Feeds reference tokens through the normal step() decode path and reports
NLL/ppl + hit rate + tok/s + RSS. Inert unless PPL=1; default path
untouched (12/12 vs ref.json verified with patch applied). Cross-checked
vs HF transformers bf16 on identical token ids: 12.11 vs 12.25 ppl (#108).
@bokiko benchmarked the feature on three hosts and found every setting is
either no faster or no longer coherent. Reproduced here on a 25 GB box, and
the numbers are worse than "a quality/speed tradeoff":
- budget=8 -> hellaswag 30% vs 90% with it off (25% is the chance floor)
- budget=4 -> decode is literal noise ("The **1...: s2151:")
- MTP acceptance 0%. Which experts survive the cap depends on cache
residency at the moment of the forward, so draft and verify do not
compute the same function -- the invariant #294 just established for
#163, violated through cache state instead of kernel dispatch. SPEC_PIN
cannot fix that and should not try: it is feature semantics, not
dispatch.
- 0.13 tok/s vs 0.30 baseline, while loading 14.66 experts per layer
against a topk=8 baseline. The cap does MORE disk I/O than not using it.
"~335 GB I/O saved" counts dropped experts, not bytes not read.
EXPERT_BUDGET>0 is now ignored unless EXPERT_BUDGET_EXPERIMENTAL=1, with the
measurements printed. The code stays compiled and developable: MoE-Spec
(arXiv 2602.16052) is not a wrong idea, this implementation just has no
point where it is both faster and correct. Re-enabling it by default needs a
quality number next to every speed number.
Also gates the cap to decode (S<=4), @woolcoxm's fix from #292/#298: during
prefill the batch union is 30-100+ experts, and capping to 4-8 drops most of
them, corrupting the hidden state and therefore the KV cache. Necessary but
not sufficient -- the run above already includes it.
Removes issue_budget.md, issue_diskio.md and issue_grouped_quant.md: design
notes in the repo root, unlinked from any README, whose only user-facing
content was "EXPERT_BUDGET=6-8 -- good speedup, minimal quality loss" and
"+83% decode at budget=4". Measurement says otherwise, and they were the
only thing on main telling anyone to switch this on. They stay in history.
Reported-by: bokiko <#303>
Co-Authored-By: woolcoxm <#292>
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Two silent failures that compound into "[engine terminated]" with no cause.
cap_for_ram() floors the expert cache at cap=1. When resident+slack have
already blown the budget, avail is negative and capmax would be 0 -- "I do
not fit in your budget". Flooring it to 1 and carrying on turns "I do not
fit" into "I overshoot", which is precisely the mid-generation OOM-kill the
function exists to prevent: it printed "projected peak 25.1 GB" against a
22 GB budget and started anyway. Now it says so, names PIN_GB when that is
what inflated the resident set, and refuses to start when the peak also
exceeds the memory actually available on the machine (COLI_RAM_OVERCOMMIT=1
overrides).
The kernel kills with SIGKILL: no error, no log, stdout just closes. coli
read that EOF and printed "[engine terminated]" without ever reaping the
child, so an OOM-kill was indistinguishable from a clean exit -- the report
in #305 (Debian 12, dies mid-generation, no message). engine_diag() now
reports the signal or exit code, names the OOM-killer when it was SIGKILL,
and shows the tail of the engine's stderr.
Reported-by: Ne00n <#305>
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
One HF 504 killed the whole bench. Now: load_dataset retries with
exponential backoff (hf_hub resumes partial downloads from cache); a task
that still fails is skipped instead of killing the rest; JSONLs are written
atomically (coli only checks existence, so a truncated file from an
interrupted run would block re-download forever); coli bench drops
still-missing tasks with a warning and refuses to run eval with none.
Co-Authored-By: Claude <noreply@anthropic.com>
Apple clang 16 (clang-1600.0.26.6) defaults objective-c++ to a pre-C++11
dialect, so the raw string literal holding the Metal shader in
backend_metal.mm fails to parse:
backend_metal.mm:12:29: error: use of undeclared identifier 'R'
backend_metal.mm:13:10: fatal error: 'metal_stdlib' file not found
Pinning gnu++17 on METALXX fixes 'make glm METAL=1' and 'make metal-test'.
Verified on macOS 15 / M4 Max: both targets build and all metal backend
tests pass.
PIN=auto resolves to <model>/.coli_usage, the history that serve mode
appends after every turn, so each restart's hot-store placement follows the
accumulated REAL workload instead of a frozen one-shot profile; stats.txt is
the fallback for a virgin model dir, and with neither present the run simply
starts unpinned. Same magic-value convention as PIN_GB=all (#80); explicit
paths and the AUTOPIN flow are untouched (PIN set skips AUTOPIN as before).
This lifts deployment entrypoints' "prefer .coli_usage over stats.txt" shell
plumbing into the engine, plus the ENVIRONMENT.md row.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Select a portable architecture from the compiler target instead of forcing x86-64-v3 on every platform. On macOS, only enable Homebrew OpenMP when its header and library actually exist, preserving the dependency-free fallback.
MTP acceptance collapses when the draft (S=1) and verify (S>=2) forwards do
not compute the same function. Three switches were S-dependent: the int4
IDOT gate (S>=g_i4s, asymmetric exactly on ISAs where g_i4s>1), the
S==1-only fused gate+up pair, and the Metal GEMM row threshold.
SPEC_PIN=1 (default) pins every forward issued while model drafts are live
to the platform S=1 kernel family, so draft and verify agree by
construction. Prefill and non-speculative decode keep their gates.
SPEC_PIN=0 restores the old behavior for A/B.
The 24-proposal <10% guard now pauses drafting for 256 tokens and re-arms
on a fresh window instead of latching off for the whole session.
Co-Authored-By: Claude <noreply@anthropic.com>
Two issues found by the diagnostic sweep in #292:
1. Profile double-count in pipe_layer_sparse: the outer t_emm span wrapped
both the shared-expert GPU dispatch AND the moe() call, but moe()
self-times its own t_emm internally (6 accumulation sites). Routed-expert
matmul was counted twice -> accounted > elapsed -> 'other' went negative,
and expert-matmul showed an inflated ~120s. Fix: split into two narrow
spans around only the GPU work moe() does NOT cover (shared-expert
dispatch, routed upload+add), letting moe() self-time as all other
callers already do. Verified: 'other' now positive (14-17s),
expert-matmul realistic (5-6s).
2. MTP blanket-disabled under CUDA (g_draft=0 when g_cuda_enabled). This
was a conservative guard from #163 before the root cause (cold-expert
fused-pair + IDOT kernel divergence) was fully diagnosed. GPU-resident
experts have no divergence; the cold subset still does but achieves
30-50% acceptance anyway (matching non-CUDA builds). Add COLI_CUDA_MTP=1
opt-in so users can test speculation under CUDA. Default unchanged.
Verified: COLI_CUDA_MTP=1 -> MTP ACTIVE (draft=3), 44% acceptance,
3 forwards for 8 tokens (vs 7 without MTP).
Refs #292#163
pin_wire() only locked the legacy private-slab path (s->slab / s->fslab),
which stay NULL under COLI_MMAP -- so MLOCK=1 on an mmap build silently
wired 0 bytes and the 'pinned' set was just warm page cache, evictable
under memory pressure (measured: hundreds of MB/s of re-reads from disk
mid-generation on a 503 GB host once the cache tightened).
Three pieces:
- qt_wire_mmap(): mlock each pinned expert's weight + scale ranges inside
the file mappings. Skips cuda_eligible QTs: VRAM-tier experts compute
from device memory, and expert_host_release() early-returns for mmap
experts (no slab) WITHOUT nulling q8/q4, so a host-pointer check alone
wires the whole VRAM tier too (~137 GB of never-touched locked pages on
a 6x32GB-class rig -- enough to starve the kernel into thrashing).
- qt_unwire_mmap(): REPIN gpu_swap promotions drop the promoted expert's
host lock instead of leaking it on every swap.
- expert_load() deliberately does NOT wire (it also runs for the transient
VRAM-staging pass); wiring happens once in pin_wire() on the final set.
Measured on GLM-5.2 int4, 4x RTX 5090 + 1x RTX 4090, 503 GB RAM,
PIN_GB=all: wired goes 0 -> 226 GB (exactly the RAM tier), decode-time
disk reads drop to zero once warm.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Windows cmd.exe always exports PROMPT (its prompt template, default "$P$G")
into a child's environment. The engine's `getenv("PROMPT")` picked that up, so
running `glm.exe` from cmd for the oracle self-test instead entered text-
generation mode — loading a tokenizer the tiny model doesn't ship and failing
with "tokenizer.json: No such file". PowerShell has no PROMPT env var, so it
worked there (galmok's 32/32) but not in cmd — same command, different shell.
New coli_user_prompt(): honors COLI_PROMPT everywhere, and on Windows ignores a
PROMPT that carries cmd's $-metacodes ($P,$G,...) — a real prompt has none. cmd
users can still pass a prompt via COLI_PROMPT or a non-$ PROMPT. Verified: $P$G
-> oracle mode, "Explain recursion" -> honored, COLI_PROMPT -> honored.
Third native-Windows bug surfaced by a from-scratch main download (after the
-lpsapi link fix and the bilingual oracle message).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Running a real model with no PROMPT lands in oracle self-test mode, which
compares against ref_glm.json — the TINY model's oracle. The guard that
detects the vocab mismatch and points the user at PROMPT=/coli chat was
Italian-only, which read as a crash to English users (#271, galmok). Lead
with English, keep an IT footer, and add the `coli chat` hint.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
compat.h's rss_gb() calls GetProcessMemoryInfo and links psapi via
#pragma comment(lib,"psapi.lib") — an MSVC-ism. MinGW gcc ignores that pragma
(emits -Wunknown-pragmas), and the Windows LDFLAGS never linked psapi, so on a
gcc that doesn't honor the pragma (e.g. 16.1.0 UCRT) the build fails with
`undefined reference to GetProcessMemoryInfo`. Add -lpsapi to the Windows
LDFLAGS; harmless on toolchains where the pragma also resolves it. Found while
building on native Windows 11 with winlibs GCC 16.1.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Opt-in (default off): prints the generated token ids to stderr after
generation, for exact comparison across decode paths (e.g. the S=1
resident pipeline vs the CPU path). Used to verify the pipe_layer_sparse
gate relaxation is token-exact vs the CPU decode path (see #273).
The grouped-int4 (fmt=4) loader hard-coded gs=128 at all three detection
sites (resident weights + the two expert-load paths: mmap and pread-slab).
A checkpoint converted with --group-size 64 (or any non-128 size) wrote a
valid file that the engine then misdetected as plain per-row int4 (fmt=2)
and read the wrong number of scales -> garbage output, silently.
The conversion path (convert_fp8_to_int4.py --group-size) already emits
arbitrary group sizes, and the compute kernel (matmul_i4_grouped) is
fully gs-generic (only constraint: gs multiple of 16, the AVX2 width).
The loader was the sole gap.
Replace the three hardcoded checks with one shared helper, detect_group_size(),
that derives gs from the scale-array byte count by probing candidate sizes
{16,32,48,64,96,128,192,256} finest-first. Data-driven: any listed size
just works; per-row int4 (ns == O*4) correctly returns gs=0 -> stays fmt=2.
Verified against real GLM-5.2 expert dims (gate/up O=2048,I=6144 and down
O=6144,I=2048): g64/g128/g256 all detect correctly, and per-row is never
misdetected as grouped. g128 (the only previously-supported size) is
unchanged -> no regression for existing checkpoints.
This unblocks the g64 lane that ZacharyZcR's #225 ablation identified as
beating shipped per-row int4 (-7.5pp vs -9.3pp) at ~19% fewer bits: the
converter can produce it, and now the engine can load it.
Refs #225
Per ZacharyZcR's 6x5090 A/B on #273: the S=1 resident-pipeline relaxation is
+49% on a single GPU (5070 Ti) but a wash on multi-GPU. With layers sharded
across N devices, each resident forward at S=1 crosses P2P per layer group and
those small hops don't amortize — the same term that killed pipe x head-shard
in #111. Multi-GPU decode walls on disk service, which pipe2 can't touch.
Make the threshold device-count-dependent:
- single GPU (g_cuda_ndev<=1): S>=1 (the breakthrough path)
- multi GPU (g_cuda_ndev> 1): S>=8 (the original prefill-only gate)
with COLI_CUDA_PIPE_S_MIN as an env override for anyone who wants to measure.
The two calibration points bracket the design space:
1x 5070 Ti, modest CPU, "other"-bound decode -> S=1 (+49%)
6x 5090, sharded, disk-service-bound -> S=1 a wash, keep S>=8
Refs #273 (comment)
CACHE_ROUTE=1 ROUTE_J=2 ROUTE_M=12 steers the MoE router to prefer
cache-resident experts, reducing the miss rate from 27% to 17% and disk
I/O from 12.4s to 8.5s. Combined with the full optimization stack
(disk tuning + CUDA pipe2 + ws_b cache fix), this achieves 1.41 tok/s
on GLM-5.2 744B int4 / RTX 5070 Ti / 32GB RAM — a 4.3x speedup over stock.
route_agree=94.6% confirms minimal quality cost (94.6% of cache-steered
picks match the true top-K the router would have chosen).
No code change — CACHE_ROUTE is an existing engine feature, opt-in via
env vars. Documented in issue_diskio.md with the full optimization journey.
The cap_for_ram reserve for the 64-slot expert working set (ws_b = 64 × eb
= 1.21 GB) is overcounted when EXPERT_BUDGET is active. At budget=4 only
ws[0..3] are populated (not all 64), so the actual working set is 4 × eb
= 76 MB — 16x less than reserved. The excess 1.06 GB was starving the LRU
cache, capping it at 3 when budget=4 needs cap>=4.
Fix: clamp ws_b to (budget+4) × eb when EXPERT_BUDGET < 64. This raises
cap from 3 to 4, matching the budget. The cache can now hold all experts
a token needs, eliminating the LRU thrashing that caused excessive disk
re-reads (the SSD hammering).
Measured (pipe2 + full stack, budget=4, RAM_GB=28):
tok/s: 0.85 -> 1.03 (+21%)
hit rate: 57% -> 73% (+28%)
expert-disk: 18.2s -> 12.4s (-32%)
decode: 37.7s -> 31.0s (-18%)
Correctness: 32/32 oracle positions.
Same fix applied to expert_avail() (the mirror function for pin budgeting).
The HWINFO snapshot read /proc/cpuinfo, /proc/meminfo and
sysconf(_SC_NPROCESSORS_ONLN) — all absent on native Windows — so the
dashboard runtime panel showed "0 GB RAM / 0 cores" while the CUDA branch
populated VRAM fine. Now: CPUID brand string (0x80000002..4, no new
deps), GetSystemInfo for logical cores, and the existing compat_meminfo
(GlobalMemoryStatusEx) for RAM. Verified live: panel reads "AMD Ryzen 9
9950X3D 16-Core Processor / 66 GB RAM 24 GB free / 32 cores".
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
On Windows the engine self-exec OMP tuning never runs (Linux/FreeBSD-only)
and posix_fadvise readahead is a compat.h no-op, so a stock Windows run
leaves large measured wins on the table. The launcher now setdefaults, on
win32 only, each independently overridable by setting the variable:
- OMP_WAIT_POLICY=active, GOMP_SPINCOUNT=200000, OMP_DYNAMIC=FALSE,
OMP_NUM_THREADS=<physical cores> (parity with the glm.c self-exec block;
COLI_NO_OMP_TUNE disables exactly this block, presence-based like the
engine). OMP_PROC_BIND/OMP_PLACES deliberately omitted and also removed
from environment_for_plan on win32: MinGW libgomp has no affinity support
("Affinity not supported on this configuration").
- DIRECT=1: unbuffered expert reads. Measured on a 9950X3D + Samsung 9100
PRO Gen5 + Win11: iobench 10.68 GB/s O_DIRECT vs 9.03 buffered (warm);
end-to-end REPLAY 0.48 -> 1.02 tok/s. Matches #162 (1.47x same class).
- PIPE=1: load/matmul overlap, byte-identical output; +8% on top of DIRECT
(PIPE_WORKERS untouched at 8 - 4/8/16 swept flat on Gen5).
- PILOT_REAL=1: real cross-layer prefetch, the only working prefetch on
Windows; +11% and expert hit rate +19 points.
Full ladder methodology and numbers: 96-token greedy REPLAY, one lever per
step, medians of 3-4 runs (see the fork tuning doc referenced in the PR).
tests/test_env_defaults.py covers the defaults, explicit-override-wins,
the kill-switch scope, and the non-win32 no-op.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Expert count 21,504 -> 19,456 (75x256 + MTP head), matching the rest of the repo.
- Replace phantom IO_THREADS with PIPE_WORKERS (default 8); state the pool only
engages under PIPE=1.
- Drop rotting precise line-count claims (glm.c ~2,400; web ~390) — keep the prose.
- Add the desktop/ directory to the repo-layout section.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
profile_print now emits 'expert-disk N.NNNs service / N.NNNs wait' but
the harness regex still expected the old single expert-disk number, so
every run died with 'benchmark output missing'. Accept both formats and
report disk = service + wait.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
os.cpu_count() returns logical processors, so on SMT machines the plan
sets OMP_NUM_THREADS to 2 threads/core, which thrashes the AVX-512 units
during expert matmul (9950X3D: 32 logical vs 16 physical). Count
RelationProcessorCore records instead, with the existing lscpu/cpu_count
fallbacks intact.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
One-line gate change: relax the pipe2 call-site gate from S>=8 to S>=1,
allowing the existing resident-pipeline (pipe_layer_sparse) to run during
single-token decode, not just prefill.
The S>=8 gate was a performance heuristic (prefill-only), not a correctness
constraint — pipe_layer_sparse is fully S-general. At decode it keeps the
residual stream x on the GPU device across all 78 layers, running rmsnorm,
residual adds, and shared-expert matmuls on-device. This eliminates the
~12.5k GPU sync interruptions per decode that caused the expert-matmul
regression (13.3s -> 9.2s), and moves the untracked 'other' CPU work
(rmsnorms, residual adds, routing) onto the GPU (29s -> 17.6s).
Measured (GLM-5.2 744B int4, RTX 5070 Ti, 32GB RAM, budget=4 + full disk stack):
tok/s: 0.72 -> 1.07 (+49%)
decode: 44.5s -> 29.9s (-33%)
expert-matmul: 13.3s -> 9.2s (regression fixed)
'other': 29s -> 17.6s (-39%)
Correctness: 32/32 oracle positions (3 consecutive runs).
Configuration: COLI_CUDA=1 CUDA_DENSE=1 COLI_CUDA_ATTN=1 COLI_CUDA_PIPE=2
CUDA_EXPERT_GB=0 EXPERT_BUDGET=4 PIPE=1 RAM_GB=28 PILOT_REAL=1 DIRECT=1
vmmlaq_s32 computes a 2x2 int32 tile (2 weight rows x 2 activation
rows) per instruction on 8-deep segments. Tile o and s in pairs,
halving weight traffic and doubling per-instruction work at S>=2.
Four independent accumulators over a 64-deep unroll keep the loop
throughput-bound (a single chained accumulator measures no better
than SDOT: latency-bound). S=1 and all tails (odd o, odd s, I not a
multiple of 16/32) keep the existing SDOT/scalar code, and scales
apply in the same order, so results are bit-identical.
Compile-time gated on __ARM_FEATURE_MATMUL_INT8. The default Darwin
build passes no -mcpu and is byte-identical (still SDOT, IDOT_KERNEL
"neon"). Opt in with ARCH=native (new Darwin Makefile knob, appends
-mcpu=<arch>), which reports IDOT_KERNEL "neon-i8mm". The same gate
lights up on any aarch64 with i8mm (Graviton3+, Grace).
test_idot grows a driver-level exactness check through matmul_qt_ex:
fmt 1 and 2, S in {2,3,4,5,8}, O in {1,2,3,64,65}, I in {16,17,100,
1408}, bitwise float equality against a plain-C reference. Green on
both build flavors.
Measured on an M5 Pro (18 threads, matmul_qt_ex microbenchmark at
GLM-5.2 expert shapes, best of 3 process runs, vs the SDOT baseline):
gateup int4 S=8 499.7 -> 1076.6 GF/s (+115%)
gateup int8 S=8 512.9 -> 1166.3 GF/s (+127%)
down int4 S=8 696.9 -> 1186.0 GF/s (+70%)
S=1 decode rows unchanged (SDOT path untouched)
The decode/prefill PROFILE line splits expert-disk time into service
(overlapped async dispatch) and wait (blocking stalls), but every
accumulation site wrote t_edisk and nothing ever wrote t_ewait, so the
wait column always printed 0.000s. Worse, the accounted sum used the
dead t_ewait instead of t_edisk, so the entire disk-read stall was
excluded from accounted and silently fell into the other bucket.
On a disk-streaming MoE the effect is large: other reads as ~60% of
decode when it is really the expert-load stall. Route the three
blocking sites (non-PIPE parallel load, the Metal drain barrier, and
the per-expert pipe_wait in the CPU matmul loop) to t_ewait, keep the
async dispatch in t_edisk, and include both in accounted.
Profiling-only; no behavioural change. Before, on a 168-expert model:
expert-disk 25.077s service / 0.000s wait | ... | other 28.401s
After:
expert-disk 0.111s service / 26.948s wait | ... | other 3.390s
other now holds only the genuinely-unbucketed work (router, norms).
Peter Groom