The engine opens/mmaps every safetensors shard of the model (144+ files
for GLM-5.2). On macOS the default soft fd limit is 256, so a stock
terminal session fails while loading with:
<model>/out-00125.safetensors: Too many open files
the engine exited while loading
Raise the soft limit toward the hard limit (capped at 65536) in the
coli launcher before spawning the engine, so users don't need a manual
'ulimit -n' in every shell. No-op on Windows and on shells whose limit
is already sufficient.
Co-authored-by: Harvad Lee <hongyanab@gmail.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
* Add make install/uninstall targets
Neither the root Makefile nor c/Makefile had an install target,
so 'build+install' never actually installed anything (fixes#164).
Adds standard PREFIX/DESTDIR/BINDIR-respecting install and uninstall
targets that place glm and coli in $(BINDIR).
* Split install: coli in bin/, engine + support files in libexec/
Addresses feedback on #164 from JustVugg and yurivict: coli goes to
$(BINDIR), glm/olmoe and their Python support modules
(resource_plan.py, doctor.py, openai_server.py, tools/) go to
$(LIBEXECDIR) (default $(PREFIX)/libexec/colibri), matching typical
Unix/FreeBSD-port conventions for a wrapper vs. its internals.
coli now resolves the engine path in this order:
1. $COLI_ENGINE if set (explicit override)
2. glm next to itself (run-in-place from a source checkout, unchanged)
3. $(LIBEXECDIR)/glm (installed layout), also added to sys.path so
the Python support modules still import correctly
Also adds a 'bench' target (builds iobench) since only cuda-bench
existed before.
Tested locally (WSL2/Ubuntu):
- run-in-place: cd c && python3 coli info -> engine ready
- installed: make install PREFIX=$HOME/.local && ~/.local/bin/coli info -> engine ready (found via libexec)
- make uninstall cleans both bin/ and libexec/colibri/ fully
Rebased onto current dev, split into 3 logical parts (all validated):
1. CPU portability (serve-mode _O_BINARY pipe fix — stock main hangs on MinGW without it; RAM detection cap 0->9/layer; POSIX guards for select/mmap/madvise; warmup script).
2. AVX-VNNI 128-bit int8/int4 dot kernel (Alder Lake+/Meteor Lake+), bit-identical to AVX2 (author-verified on Meteor Lake; compiles out to AVX2 elsewhere) + _mm256_extracti128_si256 typo fix that blocked -march=native.
3. CUDA DLL via LoadLibrary, gated behind CUDA_DLL=1 (host never links cudart; silent CPU fallback if absent; author-verified on RTX 5070 Ti).
Validated here: make check 59/59, oracle 32/32 TF, Windows cross-compile clean + glm.exe loads+runs via WSL interop. Fixes the #123 Windows build failure.
env_for() mapped --gpu/--vram only inside the --auto-tier branch, so the standalone form started a CPU-only engine with no warning (#121 — nearly published as a GPU benchmark). Now: --gpu none disables CUDA; --gpu list/auto sets COLI_CUDA/COLI_GPUS; --vram sets CUDA_EXPERT_GB; and --gpu/--vram on a CPU-only binary exit with a clear 'make glm CUDA=1' message instead of falling back silently. Tested on a CPU-only build: fail-fast fires for --gpu/--vram, --gpu none and default still start the CPU engine (no regression).
serve mode persists the compressed MLA KV-cache incrementally after every
turn (~182 KB/token appended, header count written last = crash-safe) and
resumes it at startup: the model remembers the whole conversation and zero
re-prefill happens. :reset and context-full restarts truncate the file.
The MTP layer's KV row is not saved; kv_start=-1 re-arms its decode window.
Validated: split-session answer byte-identical to an uninterrupted session
(tiny oracle, TEMP=0), and on the real 744B model a restarted chat resumed
58 tokens in 0.0s and recalled a fact from the previous session while
prefilling only the new question.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Fenced code blocks become bordered boxes with the language label, **bold**
renders as real bold, `inline code` colored, # headers, - bullets. Works
char-by-char on the live stream (markers split across chunks are held back),
inline state resets per line, and orphan ``` fences right after a close
(a known int4 glitch) are swallowed. COLI_RAW=1 restores raw output.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Each architecture maps to its own engine binary (glm today; gptoss, qwenmoe
reserved). Registry in c/models.json (local, gitignored); chat shows a picker
when more than one model is installed. Dense models stay llama.cpp territory
- documented honestly in the README.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Faithful to the official modeling (transformers glm_moe_dsa): q from the
q_a latent via wq_b (32 heads x 128), k = LayerNorm(wk(h)) shared across
heads, interleaved RoPE on the first 64 dims, ReLU(q.k/sqrt(128)) weighted
by weights_proj(h)/sqrt(32), causal top-2048 per query.
- 'full' layers compute the selection (+ maintain the indexer k-cache from
token 0); 'shared' layers reuse it (IndexShare, index_topk_freq=4).
- Selection restricts both attention paths (absorbed decode + prefill
reconstruction). MTP row stays dense.
- Auto-detected like MTP: if out-idx-* weights are present for all full
layers, DSA arms itself; DSA=0 disables; DSA_FORCE/DSA_TOPK for testing.
- Validated on the tiny oracle (which ships indexer weights): selection
machinery forced on with keep=all keys reproduces dense attention exactly
(TF 32/32, gen 20/20); sparse smoke runs clean; kill switch verified.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Learning cache: expert usage persists in <SNAP>/.coli_usage across sessions
(atomic save every turn); at startup the hottest experts are auto-pinned in
RAM with half the expert budget (AUTOPIN=0 disables). The engine gets faster
the more you use it.
- Sampling: temperature + nucleus (official 1.0/0.95 defaults in chat; TEMP=0
= greedy). MTP/n-gram speculation stays lossless via rejection sampling
(accept draft w.p. p(draft); on reject resample with draft banned).
- coli: --temp flag.
- Converter: --indexer mode extracts DSA lightning-indexer weights
(resumable; needed for future sparse attention beyond 2048 ctx).
- pin_load/stats include the MTP row; usage histogram covers layer 78.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Harvad Li