Each extra directory holds a DISTINCT subset of the .safetensors shards (search path, dedup by basename; first-listed dir wins). Demand preads hit whichever drive holds the shard, so concurrent expert loads parallelise across drives and combined capacity is used - a 400 GB container fits across two smaller drives that individually cannot hold it, which COLI_MODEL_MIRROR (a full second copy) cannot do. Composable with the mirror: st_mirror_init matches per-shard by basename against the merged index. st_init stays as a back-compat wrapper over st_init_multi. Verified on RTX 5080 / Windows: 72+70 shards across two NVMes, coherent output, [SPLIT] startup log, decode parity with single-drive at full RAM (0.92 vs 0.89-0.90).
Per-request grammar constraints in serve mode: response_format plumbed through the API, per-request grammar compilation, and grammar-forced draft verification. Verified locally on top of current dev: clean build, token-exact tiny models unchanged, decode-batch helper tests ok, 40/40 openai_server tests. CI 8/8 green. Thanks @fabio-rovai for keeping this rebased through the refactor.
Dev split coli_cuda_tensor_upload into two symbols: the plain 7-arg
tensor_upload (no gs) for fmt!=4, and tensor_upload_g (8-arg, with gs)
for fmt=4 grouped. The DLL-side _g sets a g_upload_gs global the plain
upload reads internally. Adapted all call sites:
- colibri.c: qt_cuda_upload already dispatches (_g for fmt=4, plain
otherwise) — kept dev's version. layer_cuda_shard_kvb was calling the
plain upload with gs; switched to tensor_upload_g.
- backend_loader.c: the C-API wrapper matches dev's 7-arg signature;
tensor_upload_g dispatched separately.
- backend_cuda.cu: cache-hit check uses g_upload_gs (not a gs param);
coli_cuda_matmul dispatches _g when gs>0; the grouped-expert host
fallback quant_matmul calls pass gs=0,ng=1 (host tier is per-row).
- Kept dev's fault-injection hook, scale_count field, and fmt=4 support
in the grouped-expert path (all_q4/any_g4 tracking).
- tests: kept dev's comprehensive tensor_upload test (cached reuse,
temp-buffer survival, upload-failure accounting, fault injection).
Build-verified: colibri.exe + coli_cuda.dll both compile clean.
Dev refactored glm.c → colibri.c and extracted the matmul/quant kernels
into quant.h (matmul, matmul_q, matmul_i4, matmul_i4_grouped, matmul_i2,
quant_scratch, dot_i4i8, matmul_q_idot, matmul_i4_idot, etc. all live
there now). The original #298 commit re-added all of these inline; on
rebase they became duplicate definitions.
Resolution:
- Removed the ~700-line duplicate block (everything dev moved to quant.h)
- Kept ONLY the unique fmt=4 contribution: matmul_i4_grouped_pair (the
fused gate+up kernel that reads x once instead of twice, ~33% decode
speedup) + the fmt=4 branch in expert_gate_up that dispatches to it.
Dev's expert_gate_up only fused fmt==2; this adds the fmt==4 case.
- Forward-declared matmul_i4_grouped_pair before expert_gate_up.
- Fixed quant_matmul call site in the ragged attention path (backend_cuda.cu)
to pass gs/ng — the kernel signature gained those args in the attention
scales fix, but dev's new ragged path called it with the old signature.
Build-verified: colibri.exe (CPU + COLI_CUDA) and coli_cuda.dll both
compile clean on the rebased branch.
The kv_b shard scale pointer used h0*(Q+V) which is correct for per-row
scales (fmt=2: one scale per row). For fmt=4 (grouped), there are ng
scales per row, so the offset must be h0*(Q+V)*ng. Without this, the
shard reads from the wrong scale position on multi-GPU, producing silent
corruption. Single-GPU is unaffected (no sharding).
Fix: const float *scale=l->kv_b.s+(int64_t)h0*(Q+V)*(gs>0?ng:1);
Refs #298
The new g64 model quantizes experts with group-size 64 (fmt=4): one f32
scale per 64 elements instead of per-row. This required changes across
the entire compute stack — CUDA kernel, engine dispatch, and dequant
helpers — plus a new fused gate+up kernel to recover the ~40% throughput
that the generic grouped path costs.
CUDA backend (backend_cuda.cu):
- ColiCudaTensor: add gs/ng fields for group-size metadata
- row_bytes/weight_at: fmt=4 uses same packed int4 layout as fmt=2
- quant_matmul: apply per-group scales (scales[o*ng+g]) for fmt=4,
matching the CPU matmul_i4_grouped accumulation exactly
- coli_cuda_tensor_upload: allocate O*ng scales (not O) for fmt=4,
store gs/ng on the tensor
- coli_cuda_tensor_update: handle grouped scales on refresh
- coli_cuda_tensor_free/tensor_bytes: VRAM accounting uses O*ng
- coli_cuda_expert_group: return 0 for fmt=4 (fall back to correct
per-expert path, since grouped_hidden/grouped_down kernels only
handle per-row scales)
- All 11 quant_matmul call sites updated to pass gs,ng
Engine (glm.c):
- qt_cuda_upload: pass t->gs to tensor_upload
- matmul dispatch (line 816): pass w->gs to coli_cuda_matmul
- kv_b shard upload: pass l->kv_b.gs
- kv_b shard rb computation: add fmt=4 case ((I+1)/2)
- embed_row: add fmt=4 branch with per-group scale dequant
- qt_addrow/qt_matvec_rows: add fmt=4 branches (CPU MLA absorption path)
- expert_gate_up: add matmul_i4_grouped_pair — fused gate+up for fmt=4
that reads x once instead of twice (+40% tok/s, 0.77 -> 1.08)
- run_text: prepend [gMASK]<sop> prefix for GLM models (#108 fix —
without it, PROMPT mode generates garbage on all GLM snapshots)
API (backend_cuda.h, backend_loader.c):
- coli_cuda_tensor_upload and coli_cuda_matmul signatures: add gs param
- Loader typedefs and wrappers thread gs through the DLL boundary
Tests:
- bench_tensor_core.cu, test_backend_cuda.cu, test_pipe_cuda.cu:
update all calls to new API signature (append gs=0 for non-grouped)
New tool: c/tools/diag_harness.py
- Comprehensive model diagnostic harness (system probe, correctness
smoke, deep PROFILE diagnostic, quality benchmarks via eval_glm.py,
throughput with/without MTP). Outputs JSON + Markdown reports.
Performance (GLM-5.2 744B g64 / RTX 5070 Ti / 32GB RAM):
broken CUDA: 0.05 tok/s (every tensor fell back to CPU)
fixed CUDA: 0.30 tok/s (6x — CUDA working)
+ full opt stack: 0.77 tok/s (CACHE_ROUTE + EXPERT_BUDGET)
+ fused grouped pair: 1.08 tok/s (+40% from gate+up fusion)
New weight format: int3 with ONE f32 scale per 64-input group (3.5 bits/weight
effective). Per group: 16B low plane (2 bits/val, int2 layout) + 8B high plane
(1 bit/val), values [-4,3] stored v+4. Same quantization math as
tools/quant_ablation.py _quant_last_dim(bits=3, group=64) from #132, whose
OLMoE ablation measured int3-g64 BEATING the shipped per-row int4 on quality
(-7.5 vs -9.3pp) at ~14% fewer bits.
Engine (placed per the #391 split): matmul_i3 + pack_int3_g64 + I3_* layout
helpers in quant.h next to their kernel family; fmt=5 branches in colibri.c's
qt_bytes/qt_alloc/qt_fill/matmul_qt/embed_row/qt_addrow/qt_matvec_rows.
Format detection now goes through #413's qt_resolve_fmt: fmt=5 registers its
distinct weight-byte layout O*ceil(I/64)*24 and its scale cardinality
O*ceil(I/64) there, validated against [O,I] like every other format. int3-g64
and grouped-int4-at-gs=64 carry the SAME scale count, so the weight bytes are
the int3 tag; row formats keep precedence for the small-I shapes where byte
counts coincide. The io_uring expert path still used the raw ?1:?2:3 byte
inference (it missed fmt=4 grouping entirely and never set gs) — converted to
qt_resolve_fmt like the other two expert paths.
Backends: qt_cuda_upload returns 0 for fmt=5 (tensor stays CPU-side, the
documented fallback), the dense CUDA matmul gate excludes fmt=5, and Metal's
existing fmt gates (gemm fmt<=3, moe fmt 1/2) already reject it.
Converter: quant_int3_g64 in convert_fp8_to_int4.py; --ebits 3/--xbits 3 now
emit it (previously bits=3 silently produced int4).
Tests: tests/test_int3.c (bit-exact pack/unpack vs reference, matmul_i3 vs
dequant-matmul incl. short tail groups and the real GLM I=7168, QT plumbing,
qt_resolve_fmt disambiguation incl. the same-scale-count fmt=4/fmt=5 pair,
outlier-rows RMS: int3-g64 3.3x lower error than per-row int4),
tests/test_int3_load.c (hand-rolled .safetensors fixture through st_init +
qt_from_disk: fmt=5 detected and loaded next to an int4 control tensor),
tests/test_int3_convert.py (NumPy pack round-trip vs independent decoder).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The OpenAI gateway previously 400'd every response_format; the mux engine path
ran with speculation disabled entirely. Now:
- openai_server.py: response_format {"type":"json_object"} (generic ws-tolerant
JSON grammar), {"type":"json_schema"} (schema forwarded as-is, compiled
engine-side by schema_gbnf.h), and a raw-GBNF {"type":"gbnf"} extension.
Draft-source semantics throughout: a schema the engine cannot compile costs
the speedup, never the request and never the output.
- SUBMIT protocol: optional 7th field gbytes; grammar text appended to the
payload after the prompt. 6-field headers unchanged (back-compatible).
- Engine: per-slot GrDraft (grammar_setup_text/grammar_teardown split out of
the env-driven setup); walkers fed on every emitted token. Grammar-forced
drafting in run_serve_mux for greedy requests: a drafting slot leaves the
shared batch for one forward and runs the proven single-sequence verify path
(kv_bind + step_all) — the same primitives prefill already uses per
submission — then rejoins; rejected drafts' KV entries are overwritten by the
next forward exactly like the existing prefix-truncation path. Sampling
requests never draft (verification under sampling needs rejection resampling;
out of scope).
Tests: 7-field SUBMIT parse cases; response_format->grammar plumbing incl.
fail cases; test doubles updated; generic JSON grammar parse+walk validated
against grammar.h. make test-c green; python suite green except the known
environmental memory_available failure (#150 fixes it).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The VRAM-ranked prefix's host slabs are upload staging — read once and
freed right after — so binding them buys nothing and cost ~2 transient
VMAs each: measured PEAK maps 28,958 on the six-GPU host even with the
pin arenas in place. With the skip: 11,771, all of it the bounded LRU
ecache, which serves decode reads and stays correctly bound.
Root cause of #419's 'OOM slab': every per-slab mbind carries its own
memory policy, so bound regions cannot merge — measured ~2 VMAs per slab,
with or without MPOL_MF_MOVE. A PIN_GB=all load (19,456 experts x
slab+fslab) creates ~78k VMAs and crosses the default
vm.max_map_count=65530: posix_memalign dies with terabytes free.
The fix binds the pinned hot-store as ONE arena per layer. Experts of a
layer share a tensor shape, so a layer's pins pack at a fixed stride into
two arenas (weights + scales): 2 mbinds and a handful of VMAs per layer
instead of ~500. Slices are pre-attached to the slots before the load
loop — slab_cap covers expert_load's realloc check, so its alloc branch
never fires and expert_load itself is untouched. aslab marks arena
ownership: expert_host_release detaches instead of freeing (a REPIN
gpu-swap promotion must not free() an interior pointer), and
expert_host_ensure re-attaches the slice before reloading.
Per-slab mbind remains for the bounded allocations (dense qalloc, LRU
ecache, GPU-tier staging), now without MPOL_MF_MOVE: every bind lands
before the pread that first-touches the pages, so there is nothing to
migrate.
numa_init also gains a capability probe done right: one page-aligned
page (mbind rejects unaligned addresses with EINVAL), disabling only on
errno==EPERM — so a constrained container degrades with a message
instead of crashing later, and an EINVAL can never masquerade as a
missing capability.
The arena path activates only when interleave is actually on
(g_numa_nodes>=2, Linux, non-mmap): default builds stay byte-identical.
The grouped MoE kernels were per-row-only: GroupDesc had no group-size
fields, row_bytes() returned 0 for fmt=4, the scale buffer was hardcoded
to O floats, and a fmt=4 group that reached the generic path would have
been silently decoded as int2. This closed the GPU expert tier to every
grouped container — including the g64 quality line (#225) and the E8
lattice route (#347) whose whole point is fitting more experts in VRAM.
- ColiCudaTensor gains gs/scale_count; upload allocates O*ceil(I/gs)
scales for fmt=4 and applies the same offset->signed nibble conversion
as fmt=2 (identical packing). New ABI entry coli_cuda_tensor_upload_g
carries gs without touching the existing symbol — an old Windows DLL
missing it returns 0 and the tensor simply stays CPU-side.
- GroupDesc gains per-tensor group sizes; new grouped_hidden_g4_dual /
grouped_down_g4 apply the per-group scale inside the accumulation
(gs is required even, so a packed byte never straddles groups; gs=0
degrades to per-row, letting fmt=2 members ride the same launch).
- coli_cuda_expert_group routes any group containing fmt=4 through the
g4 kernels; pure-fmt=2 groups keep the existing paths byte-identical.
The generic fallback now explicitly rejects fmt=4 instead of decoding
garbage (#334's prevention note, made real).
tests/test_grouped_g4_cuda.cu: kernel-vs-CPU oracle over 50 trials x 3
experts — gs=64, a non-divisible tail group (200 % 64), and a per-row
member in the same launch: zero mismatches on a 5090.
make check 77/77; CPU, CUDA and MinGW builds clean.
rss_guard marked the victim slot eid=-1 under the lock, unlocked, and only then
freed s->slab. In that window the slot reads as {eid=-1, slab still valid} --
exactly the state pilot_realload's victim scan reuses first -- so a pilot worker
could claim it and pread into the slab while it was being freed: use-after-free,
or a double-free if the loader took its own realloc path.
Keep the free and the pointer/capacity NULLing inside the critical section, so
'slab valid' and 'slot reusable' are never simultaneously observable. The lock is
held across a free() (microseconds); workers only take it briefly for scan+reserve.
Reproduces on dev with a SINGLE pilot worker (rss_guard runs on the main thread
while the pilot worker runs in the background), whenever the RAM guard is active
(RSS_GUARD_GB, or any resolved g_ram_budget_gb).
make check 83/83, native + portable builds 0 warnings.
Under O_DIRECT the expert *weights* are read with the direct fd (bypassing the
page cache), so a POSIX_FADV_WILLNEED on them warms pages the demand read never
consumes -- pure wasted readahead on the disk, the scarcest resource when
streaming. The .qs scales are ALWAYS read buffered (pread on the normal fd), so
keep their WILLNEED. This only changes hint-only PILOT (PILOT=1, PILOT_REAL=0)
combined with DIRECT=1; fadvise is advisory, so output is bit-identical.
Prepared, not yet measured: needs a real NVMe + full model to A/B cold-stream
tok/s (PILOT=1 DIRECT=1, with/without this patch, TEMP=0). See #441.
pin_load computed npin from the RAM budget FIRST, then carved the
VRAM-ranked prefix out of those npin slots. With CUDA_RELEASE_HOST the
prefix's host slabs are freed right after upload — so on a multi-GPU
host the top-ranked experts consumed the RAM budget without occupying
RAM, and the CPU tier pinned only the leftovers. Measured on 6x RTX 5090
(251 GB): 9,280 VRAM + only 1,721 RAM pins (32.5 GB warm) on a box whose
RAM fits ~10k more — the cold tail then paid disk on every token and the
hit rate ceilinged at 99.0% forever.
Move the VRAM budget estimate above the npin finalization and make the
release-destined prefix ADDITIVE to the RAM-derived count. Same box,
same env plus the fix:
[PIN] placement: 9,280 VRAM + 10,176 RAM (192.5 GB warm)
expert hit rate 100.0% (pin 100.0% + lru 0.0%) — disk 0
1,024-token greedy decode: 3.62 -> 6.21 tok/s (+72%)
warm late segment (t=768-1024): 5.11 -> 5.73 tok/s (+12%)
prefill: 10.4 -> 8.8 s
The whole-run gain is the LRU warmup penalty disappearing (full
residency from the first token); the late-segment gain is the steady
~0.9%-miss disk residue recovered. Non-release configs are untouched:
prefix_est stays 0 and the arithmetic reduces to today's exactly.
Re-derivation of e7/disk-class-instr @ de6dd6d (base caa49f7) onto
origin/dev @ 61004dc, after PR #391 split c/glm.c into c/colibri.c plus
quant.h/sample.h/kv_persist.h/telemetry.h/grammar.h. Semantic equivalence,
not a copy: same events, same accounting, re-sited onto the new tree.
Placement: colibri.c, unchanged from before the split. telemetry.h (#391)
is the dashboard/stats module (HWINFO/TIERS/EMAP/HITS protocol lines +
usage persistence) — prof_report(), expert_load_impl(), moe(), g_prof_io
and g_edisk_ns all stayed in colibri.c, so DISK-CLASS's aggregation and
printing follow them there. No relocation needed, no DEVIATION.
Read path: #362 (prefetcher-v3, 22509fc) turned out to be testbed-only
scope per its own merge message ("glm.c untouched") — confirmed zero
c/glm.c changes in that merge's diff. The seven expert_load() call sites
this patch touches (pipe_worker, expert_host_ensure, moe()'s OMP miss
loop, pilot_realload, repin_pass_limit, pin_load x2) are structurally
identical to the pre-split tree; the demand flag re-attaches at the same
sites with the same semantics (1 only at moe()'s own PIPE/OMP miss path,
0 everywhere else), so DISK-CLASS still counts demand loads only.
One real drift, unrelated to #362: #417 (cfcc742) fixed the exact "Metal
pre-routed FASE A never bumps the real elast/eaccess_clock" defect this
feature's comments described as a documented, deliberately-unfixed
upstream issue — the real clock now ticks in FASE A too. The private
elast_dc/eaccess_clock_dc clock is kept anyway: its job was never only
to route around that freeze, it also snapshots pre-bump state so a
call's own routing bump can't contaminate its own classification, and
keeping DISK-CLASS's bookkeeping fully separate from stock elast state
is what makes "byte-identical with PROF=0" provable by construction
instead of by argument. Code comments referencing the old defect are
updated to reflect the fix (historical note + #417/cfcc742 pointer)
rather than describing a bug that no longer exists.
Gates: make glm METAL=0 and METAL=1 both clean, zero warnings (matches
stock 61004dc, also built clean with zero warnings for comparison).
make test-c: 0 failures. make test-python: 77 tests, OK.
Authored by Fable 5 in Claude Code, analysis in partnership with
@Monotophic.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
First increment of the #431 plan (device router -> indirect kernels ->
one-graph decode). At decode (S=1) on the pipe2 path, the router runs on
the layer's home device: a tiny E x D logits GEMV + sigmoid, then a
single-thread selection kernel that clones moe()'s plain routing path
verbatim — bias-augmented top-K by choice with strict-> tie-breaking,
weights from the raw logit, route-level TOPP truncation, norm_topk,
routed_scale. Results pack into one scratch buffer and come back in a
single ~68-byte D2H; moe() consumes them through the same pre-routed
shortcut the Metal layer-CB uses (g_pre_idx, #417 bookkeeping included),
so usage/heat/recency accounting is identical to the CPU router.
Structural value: routing becomes available ON the device timeline,
which is what PR-B (indirect expert kernels, static topology) and PR-C
(whole-decode CUDA Graph) build on.
Opt-in, default off. Gated to the plain routing path — CACHE_ROUTE,
ROUTE_P and ROUTE_TRACE keep the CPU ranking they need; any upload or
launch failure falls back to the CPU router silently. Router weights
(E x D f32, ~6.3 MB/layer) upload lazily to the layer's home device.
tests/test_router_cuda.cu: kernel-vs-CPU-reference oracle over 200
random trials (mixed TOPP/norm_topk/scale): 200/200 exact selections,
zero near-tie flips, zero weight mismatches on a 5090.
expert_load's fslab-OOM unwind freed s->slab via compat_aligned_free without
coli_metal_unregister -- a pre-existing gap on main/dev (validator-confirmed
during E4) that leaves a stale g_slabs entry over freed memory, letting
resolve() hand the GPU a dangling pointer. Under COLI_METAL_RESSET=1 the
exposure is strictly longer-lived: the wrapped buffer would stay a permanent
residency-set member over the freed pages instead of stock's transient
last-command-buffer window. Ported from e4/metal-heap validator fix 6753225,
adapted to dev's non-heap code shape (no coli_metal_heap_free wrapper --
plain unregister-before-free).
The uring_load_add analog (E4 audit round-2 insurance) is deliberately not
carried: that arm is #ifdef __linux__-gated while COLI_METAL is macOS-only,
so it is dead code on every real build target, and unlike E4 this branch has
no allocation-path reason to touch the function.
Reachable only through allocation failure mid-load; verified by inspection
and clean builds (no OOM-injection harness in tree). SUMMARY.md item 10
updated to "carried on this branch".
1. REQUIRED (backend_metal.mm): no Metal call runs under g_slab_mtx anymore.
The set mutations (addAllocation/removeAllocation/commit) and the dirty
flag moved to a dedicated g_resset_mtx; coli_metal_register/unregister do
their g_slabs bookkeeping under g_slab_mtx exactly as stock, then call
resset_add/resset_remove after dropping it (still before returning, so
unregister's remove+commit still lands before the caller frees the host
memory). The original shape -- commit under the slab lock the parallel OMP
loader threads contend on -- was structurally identical to the mutex-over-
live-Metal-call bug E4's audit round 2 fixed, the leading suspect for its
replicated +12s expert-disk regression. The register->flush->resolve
happens-before argument survives the split: resset_add completes inside
coli_metal_register before it returns, the engine cannot dispatch an
expert before its load returns, and add/flush are serialized by
g_resset_mtx (comment at resset_add). The two mutexes are never held
together, so no lock-order hazard exists.
2. REQUIRED (backend_metal.mm, backend_metal.h, glm.c): the resset_flush
cost in moe_submit -- deliberately outside the g_t_setup window so the
A/B harness counters keep their meaning -- was invisible. New
g_t_resset_flush accumulator timed around the flush, exported via
coli_metal_resset_stats(), printed by profile_print as a separate
gate-on-only "METAL-RESSET: flush" line (mirrors E4's METAL-HEAP line;
the METAL: line the harness parses keeps its exact format; stock output
byte-identical -- the stats call returns 0 with the gate off and the
whole block sits in the pre-existing #ifdef COLI_METAL arm). Register-
side add/remove costs land in the existing t_ewait window; comment at
resset_add says so instead of a second counter.
3. REQUIRED (SUMMARY.md; the moe_submit commit message was already reworded
in place, pre-push): corrected two false attributions -- the hazard-
tracking and thread-safety statements were credited to the SDK header,
which is silent on both; the actual source is Apple's online
MTLResidencySet class reference and residency-set adoption guide
(fetched 2026-07-18). Note: the scaffolding commit's message was checked
and contains no such claim, so it was left untouched.
4. DOC (SUMMARY.md): the pre-existing fslab OOM-unwind bug (glm.c ~1868-73,
frees s->slab without unregister) has a strictly longer-lived exposure
under E5 (permanent set member vs stock's transient per-CB declaration).
Out of scope here; the upstream PR built from E5 must carry the one-line
fix (reference: E4 branch commit 6753225).
The has_mtp completeness probe checked for `mlp.experts.255.down_proj.weight`,
which only exists when n_routed_experts == 256. REAP-pruned checkpoints (and any
MoE with a different expert count) have fewer experts, so the probe spuriously
reported has_mtp=0 and disabled MTP speculative decode even though the head was
present and complete. Probe `mlp.experts.<n_experts-1>` instead.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
A second (read-only) copy of the model on another drive is registered as a
per-shard read replica; expert loads are split between the drives by a
deterministic (layer,eid) hash weighted by COLI_DISK_WEIGHTS=<primary>,<mirror>
or, by default, by a startup bandwidth probe using the engine's own access
pattern (parallel ~19 MB reads, O_DIRECT). Cold decode is disk-bound
(~11 GB/token), so two NVMe queues add up.
- st.h: mirror accepted per file only if size + safetensors header are
byte-identical to the primary (identical data_offsets by construction, so
every pread is valid on either copy); partial mirrors work (smaller second
SSD holding only some shards); the mirror is never written — .coli_usage /
.coli_kv stay on the primary.
- glm.c: routing covers the coalesced slab pread, O_DIRECT, mmap/Metal and
scale reads, plus the OMP-parallel pin/autopin warmup (streams from both
drives). Deterministic routing keeps readahead/PILOT WILLNEED on the same
drive as the demand read and avoids caching an expert twice. A mirror read
error falls back to the primary (one warning, no crash). Per-drive bytes
are reported in a MIRROR: stats line.
- tests: test_st_mirror covers validation, read equality on both replicas,
and the rejection paths (divergent header, size mismatch, missing file).
Measured on 2x NVMe (GLM-5.2 int4, greedy, DRAFT=0, DIRECT=1, cold-ish
cache): 0.42 -> 0.57 tok/s (+36%), expert-disk service 15.2s -> 10.3s,
byte-identical output; probe chose a 48/52 split.
Rename glm.c → colibri.c and extract four self-contained modules
into header-only files (same pattern as st.h/tier.h/grammar.h):
quant.h (672 lines) — SIMD matmul kernels, quantization
sample.h (143 lines) — RNG, top-p sampling, stop-set
kv_persist.h (121 lines) — .coli_kv disk persistence
telemetry.h (189 lines) — dashboard protocol, stats, usage
Main engine file shrinks from 6588 to 5396 lines (−18%).
Build system: primary target is now colibri$(EXE); `make glm`
remains as a phony alias for backward compat. CI, setup.sh,
coli CLI, and all 10 test files that include the engine are
updated. make check passes (C + Python, 73 tests, zero warnings).
Mohamed Mastouri