_test.cpp (CPU only) checks the order and the bytes. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…, the draft-head hint (Niko1221#474), UPDATE.bat (Niko1221#475), --expert-profile-save (Niko1221#477); setup requires it Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…pt path, grouped on the GPU (opt-in STRATA_PF_FUSED=1) Strata's own kernels (src/prefill/moe_fused.cu): the layer's input rounded to int8 once per token (a scale per 32 values), the (token, k) pairs grouped by expert on the GPU (counts, offsets, a 64-row tile list - no host sync), then per batch of up to 128 experts one gate/up launch (mma.sync m16n8k32 s8, the 2-bit codes unpacked in registers, Q2_0's offset folded into the activation blocks' sums, SwiGLU and H's int8 rounding in the epilogue) and one down launch into the per-slot rows the existing combine reads. Experts are read where they are (cache slot or streamed ring slot, a pointer per expert): no gather. Used only on the streamed walk (chunks from stream_all_min()) of the canonical Q2_0 layout on sm_80+ CUDA, with STRATA_PF_FUSED=1; it reuses the MMQ buffers (Xq, H, GU). Everything else - the default, native/IQ packs, small chunks, sm_75, HIP - runs the MMQ path unchanged (the old block is only wrapped in the else branch). tests/cuda/prefill_fused_moe_test (exit 77 below sm_80), RTX 5070: vs a double-precision reference, MMQ rel RMS 1.144e-2 (worst row 4.15e-2), fused 1.144e-2 (4.15e-2); fused vs MMQ 9.0e-5. One layer at 2048 tokens x top 10 over 512 experts: MMQ path 11.12 ms, fused 3.13 ms. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…e layer's experts) by default The fused path launches on batches of a layer's streamed experts, so with the 384-slot ring a layer's last batch waited for slots its own first batch frees. With STRATA_PF_FUSED=1 (and the canonical Q2_0 layout) the pinned-RAM ring is 512 slots; without it nothing changes (384 / 96 as before; STRATA_PREFILL_RING still overrides). RTX 5070, Q2_0 (cfg-q2rel), greedy, 3 alternating pairs, prompt tok/s: 4K 1,275 -> 1,516 (+18.9%), 32K 2,176 -> 2,422 (+11.3%); at 384 slots the fused path gave +4-7% / +3%. 1024 slots: 4K +24-27%, but the 32K chunk falls from 8192 to 6144 (+6%). The default path's output is identical to 0.1.35's on both prompts. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

… hold only what the fused kernels need In a layout whose chunks run the fused experts (STRATA_PF_FUSED=1, the Q2_0 pack, a streamed chunk) GU holds the grouping tables, H the int8 H rows and Xq the per-token int8 activations, and Hq nothing - instead of MMQ's FP32 GU / H and per-slot q8_1 rows: ~100 KB a token less in the MoE set, which then no longer sets the shared region's size (the GDN set does). A last chunk below stream_all_min() still runs MMQ in the same buffers, so each keeps MMQ's size for stream_all_min() - 1 tokens. The ring may grow to 1024 slots with the fused experts (STRATA_PREFILL_RING; the default stays 512). Without STRATA_PF_FUSED nothing changes: the same buffers, ring cap (512) and events. The fused walk now counts the experts it reads from the cache in experts_resident (it stayed 0): every resident expert of the layer, routed or not - at a streamed chunk's size nearly all are. RTX 5070, Q2_0 (cfg-q2rel), --prefill auto, 2 alternating pairs, prompt tok/s (borrowed slots): 4K: MMQ 1,305 (1621), fused 1,517 (1749), fused + P3 1,512 (1534), + 1024-slot ring 1,605 (2046) 32K: MMQ 2,177 (3071), fused 2,453 (3199), fused + P3 2,485 (2641), + 1024-slot ring 2,660 (3153) 128K (--max-context 131072 --kv-resident 32768): MMQ 2,108 (chunk 8192), fused 2,140 (6144), fused + P3 2,346 (8192), + 1024-slot ring 2,392 (6144) A 16K chunk still does not fit the 12 GB card: at 8192 tokens the loan is 2,641 of 3,690 slots, ~2.1K of them per 8K tokens. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…ault (two layers' experts) With P3's smaller buffers the 1024-slot ring fits beside an 8192-token chunk at 32K (borrowed 3,153 of 3,690 slots, under the 90% cap). RTX 5070, Q2_0 (cfg-q2rel), --prefill auto, prompt tok/s at 512 / 1024 slots (2 alternating pairs): 4K 1,512 / 1,605, 32K 2,485 / 2,660, 128K (KV streaming) 2,346 / 2,392 - there the chunk falls from 8192 to 6144 and is still faster. Against MMQ (one more pair on this build): 4K 1,299 -> 1,609 (+23.9%), 32K 2,187 -> 2,656 (+21.5%), 128K 2,128 -> 2,454 (+15.3%). The ring does not change the numbers (the same output as at 512 slots). Without STRATA_PF_FUSED nothing changes (384 / 96 slots, cap 512). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…y argmax, the same ids and tokens - qsa_block_topk: calls of up to 16 queries (decode windows) run on a cluster of 8 CTAs per query (block_topk_cluster_kernel): keys in shared memory, digit histograms pushed through distributed shared memory, one cluster barrier per radix pass, a parallel digit search, cluster-wide offsets for the ordered emit. The threshold and the cut at it are the one-CTA kernels' (same tie rule, NaN keys 0). STRATA_QSA_CLUSTER=0: off. RTX 5070 per call: 21.9 -> 15.6 us at 32K, 58 -> 18 at 128K, 200 -> 22 at 262K. - sample_tokens greedy without penalties: a cluster of 8 CTAs per row (sampler_greedy_cluster_kernel), four loads in flight per thread, results merged in CTA 0 (lowest index on ties, NaN never taken, 0 when none). STRATA_ARGMAX_MULTI=0: off. 39.6 -> 5.9 us per call at 248,320 logits. - Both launch through cudaLaunchKernelEx with the cluster attribute (captured in the decode graphs) only where they run: CUDA, compute capability 9+ (STRATA_EMULATE_CC respected), the kernel's own PTX/SASS at sm_90+ (an older build's JIT copy is a trap), room for one cluster - checked once per device. HIP and sm_75/86/89 keep the one-CTA kernels. - decode_cluster_parity (ctest; skips below sm_90): bitwise vs the one-CTA dispatch and the reference kernel over 434 top-k cases (contexts 7..262,144, every tail weight, 1-16 queries, ties / NaN / +-inf / -0 / denormal / all NaN) and 216 argmax cases, plus a CUDA graph capturing both (first calls inside the capture); --bench timings. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…gate/up IQ2_XXS IQ2_XS IQ2_S IQ3_XXS IQ3_S IQ4_XS, down Q2_0 IQ4_NL; opt-in STRATA_PF_FUSED=1) src/prefill/moe_fused_iq.cu: the Q2_0 path's design (moe_fused.cu: the grouping on the GPU, a pointer per expert - cache slot or ring slot, no gather -, SwiGLU and H's int8 rounding in the gate/up epilogue) for the raw GGUF blobs of the native packs. The load stage decodes the i-quant blocks to int8 in shared memory (llama.cpp's codebooks and sign tables, held in shared memory; a 32-value sub-block per thread per 64 values of K, read one stage ahead with 16-bit loads and two 256-value blocks ahead into L2), with each sub-block's scale (per 16 values for IQ2_XS / IQ2_S, which multiply at m16n8k16). The activations are int8 per 32 values in natural order (quantize_act_native); fragments by ldmatrix; the magic bias 1.5 * 2^23 enters as the mma's C, so a dot becomes a float with one add. A work item is 256 weight rows x 64 routed rows, or 128 x 128 when the experts have 56-112 rows each (STRATA_PF_FUSED_TILE=64|128). Wiring (prefill.cpp): a native layer takes these kernels when both its formats are covered, else MMQ (IQ1_M keeps the FP16 path); the 512-slot ring of the fused path now also for a native pack with such layers. sm_75, HIP and the default (no variable) are unchanged: on IQ2_XS, IQ3_XXS and IQ3_S the default output is 0.1.35's (256/256 greedy tokens, 4K and 32K). tests/cuda/prefill_fused_iq_test (exit 77 below sm_80), RTX 5070, 256 tokens x 64 experts against a double-precision reference from ggml's to_float, rel RMS / worst row - MMQ and fused equal to four digits: IQ2_S/Q2_0 1.489e-2 / 2.93e-2 (fused vs MMQ 2.6e-4), IQ2_XXS/Q2_0 1.464e-2 / 2.96e-2 (1.9e-4), IQ2_XS/IQ4_NL 1.465e-2 / 2.41e-2 (3.5e-5), IQ3_XXS/IQ4_NL 1.458e-2 / 2.73e-2 (1.6e-4), IQ3_S/IQ4_NL 1.470e-2 / 2.65e-2 (6.5e-5), IQ4_XS/Q2_0 1.446e-2 / 2.72e-2 (4.4e-5); both tile shapes. One layer (512 experts, top 10), ms, MMQ path -> fused at 2048 / 3584 / 8192 tokens: IQ2_S 10.5 -> 5.2, 12.5 -> 9.3, 21.4 -> 17.5; IQ2_XXS 8.8 -> 4.2, 10.7 -> 7.6, 17.9 -> 13.6; IQ3_S 10.6 -> 5.6, 12.3 -> 8.8, 19.8 -> 18.4; IQ3_XXS 10.2 -> 5.1, 11.9 -> 8.3, 19.1 -> 16.5. MMQ's products alone (no gathers) at 3584 / 8192: IQ2_S 8.9 / 17.9, IQ3_S 8.1 / 15.6: the kernels are about even with MMQ's, the gain is the path around them. (The test syncs the device after its uploads: a cudaMemcpy from pageable memory on the legacy stream can still be queued on Windows while a non-blocking stream runs - the grouping then read zeroed ids, every pair on expert 0. prefill_fused_moe_test's timing part has the same pattern.) End to end, RTX 5070, speed_135.py's settings, greedy, A (MMQ) / B (STRATA_PF_FUSED=1) alternating, 2 pairs, prompt tok/s: IQ2_XS 4K 1,228 -> 1,378 (+12%; a cold first A run of 590 left out), 32K 2,107 -> 2,170 (+3.0%); IQ3_XXS 4K 1,001 -> 1,020 (+1.8%), 32K 1,745 -> 1,739 (-0.4%); IQ3_S 4K 927 -> 925 (-0.2%), 32K 1,382 -> 1,362 (-1.4%). The IQ3 packs' prompts here wait on the host copies of their unpinned experts (IQ3_XXS 4K: 5,062 of 22,573 streamed, ~3.0 s of 3.4 s), so faster kernels change little. Each arm repeats itself (256/256; IQ3_XXS 32K B and IQ3_S 32K A split at 162 / 223, the decode's timing); A vs B greedy agreement, identical prefix of 256: IQ2_XS 204 / 88, IQ3_XXS 182 / 89, IQ3_S 31 / 256. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Rb4SRxFuLpffQAAXBf4HxA

…(STRATA_PF_FUSED=0 off), the native IQ packs' fused kernels opt-in (=1), cluster decode kernels; the fused test waits for its uploads The ring rule: 1024 slots for the Q2_0 pack's fused experts (P3), 512 for native packs with fused layers. Quality (teacher-forced vs the FP16 path, Q2_0): fused as close as MMQ at 8K, closer at 32K (phaseA-tests s18-tf). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

device, expert_source, native_head, prefill gemm, prefill.cpp, generate.cpp merged from dpct of aeb35be and d9ab843 with sycl/tools/merge_upstream.py (the 3-way merge restricted to files upstream changed, dpct kernel-name hashes canonicalized to the port's). Version 0.1.35. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Rb4SRxFuLpffQAAXBf4HxA

…ls) and their switches Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

The PCIe probe sets the share of the missed experts the GPU reads over the link (--pcie-frac): the native default 0.55 from 20 GB/s up, less on a slower link (Niko1221#44). On Windows HIP it timed its copies with events that do not bracket them there: an RX 6800 read between 3,348 and 25,999 GB/s in 24 engine starts, so every link kept the x16 share. A standalone copy of the probe on that card: its events measure 0.13 ms for the 1 GiB, the host clock 43 ms (25.1 GB/s). On Windows HIP the probe now times the same copies on the host, between two synchronizes (1 GiB, tens of milliseconds): the same RX 6800 on PCIe 4.0 x16 reads 27.2 GB/s in every run, which keeps 0.55 there. On a PCIe 4.0 x8 or 3.0 x16 link the share now shrinks as Niko1221#44 intended. CUDA and Linux keep the events. Claude-Session: https://claude.ai/code/session_015Ld5YDzuKW1hgMVwwgRZDP Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…DGET) On Windows, hipMemGetInfo (ROCclr's PAL backend) reports the card's size minus this process's own allocations; what the desktop and other programs hold on the card is not subtracted. On a card that also drives the desktop the engine counts the ~930 MiB those hold as free, and --expert-cache auto fills the card past what Windows lets the process keep in VRAM: WDDM moves part of it out to system RAM. On an RX 6800 (HIP SDK 7.2) with setup's configuration: a 9.83 GiB cache, the card at 16132 of its 16368 MiB, 929 MiB more of the engine in system RAM than with this change, 30.1-31.0 tok/s decode. On a 0.1.30 build, --vram-reserve-mib 1500 left the card as full (16037 MiB), since the reserve is taken from the same figure. Windows gives every process a video memory budget that does account for the others (DXGI QueryVideoMemoryInfo). On that card it is 799 MiB below the card's size while the process is small and about 1.8 GiB below once it holds 11 GiB; allocations past it still succeed. cudaMemGetInfo, which the HIP shim maps to hipMemGetInfo, now lowers hipMemGetInfo's free figure by what the budget withholds (the card's size minus the budget), on Windows only. What is sized from it then stays within the budget: --expert-cache auto sizes the cache from it, and the existing check after the slots are written shrinks it ("only 8 MiB free once the slots are written ... shrinking"): an 8.05 GiB cache, the card at 15230 MiB, 40.6-41.5 tok/s. Lowering HIP's own figure, instead of taking the budget minus DXGI's usage, keeps memory the HIP runtime has freed but still caches counted as free, as HIP counts it. dxgi.dll is loaded at run time, so nothing new is linked; the adapter is found by the device's LUID. Without dxgi.dll, the adapter or a budget below the card's size, hipMemGetInfo's figure stands. The first query logs the budget once. STRATA_WDDM_BUDGET=0 turns it off. CUDA and Linux builds are unchanged. Claude-Session: https://claude.ai/code/session_015Ld5YDzuKW1hgMVwwgRZDP Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

…g forever (Niko1221#481) A request whose engine prints nothing for engine_silence_s (default 300 s; while a prompt is read, a chunk may take 3x the previous one's time, the first up to its tokens at 50 tok/s more) ends with EngineSilent (an EngineDied): the engine is killed and the next request starts it again. The STOP drain, under the request FIFO, is no longer an untimed wait. "engine_silence_s": 0 keeps the old behaviour. Tests: unit (silence, PP chunks, first chunk, STOP never acknowledged, 0, config) and over HTTP with a fake engine process (non-stream, stream, stop token). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

…gh to overlap the others on a busy PC Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

…y fit in VRAM On a card whose experts mostly run on the CPU the slots cost speed per request (RTX 5070, Q2_0: 11-24 %), so setup now recommends slots only when the expert cache (every card of a split counted) still holds half of the model's experts beside them (Q2_0: 24 GB and up, or a split); elsewhere it stays at one at a time and says "parallel N reduces waiting for several users but costs about 10-25% speed per request on this card". --parallel N is honoured as asked, as before. BATCHING.md: the table with the change against one at a time. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

…ted count opt-in (STRATA_RING_BYTES=1) Default: 0.1.39's 384/1024-slot ring and bytes_needed count, so the default loan and output stay as they were. The auto chunk scan (the PR's second commit) is not taken. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

…the better buy `--prefill auto` walked a fixed list of chunk sizes - 32768, 16384, 8192, 6144, ... - and took the first that fitted, with the ring a fixed 384 slots taken off the top before the chunk was ever considered. Both halves of that were wrong on a rig like this one. The list is coarse exactly where it matters. `bytes_needed` is a sum of (T x positive constant) terms plus a max of such sums, so it rises monotonically with T, and so does every test the scan applies - which makes the largest chunk that fits a bisection on the 256-token grid the prompt path already works on. Seven probes against the list's ten, at one `bytes_needed` each. It matters because this rig affords ~8,700 tokens and was handed 8192; 8704 is not on the list. The order was wrong because chunk and ring spend the same borrowed VRAM, and a ring slot is worth far more than a chunk token. Measured here (2x RTX 3060 + 2x RTX 5060, CUDA3 lending at its 90% cap, 120K prompt): 8960 tokens with the 17-slot ring that leaves reads at 963 tok/s, 8192/130 at 1,008, 7168 with the ring full at its 199-slot byte budget at 1,000, 5632/199 at 915. A ring slot is worth ~0.53 tok/s and a chunk token ~0.05, so the 69 slots between a full ring and 8192's 130 are worth more than the 512 chunk tokens they cost - and once the ring IS full, a smaller chunk buys nothing. So: the largest chunk that still leaves the ring full, and only a rig where no chunk can afford one falls back to the old rule with a kRingMin floor. The room is counted in BYTES, not slots. A ring slot is max_blob, but a cache slot holds its own layer's blob - 2.15 MiB against a 2.54 MiB max_blob here. In slots the ring looked 18% cheaper than it is, a chunk whose ring fitted only after that discount was refused outright, and the scan stopped a step short. `bytes_from_slots` is the inverse of the existing `slots_from_bytes`, and `cache_slots_for` factors the latter out for the serve path's per-participant version. Both inverses clamp at k <= 0: the lend budget is `min(slots - 128, pct * slots / 100)` and goes negative on a cache under 128 slots, where `slots - k` is off_[slots] - the end of the prefix sum, so that cache lends 0 bytes rather than reading past its offsets. Upstream's per-participant `fits`/`fits_one` and the Niko1221#448 diagnostic are kept: the ring room is taken over the smallest participant, `alone` is the chunk CUDA0 alone would read in, and the small-card warning is unchanged. Verified on the rig: the scan picks 7680/199 against the old rule's 8960/17, 1018.8 and 1021.1 tok/s on two cold 120K prompts against 963.1 - +5.9%. Q8_0 picks 5632/101, its 506 MiB budget over a 5,222,400 B blob. Co-Authored-By: Claude Code <noreply@anthropic.com> 0.1.39b (Niko1221): behind STRATA_RING_BYTES=1 with the byte-budget ring; the default keeps 0.1.39's chunk list (plan_lend and the serve scan), its log line gains the ring. The INFO fields (the Monitor commit) are not taken. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

…mbers and quality check Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

's fused_gr carveout on HIP) Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

Added where a config goes to several GPUs (setup --gpus / a multi-GPU install, start --gpus, "use both" at start). Single-GPU configs are not touched. Opt-out: setup --no-remote-expert-opt, or "remote_expert_opt": false in the config (a user key, kept on re-runs). The engine uses it only with a helper cache (--expert-cache-device1..3), so a layer split runs as before. tools/test_setup_remote_opt.py; the 2x16 GB golden configs gain the flag. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

…S.md with the measured quality Changes default output on long prompts of native packs against 0.1.39 (expected; gate notes in the S40 report). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

…8, Linux 525); the wheel pins match the 12.9.1 toolkit Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

… keep 0.1.39's ring - a prompt that fits 0.1.39's auto chunk keeps 0.1.39's ring (Prefill::set_ring_budget's small_max): 4K prompts were 1-17% slower with the smaller ring (Coder -15%, IQ3_S -5%, IQ3_XXS -2%; 5 interleaved pairs each); - ring slots are given up for a bigger chunk only where 0.1.39's rule held the chunk under 6144 (ring_cap_for); from 6144 on the ring keeps its 0.1.39 size (the Coder 6144/384 -> 7936/199 was -12% at 32K); - the unpinned arm's cap stays 96 (the byte budget made it ~49 on IQ3_S); - the scan sets its own ring budget instead of set_ring_override, so a layer split's 96-slot override (Niko1221#340) is no longer clobbered by the scan. RTX 5070 vs STRATA_RING_BYTES=0: 4K -0.7..+1.1% (bit-identical); 32K IQ3_XXS +18.5% (cache 1500) / +9.4%, Coder +3.1%, IQ3_S +0.2%. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJJwUv9g3cD2ztX6KWS8rk

# Conflicts: # setup.py

# Conflicts: # src/core/expert_source.cpp # src/kernels/cuda/iq_kernels.cu

# Conflicts: # src/core/verify.cpp # src/program/generate.cpp

…l); UD-Q4_K_XL stays experimental - "experimental" is per model now: MODELS["UD-Q4_K_XL"] keeps it, the unsloth family and UD-IQ4_XS drop it. The first menu lists the family without [experimental], with a one-line about (UD-IQ4_XS: a 94 GB download; under ~80 GB of RAM part of its experts are read from the SSD). Its size menu lists UD-IQ4_XS first (the default) and UD-Q4_K_XL second; an experimental size always sorts last. --model names are unchanged. - UD-IQ4_XS: no EXPERIMENTAL warning, --check verdict without it. Images are asked (off by default), per model: the engine's image path has no restriction for this pack, which uses the original model's image encoder; not yet run with images. UD-Q4_K_XL keeps images off. - MCP: the fallback tables mirror setup's (per-model experimental/vision), sizes in setup's order, per-size "experimental" and "images", the vision check per model. - Docs: README and its six translations, MODELS, UNSLOTH_Q4, AI_SETUP, MCP_SERVER. - Tests: the menus, the default, --model by name, images, --check, the pack/tokenizer paths; the golden configs are unchanged. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJJwUv9g3cD2ztX6KWS8rk

…e-mib 1000 if a request stalls With the image encoder on the GPU, the 700 MiB reserve can leave ~200 MiB free on a 12 GB card (UD-IQ4_XS end-to-end check on an RTX 5070). Setup prints a one-line recommendation; the written config is unchanged. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJJwUv9g3cD2ztX6KWS8rk

midhatn · 2026-10-04T15:03:51Z

I ran the real-model parity test on a Ryzen 9 7940HS (AVX-512), Windows/MSVC, using a matching Swift 1.5 IQ3_XXS pack and GGUF. All 810 bitwise comparisons passed, including the independent serial reference.

Two small test-port changes were needed: register pool_pipeline_parity under if((UNIX OR WIN32) AND STRATA_NATIVE_EXPERTS), and provide the Windows equivalents for setenv and fseeko/64-bit offsets in the test. For the v0.1.39 rebase, I also kept its q2_native_kernels(...) ISA dispatch inside the refactored helpers.

The bounded end-to-end code/tool/cache tests passed too, but I don't have a convincing speedup to report. Code decode was 31.8 tok/s with the pipeline on; the two off controls were 33.4 and 30.6 tok/s. Generated reasoning length varied substantially even between the off controls, so completion-time differences aren't an equal-work comparison.

This was a local combined v0.1.39 build with #711 (INT8 KV selected), #732 (prefetch disabled) and #749 also present. I compared the pipeline switch within that same binary. No ThreadSanitizer run or broader concurrency claim.

Niko1221 · 2026-10-06T10:10:18Z

Heads-up: today we cleaned up this repository's commit history, and GitHub closed this PR automatically when main was force-pushed (that is the "base branch was force-pushed" event above). It is not a rejection.

GitHub cannot reopen it, because its branch is built on the old history. If you want to keep it going: start a new branch from the new main, cherry-pick your own commits onto it (git fetch upstream && git switch -c my-fix upstream/main && git cherry-pick <your commits>), and open a new PR. Or simply leave it: when we take your change for a release we port it onto the new main ourselves and keep you as its author.

Sorry for the noise, and thank you for the contribution.

did-technomancer · 2026-10-06T11:16:03Z

STRATA_POOL_PIPELINE=1 on an RX 6800 under Windows (v0.1.39 + #835 #849 #851 #903 #704 #733 #732, merged and built
here, IQ3_XXS, ~4,290 expert slots): no difference.

	English	Ukrainian	Code
switch off	49.9	55.5	55.9
`STRATA_POOL_PIPELINE=1`	49.6	56.0	54.6

tok/s, median of 3 replies, temperature 0, one run per row. RX 6800 16 GB, Ryzen 7 5700X3D (8 cores, AVX2),
64 GB DDR4-3200, Windows 11, ROCm 10.2.0a20260930.

Niko1221 and others added 30 commits October 2, 2026 09:33

Merge branch 'f-135-eng' into rel-0.1.35

d8d4ad3

Merge branch 'f-135-small' into rel-0.1.35

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Merge upstream main (0.1.35) into b70

e5c2836

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Rb4SRxFuLpffQAAXBf4HxA

Merge branch 'f-136-fused' into rel-0.1.36

bb47c5c

docs: 0.1.36's speed (fused Q2_0 prompt experts, cluster decode kerne…

78f071c

…ls) and their switches Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

docs: the cluster decode kernels run on sm_90+ (RTX 50), not RTX 40

36fa455

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN

Niko1221 and others added 19 commits October 4, 2026 03:34

Niko1221#465 serve/test_parallel: the first request decodes long enou…

0b651f8

…gh to overlap the others on a busy PC Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

docs: STRATA_RING_BYTES=1 (Niko1221#583, opt-in) with its RTX 5070 nu…

06586e3

…mbers and quality check Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

hip_compat: cudaFuncAttributePreferredSharedMemoryCarveout (Niko1221#646

f166564

's fused_gr carveout on HIP) Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

setup: the CUDA 12 engine's driver floor per OS (Windows 527.41 -> 52…

bc0fc49

…8, Linux 525); the wheel pins match the 12.9.1 toolkit Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

Pipeline CPU expert work without a batch-wide phase barrier

6b22c97

Merge branch 'f-139b-cpu' into f-139-rel

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Merge branch 'f-139b-old' into f-139-rel

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# Conflicts: # setup.py

Merge branch 'f-139b-arc' into f-139-rel

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Merge branch 'f-139b-spd' into f-139-rel

cb97e6c

# Conflicts: # src/core/expert_source.cpp # src/kernels/cuda/iq_kernels.cu

Merge branch 'f-139b-par' into f-139-rel

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# Conflicts: # src/core/verify.cpp # src/program/generate.cpp

docs: README refined + six translations

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midhatn mentioned this pull request Oct 4, 2026

kv: --kv k8v4 streams with --kv-resident #711

Closed

Merge v0.1.39 and retain ISA dispatch and portable parity tests

f2625a5

Unmaple mentioned this pull request Oct 5, 2026

cpu: allow configuring expert-pool tasks per phase #949

Closed

Niko1221 closed this Oct 6, 2026

Niko1221 force-pushed the main branch from 1735d64 to 82f46a8 Compare October 6, 2026 09:48

Unmaple mentioned this pull request Oct 6, 2026

cpu: allow configuring expert-pool tasks per phase #1087

Closed

This was referenced Oct 6, 2026

STRATA_HC_Q8: hyper-connection projections as int8 + fp32 scale per 32 #704

Closed

Overlap streamed KV uploads with prefill computation #732

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