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The thread that launches a chunk's kernels also issued the expert copies, and waited there for the host copies of the unpinned blobs (the arena part Windows would not pin): the GPU idled - the 'wait copy' and 'dequant' time of the i-quant prompts. A per-chunk issuer thread now walks the stream: the same copies in the same order into the same slots, a slot refilled only after the compute stream recorded it free. STRATA_PREFILL_ISSUER=0 keeps the inline issue. IQ3_S 32K prompt: 1143 -> 1213 tok/s, 'wait copy' 15.2% -> 7.5%. Exact: residual dumps and continuations byte-identical to 0.1.21 at 20K (IQ3_S, IQ3_XXS: +16% prompt tok/s with Phase C). The stager's ring and threads are tunable (STRATA_STAGER_RING / _THREADS); the defaults stay (more copy threads were slower: 816 tok/s at 6). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
Agent clients put screenshots in tool results; flattening non-user messages to text made the model blind to them. The template and images_of already handle them; only the conversion was too strict.
…yer-split-limits Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
… a host sync per group A group of <= --spec rows was staged in mapped memory and synced before the next: ~5,500 host round trips on a 32K prompt. The rows' token / step / position records are now uploaded once per call and each group is device copies plus a graph on the one stream, one sync at the end (a graph variant without the mapped staging). The same work in the same order: continuations byte-identical to 0.1.21 (4K, 20K). Q2_0 32K: the draft layer's share 1091 -> 905 ms; prompt 1213 (Phase B) -> 1387 tok/s with Phase C and D-5. STRATA_MTP_PREFILL_SYNC=1 keeps the old loop. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…g from the host After routing, one thread checks the group's entries against the device residency table; when all are resident it writes the plan the host pool would (same layout, same order) and marks the ring. The layer's waits on the host (plan, PCIe share, CPU share) then return at once and the CPU rows are zeros; the host still runs its loop, off the GPU's critical path. STRATA_VERIFY_DEVICE_PLAN=0: off. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…utput norm in its own kernel One 512-thread block per head (48 blocks) walked the chunk. The value columns' recurrences are independent; only the per-token output norm couples a head's columns. Now 4 blocks of 32 columns per head (192 blocks, 128 threads per __syncthreads), and a parallel norm kernel with the same warp sums. Per column the same arithmetic in the same order: residual dumps and continuations byte-identical to 0.1.21 (4K, 20K). Q2_0 32K, back to back: GDN 5118 -> 4264 ms, prompt 1258 -> 1308 tok/s. STRATA_GDN_REC_HEADS=1: the per-head kernel. (A chunked delta-rule kernel - not bitwise - is the larger step left.) Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
Exact on RIBPC (5080 alone and 5080+3090, device plan off/on 10/10 identical), but decode and prompt speed within noise (1 GPU story 86.7 -> 84.5, code 103.3 -> 102.0 tok/s; 2 GPUs 81.5 -> 82.4, 113.9 -> 109.9). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…rve) Two gaps kept the routing trace one-shot-only: - drive_pool_multi, the verify-window dispatch the serve path uses for every decode call, had no trace hook; --dump-routing under --serve opened the file and wrote nothing. Records now go out per token in drive_pool's format (layer, k, ids, weights). The multi dispatch fuses the router weights into the kernel and does not surface them, so records carry unit weights: make_profile.py ranks pairs by routed frequency, which is the signal that matters; a one-shot run still records true weights. - the trace sat in a stdio buffer until teardown, so a crash lost the whole session's data. It now flushes at every request's DONE. Verified live: a serve-mode trace grows per request, parses cleanly in make_profile.py's format (all 48 layers, records per decode position), and captures real multi-session traffic for expert-profile building.
…pu 0,2 means --gpus 0,2
A user reported that he could pick a GPU but not two. With v0.1.21 (reproduced on a 5080 + 2080 Ti + 3090 PC):
* START-HERE.bat --gpus 0,2 on an installed model started it on GPU 0 alone: --gpus was saved only with --setup,
and the start passed its first card as --gpu, which overrides the saved choice;
* START-HERE.bat --gpu 0,2 stopped with "argument --gpu: invalid int value";
* an update unzipped next to a layer-split install stopped at step 1: "there is no GPU [0, 2]".
Now the first two start across both cards (the server gets --gpu 0,2 and splits the layers, auto), and an update
sets itself up across the same cards with the saved layer split. --gpu N still runs one card.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
One block per (query, KV head) walks its selected cells in chunks of 32 with an online softmax (no split-K scratch, no merge kernel). q.k and p.v are FP16 MMAs with FP32 accumulation; int8 codes enter exactly, their scales in FP32; q and p are split into hi + lo halves after an exact power-of-two prescale, so the result keeps FP32-level accuracy. Int8 KV: each warp owns one 64-dim scale group and gathers its own K/V slices with double-buffered cp.async. FP16 KV: the first version. Q4_0 KV, or STRATA_PROMPT_ATTN_OLD=1: the old kernel. qsa_prompt_attn_parity (5070, synthetic): vs FP64 3.2e-6 (old kernel 2.5e-6); int8 4.0x faster (26.6 -> 6.6 ms per 2,048-query chunk at 32K), fp16 1.45x. Q2_0, 32K prompt: attention 5,216 -> 1,318 ms, prompt 1,386 -> 1,646 tok/s (+18.8%). Not bitwise: the model amplifies any FP32-level change (v1 vs v2 of this kernel, same accuracy, another summation order, differ as much as old vs new: residual rel L2 mean 0.060 vs 0.061). Needles 8K/16K/32K x 5 depths: 15/15 with both kernels. Also STRATA_SEL_OVERLAP (debug): how many of the cells 16-query tiles read compared to one query at a time. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…nd mid-prompt checkpoints across GPUs Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
… asked, "not supported" said plainly
Several users could pick one GPU but not two. Now START-HERE.bat / setup.sh:
* lists every NVIDIA card with "can be used" or why not (older than the RTX 30 series; under 8 GB to share a model);
* asks, when two or more cards can share the model, which to use - the two best together recommended (newest
generation first), every combination and each single card offered; --yes takes the recommendation;
* asks once, at the next start of a model installed on one card, whether to use both from now on (kept);
* --gpus 0,2 / --gpus all / --gpu 0,2 at a start saves the cards for that model; --gpu N is for that start only;
* a card that cannot be used (or is gone) stops with its reason and the command that works instead.
The engine gets code for every chosen card (a local build compiles all their generations; the ready-made one is
checked against each), the context is recommended from the smallest card, and --calibrate measures the split.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…d next to it vswhere -latest picked Visual Studio 2026 (version 18) on a PC that also has 2022, and CUDA 13.1's nvcc stops with "unsupported Microsoft Visual Studio version". Now only 2019/2022 are looked for; with only a newer one, the 2022 Build Tools are installed as when none is there. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…,2, or one card: --gpu 0 or --gpu 2) Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
D-3 measurement: Q2_0, 128K prompt with KV streaming, 5070: staging 1,331 ms of 81.3 s (1.6%); staging only the selected union could save part of that, so D-3 is not done. The same prompt: 1,144 tok/s on 0.1.21, 1,596 on this branch (+39.5%), time to first token 115.7 -> 83.4 s. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
… one wait The same copies on the same (legacy) stream, but queued instead of waited on one by one: the same bytes, ordered the same against earlier work. Q2_0 on a 5070 (1,782 slots): 162 -> 96 ms per prompt, 4K and 32K outputs identical. The rest (publishing slots in the background while decode starts) would hide the last ~2% of the time to first token on a 4K prompt; not done. STRATA_REFILL_BLOCKING=1 keeps the old loop. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
The GPU, VRAM, temperature and power cards read all the cards the model runs on: their total (VRAM, power, PCIe traffic), mean (load) or hottest (temperature), with each card's own reading underneath; the GPU name lists them. One card: unchanged. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
Expose loaded-model metadata through /models and /v1/models. Add /props and single-slot /slots responses with existing API-key enforcement and regression coverage. Validation: all 30 server tests pass. Pi 0.87.1 discovered the loaded model with 262144-token context and thinking support, and completed a chat smoke test successfully. Fixes Niko1221#117. Co-authored-by: Codex (gpt-6-astra-medium) <noreply@openai.com>
…s it Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
The expert-arena load runs at ~0.05 GiB/s (13-14 min to ready) when the serve is started from Task Scheduler with its registration defaults (Priority 7 / Below normal, least-privilege token), versus 1.4-1.5 GiB/s (~35 s) from a terminal or SSH - same binary, args and cache state. - docs/DETAILS.md: measured table + the two registration settings that fix it - src/program/generate.cpp: when load_gib_per_second() < 0.2 GiB/s, print a hint naming this cause under the load line
…l sm_75 build compiles again) Its MMA and cp.async need sm_80: an sm_75 build stopped with ptxas errors. They now compile to a trap below sm_80, and qsa_prompt_attn_batch refuses such a device at run time, so the old kernel runs there. RTX 30/40/50: the same code (parity unchanged: vs FP64 3.17e-6). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
# Conflicts: # serve/server.py
…ccepted), from PR Niko1221#83 Co-Authored-By: mikicvi <mikicvi@users.noreply.github.com> Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
# Conflicts: # serve/server.py
…_K/Q5_1/Q8_0 multi-token kernels from two tokens on - Registering 30 GiB of a 40 GiB RAM tier left the driver unable to page-lock the prompt path's token id buffer afterwards (RTX 5070, WDDM: "prefill: the token id buffer"); the prefix is now capped at 24 GiB, the size a whole 24 GiB budget page-locks with, and STRATA_PARTIAL_PIN_GIB sets it. - A verify window's expert groups hold ~1.4 tokens on average; one token goes to ggml's own dot (the same bits, without the multi-token kernel's per-token arrays). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…ssed slices at once (PrefetchVirtualMemory) The copies' page faults read a few clusters each; one PrefetchVirtualMemory call for every role slice about to be copied lets the memory manager queue large reads at once, and the parallel copies then find the pages resident or in flight. Only the GGUF-in-place source's prefetch uses it; STRATA_FETCH_PVM=0 is the A/B arm. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…IN_GIB; the split's prompt chunk steps down - The expert arena was pinned at most 8 GiB whenever a layer split (or remote experts) ran, on every OS - a Windows/WDDM workaround that cost a Linux 4090 + 3060 split two thirds of its prompt speed (Niko1221#253, bettercallcaleb). The cap now applies only under WDDM (_WIN32, or WSL2 detected by /dev/dxg), for multi_gpu and expert_cache_remote alike. STRATA_ARENA_PIN_GIB overrides it on any OS: "0" pins the whole arena, N > 0 at most N GiB (arena_pin_cap_gib(), -1 when unset: the same name and meaning as the S2 branch's helper, file-local here). - The split's prompt chunk no longer stops the start when its buffers do not fit: a prompt path without a loan first gets the largest chunk whose buffers (Prefill::bytes_needed) leave 512 MiB free on its device, and an init that still fails steps down one size (8192 -> 6144 -> ... -> 2048 -> 1536 -> 1024 -> 512): Prefill::reset() frees what the failed init made, the loans are re-priced, and the failed allocation's sticky error is consumed (else the next launch check reported "out of memory"). - pinned.cu: the capped note no longer says "for CUDA1". - docs: MULTI_GPU.md, SECOND_GPU.md. Measured on amdbox (Linux, R9700 + RX 9070 XT, gpu [1, 0], Coder IQ1_M, 128 greedy tokens; tok/s): | cache | arm | 4K cold | 4K warm | 16K cold | 16K warm | decode | | auto (holds ~all) | 8 GiB cap | 678 | 1,355 | 1,709 | 1,880 | 44.5-51.5 | | | whole arena | 703 | 1,234 | 1,729 | 1,864 | 44.6-51.4 | | fixed 1500 on CUDA0 | 8 GiB cap | 608 | 1,101 | 1,287 | | 34.5-36.1 | | | whole arena | 716 | 1,160 | 1,803 | | 34.8-36.0 | (2 starts per arm for auto, 5 for fixed.) With most experts streamed the whole arena reads a 16K prompt 40% faster; with every expert already on the cards the two are within noise except 4K warm (-9%: the pinned share raises the auto lend to 90% and the streamed ring to 384 slots). Identity: the fixed-cache arms gave the same tokens in all 5 + 5 starts (4K, 16K, 4K again), and the auto arms 2 + 2. The step-down was exercised on the 9070 XT with --expert-cache 3000 --no-prefill-borrow --prefill 8192 ("need 4528 MiB, 2910 MiB free: 2048-token chunks"; with the init retry alone it went 8192 -> ... -> 2048, and 1536 ran all four requests). Known, also on main: that own-buffer configuration at 2048 makes hipBLAS pick hipBLASLt kernels whose code objects this ROCm 7.14 lacks (TensileLibrary_..._gfx1201-xnack+.co not found, "launchKernel failed") and the prompt hangs. The WDDM side (Windows, WSL2) is unchanged and not re-measured here. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…one when it holds every profiled expert + KV
Before any later stage loads, split-auto checks whether CUDA0 alone holds every profiled expert pair plus the whole
session (KV of every layer), the drafter + head, the verify windows and the reserve; if so it clears the split (one
GPU) and logs why, else it logs how much of the profile CUDA0 would hold and keeps the split. Default behaviour is
unchanged: the engine flag is opt-in, and the server passes it only for a config with "split_skip_if_fits": true
(and a GPU list). Not with remote expert caches.
Measured on amdbox (Coder IQ1_M, --expert-cache auto --prefill auto, 128 greedy tokens, gpu [1, 0] = R9700 first,
2 starts per arm, warm = the second request of each size):
| arm | 4K warm | 16K warm | decode |
| split (flag off) | 1,244 | 1,897 | 50.5-53.0 |
| --split-skip-if-fits (on) | 1,776 | 1,791 | 59.7-61.8 |
| R9700 alone | 1,787 | 1,801 | 59.9-61.3 |
The flag's run is the R9700-alone run (same tokens, all four requests); with the 9070 XT first it keeps the split
("would hold only 9.84 of the profile's 23.42 GiB"). On this pair the split still reads 16K prompts ~5% faster.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
# Conflicts: # src/core/pinned.cu
…aster on an AVX2-only CPU (Ryzen 9 3900X: gate/up -2.6..-4.7% at 1 token, down -3..-5% at 2 tokens) This reverts commit 9e9dce4. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…t an expert boundary; the Q4_K/Q5_1/Q8_0 multi-token kernels opt-in (STRATA_KQ256=1) - Locking the whole 40 GiB arena in the working set and then registering its prefix made the next device allocation fail (the prompt path's token id buffer); the registered prefix is page-locked by the driver already, so only the rest is locked now. 24 GiB registered out of a 40 GiB budget then starts and runs. - The registered prefix ends at an expert boundary: a blob that began inside it and ran past it was taken as page-locked by cudaMemcpyAsync and refused (an adaptive refill failed, with 16 GiB registered). - The prompt path's token id buffer error names the CUDA error. - kq_avx2 multi-token kernels: bit-exact, but no faster in the engine (the rows phases stayed at 72 ms per verify round at a 40 GiB budget; a window's expert groups hold ~1.4 tokens and ggml's per-token calls find the weights in L1), so they are opt-in with STRATA_KQ256=1; native_expert_parity still checks them bit for bit. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…m-split down kernel (164 blocks), rs/lo/inject in up's prologue (STRATA_GR_V3=0 = old)
…); its shared memory is sized from the card's opt-in limit - one or two column halves, else the default kernels (Turing/HIP 64 KB fit with two; capability-based split as proposed by @Adamyno); the neutral knobs (DOWN_R2, UP_PF, KSPLIT) dropped (review)
…ure was generated, as its generator does; a partly missing set still fails; the open handle of a half-present fixture is closed Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…er on this layer's MoE input warms its predicted experts' pages - RouterLookahead: when the CPU pool starts layer l, a worker thread applies layer l+1's router (a BF16 host copy of ffn_gate_inp, 126 MB for 48 layers) to layer l's MoE input - the router's own input - for each token of the window, takes the top STRATA_LOOKAHEAD_K (default 10), drops the experts the GPU cache (host_res) or the RAM copy holds, and asks the source to warm the rest. It never waits: a prediction still running makes the next one skip. STRATA_LOOKAHEAD=0 is the A/B arm. - ExpertSource::warm / warms(): FileExpertSource with the GGUF in place issues one PrefetchVirtualMemory for the predicted slices (madvise(WILLNEED) elsewhere). Pages only: blob(), the experts computed and their bytes do not change (the same answers in every A/B run). Every other source: no-op, and the predictor is not started. - bf16_rows_dot_multi (kq_avx2.cpp, AVX2): the router read once for all tokens of the window. - Stats: experts warmed, how many of the decode's file-tier reads had been warmed, the predictor's time and skips. UD-Q4_K_XL at a 40 GiB budget (single-token-router version): ~48-54% of the file tier's reads had been warmed; reading time 850-968 -> 630-730 ms per verify round; decode 5.31/6.60 tok/s off vs 7.08/6.79/6.91 on. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
Add preliminary native v0.1.26 results, raw run JSON, scrubbed guest hardware facts, and the local API harness. Prompt and memory topology limitations are documented.
On the RTX 5070 / 64 GB PC the GPU computing a share of the misses over PCIe measured no faster than the CPU: 7.30 and 7.44 tok/s with 24 and 16 GiB of a 40 GiB budget registered, against 7.05-7.74 unpinned, and registering costs startup time and driver memory. The default budget mode is now locked-only; the routing-aware prefetch (multi-run A/B: 6.93 -> 7.91 tok/s mean, identical answers) stays on with the GGUF in place. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
… the mapped mode without experts.bin, --resident-budget-gib, the tier counters - docs/UNSLOTH_Q4.md: what UD-Q4_K_XL is (Hugging Face revision 38bb39e, the four shards with sizes and SHA-256), disk/RAM/GPU needs (111.3 GB download, 1.4 GB pack, no experts.bin), the iq_pack.py command and why --compat-bf16, the numerical policy (native experts/embedding/head/projections/PLE; the converted small tensors listed in conversions.json), the MTP draft layer, a server config (--native shard 1, --ple-gguf found by name, --resident-budget-gib N with N = RAM minus 20-24 GB), measured speed on an RTX 5070 / 64 GB PC (7-8.5 tok/s at 40 GiB; ~60 s to the first answer), the switches (STRATA_LOOKAHEAD=0, STRATA_KQ256=1, STRATA_PARTIAL_PIN=1, STRATA_FETCH_THREADS), scope and validation, credits (eddoursul Niko1221#245, gopinath87607 Niko1221#255, jagsan-cyber Niko1221#247). Experimental, not in setup. - docs/DETAILS.md: the mapped low-RAM mode reads a native pack's experts from the GGUF when it has no experts.bin (checked, identical answers on the Coder); --resident-budget-gib and the routing-aware prefetch; where the tier counters are (--stats, the server log, /metrics' ram_blobs / file_blobs / file_mb); a pointer from "Which model?". Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…plit GGUF, Q4_K/Q5_K/Q5_1/Q8_0 experts, RAM budget + SSD tier with routing prefetch), the low-RAM mode reads GGUF experts in place, Windows GGUF load 2x (Niko1221#230), server status race (Niko1221#266) and tokenizer (Niko1221#268) fixes, truncated tool calls (Niko1221#211/Niko1221#231), AMD intrinsics (Niko1221#262); setup requires it Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
Contributor
|
Suggestion for these templates: a few extra fields would make hardware reports and performance PRs easier to review, based on the maintainer's review comments in #390, #492, #583 and #663. For the bug/question templates: GPU(s) with PCIe width, CPU and RAM (size, speed, channels), the build (release or source commit, flags), model and quant, context; and a "What I did not test" line. For the PR template: "Default path unchanged when no flag is given? (how checked)", "Builds on: CUDA / HIP / SYCL (compiled / run)", "Measured on: hardware, model, quant, context, runs" and "Not tested". Happy to send these as a patch on top if useful. |
Owner
|
Heads-up: today we cleaned up this repository's commit history, and GitHub closed this PR automatically when 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 Sorry for the noise, and thank you for the contribution. |
Niko1221
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…t template (#401) Short forms with the required fields, a link to the troubleshooting table, and the pull request template. Taken without CODEOWNERS and without the template's checklist (the changes the plan asked for); blank issues stay allowed.
Owner
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Thank you, Joe-Huber. The issue forms and the pull request template are in 0.1.40.2 with your authorship kept, without CODEOWNERS and the template checklist. Already closed with the history rewrite. |
0xrjman
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…t template (Niko1221#401) Short forms with the required fields, a link to the troubleshooting table, and the pull request template. Taken without CODEOWNERS and without the template's checklist (the changes the plan asked for); blank issues stay allowed.
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Summary
Created a
.githubwith small configs.What changed
CODEOWNERSfile with @Niko1221 as the default ownerfeature_request.ymlbug_report.ymlquestion.ymlconfig.ymlfor issue configurationpull_request_template.mdas a PR template