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…, 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
…0 log lines (Niko1221#496) Every start failure, not only the draft-head case (Niko1221#474), now carries the engine's own reason: the last 20 lines this start wrote to the engine log (never an earlier start's). The failed start also closes its pipes and its handle on the log. Tests: the tail itself, and a fake engine that exits with a reason in its log. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…p-experts (Niko1221#505) The server window said "loading the experts into RAM (about N GB)" whatever the flags. Now: --mmap-experts maps them from the model files (the OS file cache reads them, nothing is loaded up front; its "experts via mmap" line triggers the message when the model has no PLE line), --resident-experts copies the ones the GPU does not hold, and --resident-budget-gib N copies up to N GiB. setup's start banner says "maps" for the plain mapped mode too. Tests: the words per flag set, and the narrator over a mapped and an arena log. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…#495) hf() takes the host from HF_ENDPOINT (default huggingface.co, a trailing slash dropped); the pinned revisions, the fallback to main and the SHA-256 checks are unchanged. tools/mtp_fetch.py reads the MTP tensors from the same host. Step 5 prints the folder setup expects the model files in and how to use files downloaded by hand (that folder with their original names, or --gguf-dir), and the mirror when one is set. INSTALL.md says the same. Tests: the endpoint in hf()/hf_unpinned()/mtp_fetch, and step 5's words over a mocked install. 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
…ds it; no budget on a --gpus start (Niko1221#498) The engine refuses --resident-budget-gib with a layer split, so setup always dropped UD-Q4_K_XL to one GPU. With RAM >= the GGUF size + UNSLOTH_RAM_LEFT_GB (~135 GB) and 2+ usable cards, setup now asks (one GPU stays the default and --yes keeps it; an explicit --gpus is honoured; an explicit --resident-budget-gib keeps one GPU) and the split writes "gpu": [..], "layer_split": "auto" without the budget: all the experts loaded into RAM at start, the files through the OS file cache (the config Niko1221#498 measured: 2x RTX 3090, 31 -> 64-78 tok/s). Latent bug: START-HERE --gpus 0,1 (or the start-time offer, or a hand-edited config) on a UD-Q4 config kept the budget and the engine exited with code 2; the split now drops it, or setup stops before saving when the RAM is too small. docs/UNSLOTH_Q4.md says so. Tests in tools/test_setup_unsloth.py; 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
The engine already leaves --vram-reserve-mib MiB of VRAM free (default 700). INSTALL.md now says what it does, its default, and how to set it (the flag, or "--vram-reserve-mib", "2048" in the config's args). Setup's new flag writes it into the config only when given (one value: it replaces the images' own 700, with a note when it is less than the GPU image encoder needs); on a start of an installed model it is saved into that model's args, and a new copy set up from an earlier install keeps it. The default config is unchanged (golden test). Tests in test_setup_choices.py. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…are, the samples logged The probe timed one 1 GiB burst; an x16 PCIe 4 laptop (RTX A3000) read 18.5, 6.9 and 5.8 GB/s in three starts, and the low reading set pcie_frac (0.12-0.15 where calibrate.py found 0.35). The same 1 GiB is now four bursts of 256 MiB, each timed on its own (events between them on the stream; on Windows HIP the host clock, Niko1221#377), and the fastest is the link's figure: a disturbance only ever slows a copy, while the median would still follow one lasting through half the bursts. The probe copies no more than before. The share was base above 20 GB/s and base * bw / 26 below (19.9 GB/s -> 0.42, 20.0 -> 0.55; 4.0 -> 0.08, 3.9 -> 0). It is now base * min(1, bw / 20): continuous, unchanged from 20 GB/s up (an x16 PCIe 4/5 link reads ~26-28 GB/s, so the default stays 0.55 there), in proportion to the link below, so the link's time on its share stays about what the x16 share costs. The rule is one function for CUDA0 and the layer split's stages. The log names every burst: "PCIe probe: 28.9 GB/s host->device (best of 28.9 28.9 28.9 28.9)" (the RTX 5070 here; pcie_frac 0.55 as before). Noted, not changed: a given --pcie-frac is every stage's share and skips the stages' probes. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…#380) and the host-timed PCIe probe (Niko1221#377) Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…MD counts the desktop's VRAM (Niko1221#380 Niko1221#377 Niko1221#497), a steadier PCIe probe (Niko1221#485), fixes Niko1221#496 Niko1221#495 Niko1221#498 Niko1221#505 Niko1221#493; setup requires it Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…iko1221#374 Niko1221#363 Niko1221#407 Niko1221#413 onto 0.1.35 One dpct run of upstream main + the seven PRs, 3-way merged into the files they touch (sycl/tools/merge_upstream.py); their header changes are forked into sycl/include until upstream merges them. - Niko1221#385 stager waits for a buffer's previous DMA on every job (port: polled seq) - Niko1221#463 decode waits for an adaptive swap before reading residency - Niko1221#453 drafter's batched K/V on the KV-streaming ring - Niko1221#374 first chunk's PLE rows read beside layer 0; port fixes: the next read starts at c0 + T (the port's first chunk is shorter), and the upload is marked by a polled sequence number instead of a host event wait, which deadlocked prompts past ~4K tokens under the Level Zero v2 adapter - Niko1221#363 group stride + fused SwiGLU/q8_1 for the PCIe expert call, re-applied to the port's lane kernels (fp contract off, as upstream's HIP form) - Niko1221#407 --adapt-tuned (opt-in), Niko1221#413 key-head DeltaNet recurrence (opt-in via STRATA_GDN_KEYHEAD=1: 8% slower on the B70; its NVIDIA gate and SM-holding parity bench not ported) Tested on the B70: native_grouped_parity, iq_multi, gr, qsa, kv_hybrid, quantize_act, qsa_prompt_attn parity pass; Coder 19/2,184-token, IQ2_XS and 40K outputs identical to 0.1.33; Coder 2,184-token prompt 784 -> 825 tok/s; 128K int8 KV streaming 895 / 67.0 tok/s; IQ2_XS decode +1.7% (Niko1221#363). Co-authored-by: sergqwer, constantindjonkam, architectds, BlueKingMuch (upstream PR authors) Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Rb4SRxFuLpffQAAXBf4HxA
…er_intel) - engine (serve mode): GEN key logprobs=K (0..20) writes "LP logprob id:lp ..." after each "T id", from the verify window's head logits (Verifier::copy_logits; before temperature, penalties and sampling); absent key, no LP lines - server_intel.py: pairs LP lines with their tokens (StrataEngine._pump), passes logprobs/top_logprobs to the engine, and fills OpenAI's choices[].logprobs (content / reasoning_content) in streamed chunks and the collected reply; the stop token is not listed. serve/ itself is unchanged. Tested: a fake engine on hardac (streamed, not streamed, without logprobs, stop token excluded); on the B70 with the IQ2_XS: same answer with and without, "Paris" -0.0019 with top-1 equal to the chosen token, no measurable time cost. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Rb4SRxFuLpffQAAXBf4HxA
… wait, released by one event In the streamed walk every expert put a cross-stream wait (its copy) and an event record (its ring slot back) on the compute stream around a ~3 us gather. Under WDDM each pair left ~10 us of GPU idle even when the copy had long landed: an nsys trace of a 32K prompt showed 226 ms of idle between back-to-back gathers while the host was 3-11 ms ahead of the GPU. Now a group of 16 is gathered by one launch (copy16_group_kernel) after one wait on its last streamed copy - the copy stream is in order - and one event releases all of its ring slots: the issuer waits on used[used_of[slot]]. A ring entry the routing skipped inside an open group first gathers what the group holds so far, so no more than a group's entries are ever held back from the issuer. The same bytes in the same group slots. STRATA_PREFILL_GROUP_GATHER=0: one at a time, as before. IQ2_XS, RTX 5090, 262K context: 32K prompt -8.6%, 8K -10%, 2K -12.6%; first-token logits bit-identical at 2K, 8K, 16K and 32K and with a 96-slot ring. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VZy1yKaDDiA8a7svdwaHio
… 256 at a time Layer 1 adds the per-layer embedding to every token of a chunk, so its rows must be in. A later chunk's rows were already read on a thread during the chunk before; the first chunk's were read synchronously before layer 0, so the GPU idled while the SSD read them (an nsys trace: ~245 ms of a single-chunk 32K prompt). Now the first chunk's gather starts on a thread too and lands just before layer 1 (ple_land), which also starts the next chunk's. The reads themselves went 64 at a time: a 32K prompt is 512,720 rows in 213,358 4 KiB reads, ~620K IOPS at queue depth 64. --ple-inflight now defaults to 256, where the drive saturates (1024 is no faster). Decode reads 16 rows a token and is unchanged. The same rows: the same results. IQ2_XS, RTX 5090: a 32K prompt 5986 / 6013 -> 5843 / 5808 ms, 8K 1714 -> 1624 ms; first-token logits bit-identical. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VZy1yKaDDiA8a7svdwaHio
…e checked From an audit: the upload of a chunk's rows, the event recorded after it and the wait on the other buffer's event before the SSD thread refills it were unchecked, so a failed record or wait let the thread write a buffer an upload might still read. A failure now ends the chunk with the CUDA error. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01VZy1yKaDDiA8a7svdwaHio
# 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
Merge Niko1221/Strata main at 6f32ec0 (v0.1.39). Preserve Windows ADL monitoring and English-default Arabic UI; localize the new conversation cache, PCIe share, and model settings controls without resetting unsaved values. Validation: 226 server/telemetry/API tests, 116 setup tests, JS syntax checks, translation placeholders, and Playwright UI regression checks passed.
…indows AMD telemetry
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Hardware validation on 2026-10-09: RX 7900 XTX 24 GB, Ryzen 7 7800X3D, 64 GB RAM, Windows 11 build 26200, AMD driver 32.0.31041.1004. The installed HIP engine was Strata 0.1.41, Coder IQ1_M, 131072 context. Each PR was checked out separately at the commit below. The hardware reader was sampled 15 times at one-second intervals while the existing model server continued its ordinary workload. These were not controlled performance benchmarks. For HTTP integration, a separate loopback server used MockEngine plus the real GPU reader; Tested head: The AMD reader works on this discrete RDNA3 card, including board power.
Repeatable admission-query check from the PR checkout: import gc
import psutil
from serve.telemetry import free_vram_mib
for _ in range(3):
free_vram_mib(0, amd=True)
gc.collect()
process = psutil.Process()
before = process.num_handles()
for _ in range(40):
free_vram_mib(0, amd=True)
gc.collect()
print(before, process.num_handles())Limitations: one discrete card only; no independent PCIe-link or temperature calibration, no browser rendering/Arabic UI test, and no continuous-hours soak. The telemetry unit tests and HTTP/JSON integration above are the scope of this report. |
Keep the previous main history as a merge parent while adopting the current installation tree after upstream history cleanup.
…D telemetry and Abliterated models
…telemetry and Abliterated models
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Thanks for the pull request. We're resetting the tracker for 0.2.0-beta. There are more than 1,300 open issues and pull requests, too many for us to review properly, so we're closing everything that isn't being worked on right now. Closed here does not mean rejected. To reopen it takes about two minutes:
The rules are in CONTRIBUTING.md. |
On Windows with AMD GPUs, the web Monitor leaves GPU load, dedicated VRAM, temperature, power and PCIe link fields blank. Add an ADL reader from the installed display driver, including HIP-to-ADL device matching, unit conversion and cleanup. Unsupported readings remain unavailable.
Add an optional Arabic interface to Chat, Monitor and API Monitor. English stays the default; the browser remembers the language. Arabic uses RTL layout while code and protocol values retain their direction and values. Language switching preserves drafts and in-flight responses, and the current conversation-cache and model-settings controls are translated.
Replaces #712, which GitHub closed automatically after the upstream history rewrite. This branch starts from the new upstream
main(82f46a8) and cherry-picks only the contribution and screenshot commits, preserving original authorship.Validation:
python -m unittest serve.test_amd_windows serve.test_server.AmdTelemetry serve.test_monitor serve.test_runconfig -q.Arabic Monitor screenshot from the original contribution, captured with the model running on an RX 9070 XT: