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.
Tools-only step of the #452 plan: iq3_pack.py index codec + IQ3_XXS grid + quant_ablation hook, with round-trip tests (6/6 locally, CI 8/8). No engine changes.
Co-authored-by: ZacharyZcR
CI keeps the runtime path dependency-free, so the Python test job runs
without numpy and the new codec tests errored on import. Guard the import
and skip the class instead — verified both ways: 6/6 run where numpy is
present, 6/6 skip cleanly where it is not.
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>
Addresses the #192 review: server usage documented in docs/grammar-draft.md
(incl. back-compat statement for the additive SUBMIT field and the #100-class
near-tie caveat); gateway pre-checks grammar payloads at 1 MiB (matching the
engine's gbytes bound); negative tests for non-dict response_format, empty and
oversized grammars, plus an explicit test that malformed GBNF passes the
gateway by design (engine fail-soft, draft-source semantics). Measured compile
overhead: 7.8 us/request typical schema, 17.9 us at the 32-level nesting cap.
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 #270 rebase resolved the TEST_BINS list but missed the test_pipe_block
rule prerequisite and its #include, both still pointing at glm.c (which #391
renamed to colibri.c) — 'No rule to make target glm.c' broke the Linux C test
suite on dev. Point both at colibri.c. Build-verified locally.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
On Windows a bare 'coli chat' (no --gpu/--vram/--auto-tier) ALWAYS ran
CPU-only, even on a CUDA build with a GPU present. Two defects:
1. cuda_binary() returned False on Windows. It detects CUDA by running
'ldd glm | grep libcudart', which is Linux-only (no ldd on win32) and
meaningless anyway because the Windows engine links cudart only inside a
runtime-loaded coli_cuda.dll, not as a libcudart symbol in glm.exe. So
the --gpu/--vram/--auto-tier gates (which call cuda_binary()) never opened.
2. Even with detection fixed, bare 'coli chat' set no CUDA env: env_for's
else-branch only enables CUDA when --gpu/--vram is passed. Nothing
auto-enabled the GPU.
Now: cuda_binary() on non-Linux returns True iff coli_cuda.dll exists next
to glm.exe — the exact file backend_loader.c loads from the engine's own
directory, so its presence is a faithful, cheap, DLL-hijack-safe proxy for a
CUDA-capable build. And env_for, scoped to win32 (Linux keeps its working
explicit-flag UX), auto-enables CUDA when a bare chat detects a CUDA build
plus a GPU via nvidia-smi, sizing the expert-tier VRAM budget from real free
VRAM via the existing build_plan/environment_for_plan machinery (same as
--auto-tier, no guessed budget). If nvidia-smi is missing it falls back to
CPU with a clear warning; --gpu none still forces CPU; explicit --vram/--gpu
still win. CUDA_DENSE stays an explicit opt-in (matches --auto-tier).
Verified on a Windows + RTX 5070 Ti box: bare 'coli chat --model <g64>' now
prints '[GPU] auto-enabled CUDA ... 13.0 GB expert tier' and emits
COLI_CUDA=1 / COLI_GPUS=0 / CUDA_EXPERT_GB=13.044 (was: all unset, CPU-only).
Tests: 4 new cases (auto-enable, nvidia-smi-missing fallback, CPU-build
silent, Linux-unchanged) plus the 4 existing default-I/O tests guarded to
mock cuda_binary() so they stay host-independent. Full python suite green
(env_defaults 8, resource_plan 10, doctor 8, makefile_platform 3, cli_output 3).
Out of scope: doctor.cuda_linkage is also POSIX-only and mis-reports on
Windows — separate follow-up.
JustVugg caught a regression by running the PR: the code asked
`lscpu -p=CPU,Core,Socket` and read fields[1]/fields[2], but the comment's
claim was inverted -- `lscpu -p=<list>` emits EXACTLY the requested columns
(no CPU prefix), while bare `lscpu -p` prepends CPU.
On machines where the requested list is short or lscpu collapses to two
columns, every line was skipped by the `< 3` guard, cores stayed empty, and
the code fell through to os.cpu_count() -- the LOGICAL count. Result: 6
physical cores reported as 12 (SMT over-subscription), the opposite of the
fix.
Correct per review:
- ask lscpu for exactly 'core,socket'
- take fields[-2], fields[-1] (correct whether or not CPU is prepended)
- keep the warning scaffolding + the _resolve_physical_cores clamp
(those are the actual #325 fix; only the indexing was wrong)
Test now exercises BOTH the 2-column (-p=core,socket) and 3-column
(bare -p, CPU prefix) layouts, asserting 12 physical cores each. The
2-column case is the one that regressed: old parser returns 24, new
returns 12.
The core-count fix was necessary but not sufficient: @liangstein confirmed
physical_cpu_count() now returns 64 on his box, yet --auto-tier STILL pinned
decode to one core. A complete env diff between the plain (working) and
--auto-tier (broken) paths showed exactly three keys the plan adds:
OMP_NUM_THREADS = 64 (correct, verified)
OMP_PROC_BIND = spread
OMP_PLACES = cores
Since OMP_NUM_THREADS was already correct, the culprit is the affinity pair.
The mechanism: environment_for_plan() sets OMP_PROC_BIND=spread + OMP_PLACES=cores
in the launcher's env. The engine's hot-thread tuning (glm.c main, the
COLI_OMP_TUNED self-exec) then tries setenv("OMP_PROC_BIND","close", overwrite=0)
-- but overwrite=0 cannot replace an already-set var, so the plan's "spread"
wins. On the reporter's libgomp + multi-socket topology, spread + places=cores
collapsed the team to a single CPU even with 64 threads configured.
Fix: don't set affinity from the plan at all. The engine deliberately chose
"close" for cache locality (the tiny back-to-back per-expert matmuls want
adjacent cores), and the plain path already leaves affinity to the engine.
Removing the plan's spread/places makes --auto-tier match the working plain
path; a user wanting a specific policy can still set OMP_PROC_BIND/OMP_PLACES
in their own environment (environment_for_plan only setdefaults OMP_NUM_THREADS).
Verified via env diff: after the fix, --auto-tier adds ONLY OMP_NUM_THREADS
beyond the plain env -- the engine's own close/bind tuning now wins on both
paths identically.
Note: this could not be reproduced on Windows (MinGW libgomp prints "Affinity
not supported on this configuration" and ignores the vars entirely); it is
Linux-libgomp-specific, matching the reporter's Rocky 9 box.
Tests: rewrite test_applies_plan_without_overriding_explicit_settings to assert
the plan sets NO affinity vars on any platform (the old test encoded the buggy
platform-dependent spread/cores contract). Add
test_plan_does_not_set_omp_affinity_vars as a focused regression. 78/78 pass.
physical_cpu_count() silently returned 1 in two situations, and that value
flowed through build_plan -> OMP_NUM_THREADS to pin every matmul region to a
single thread under --auto-tier (reported on Rocky Linux 9, 512 GB RAM).
Two root causes:
1. The lscpu parse counted the wrong thing. `lscpu -p=core,socket` prepends the
CPU column, so the output is actually CPU,Core,Socket; the old set
comprehension collected (CPU,core,socket) tuples that were unique per logical
CPU. Now parse CPU,Core,Socket and dedupe on (core, socket) to get true
physical cores (the SMT-doubling the surrounding comments warn against was
the actual behavior).
2. Any probe failure fell through to `os.cpu_count() or 1`. On a cgroup'd or
otherwise constrained box os.cpu_count() can be 1 (or None), silently
capping the run. Skip offline core/socket fields ("-" instead of raising
ValueError) so a single offline row no longer discards the whole parse, and
replace the silent `or 1` fallback with os.cpu_count() (logical) plus an
explicit warning. Only return 1 when nothing at all is detected, and warn.
Also harden the win32 branch: declare argtypes/restype on
GetLogicalProcessorInformationEx (an undeclared 64-bit WinAPI returns c_int and
takes c_int pointers, so the probe could silently fail), and warn on its
fallbacks. Replace the silent max(1, int()) clamp in build_plan with
_resolve_physical_cores() that clamps to 1 with a warning instead of masking.
Tests: the existing OMP_NUM_THREADS test passed physical_cpus=24 explicitly, so
it never exercised the real probe -- that is why this regressed. Add regression
coverage for the lscpu physical-core dedup, offline fields, lscpu-missing
fallback, zero-cores degenerate case, and an end-to-end build_plan +
environment_for_plan check that OMP_NUM_THREADS reflects physical (not logical)
cores.
CI runs 'make check' = dependency-free tests, no model downloads (by design,
#140). glm_tiny/ is a gitignored generated fixture, so test_inefficiency.py
hard-failed on the Windows/macOS/Linux runners with 'config.json: No such file
or directory' instead of skipping.
_engine_present() now requires BOTH glm.exe AND glm_tiny/config.json, and
_skip_reason() names exactly which prerequisite is missing so the skip is
actionable. Verified: 8 skipped (0 failed) with the fixture absent; 5 pass +
3 CUDA-skip with it present.
Two layers of efficiency coverage for the engine, both parsing the telemetry
glm.c already emits (REPLAY/PROFILE/[PROF]/CUDA-tier) but nothing previously
asserted on:
1. test_inefficiency.py — tiny-model asserted regression tests (8 tests, run
in make test via test-python). Gate on: throughput floor, PROFILE phase
accounting sanity, disk-wait not dominant on a resident model, CPU greedy
determinism, and (when a CUDA build is present) CUDA init, dense VRAM
upload, and CPU-vs-CUDA argmax agreement >= 70%. CUDA tests auto-skip with
a clear build hint on CPU-only binaries.
2. test_efficiency_report.py — opt-in optimization dossier for a real model.
Turns on every instrumentation flag (PROF, COLI_CUDA_PROFILE, CACHE_ROUTE,
DISK_SPLIT, LOOKA) and prints 9 sections (provenance, throughput + tail
latency, where-time-goes, attention breakdown, expert cache, disk I/O +
phase split, routing quality + predictability, speculation, GPU tiers),
each flagging inefficiency with the concrete knob to move tok/s. Never
fails CI.
tools/efficiency.py is the shared harness: parse_run() captures every signal,
run_engine() wraps the subprocess. Reuses PROFILE_RE/SPEED_RE from
tools/benchmark_cuda_fixture.py and extends the tok/s regex to also catch the
run_text (parenthesized) format the full-model PROMPT path uses.
Makefile adds: efficiency / efficiency-cuda / efficiency-report targets.
Verified end-to-end on the full glm52_i4_g64 model (CPU + CUDA).
#453 settled the scheme; this produces the actual bytes. iq3_pack.encode/decode
implement the container layout so the converter, the engine and the decode
kernels can all be written against one spec:
98 bytes per 256 weights = 3.0625 bpw
[ 0..63] uint8 grid index per 4-dim magnitude block
[64..95] uint32 x8 — four 7-bit sign words + 4-bit sub-scale per 32
[96..97] fp16 super-scale
Signs use the published odd-parity trick: 7 of every 8 are stored and the 8th
is derived, so the encoder flips the smallest-magnitude weight of any block
whose true signs would violate parity — the cost the #453 ablation priced in,
now actually paid. Sub-scale is searched over all 16 codes against the stored
(fp16-rounded) super-scale, so encode-time and decode-time arithmetic agree.
GLM-5.2 routed experts under this container: 372.7 -> 281.2 GB (-24.6%), and a
176 GB VRAM tier holds ~12,180 experts instead of ~9,190 (+33%).
tests/test_iq3_pack.py: byte budget, deterministic encode, decode checked
value-by-value against an independent loop-based reader written straight from
the layout, sign-parity closure, and reconstruction quality in the band the
chosen scheme measured (rel-RMSE 0.195 vs the torch model's 0.195).
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.
Re-derives the campaign's rtop8 breakthrough (fuse/rtop8-par @ b32439b,
commits 48b3a98 + b32439b) cleanly onto origin/dev @ 61004dc (post-#391
split), dropping the cb-chain commit (1130ac9) that branch was stacked
on. cb-chain touched only c/glm.c (now split into c/colibri.c); rtop8
touches only c/backend_metal.{h,mm} and c/tests/test_backend_metal.mm —
disjoint files, verified zero cb-chain remnants in this diff.
- r_top8_par: one SIMDGROUP per row (blocked lane ownership, taken
bitmask, shuffle-down argmax with ties->lower-index) replicating the
serial r_top8's first-max-wins ascending order exactly; topp/normk/
rscale tail verbatim on lane 0. Bench: serial 0.465 ms/layer (~55% of
the layer CB), parallel ~93x faster on the kernel, bit-exact.
- COLI_RTOP8 gate, default ON (renamed and inverted from the campaign's
COLI_RTOP8_PAR=0-default gate; COLI_RTOP8=0 is the opt-out escape).
- Expert-count generality (new hard requirement, REAP E=168 packages):
the parallel kernel's E<=256 contract is now enforced at EVERY call
site in host code (g_rtop8_width_ok / E<=256 checked before selecting
the pipeline), not just as an in-kernel defensive no-op -- closes a
latent gap where the campaign's SIMD-width guard only protected the
engine's automatic dispatch, not the standalone coli_metal_rtop8(par=1,
...) probe function metal-test itself uses. Out-of-contract requests
(E>256, or non-32-wide SIMD) now transparently run the serial kernel
instead of silently no-op'ing.
- metal-test: 16 new cases -- the campaign's 10-case exact-match fuzz
(now E-parametric) plus 6 new E-generality cases: E=168 (REAP) generic
+ massed-dup-ties, E=24 (<32 lane width) generic + ALL-EQUAL ties,
E=200 (a lane straddles the E boundary -- per=ceil(200/32)=7 doesn't
divide 200, so lane 28's ch[] block mixes 4 real indices with 3
sentinel ones; input is constructed to force those 4 into the top-8
deterministically, and the test asserts they were actually selected,
not just that serial==parallel), and E=257 (out-of-contract, proves
the auto-serial-fallback engages: this case is confirmed load-bearing
-- it fails without the new host-side guard). 15 stock + 10 ported +
6 new = 31 total metal-test cases.
Builds (METAL=0/1) and full C/python suites verified unchanged vs stock
61004dc (0 new warnings, identical pass counts). On-box A/B on this
branch is pending (see PR_BODY.md) -- no model runs performed here.
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.
Extend resource_plan to classify the hardware into bottleneck regimes
(disk / memory / mixed / compute) and derive tuning knobs automatically:
MTP: off when compute-bound (42% loss at full residency, #389)
or disk-bound with <90% hit (union growth adds reads)
PIPE: COLI_CUDA_PIPE=1 single-GPU, =2 multi-GPU, PIPE=1 CPU disk
NUMA: selective interleave for GPU hosts, blanket hint for CPU-only
PIN: PIN_GB=all when fully resident + no GPU
OMP: COLI_NO_OMP_TUNE=1 for Metal (spin steals GPU power)
`coli plan` now shows an auto-tune section with each knob and its
reason. `environment_for_plan()` applies them via setdefault so
explicit user settings always win.
plan version stays at 2 (additive fields: bottleneck_class,
projected_hit_rate, tune). 7 new tests covering all regimes.
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).
CreateProcess cannot exec a shebang script, so the gateway exits during
setUpClass on the windows CI job. The gateway logic under test is
platform-independent and stays covered by the POSIX jobs.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The gateway's tool-calling path had unit coverage (parse_tool_calls,
render_chat) but nothing exercised the real subprocess wire protocol or the
HTTP surface a coding client actually hits. #401 reports plain-text replies
where tool_calls were expected; every documented path checks out, so pin the
whole path down with a mock engine speaking SUBMIT/DATA/DONE and assert:
- non-stream: tool_calls populated, finish_reason tool_calls, no raw markers
- stream: markers suppressed across 20-way chunk splits, tool_calls delta
- tool-result round trip: <|observation|><tool_response> rendering, text reply
- no tools: plain text untouched
Also emit a stderr diagnosis when tools are declared and tool-call markers
are present in the reply but the strict parse matches nothing (typically
quantization-mangled output) pointing at COLI_TOOL_SALVAGE=1 -- the likely
field condition behind #401.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
A single NaN or +Inf logit silently broke the default sampling path. +Inf
became `mx`, then `expf((Inf-mx))`/`expf((NaN-mx))` is NaN, the softmax sum went
NaN, every probability went NaN — and dist_sample's fallback loop
`if(g_pbuf[i]>0)` is false for NaN at every index, so it returned 0. Every
subsequent token: 0. No error, no warning. @KingIcyCreamProjects found it.
dist_build now takes `mx` over finite logits only, gives a non-finite logit
probability 0, and when the distribution is unusable (no finite logit, or a
non-finite/zero sum) collapses to a delta on the finite argmax and warns ONCE
on stderr — degraded, but a valid token and a visible cause, never a silent
stream of zeros. The finite argmax uses the index found during the mx pass
(robust even when lo[0] itself is NaN, where argmax_v would wrongly return 0).
tests/test_sample_nan.c: healthy logits still sample correctly; NaN/+Inf
injected at lo[0], the middle, and the last position all pick the finite
argmax; an all-non-finite vocab leaves no NaN in the buffer and doesn't crash.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
@KingIcyCreamProjects caught a real artifact on the 9950X3D (#357 thread): the
nk=8192 cell reported ~75x, far above the real ~13-40x algorithm. Root cause was
two compounding bench bugs, both verified against the code:
1. ONE frozen input per cell. partial_select_desc's median-of-three pivot is
fully deterministic (no RNG), and bench_dsa_select froze the input then ran
2000 reps on that identical array -- so all 2000 reps hit the exact same
pivot sequence. A single lucky input spiked one nk row (stable across re-runs
because it's deterministic, not because it's real).
2. brng was a static global, initialized once and NEVER reset between cells.
So each (shape,nk) cell's input depended on every prior cell's brand() draws
-- reordering nks[] or adding a shape silently shifted all later inputs.
Fix:
- brng_seed() reseeds per (shape, nk, seed) so every cell is reproducible and
independent of cell ordering.
- Report the MEDIAN of N_SEEDS=11 independent inputs, each itself a median over
N_REPEAT=2000 timing reps. A lucky pivot now moves one of 11 samples, not the
reported number.
Measured on Intel Core Ultra 9 185H (this fix, median of 11 seeds):
realistic@8192 10.97x (was 13.57x single-seed on this box)
uniform@8192 9.39x (was 7.48x)
plateau@8192 16.11x (plateau ignores the RNG, unchanged as expected)
band: 6-26x across all cells, no anomalous spikes.
Correctness is unaffected (test_dsa_select, 129 cases, unchanged). This is a bench
fidelity fix only -- no engine code touched.
Per review: the collapse starts one line before the sum — seeding
mx=lo[0] means a NaN at index 0 makes mx NaN, every (lo[i]-mx) NaN, and
the softmax is doomed at the max-finding, not the normalize. Seed mx
from -INFINITY and skip NaNs (x==x), mirroring the argmax_v change; if
nothing finite survives, fall back to mx=0 and let the post-sum guard
decide. The isfinite(s) guard is now the second line of defense rather
than the only one.
Clean logits take a byte-identical path (the extra x==x compare is
noise next to V expf calls). test_logit_nan gains the NaN-at-index-0
and all-NaN dist_build cases; test_topp's 123-case sweep still passes
on this tree, confirming no interaction with the #354 heap select.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
On the default serve path (TEMP>0, 0<NUCLEUS<1) a single NaN or +Inf in
the logits — a bad streamed expert tile, or an fp overflow in the matmul
at a low-RAM eviction boundary — poisoned softmax: g_pbuf became all-NaN,
dist_sample never satisfied cum>=u, and the fallback returned token 0. The
engine then emitted an unbroken run of token 0 with NO error. The greedy
path was equally blind: argmax_v started bv=lo[0] and `lo[i]>NaN` is always
false, so a NaN at index 0 pinned the argmax to 0.
- argmax_v: skip NaN (x==x) and seed from -inf, so it returns the max
finite/+Inf entry instead of being NaN-pinned to 0. Covers greedy decode
and the speculative-verify argmax path.
- dist_build: after the softmax sum, if s is non-finite or <=0, collapse
g_pbuf to a one-hot over the finite argmax and warn once, instead of
dividing every entry into NaN. Covers the nucleus and verify paths.
Both are O(1)/free on the happy path (one branch after the existing loop;
one extra comparison inside the existing argmax loop). Degrade + diagnose,
never silently corrupt.
test_logit_nan (wired into TEST_BINS): asserts argmax_v skips NaN/picks
+Inf, dist_build yields a finite normalized one-hot on the max finite
logit, dist_sample emits that token (not 0), and clean logits still give a
valid distribution. Fails on stock dev, passes with this change.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Vincenzo Fornaro