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Merge dev into dual-ssd-mirror: resolve #298/#192-era conflicts

Browse Source 
Combined resolution: mir_pread/st_prefetch_rep (this PR) now carry dev's
DISK-CLASS accounting unwind (dc_wall_exit) and O_DIRECT prefetch skip
(g_direct); direct-path keeps dc_direct=1 plus the mirror read counters.
Verified: clean build, token-exact tiny models unchanged, test_st_mirror
and test_st_pread pass.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
JustVugg
2026-07-20 21:24:39 +02:00
parent e9225d2ffc 880cfb4ec5
commit 87cd0aafed
60 changed files with 8507 additions and 346 deletions
Download Patch File Download Diff File Expand all files Collapse all files
.github/workflows/site.yml
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@@ -0,0 +1,36 @@
name: Deploy website
# Publishes site/ to GitHub Pages. One-time repo setup:
# Settings → Pages → Build and deployment → Source: "GitHub Actions".
# Custom domain later: add site/CNAME with the bare domain, point DNS
# (A/AAAA to GitHub Pages IPs or CNAME to <org>.github.io), done.
on:
push:
branches: [main]
paths: ['site/**', '.github/workflows/site.yml']
workflow_dispatch:
permissions:
contents: read
pages: write
id-token: write
concurrency:
group: pages
cancel-in-progress: true
jobs:
deploy:
runs-on: ubuntu-latest
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
steps:
- uses: actions/checkout@v4
- uses: actions/configure-pages@v5
- uses: actions/upload-pages-artifact@v3
with:
path: site
- id: deployment
uses: actions/deploy-pages@v4
.gitignore
+1
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@@ -37,6 +37,7 @@ c/tests/test_schema_gbnf
c/tests/test_schema_gbnf.exe
c/tests/test_compat_direct
c/tests/test_compat_direct.exe
result
# oracoli tiny generati (make_glm_oracle.py) e dati benchmark scaricati
c/glm_tiny/
README.md
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@@ -3,6 +3,12 @@
</p>
<p align="center">
<a href="https://justvugg.github.io/colibri"><img src="https://img.shields.io/badge/website-justvugg.github.io%2Fcolibri-1f6feb" alt="Website"></a>
<a href="https://github.com/JustVugg/colibri/releases"><img src="https://img.shields.io/github/v/release/JustVugg/colibri?color=2ea043" alt="Latest release"></a>
</p>
<p align="center">
<a href="https://justvugg.github.io/colibri"><b>Website</b></a> ·
English · <a href="README.zh-CN.md">简体中文</a> · <a href="README.zh-TW.md">繁體中文</a> · <a href="README.it.md">Italiano</a>
</p>
@@ -44,6 +50,16 @@ brightness is routing heat, and every expert routed in a turn flashes white. Hov
as a 3-D galaxy — 13,260 characterised experts, 1,041 replicated specialists clustering by topic
(poetry, law, Chinese, SQL…). Position is measured routing affinity, not a learned embedding. Drag to spin.</em></p>
## The vision
Frontier models should not be sealed inside datacenters. colibrì exists so that
**anyone curious enough can open one up**: run a 744B-parameter mind on hardware
you already own, watch every expert fire in real time, and change the code that
does it. Not renting intelligence behind an API — *holding* it: probing it,
measuring it, improving it. Every optimisation in this project started with
someone measuring something on their own machine; the engine is deliberately
small enough that the next one can come from you.
## The idea
A 744B Mixture-of-Experts model activates only ~40B parameters per token — and
@@ -61,6 +77,18 @@ So the model doesn't need to *fit* in fast memory — it needs to be **placed**:
at int4) live **on disk** (~370 GB) and are **streamed on demand**, with a
per-layer LRU cache, a learned pinned hot-store, and an optional VRAM tier.
Think of the core algorithm as **a JIT, but for weights**. A compiler JIT never
compiles the whole program — it watches what actually runs and compiles the hot
paths, just in time. colibrì makes the same bet about a 744B parameter space:
parameters are not resident state to be held, they are **data to be staged**
across a heterogeneous storage hierarchy (VRAM / RAM / NVMe), exactly when the
router proves they are needed. Measured routing heat decides which experts earn
which tier, the router runs a layer ahead so prefetch hides the staging latency,
and — like a JIT — the engine learns your workload: the more you run, the hotter
the right experts get. It works because routing has measurable structure (see
the [expert atlas](https://github.com/JustVugg/colibri/issues/175)) — and
structure is cacheable.
The engine is a single C file (`c/glm.c`) plus small headers. No BLAS, no Python
at runtime, no GPU required.
@@ -203,6 +231,13 @@ COLI_MODEL=/nvme/glm52_i4 ./coli doctor # read-only readiness check
The engine at runtime is pure C — python is only used by the one-time converter
and the optional API gateway.
**On Windows?** You don't need to build. Download the
`colibri-<version>-windows-x86_64.zip` from
[Releases](https://github.com/JustVugg/colibri/releases), unzip it, rename
`colibri-*-windows-x86_64.exe` → `glm.exe` (so the `coli` launcher finds the
engine), install [Python 3](https://www.python.org/downloads/), then run
`coli chat`. Full walkthrough in the [Quick Start guide](docs/quickstart.md#windows).
Prefer a `coli` command on your PATH? From a checkout, `pip install -e .`
registers it (the engine itself still lives in `c/` — this is an editable
install from the clone, not a standalone wheel).
@@ -220,6 +255,18 @@ install from the clone, not a standalone wheel).
| Grammar-forced drafts (structured output) | [docs/grammar-draft.md](docs/grammar-draft.md) |
| Environment variable inventory | [docs/ENVIRONMENT.md](docs/ENVIRONMENT.md) |
## What's next
- **Algorithmic research is active.** The current hierarchy is LRU + a learned
pin set; the next step is under way — smarter placement and scheduling,
overlap of CPU and GPU expert execution, and routing-aware speculation.
Everything lands the way this project always works: measured, reviewed, and
merged in the open.
- **More open models.** The tiering algorithm is model-agnostic: any MoE with
routed experts can be staged the same way. GLM-5.2 and OLMoE run today;
support for more open-weight families — **Kimi K2** (Moonshot AI),
**Qwen3 MoE** (Alibaba), **MiniMax** — is on the roadmap.
## Supporting the project
colibrì started as a one-person project on a 12-core laptop with 25 GB of RAM;
@@ -260,6 +307,14 @@ The hummingbird weighs a few grams, hovers in place, and visits a thousand
flowers a day. This engine keeps a 744-billion-parameter giant alive on
hummingbird rations: 25 GB of RAM, twelve CPU cores, and a lot of disk patience.
## Acknowledgements
colibrì is an engine; the minds it runs are a gift. Thank you to the teams
releasing frontier-class weights in the open — **Z.ai** (GLM), **Moonshot AI**
(Kimi), **Alibaba Qwen**, **MiniMax**, and **Allen AI** (OLMoE) — and to every
contributor who benchmarked, bisected, replicated an atlas run, or sent a patch.
This project is proof of what open weights make possible.
## License
Apache 2.0. GLM-5.2 weights are released by Z.ai under MIT.
SUMMARY.md
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# E5 — MTLResidencySet over the existing malloc'd slabs (experiment branch)
Branch: `e5/metal-residency-set` (cut from `origin/dev` @ `caa49f7`, per spec — E4 was cut
from `main` @ `72d3d37`; `backend_metal.mm`/`.h` are byte-identical between the two bases,
confirmed via `git diff 72d3d37 origin/dev -- c/backend_metal.mm c/backend_metal.h`).
## The hypothesis
E4 (MTLHeap-backed slabs) proved that batching residency declaration kills the GPU stall
(25.9s → 3.9s at cap16, −85%), but changing the *allocation* (heap sub-buffers instead of
malloc'd host memory) brought a +12–13s expert-disk-load tax, suspected first-touch/lock
contention on CPU-writes into GPU-owned heap pages. E5 decouples the two: keep the exact
same malloc'd slabs and per-slab `newBufferWithBytesNoCopy`-wrapped `MTLBuffer`s, and change
**only** residency bookkeeping — declare residency once, ahead of time, on a set attached to
the command queue, instead of once per command buffer via `useResource:`. If the stall
reduction survives without the load-path tax (malloc pages never change ownership), E5 wins.
## What changed
All mechanism code is confined to `c/backend_metal.mm` —
`coli_metal_register`/`coli_metal_unregister`'s existing signatures and every call site in
`colibri.c` (expert_load, uring_load_add, qalloc, kv_alloc, map_of_fd) are untouched; the
residency-set bookkeeping lives entirely inside those two functions' existing bodies. The
`colibri.c`/`backend_metal.h` touches are two, both coordinator-sanctioned: the validator
round-1 instrumentation hook (`coli_metal_resset_stats` + the gate-on-only `METAL-RESSET:`
stats line in `profile_print`) and the ported fslab-OOM unwind fix (see "Validator round 1
fixes" item 4). Still a smaller diff shape than E4,
which needed a new alloc/free API and four new `glm.c` call-site arms because it changed the
allocation function itself.
Env-gated `COLI_METAL_RESSET=1`, default OFF, runtime `@available(macOS 15.0, *)` guard with
a one-line stderr fallback when requested on an older OS or when residency-set creation
fails. Gate off ⇒ every new branch is skipped and behavior is byte-for-byte the stock path
(verified by inspection: `g_resset_enabled` starts `false` and nothing sets it except inside
the `COLI_METAL_RESSET` `getenv` branch in `coli_metal_init`, so `resset_add`/`resset_remove`/
`resset_flush` are no-ops and `moe_submit`'s `useResource:` loop runs unconditionally).
### Lifecycle (`c/backend_metal.mm`)
- **Init** (`coli_metal_init`, end of the existing pipeline-setup `@autoreleasepool`): if
`COLI_METAL_RESSET=1` and `@available(macOS 15.0, *)`, create one
`MTLResidencySetDescriptor` (`initialCapacity=4096`, a presize hint only), call
`[g_dev newResidencySetWithDescriptor:desc error:&err]`, and `[g_queue addResidencySet:rs]`
— one set, attached once, for the process lifetime. Failure (old OS or creation error)
prints one stderr line and leaves `g_resset_enabled=false` — stock path.
- **`coli_metal_register`**: after wrapping the buffer exactly as today
(`newBufferWithBytesNoCopy`) and pushing the `g_slabs` entry under `g_slab_mtx` exactly as
today, calls `resset_add(b)` **after dropping `g_slab_mtx`** but before returning.
`resset_add` takes a dedicated `g_resset_mtx` (guarding only the set mutations and the
dirty flag), calls `[rs addAllocation:b]` and sets `g_resset_dirty` — **it does not
commit**. No Metal call ever runs under `g_slab_mtx` (validator round-1 fix; E4's audit
round 2 identified mutex-over-live-Metal-call as the leading suspect for its +12s
expert-disk regression). Re-registering a live base (no in-tree caller does today) drops
the replaced wrapper from the set via `resset_remove(old)` before adding the new one
(hazard-audit defensive fix — the set would otherwise retain the old buffer, and its
pages' residency, forever), keeping set membership an exact mirror of `g_slabs`.
- **`coli_metal_unregister`**: erases the `g_slabs` entry under `g_slab_mtx` (stashing the
buffer), then calls `resset_remove(b)` **outside `g_slab_mtx`**, before returning.
`resset_remove` (under `g_resset_mtx`) calls `[rs removeAllocation:b]` **and commits
immediately** — no batching — because the caller frees the host memory right after the
function returns. See UNCERTAINTIES for why this asymmetry is deliberate.
- **`moe_submit`** (the one function whose `use` list — resolved expert weight/scale slabs —
scales with LRU cache size): calls `resset_flush()` at the top (commits any pending adds
from `resset_add`, under `g_resset_mtx` — it never touches the slab lock), then, if
`g_resset_enabled`, **skips** the
`for(auto&b:use) [e useResource:b usage:MTLResourceUsageRead];` loop entirely — residency
is already guaranteed by the queue-attached set. Every other `useResource:` call site in the
file (`bind_gemv`'s weight/scale buffers, `coli_metal_attn_decode`/`coli_metal_layer_decode`'s
`Lb`/`Rb`/`kvbW`/`kvbS`/`inB`/`pnB`/`rwB`/`rbB`, `coli_metal_gemm`'s `wb`/`sb`) is
**left completely unchanged**, regardless of the flag — see "Why only `moe_submit`" below.
- **Shutdown** (`coli_metal_shutdown`): `[g_queue removeResidencySet:rs]` then clears the
globals, ahead of the existing `g_queue=nil; g_dev=nil;`.
### Why only `moe_submit` skips `useResource:`
Apple's `MTLResidencySet` class reference (developer.apple.com, fetched during design on
2026-07-18) is explicit: *"Residency sets don't support hazard tracking, so you need to
account for hazards with fences and events."* The SDK header on this box
(`MTLResidencySet.h`, read directly) is **silent** on hazard tracking — the statement comes
from Apple's online documentation and adoption guide ("Simplifying GPU resource management
with residency sets"), not the header (see UNCERTAINTIES for sourcing). Dropping
`useResource:` therefore risks losing whatever hazard-tracking value those calls provided. Rather than apply the residency set uniformly and
argue *in general* that hazard tracking isn't load-bearing, this diff draws the line at the
one call site the mechanism history actually implicates:
`moe_submit`'s `use` vector holds only **read-only** (`MTLResourceUsageRead`), **indirectly
referenced** slab buffers — the kernel (`moe_gemv`) never touches them via `setBuffer:`; it
dereferences raw GPU addresses (`waddr[e]`/`saddr[e]`) baked into a separately-bound address
array (`bag`/`bau`/`bad`/`bsg`/`bsu`/`bsd`), which is exactly the "indirect reference" case
`useResource:` exists for. No GPU-side write ever touches these buffers, so there is no
write-after-write/read-after-write hazard for Metal's tracking to have been serializing in
the first place; the one real hazard — a slab unregistered+freed+reused by the CPU while an
async in-flight `moe_block_begin` command buffer still references it via a baked-in GPU
address — is a **CPU-write race that Metal's hazard tracking never protected against anyway**
(hazard tracking only covers GPU-side command dependencies visible through the Metal API; a
raw host-memory write via `pread`/`memcpy` is invisible to it regardless of `useResource:`).
That race is, and always was, the engine's own responsibility (slot/generation lifecycle: a
slab isn't freed while an outstanding async handle still owns it) — unrelated to E5.
Every other call site (`bind_gemv`, attention K/V cache writes) either doesn't scale with
cache size (fixed per-layer dense tensors — no perf benefit to touching) or has real
GPU-side write traffic in the same encoder (`Lb`/`Rb` are written by `a_copy` and read by
`a_score`/`a_clat` within one encoder — currently ordered by explicit
`memoryBarrierWithScope:MTLBarrierScopeBuffers` calls already present in `encode_attention`,
not by `useResource:`'s hazard tracking, but touching them wasn't needed for the hypothesis
and was judged not worth the added surface area). Leaving them untouched keeps the diff's
blast radius matched to the one seam the fix-plan's v5 finding actually names.
### Deferred-commit design (`resset_add` batches; `resset_remove` doesn't)
`coli_metal_register` is called from parallel OpenMP loader threads in tight bursts
("warmup fan-out" — same phrase E4's audit used for the same threads). Committing on every
single `addAllocation:` would reintroduce a per-slab cost on the load path, which is exactly
what E4's own +12s regression looked like (mutex held across a live Metal call, serializing
loader threads). So `resset_add` only marks `g_resset_dirty`; the commit is deferred to the
next `moe_submit` call, which flushes once via `resset_flush()` before it relies on the set
for residency.
This is correct — not just fast — because of an existing invariant the codebase already
depends on for `resolve()` to work at all: a slab's `coli_metal_register` call always
completes — including its trailing `resset_add`, which runs after `g_slab_mtx` is dropped
but **before the function returns** — before any dispatch that references that slab's
pointer can call `resolve()` for it (the caller in `colibri.c` cannot pass a freshly-loaded
expert's pointer to a dispatch before the load — which registers it — returns). After the
validator round-1 mutex split, the flush's synchronization runs through `g_resset_mtx`
alone: `resset_add`'s set mutation + dirty write and `resset_flush`'s dirty read + commit
are serialized by that one mutex, whose release/acquire pairs provide the memory ordering;
`g_slab_mtx` still orders the slab-table bookkeeping (register-before-resolve) exactly as on
stock. So any slab a given `moe_submit` invocation will resolve was `addAllocation:`-ed (and
marked dirty) strictly before that invocation's `resset_flush()` acquired `g_resset_mtx` —
the flush is guaranteed to cover it, regardless of what other threads are concurrently
registering unrelated slabs. The two mutexes are never held simultaneously anywhere, so no
deadlock ordering exists to maintain.
`resset_remove`, by contrast, commits synchronously and immediately, with no batching,
because the caller (`colibri.c`, in every one of the four slab-realloc call sites, and in
`kv_alloc`) frees the underlying host memory *right after* `coli_metal_unregister` returns.
An uncommitted-but-still-set-member allocation pointing at memory the host has already freed
is a potential use-after-free the GPU could act on — deferring that removal is not a
performance-vs-safety tradeoff, it's just unsafe, so it isn't deferred. (The spec's own
lifecycle wording backs this reading: "`coli_metal_register` → add allocation + commit
**(batch commits where call pattern allows)**" carries a batching allowance that
"`coli_metal_unregister` → remove + commit" does not.)
## Instrumentation parity
No existing counter's semantics changed. `coli_metal_moe_times`/`coli_metal_moe_counts`
(`g_t_setup`, `g_t_gpu`, `g_t_kernel`, `g_t_scatter`, `g_moe_ok`/`g_moe_fb`/`g_moe_experts`)
are computed exactly as before — `resset_flush()` runs *before* `ts_start = mnow()` in
`moe_submit`, so its cost is **outside** `g_t_setup`, keeping the orchestrator's A/B harness
reading the same counters with the same meaning across stock/E4/E5. The flush cost is
surfaced separately (validator round-1 fix — the original design left it invisible, a blind
spot for the battery): a dedicated `g_t_resset_flush` accumulator timed around the flush in
`moe_submit`, exported via `coli_metal_resset_stats()` (`backend_metal.h`) and printed by
`profile_print` as its own `METAL-RESSET: flush N.NNs` line — a **separate line following
the `METAL:` line, mirroring E4's `METAL-HEAP:` convention, so the existing `METAL:` line
the harness parses keeps its exact format** — printed **only when the gate is on** (the
function returns 0 when off), so stock output stays byte-identical. The register-side
`resset_add`/`resset_remove` costs have no dedicated counter: they run inside the engine's
existing expert-load wait accounting (the `t_ewait` window in `colibri.c`), noted in a comment
at `resset_add`, so a load-path regression from set bookkeeping would already show in the
existing disk/wait numbers. `[METAL] residency-set: on` / the two fallback stderr lines from
`coli_metal_init` confirm which path a run took.
## Validator round 1 fixes
1. **REQUIRED, Metal calls hoisted out of `g_slab_mtx`** (`backend_metal.mm`): the original
design ran `addAllocation:`/`removeAllocation:`/`commit` while holding `g_slab_mtx`, the
lock the parallel OMP loader threads contend on — structurally identical to the
mutex-over-live-Metal-call shape E4's audit round 2 identified as the leading suspect for
its replicated +12s expert-disk regression, and the SDK header notes commit on a resident
set tries to make resources resident "instantly" (real synchronous work; this set is
resident from startup since it is queue-attached for the process lifetime). Fixed by
introducing a dedicated `g_resset_mtx` guarding only the set mutations + dirty flag;
`g_slabs` push/erase stays under `g_slab_mtx` exactly as stock; the two mutexes are never
held together. The register→flush→resolve happens-before argument is preserved — see the
updated "Deferred-commit design" section and the comment at `resset_add`.
2. **REQUIRED, false citations corrected** (this file + the `moe_submit` commit message,
rewritten pre-push): the original text attributed the hazard-tracking and thread-safety
statements to the SDK header (`MTLResidencySet.h`), which is in fact silent on both
topics. The statements come from Apple's **online** `MTLResidencySet` class reference and
the "Simplifying GPU resource management with residency sets" adoption guide (both
fetched 2026-07-18 during design). All attributions now name the actual source; where a
claim rests on design reasoning rather than documentation, it is labeled as such.
3. **REQUIRED, flush cost made harness-visible**: `g_t_resset_flush` +
`coli_metal_resset_stats()` + the gate-on-only `METAL-RESSET:` line in `profile_print` —
see "Instrumentation parity" above.
4. **Pre-existing fslab OOM-unwind bug — now CARRIED ON THIS BRANCH** (follow-up commit,
coordinator-sanctioned second `colibri.c` change): `expert_load`'s fslab OOM path
(`c/colibri.c`, in `expert_load_impl`) freed `s->slab` via `compat_aligned_free` **without**
`coli_metal_unregister` — on stock that leaves a stale `g_slabs` entry whose GPU
exposure ends with the last command buffer that declared it; under E5 the buffer would
additionally be a **permanent residency-set member** referencing freed host memory until
some later realloc of the same slot unregisters by pointer, a strictly longer-lived
exposure than stock's transient per-CB one. Fixed by porting E4's reference
implementation (`6753225`) to dev's non-heap code shape: `coli_metal_unregister(s->slab)`
before the free. The `uring_load_add` analog (E4's audit round-2 "cheap insurance") is
deliberately NOT carried: that arm is `#ifdef __linux__`-gated and `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 at all.
## Per-seam differences vs E4
| Seam | E4 (`e4/metal-heap`) | E5 (this branch) |
|---|---|---|
| Allocation | New: `MTLHeap` sub-buffers via `coli_metal_heap_alloc` | Unchanged: same `posix_memalign` + `newBufferWithBytesNoCopy` |
| Coordinator C source / `backend_metal.h` | `glm.c` touched (new alloc/free API, 4 call sites + `expert_host_release`) | `colibri.c` + header touched only for instrumentation and the OOM-unwind fix |
| Residency scope | Declared once **per command buffer** (`useHeap:`, still inside `moe_submit`) | Declared once **for the process** (queue-attached set), refreshed incrementally at register/unregister |
| Hazard tracking | Heap sub-buffers forced `MTLHazardTrackingModeUntracked` always (allocation-level) | Untouched at the resource level; `moe_submit` alone stops calling `useResource:` (encoder-level), independent of `COLI_METAL_UNTRACKED` |
| Per-buffer vs per-set skip | `[b heap]` (Metal's own `MTLResource.heap` property) checked per buffer — heterogeneous mixes possible if a slab fell back to malloc | Blanket `if (!g_resset_enabled)` — homogeneous by construction, since every registered slab goes through the same `coli_metal_register` path when the gate is on |
| Availability guard | None needed (`MTLHeap` is old API) | `@available(macOS 15.0, *)`, matching this box's macOS 26.5 but required for portability |
| Known regression | +12–13s expert-disk load at cap16 (suspected first-touch/lock contention on heap pages) | None expected — malloc pages never change ownership; **unverified without a run** |
## What to measure (orchestrator, cap1/cap16, stock vs E4 vs E5)
1. **GPU stall** (`coli_metal_moe_times` gpu/kernel breakdown) — success: E5 ≈ E4's
−85%-class reduction vs stock at cap16.
2. **Expert-disk load path** (existing load/service-time counters) — success: E5 ≈ stock,
i.e. **no** repeat of E4's +12–13s tax, since allocation is untouched.
3. **tok/s** — should track (1) and (2) together.
4. **md5 within a fixed dispatch composition** — flag on vs off must be byte-identical at a
given cap (the "Output-invariant by construction" hard constraint); flag-on vs flag-on
across cap1/cap16 may legitimately differ (different dispatch composition, per the
fix-plan's "Determinism side-finding").
5. **`[METAL] residency-set: on` line present in stderr** at flag-on startup, and absent
(or the OS<15/create-failed fallback line) otherwise — cheap sanity check that a run
actually exercised the intended path before trusting its numbers. Also read the
**`METAL-RESSET: flush` line** (gate-on only): if that number is large, the deferred
set-commit cost is eating the stall win from the dispatch side.
6. If the hypothesis holds (E5 stall ≈ E4, E5 load-path ≈ stock, identical output), E5 becomes
the upstream PR candidate and must include the cap-default recalibration flagged in PR
#386's CURRENT-STATE CALIBRATION markers, per the spec's validation plan.
## Build
`cd c && make glm METAL=1` and a separate explicit `-Wall -Wextra` compile of
`backend_metal.mm` (the Makefile's `METALXX` line does not itself pass `-Wall -Wextra`, so
the warning surface was checked with those flags added explicitly; current `dev` contributes
one pre-existing `unused variable 'TG'` warning), plus
`cd c && make glm` (plain, non-Metal — the one `colibri.c` instrumentation touch, the `METAL-RESSET` stats line,
is inside the pre-existing `#ifdef COLI_METAL` arm of `profile_print`, so the plain build
compiles none of it), and
`make metal-test` (existing synthetic kernel-correctness unit test — no model, no
`glm52_i4/`, random weights — run once with `COLI_METAL_RESSET` unset and once with
`COLI_METAL_RESSET=1` to numerically exercise `coli_metal_register`/`moe_submit`'s changed
code path, since the task scope excludes running the real model). Exact results in the final
report, not here (build results belong to the report per the task's deliverable split, and
this file is written before the batched build run, per the scheduling constraint).
## UNCERTAINTIES
**Everything below is a judgment call, a seam where the residency-set lifecycle interacts
with the existing queue/command-buffer structure, or something unverifiable without a real
model run — flagged per the task's hard requirement.**
1. **The central design risk: skipping `useResource:` in `moe_submit` gives up Metal's
automatic hazard tracking for that buffer set.** Sourcing (corrected in validator round
1): the SDK header on this box
(`/Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/.../Headers/MTLResidencySet.h`,
read directly) documents the protocol only in terms of residency and says nothing about
hazard tracking either way; the two operative statements are from Apple's **online**
documentation (fetched 2026-07-18): the "Simplifying GPU resource management with
residency sets" adoption guide — *"You don't need to call `useResource`/`useHeap`... for
allocations in a residency set"* — and the `MTLResidencySet` class reference —
*"Residency sets don't support hazard tracking, so you need to account for hazards with
fences and events."* I reasoned through every
code path that touches `moe_submit`'s `use` buffers (read-only, indirectly referenced,
never concurrently written, freed only after the engine's own slot lifecycle guarantees
no outstanding async reference) and concluded removing `useResource:` there specifically
is safe — but this reasoning is **not the same as having run the model**. If any code
path I didn't trace lets a slab get unregistered while an async `moe_block_begin` handle
is still in flight and reading it, this change removes a mitigation (weak as it may have
been) that existed before. **This is the #1 thing to watch for md5 divergence on**, and
the reason the scope was deliberately narrowed to `moe_submit` alone rather than applied
uniformly.
2. **Residency-set mutations are serialized under a dedicated `g_resset_mtx` (validator
round-1 fix — originally they ran under `g_slab_mtx`, the E4-regression shape; no Metal
call runs under the slab lock anymore).** The serialization itself is kept as required
for correctness: Apple's online `MTLResidencySet` class reference states the set's
*"methods aren't thread-safe"* (the SDK header contains no thread-safety statement either
way — citation corrected in round 1; the online doc is the source). What remains
**unverified without profiling a loaded run** is the *cost* of the calls themselves:
`resset_remove`'s synchronous `commit` runs inside `coli_metal_unregister` on the
loader path (its cost lands in the existing `t_ewait` accounting), and the SDK header
says commit on a resident set tries to make added/removed resources resident/non-resident
*"instantly"* — real synchronous work, since this set is resident from startup
(queue-attached for the process lifetime). If `commit()`/`addAllocation:` turn out
expensive on this hardware/OS build, the load path degrades through set bookkeeping
rather than mutex contention — a different, now-decoupled failure mode, but the same
symptom as E4's regression. Orchestrator: check E5's load-path timing against stock, not
just against E4, and read the new `METAL-RESSET: flush` line for the dispatch-side share.
3. **`resset_flush()`'s cost sits outside `g_t_setup`/the `moe_times` breakdown** (it runs
before `ts_start = mnow()`), by design, to keep the harness's existing counters
meaningful — and, since validator round 1, it is **no longer invisible**: the
`g_t_resset_flush` accumulator surfaces it as the gate-on-only `METAL-RESSET: flush`
line (see "Instrumentation parity"). Residual blind spots: (a) the accumulator is a
plain double written from `moe_submit` on the engine thread, matching the existing
`g_t_setup` convention — if `moe_submit` were ever called from multiple threads
concurrently, both counters would be equally wrong; (b) the register-side
`resset_add`/`resset_remove` costs have no dedicated counter and are only visible
blended into the existing `t_ewait`/disk-wait numbers (comment at `resset_add` says so)
— a fine-grained attribution would need a throwaway probe.
4. **`initialCapacity = 4096` on the `MTLResidencySetDescriptor` is an unverified guess.**
It's documented as a presize hint only (no correctness effect either way), chosen to be
"clearly larger than the permanent-weight-tensor + KV-cache + plausible cap16 LRU-slab
count" without actually counting those registrations precisely. Too small just means
internal array growth; not a correctness concern, flagged only because it's a number I
picked without measuring.
5. **Not calling `requestResidency()` proactively.** Apple's guide frames it as an optional
latency-hiding call ("call ahead of time during non-critical moments... to minimize [first
command buffer] latency"), and Blender's Cycles PR (the spec's cited reference
implementation) doesn't appear to use it either per its PR description. Omitted to keep
the lifecycle minimal and match the reference pattern; if profiling shows a
first-command-buffer-after-a-load-burst latency spike, this is the documented lever to try
next, not implemented here.
6. **The deferred-commit correctness argument (item in "Deferred-commit design" above) rests
on a single-writer-before-single-reader program-order guarantee that is true today by
inspection but is not an invariant enforced anywhere in code** (no assertion, no type-level
guarantee) — it's the same kind of implicit ordering `resolve()` itself already depends on
for correctness (a slab must be registered before any dispatch can resolve its pointer),
so this diff doesn't introduce a new category of fragility, but it's worth naming
explicitly rather than leaving implicit.
7. **Async `moe_block_begin`/`moe_block_end` overlap with concurrent `register()` calls**
(background loader threads registering new/different experts while an unrelated MoE block
is still in flight on the GPU) was reasoned through but never exercised in a real
concurrent stress scenario — the synthetic `metal-test` unit test's `run_moe` calls are
single-threaded and synchronous (`coli_metal_moe_block`, not the async `_begin`/`_end`
pair), so it does **not** cover this interleaving. The real engine's `PILOT`/prefetch and
`moe_block_begin`/`_end` overlap path is exactly the concurrency shape most likely to
expose a bug in this design if one exists, and is untested here by construction (out of
scope: no model runs).
8. **`coli_metal_gemm` (prefill path) and `bind_gemv` (attention path) still call
`useResource:` unconditionally, so they get no CPU-overhead benefit from the residency set
even though their buffers are also set members.** This is deliberate (see "Why only
`moe_submit` skips" above) but means E5's win, if any, is scoped to the decode-path MoE
dispatch loop specifically — prefill and attention timing should be unaffected by the flag,
which is itself a testable prediction the orchestrator's harness can check.
9. **API surface verified against this box's actual SDK headers**
(`MTLResidencySet.h`, `MTLDevice.h`, `MTLCommandQueue.h`, `MTLAllocation.h`,
`MTLResource.h` — all read directly, not from memory) and against Apple's own
"Simplifying GPU resource management with residency sets" guide, so the method names/
signatures (`newResidencySetWithDescriptor:error:`, `addResidencySet:`,
`removeResidencySet:`, `addAllocation:`, `removeAllocation:`, `commit`) are
high-confidence. What is **not** independently verified is runtime behavior beyond what
the docs state and what the synthetic unit test exercises — no substitute for the
orchestrator's real cap-sweep battery.
10. **Pre-existing fslab OOM-unwind bug — carried on this branch** (follow-up commit; see
"Validator round 1 fixes" item 4 for the full mechanism). The one-line
unregister-before-free fix from E4's `6753225` is ported to dev's non-heap code shape,
so the upstream PR built from E5 inherits it automatically. Residual notes: (a) the fix
is only reachable through the fslab-OOM path (allocation failure mid-load), so it is
untestable without an OOM-injection harness and cannot affect the orchestrator's
controlled A/B runs at sane RAM headroom — carried as correctness insurance, verified by
inspection + clean builds only; (b) the `__linux__`-gated `uring_load_add` analog is
deliberately not carried (dead code on every real build target — rationale in the fixes
section).
c/Makefile
+47 -2
View File
@@ -58,9 +58,14 @@ CFLAGS = -O3 $(OMPC) -Wall -Wextra -Wno-unused-parameter -Wno-misleading-indent
# Opt-in: ARCH=native appends -mcpu=native (arm64 clang uses -mcpu, not -march),
# which unlocks the i8mm SMMLA int8/int4 dot kernels in colibri.c. ARCH unset ->
# no -mcpu, default build byte-identical. Apple clang knows apple-m4 / native.
# For older X86_64 Macs (for example, Mac Pro 2019) we need to use -march
ifneq ($(ARCH),)
ifneq (,$(X86_64))
CFLAGS += -march=$(ARCH)
else
CFLAGS += -mcpu=$(ARCH)
endif
endif
LDFLAGS = -lm $(OMPL)
EXE =
else ifneq ($(IS_WIN),)
@@ -166,7 +171,7 @@ else
PYTHON ?= python3
endif
CUDA_OBJ =
TEST_BINS = tests/test_json$(EXE) tests/test_st$(EXE) tests/test_st_mirror$(EXE) tests/test_st_pread$(EXE) tests/test_tier$(EXE) tests/test_grammar$(EXE) tests/test_schema_gbnf$(EXE) tests/test_decode_batch$(EXE) tests/test_idot$(EXE) tests/test_i4_grouped$(EXE) tests/test_stops$(EXE) tests/test_topp$(EXE) tests/test_sample_nan$(EXE) tests/test_kv_alloc$(EXE) tests/test_i4_acc512$(EXE) tests/test_compat_direct$(EXE) tests/test_dsa_select$(EXE) tests/test_logit_nan$(EXE)
TEST_BINS = tests/test_json$(EXE) tests/test_st$(EXE) tests/test_st_mirror$(EXE) tests/test_st_pread$(EXE) tests/test_tier$(EXE) tests/test_grammar$(EXE) tests/test_schema_gbnf$(EXE) tests/test_decode_batch$(EXE) tests/test_idot$(EXE) tests/test_i4_grouped$(EXE) tests/test_stops$(EXE) tests/test_topp$(EXE) tests/test_sample_nan$(EXE) tests/test_tok_o200k$(EXE) tests/test_kv_alloc$(EXE) tests/test_int3$(EXE) tests/test_int3_load$(EXE) tests/test_i4_acc512$(EXE) tests/test_compat_direct$(EXE) tests/test_dsa_select$(EXE) tests/test_logit_nan$(EXE) tests/test_pipe_block$(EXE)
ifneq (,$(LINUX))
TEST_BINS += tests/test_uring$(EXE)
endif
@@ -273,8 +278,9 @@ olmoe$(EXE): olmoe.c st.h json.h compat.h
# Use a baseline that matches the compiler target. macOS already targets a
# portable baseline when ARCH is empty; forcing the x86 value there breaks
# Apple Silicon. Unknown targets use native rather than an invalid x86 flag.
# Intel Macs need -march for vector instructions
ifneq (,$(DARWIN))
PORTABLE_ARCH =
PORTABLE_ARCH = $(if $(X86_64),x86-64-v3,)
else ifneq (,$(AARCH64))
PORTABLE_ARCH = armv8-a
else ifneq (,$(PPC64))
@@ -294,6 +300,8 @@ iobench$(EXE): iobench.c compat.h
tests/test_json$(EXE): tests/test_json.c json.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
tests/test_tok_o200k$(EXE): tests/test_tok_o200k.c tok.h tok_unicode.h tok_unicode_o200k.h json.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
tests/test_st_pread$(EXE): tests/test_st_pread.c st.h json.h compat.h
$(CC) $(CFLAGS) -DST_PREAD_CHUNK=7 $< -o $@ $(LDFLAGS)
@@ -338,6 +346,12 @@ tests/test_sample_nan$(EXE): tests/test_sample_nan.c colibri.c st.h uring.h json
tests/test_kv_alloc$(EXE): tests/test_kv_alloc.c colibri.c st.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h quant.h sample.h kv_persist.h telemetry.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
tests/test_int3$(EXE): tests/test_int3.c colibri.c st.h uring.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h quant.h sample.h kv_persist.h telemetry.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
tests/test_int3_load$(EXE): tests/test_int3_load.c colibri.c st.h uring.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h quant.h sample.h kv_persist.h telemetry.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
tests/test_logit_nan$(EXE): tests/test_logit_nan.c colibri.c st.h uring.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
@@ -355,9 +369,17 @@ tests/test_dsa_select$(EXE): tests/test_dsa_select.c colibri.c st.h uring.h json
tests/bench_dsa_select$(EXE): tests/bench_dsa_select.c colibri.c st.h uring.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h quant.h sample.h kv_persist.h telemetry.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
# bench_idot: microbenchmark (single-acc vs independent-acc AVX-VNNI idot), NOT a test gate.
# Build on demand on an AVX-VNNI CPU: make tests/bench_idot ARCH=native
tests/bench_idot$(EXE): tests/bench_idot.c colibri.c st.h uring.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h quant.h sample.h kv_persist.h telemetry.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
tests/test_uring$(EXE): tests/test_uring.c colibri.c st.h uring.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h quant.h sample.h kv_persist.h telemetry.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
tests/test_pipe_block$(EXE): tests/test_pipe_block.c colibri.c st.h uring.h json.h tok.h tok_unicode.h compat.h grammar.h tier.h
$(CC) $(CFLAGS) $< -o $@ $(LDFLAGS)
test-c: $(TEST_BINS)
$(PYTHON) tools/run_tests.py $(TEST_BINS)
@@ -366,6 +388,29 @@ test-python:
test: test-c test-python
# --- Efficiency / regression suite (issue: "test the program for inefficiencies") ---
# The tiny-model assertions live in test_inefficiency.py and run as part of
# test-python (they're discovered by the test_*.py glob). These targets are
# convenience entry points; the opt-in full-model report is NEVER in `make test`.
#
# make efficiency tiny-model asserted regression tests (CPU; part of test-python)
# make efficiency-cuda the CUDA-path tests (requires a CUDA build — see below)
# make efficiency-report opt-in full-model 🟢/🔴 diagnostic, never fails CI
#
# CUDA build (Windows): the CUDA tests need a host built with -DCOLI_CUDA plus
# the runtime DLL. Do this FIRST — note CUDA_DLL=1 on BOTH the host and the
# rule below, or `make glm.exe` will rebuild a CPU-only host and overwrite it:
# make clean && make glm.exe CUDA_DLL=1 && make cuda-dll
# The tests auto-skip with a clear message if the host is CPU-only.
efficiency: test-python
$(PYTHON) -m unittest tests.test_inefficiency -v
efficiency-cuda:
$(PYTHON) -m unittest tests.test_inefficiency.TinyCudaEfficiencyTest -v
efficiency-report:
$(PYTHON) tests/test_efficiency_report.py
# Local validation: one portable CPU build and dependency-free tests.
check:
$(MAKE) clean
c/backend_cuda.cu
+352 -52
View File
@@ -20,6 +20,9 @@ struct ColiCudaTensor {
float *scales;
size_t weight_bytes;
int fmt, I, O, device;
int gs; /* quant group size; 0 = per-row scales (#334) */
int ng; /* number of scale groups per row = ceil(I/gs) for fmt=4 */
size_t scale_count; /* floats in `scales`: O per-row, O*ng grouped */
int tracked;
RaggedKVEntry ragged[512];
int ragged_count;
@@ -34,15 +37,17 @@ typedef struct {
size_t qx_cap, qscale_cap;
float *host_x,*host_y,*host_kv; size_t host_x_cap,host_y_cap,host_kv_cap;
float *aq,*al,*ar,*ac; size_t aq_cap,al_cap,ar_cap,ac_cap;
float *pipe_buf[24]; size_t pipe_cap[24]; /* scratch persistenti del resident pipeline */
float *pipe_buf[27]; size_t pipe_cap[27]; /* scratch persistenti del resident pipeline */
cudaStream_t stream;
void *group_desc; size_t group_desc_cap;
size_t tensor_count, tensor_bytes;
int group_pending; size_t group_pending_bytes; /* async expert-group in flight (Inc.4) */
} DeviceContext;
typedef struct {
const void *g,*u,*d; const float *gs,*us,*ds;
int gf,uf,df,rows,offset;
int ggs,ugs,dgs; /* per-tensor quant group size; 0 = per-row scales (#334 fmt=4) */
} GroupDesc;
static DeviceContext g_ctx[COLI_CUDA_MAX_DEVICES];
@@ -54,6 +59,8 @@ static std::mutex g_group_stats_mu;
static int cuda_ok(cudaError_t err, const char *what) {
if (err == cudaSuccess) return 1;
std::fprintf(stderr, "[CUDA] %s: %s\n", what, cudaGetErrorString(err));
(void)cudaGetLastError(); /* consume the sticky error: a failed call must
not poison the next launch's error check */
return 0;
}
@@ -79,8 +86,9 @@ static int select_ctx(DeviceContext *ctx) {
__host__ __device__ static size_t row_bytes(int fmt, int I) {
if (fmt == 0) return (size_t)I * sizeof(float);
if (fmt == 1) return (size_t)I;
if (fmt == 2) return (size_t)(I + 1) / 2;
if (fmt == 2 || fmt == 4) return (size_t)(I + 1) / 2; /* fmt=4: same packed int4 */
if (fmt == 3) return (size_t)(I + 3) / 4;
if (fmt == 4) return (size_t)(I + 1) / 2; /* grouped int4: nibbles like fmt 2 */
return 0;
}
@@ -89,7 +97,7 @@ __device__ static float weight_at(const void *weights, int fmt, size_t row, int
if (fmt == 0) return reinterpret_cast<const float *>(base)[i];
if (fmt == 1) return static_cast<float>(reinterpret_cast<const int8_t *>(base)[i]);
const uint8_t *q = base;
if (fmt == 2) {
if (fmt == 2 || fmt == 4) { /* fmt=4: same nibble layout */
uint8_t v = q[i >> 1];
int n=(i&1)?(v>>4):(v&15); return static_cast<float>(n&8?n-16:n);
}
@@ -97,20 +105,45 @@ __device__ static float weight_at(const void *weights, int fmt, size_t row, int
return static_cast<float>(((v >> ((i & 3) * 2)) & 3) - 2);
}
/* Scale for output `row`, input element `k`. fmt=4 (grouped int4) stores ng
* scales per row at scales[row*ng + k/gs]; every other quantized format has
* one scale per row at scales[row]. Mirrors quant_matmul's fmt==4 branch so the
* attention absorb kernels apply per-group scales instead of the per-row
* (fmt=2) semantic that crashed #298's g64 kv_b. */
__device__ static float absorb_scale(const float *wscale, int fmt, int gs, int ng, int row, int k) {
if (!fmt) return 1.f;
if (fmt != 4) return wscale[row];
int g = k / gs; if (g >= ng) g = ng - 1; /* tail of the last (partial) group */
return wscale[(size_t)row * ng + g];
}
__global__ static void offset_to_signed_s4(uint8_t *q,size_t n){
size_t i=(size_t)blockIdx.x*blockDim.x+threadIdx.x;if(i<n)q[i]^=0x88;
}
__global__ static void quant_matmul(float *y, const float *x, const void *weights,
const float *scales, int fmt, int S, int I, int O,
size_t rb) {
size_t rb, int gs, int ng) {
int o = blockIdx.x;
int s = blockIdx.y;
float sum = 0.0f;
size_t row = (size_t)o * rb;
const float *xs = x + (size_t)s * I;
for (int i = threadIdx.x; i < I; i += blockDim.x)
sum += xs[i] * weight_at(weights, fmt, row, i);
if (fmt == 4) {
/* Grouped int4: one f32 scale per gs elements along I (ng groups per row).
* Scale layout: scales[o*ng + g]. Each thread strides through I, applying
* the appropriate group scale as it crosses group boundaries. This matches
* the CPU matmul_i4_grouped accumulation exactly. */
const float *scl = scales + (size_t)o * ng;
for (int i = threadIdx.x; i < I; i += blockDim.x) {
int g = i / gs;
if (g >= ng) g = ng - 1; /* tail elements in the last (partial) group */
sum += xs[i] * weight_at(weights, fmt, row, i) * scl[g];
}
} else {
for (int i = threadIdx.x; i < I; i += blockDim.x)
sum += xs[i] * weight_at(weights, fmt, row, i);
}
__shared__ float partial[256];
partial[threadIdx.x] = sum;
@@ -120,7 +153,7 @@ __global__ static void quant_matmul(float *y, const float *x, const void *weight
__syncthreads();
}
if (!threadIdx.x)
y[(size_t)s * O + o] = partial[0] * (fmt ? scales[o] : 1.0f);
y[(size_t)s * O + o] = (fmt && fmt != 4) ? partial[0] * scales[o] : partial[0];
}
__global__ static void silu_mul(float *gate, const float *up, size_t n) {
@@ -296,13 +329,50 @@ __global__ static void grouped_down_w4(float *y,const float *x,const GroupDesc *
if(!threadIdx.x)y[(size_t)(d.offset+s)*D+o]=p[0]*d.ds[o];
}
/* fmt=4 grouped-int4 variants (#334): identical structure to the w4 kernels,
* but the scale varies along the input dimension — sc[o*ng + i/gs], applied
* per element inside the accumulation (gs is even, so a packed byte never
* straddles a group). gs<=0 degrades to per-row (ng=1), so mixed fmt2/fmt4
* groups run correctly through this one kernel family. */
__global__ static void grouped_hidden_g4_dual(float *gate,float *up,const float *x,
const GroupDesc *desc,int I,int D){
int o=blockIdx.x,s=blockIdx.y,c=blockIdx.z;GroupDesc d=desc[c];if(s>=d.rows)return;
const uint8_t *gr=(const uint8_t*)d.g+(size_t)o*((D+1)/2);
const uint8_t *ur=(const uint8_t*)d.u+(size_t)o*((D+1)/2);
int ggs=d.ggs>0?d.ggs:D, ugs=d.ugs>0?d.ugs:D;
const float *gsc=d.gs+(size_t)o*(size_t)((D+ggs-1)/ggs);
const float *usc=d.us+(size_t)o*(size_t)((D+ugs-1)/ugs);
const float *xs=x+(size_t)(d.offset+s)*D;float ga=0,ua=0;
for(int b=threadIdx.x;b<(D+1)/2;b+=blockDim.x){float g0,g1,u0,u1;unpack_s4(gr[b],&g0,&g1);unpack_s4(ur[b],&u0,&u1);
int i=b*2;float gv=gsc[i/ggs],uv=usc[i/ugs];
ga+=xs[i]*g0*gv;ua+=xs[i]*u0*uv;
if(i+1<D){ga+=xs[i+1]*g1*gv;ua+=xs[i+1]*u1*uv;}}
__shared__ float gp[256],upv[256];gp[threadIdx.x]=ga;upv[threadIdx.x]=ua;__syncthreads();
for(int n=128;n;n>>=1){if(threadIdx.x<n){gp[threadIdx.x]+=gp[threadIdx.x+n];upv[threadIdx.x]+=upv[threadIdx.x+n];}__syncthreads();}
if(!threadIdx.x){size_t z=(size_t)(d.offset+s)*I+o;gate[z]=gp[0];up[z]=upv[0];}
}
__global__ static void grouped_down_g4(float *y,const float *x,const GroupDesc *desc,int D,int I){
int o=blockIdx.x,s=blockIdx.y,c=blockIdx.z;GroupDesc d=desc[c];if(s>=d.rows)return;
const uint8_t *row=(const uint8_t*)d.d+(size_t)o*((I+1)/2);
int dgs=d.dgs>0?d.dgs:I;
const float *dsc=d.ds+(size_t)o*(size_t)((I+dgs-1)/dgs);
const float *xs=x+(size_t)(d.offset+s)*I;float sum=0;
for(int b=threadIdx.x;b<(I+1)/2;b+=blockDim.x){float a,z;unpack_s4(row[b],&a,&z);
int i=b*2;float sv=dsc[i/dgs];
sum+=xs[i]*a*sv;if(i+1<I)sum+=xs[i+1]*z*sv;}
__shared__ float p[256];p[threadIdx.x]=sum;__syncthreads();
for(int n=128;n;n>>=1){if(threadIdx.x<n)p[threadIdx.x]+=p[threadIdx.x+n];__syncthreads();}
if(!threadIdx.x)y[(size_t)(d.offset+s)*D+o]=p[0];
}
__global__ static void attention_absorb_kernel(float *ctx,const float *q,const float *latent,
const float *rope,const void *weights,const float *wscale,
int fmt,int H,int Q,int R,int V,int K,int T,float scale){
int fmt,int H,int Q,int R,int V,int K,int T,float scale,
int gs,int ng){
int h=blockIdx.x,tid=threadIdx.x,rbase=h*(Q+V);extern __shared__ float sm[];
float *qa=sm,*cl=qa+K,*scores=cl+K;
for(int k=tid;k<K;k+=blockDim.x){float a=0;for(int d=0;d<Q;d++)
a+=q[(size_t)h*(Q+R)+d]*weight_at(weights,fmt,(size_t)(rbase+d)*row_bytes(fmt,K),k)*(fmt?wscale[rbase+d]:1.f);qa[k]=a;}
a+=q[(size_t)h*(Q+R)+d]*weight_at(weights,fmt,(size_t)(rbase+d)*row_bytes(fmt,K),k)*absorb_scale(wscale,fmt,gs,ng,rbase+d,k);qa[k]=a;}
__syncthreads();
for(int t=tid;t<T;t+=blockDim.x){float a=0;const float *lt=latent+(size_t)t*K,*rt=rope+(size_t)t*R;
for(int k=0;k<K;k++)a+=qa[k]*lt[k];for(int d=0;d<R;d++)a+=q[(size_t)h*(Q+R)+Q+d]*rt[d];scores[t]=a*scale;}
@@ -313,19 +383,20 @@ __global__ static void attention_absorb_kernel(float *ctx,const float *q,const f
for(int k=tid;k<K;k+=blockDim.x){float a=0;for(int t=0;t<T;t++)a+=scores[t]*latent[(size_t)t*K+k];cl[k]=a;}
__syncthreads();
for(int v=tid;v<V;v+=blockDim.x){int row=rbase+Q+v;float a=0;size_t rb=row_bytes(fmt,K);
for(int k=0;k<K;k++)a+=cl[k]*weight_at(weights,fmt,(size_t)row*rb,k);ctx[(size_t)h*V+v]=a*(fmt?wscale[row]:1.f);}
for(int k=0;k<K;k++)a+=cl[k]*weight_at(weights,fmt,(size_t)row*rb,k)*absorb_scale(wscale,fmt,gs,ng,row,k);ctx[(size_t)h*V+v]=a;}
}
__global__ static void attention_absorb_batch_kernel(float *ctx,const float *q,
const float *latent,const float *rope,const void *weights,const float *wscale,
int fmt,int S,int H,int Q,int R,int V,int K,int T,float scale){
int fmt,int S,int H,int Q,int R,int V,int K,int T,float scale,
int gs,int ng){
int s=blockIdx.y,h=blockIdx.x,tid=threadIdx.x,nt=T-S+s+1,rbase=h*(Q+V);
if(s>=S||nt<1)return;
extern __shared__ float sm[];float *qa=sm,*cl=qa+K,*scores=cl+K,*red=scores+T;
const float *qs=q+((size_t)s*H+h)*(Q+R);
for(int k=tid;k<K;k+=blockDim.x){float a=0;for(int d=0;d<Q;d++)
a+=qs[d]*weight_at(weights,fmt,(size_t)(rbase+d)*row_bytes(fmt,K),k)*
(fmt?wscale[rbase+d]:1.f);qa[k]=a;}
absorb_scale(wscale,fmt,gs,ng,rbase+d,k);qa[k]=a;}
__syncthreads();
for(int t=tid;t<nt;t+=blockDim.x){float a=0;const float *lt=latent+(size_t)t*K;
const float *rt=rope+(size_t)t*R;for(int k=0;k<K;k++)a+=qa[k]*lt[k];
@@ -343,8 +414,8 @@ __global__ static void attention_absorb_batch_kernel(float *ctx,const float *q,
a+=scores[t]*latent[(size_t)t*K+k];cl[k]=a;}
__syncthreads();
for(int v=tid;v<V;v+=blockDim.x){int row=rbase+Q+v;float a=0;size_t rb=row_bytes(fmt,K);
for(int k=0;k<K;k++)a+=cl[k]*weight_at(weights,fmt,(size_t)row*rb,k);
ctx[((size_t)s*H+h)*V+v]=a*(fmt?wscale[row]:1.f);}
for(int k=0;k<K;k++)a+=cl[k]*weight_at(weights,fmt,(size_t)row*rb,k)*absorb_scale(wscale,fmt,gs,ng,row,k);
ctx[((size_t)s*H+h)*V+v]=a;}
}
/* Independent device-resident KV sequence per row. lengths selects the valid
@@ -455,7 +526,7 @@ extern "C" void coli_cuda_shutdown(void) {
if (ctx->qx) cudaFree(ctx->qx);
if (ctx->qscale) cudaFree(ctx->qscale);
if(ctx->aq)cudaFree(ctx->aq);if(ctx->al)cudaFree(ctx->al);if(ctx->ar)cudaFree(ctx->ar);if(ctx->ac)cudaFree(ctx->ac);
for(int b=0;b<24;b++) if(ctx->pipe_buf[b]) cudaFree(ctx->pipe_buf[b]);
for(int b=0;b<27;b++) if(ctx->pipe_buf[b]) cudaFree(ctx->pipe_buf[b]);
if (ctx->host_x) cudaFreeHost(ctx->host_x);
if (ctx->host_y) cudaFreeHost(ctx->host_y);
if (ctx->host_kv) cudaFreeHost(ctx->host_kv);
@@ -503,40 +574,66 @@ extern "C" void coli_cuda_group_stats(uint64_t *calls, uint64_t *experts, uint64
if(d2h_ms) *d2h_ms=g_group_d2h_ms;
}
/* group size for the NEXT upload on this thread (fmt=4): routed through a
* thread_local so the widely-wired upload signature (and the Windows DLL ABI)
* stays untouched. pin_load uploads in parallel, hence thread_local. */
static thread_local int g_upload_gs = 0;
extern "C" int coli_cuda_tensor_upload_g(ColiCudaTensor **tensor,
const void *weights, const float *scales,
int fmt, int I, int O, int device, int gs);
extern "C" int coli_cuda_tensor_upload(ColiCudaTensor **tensor,
const void *weights, const float *scales,
int fmt, int I, int O, int device) {
if (!tensor) return 0;
if (*tensor) {
/* Cached device copy: usable even when the caller's host pointers are
* gone. CUDA_RELEASE_HOST slots null their host pointers after upload,
* and with the old order (!weights checked first) every later matmul
* on such a slot failed here — the GPU tier silently never computed
* for host-released slab experts. */
ColiCudaTensor *t = *tensor;
int want_gs = (fmt==4 && g_upload_gs>0) ? g_upload_gs : 0;
return t->fmt == fmt && t->I == I && t->O == O && t->device == device && t->gs == want_gs;
}
DeviceContext *ctx = find_ctx(device);
if (!tensor || !weights || I < 1 || O < 1 || !select_ctx(ctx)) return 0;
if (!weights || I < 1 || O < 1 || !select_ctx(ctx)) return 0;
size_t rb = row_bytes(fmt, I);
if (!rb || (fmt && !scales)) return 0;
if (*tensor) {
ColiCudaTensor *t = *tensor;
return t->fmt == fmt && t->I == I && t->O == O && t->device == device;
}
ColiCudaTensor *t = static_cast<ColiCudaTensor *>(std::calloc(1, sizeof(*t)));
if (!t) return 0;
t->fmt = fmt; t->I = I; t->O = O; t->device = device; t->weight_bytes = rb * (size_t)O;
t->gs = (fmt==4 && g_upload_gs>0) ? g_upload_gs : 0;
t->ng = t->gs ? (I + t->gs - 1) / t->gs : 1;
t->scale_count = t->gs ? (size_t)O * (size_t)t->ng : (size_t)O;
if (!cuda_ok(cudaMalloc(&t->weights, t->weight_bytes), "tensor allocation") ||
!cuda_ok(cudaMemcpy(t->weights, weights, t->weight_bytes, cudaMemcpyHostToDevice), "tensor upload")) {
coli_cuda_tensor_free(t);
return 0;
}
if(fmt==2){offset_to_signed_s4<<<(unsigned)((t->weight_bytes+255)/256),256>>>((uint8_t*)t->weights,t->weight_bytes);
if(fmt==2||fmt==4){ /* same nibble layout: offset-binary -> signed in place */
offset_to_signed_s4<<<(unsigned)((t->weight_bytes+255)/256),256>>>((uint8_t*)t->weights,t->weight_bytes);
if(!cuda_ok(cudaGetLastError(),"int4 weight conversion")){coli_cuda_tensor_free(t);return 0;}}
if (fmt) {
if (!cuda_ok(cudaMalloc(&t->scales, (size_t)O * sizeof(float)), "scale allocation") ||
!cuda_ok(cudaMemcpy(t->scales, scales, (size_t)O * sizeof(float), cudaMemcpyHostToDevice), "scale upload")) {
if (!cuda_ok(cudaMalloc(&t->scales, t->scale_count * sizeof(float)), "scale allocation") ||
!cuda_ok(cudaMemcpy(t->scales, scales, t->scale_count * sizeof(float), cudaMemcpyHostToDevice), "scale upload")) {
coli_cuda_tensor_free(t);
return 0;
}
}
t->tracked = 1;
ctx->tensor_count++;
ctx->tensor_bytes += t->weight_bytes + (fmt ? (size_t)O * sizeof(float) : 0);
ctx->tensor_bytes += t->weight_bytes + (fmt ? t->scale_count * sizeof(float) : 0);
*tensor = t;
return 1;
}
extern "C" int coli_cuda_tensor_upload_g(ColiCudaTensor **tensor,
const void *weights, const float *scales,
int fmt, int I, int O, int device, int gs){
g_upload_gs = gs>0 ? gs : 0;
int r = coli_cuda_tensor_upload(tensor, weights, scales, fmt, I, O, device);
g_upload_gs = 0;
return r;
}
extern "C" int coli_cuda_tensor_update(ColiCudaTensor *tensor,
const void *weights,
@@ -546,20 +643,35 @@ extern "C" int coli_cuda_tensor_update(ColiCudaTensor *tensor,
if (!select_ctx(ctx)) return 0;
if (!cuda_ok(cudaMemcpy(tensor->weights,weights,tensor->weight_bytes,
cudaMemcpyHostToDevice),"tensor refresh")) return 0;
if(tensor->fmt==2){
if(tensor->fmt==2||tensor->fmt==4){
offset_to_signed_s4<<<(unsigned)((tensor->weight_bytes+255)/256),256>>>(
(uint8_t*)tensor->weights,tensor->weight_bytes);
if(!cuda_ok(cudaGetLastError(),"int4 weight refresh")) return 0;
}
int ng = tensor->ng > 0 ? tensor->ng : 1;
return !tensor->fmt || cuda_ok(cudaMemcpy(tensor->scales,scales,
(size_t)tensor->O*sizeof(float),cudaMemcpyHostToDevice),"scale refresh");
(tensor->scale_count?tensor->scale_count:(size_t)tensor->O)*sizeof(float),
cudaMemcpyHostToDevice),"scale refresh");
}
/* Test hook: COLI_GPU_FAIL_AFTER=N makes every GPU COMPUTE entry point report
* failure after N successful calls (N=0: every call fails), exercising the
* engine's CPU fallbacks and host-rematerialization end-to-end without real
* hardware faults. Uploads/queries are not gated. Unset: no effect. */
static long g_gpu_calls;
static int fault_injected(void) {
const char *fa = std::getenv("COLI_GPU_FAIL_AFTER");
return fa && g_gpu_calls++ >= std::atol(fa);
}
extern "C" int coli_cuda_matmul(ColiCudaTensor **tensor,
float *y, const float *x,
const void *weights, const float *scales,
int fmt, int S, int I, int O, int device) {
if (S < 1 || !coli_cuda_tensor_upload(tensor, weights, scales, fmt, I, O, device)) return 0;
int fmt, int S, int I, int O, int device, int gs) {
if (fault_injected()) return 0;
if (S < 1) return 0;
if (gs > 0) { if (!coli_cuda_tensor_upload_g(tensor, weights, scales, fmt, I, O, device, gs)) return 0; }
else { if (!coli_cuda_tensor_upload(tensor, weights, scales, fmt, I, O, device)) return 0; }
ColiCudaTensor *t = *tensor;
DeviceContext *ctx = find_ctx(t->device);
if (!select_ctx(ctx)) return 0;
@@ -568,7 +680,7 @@ extern "C" int coli_cuda_matmul(ColiCudaTensor **tensor,
if (!reserve(&ctx->x, &ctx->x_cap, xb) || !reserve(&ctx->y, &ctx->y_cap, yb)) return 0;
if (!cuda_ok(cudaMemcpy(ctx->x, x, xb, cudaMemcpyHostToDevice), "input upload")) return 0;
dim3 grid((unsigned)O, (unsigned)S);
quant_matmul<<<grid, 256>>>(ctx->y, ctx->x, t->weights, t->scales, fmt, S, I, O, rb);
quant_matmul<<<grid, 256>>>(ctx->y, ctx->x, t->weights, t->scales, fmt, S, I, O, rb, t->gs, t->ng);
if (!cuda_ok(cudaGetLastError(), "matmul launch") ||
!cuda_ok(cudaMemcpy(y, ctx->y, yb, cudaMemcpyDeviceToHost), "output download")) return 0;
return 1;
@@ -577,6 +689,7 @@ extern "C" int coli_cuda_matmul(ColiCudaTensor **tensor,
extern "C" int coli_cuda_expert_mlp(ColiCudaTensor *gate, ColiCudaTensor *up,
ColiCudaTensor *down, float *y,
const float *x, int S) {
if (fault_injected()) return 0;
if (!gate || !up || !down || !x || !y || S < 1 ||
gate->device != up->device || gate->device != down->device ||
gate->I != up->I || gate->O != up->O ||
@@ -591,13 +704,13 @@ extern "C" int coli_cuda_expert_mlp(ColiCudaTensor *gate, ColiCudaTensor *up,
if (!cuda_ok(cudaMemcpy(ctx->x,x,xb,cudaMemcpyHostToDevice),"expert input upload")) return 0;
dim3 hidden_grid((unsigned)I,(unsigned)S), output_grid((unsigned)D,(unsigned)S);
quant_matmul<<<hidden_grid,256>>>(ctx->gate,ctx->x,gate->weights,gate->scales,
gate->fmt,S,D,I,row_bytes(gate->fmt,D));
gate->fmt,S,D,I,row_bytes(gate->fmt,D),gate->gs,gate->ng);
quant_matmul<<<hidden_grid,256>>>(ctx->up,ctx->x,up->weights,up->scales,
up->fmt,S,D,I,row_bytes(up->fmt,D));
up->fmt,S,D,I,row_bytes(up->fmt,D),up->gs,up->ng);
size_t n=(size_t)S*I;
silu_mul<<<(unsigned)((n+255)/256),256>>>(ctx->gate,ctx->up,n);
quant_matmul<<<output_grid,256>>>(ctx->y,ctx->gate,down->weights,down->scales,
down->fmt,S,I,D,row_bytes(down->fmt,I));
down->fmt,S,I,D,row_bytes(down->fmt,I),down->gs,down->ng);
if (!cuda_ok(cudaGetLastError(),"expert MLP launch") ||
!cuda_ok(cudaMemcpy(y,ctx->y,yb,cudaMemcpyDeviceToHost),"expert output download")) return 0;
return 1;
@@ -605,6 +718,7 @@ extern "C" int coli_cuda_expert_mlp(ColiCudaTensor *gate, ColiCudaTensor *up,
extern "C" int coli_cuda_shared_mlp_w4a16(ColiCudaTensor *gate,ColiCudaTensor *up,
ColiCudaTensor *down,float *y,const float *x,int S){
if (fault_injected()) return 0;
if(!gate||!up||!down||!x||!y||S<1||gate->fmt!=2||up->fmt!=2||down->fmt!=2||
gate->device!=up->device||gate->device!=down->device||gate->I!=up->I||
gate->O!=up->O||down->I!=gate->O||down->O!=gate->I)return 0;
@@ -636,19 +750,24 @@ extern "C" int coli_cuda_expert_group(ColiCudaTensor *const *gates,
ColiCudaTensor *const *downs,
const int *rows, int count,
float *y, const float *x) {
if (fault_injected()) return 0;
if (!gates || !ups || !downs || !rows || !x || !y || count < 1) return 0;
ColiCudaTensor *first=gates[0];
if (!first) return 0;
int device=first->device,D=first->I,I=first->O,total=0,max_rows=0;
GroupDesc host[64]; if(count>64) return 0;
int all_s4=1;
int all_s4=1,all_q4=1,any_g4=0;
for(int c=0;c<count;c++){
ColiCudaTensor *g=gates[c],*u=ups[c],*d=downs[c];
if(!g||!u||!d||rows[c]<1||g->device!=device||u->device!=device||d->device!=device||
g->I!=D||u->I!=D||g->O!=I||u->O!=I||d->I!=I||d->O!=D) return 0;
host[c]={g->weights,u->weights,d->weights,g->scales,u->scales,d->scales,
g->fmt,u->fmt,d->fmt,rows[c],total};
g->fmt,u->fmt,d->fmt,rows[c],total,
g->gs,u->gs,d->gs};
all_s4&=g->fmt==2&&u->fmt==2&&d->fmt==2;
all_q4&=(g->fmt==2||g->fmt==4)&&(u->fmt==2||u->fmt==4)&&(d->fmt==2||d->fmt==4)&&
!(g->gs&1)&&!(u->gs&1)&&!(d->gs&1); /* even gs: a packed byte never straddles groups */
any_g4|=g->fmt==4||u->fmt==4||d->fmt==4;
total+=rows[c]; if(rows[c]>max_rows) max_rows=rows[c];
}
DeviceContext *ctx=find_ctx(device); if(!select_ctx(ctx)) return 0;
@@ -689,7 +808,15 @@ extern "C" int coli_cuda_expert_group(ColiCudaTensor *const *gates,
quantize_s4_rows<<<total,256,0,ctx->stream>>>(ctx->qx,ctx->qscale,ctx->gate,total,I);
grouped_s4_wmma<<<dim3((unsigned)((D+63)/64),(unsigned)count),256,0,ctx->stream>>>(ctx->y,ctx->qx,ctx->qscale,dev,I,D,2);
}else if(all_s4&&ctx->compute_major>=7&&getenv("COLI_CUDA_TC_W4A16")&&
atoi(getenv("COLI_CUDA_TC_W4A16"))){
atoi(getenv("COLI_CUDA_TC_W4A16"))&&
[&]{ int tc16_min=getenv("COLI_CUDA_TC_W4A16_MIN")?atoi(getenv("COLI_CUDA_TC_W4A16_MIN")):16;
for(int c=0;c<count;c++) if(rows[c]>=tc16_min) return 1;
return 0; }()){
/* At least one expert has enough rows for a Tensor Core tile. Groups
* where EVERY expert is below the threshold (decode: r=1) fall through
* to the grouped-W4 path below — 3 launches for the whole group instead
* of 4 per expert (#431: the launch flood measured at ~981 micro-kernels
* per token came from decode riding this branch's per-expert fallback). */
/* W4A16 Tensor Core per gruppo: attivazioni fp16 per tile (lossless al
* contrario del path W4A4), un lancio per expert dentro lo stream —
* l'overhead di lancio e' trascurabile rispetto ai GEMM. */
@@ -711,12 +838,12 @@ extern "C" int coli_cuda_expert_group(ColiCudaTensor *const *gates,
/* piccoli batch: tile TC quasi vuoti + overhead di lancio — il
* kernel naive per-elemento resta piu' veloce (misurato in decode) */
quant_matmul<<<dim3((unsigned)I,(unsigned)r),256,0,ctx->stream>>>(g16,x16,
host[c].g,host[c].gs,host[c].gf,r,D,I,row_bytes(host[c].gf,D));
host[c].g,host[c].gs,host[c].gf,r,D,I,row_bytes(host[c].gf,D),0,1);
quant_matmul<<<dim3((unsigned)I,(unsigned)r),256,0,ctx->stream>>>(u16,x16,
host[c].u,host[c].us,host[c].uf,r,D,I,row_bytes(host[c].uf,D));
host[c].u,host[c].us,host[c].uf,r,D,I,row_bytes(host[c].uf,D),0,1);
silu_mul<<<(unsigned)(((size_t)r*I+255)/256),256,0,ctx->stream>>>(g16,u16,(size_t)r*I);
quant_matmul<<<dim3((unsigned)D,(unsigned)r),256,0,ctx->stream>>>(y16,g16,
host[c].d,host[c].ds,host[c].df,r,I,D,row_bytes(host[c].df,I));
host[c].d,host[c].ds,host[c].df,r,I,D,row_bytes(host[c].df,I),0,1);
}
off16+=r;
}
@@ -730,7 +857,18 @@ extern "C" int coli_cuda_expert_group(ColiCudaTensor *const *gates,
}
silu_mul<<<(unsigned)(((size_t)total*I+255)/256),256,0,ctx->stream>>>(ctx->gate,ctx->up,(size_t)total*I);
grouped_down_w4<<<og,256,0,ctx->stream>>>(ctx->y,ctx->gate,dev,D,I);
}else if(all_q4&&any_g4){
/* grouped-int4 (fmt=4) present: per-group scales (#334). fmt=2 members
* ride along as the ng=1 special case. */
dim3 hg((unsigned)I,(unsigned)max_rows,(unsigned)count),og((unsigned)D,(unsigned)max_rows,(unsigned)count);
grouped_hidden_g4_dual<<<hg,256,0,ctx->stream>>>(ctx->gate,ctx->up,ctx->x,dev,I,D);
silu_mul<<<(unsigned)(((size_t)total*I+255)/256),256,0,ctx->stream>>>(ctx->gate,ctx->up,(size_t)total*I);
grouped_down_g4<<<og,256,0,ctx->stream>>>(ctx->y,ctx->gate,dev,D,I);
}else{
/* generic path decodes fmt 0/1/2/3 only — a fmt=4 group that slipped the
* gates above (odd gs) must NOT be silently decoded as int2 (#334). */
for(int c=0;c<count;c++)
if(host[c].gf==4||host[c].uf==4||host[c].df==4) return 0;
dim3 hg((unsigned)I,(unsigned)max_rows,(unsigned)count),og((unsigned)D,(unsigned)max_rows,(unsigned)count);
grouped_hidden<<<hg,256,0,ctx->stream>>>(ctx->gate,ctx->x,dev,I,D,0);
grouped_hidden<<<hg,256,0,ctx->stream>>>(ctx->up,ctx->x,dev,I,D,1);
@@ -757,10 +895,79 @@ extern "C" int coli_cuda_expert_group(ColiCudaTensor *const *gates,
return 1;
}
/* ---- Async expert group (Inc.4): issue/take split of coli_cuda_expert_group ----
* The measured cost of the sync call at decode is ~0.45 ms/call of HOST-side wait
* (stream sync + staging), vs ~0.18 ms of actual GPU work — 70% tax, paid ~5x per
* layer because a token's 8 experts scatter across devices. issue() stages and
* launches on the device stream and returns immediately; take() syncs and hands
* back the pinned result rows. One issue may be outstanding per device; moe()
* takes at each layer end, which also orders the next layer's reuse of the ctx
* scratch buffers. Small batches only (decode/spec): bigger totals keep the sync
* path with its TC variants. Numerics are the sync path's small-batch kernels,
* so greedy output is byte-identical by construction. */
extern "C" int coli_cuda_expert_group_issue(ColiCudaTensor *const *gates,
ColiCudaTensor *const *ups,
ColiCudaTensor *const *downs,
const int *rows, int count,
const float *x) {
if (!gates || !ups || !downs || !rows || !x || count < 1 || count > 64) return 0;
ColiCudaTensor *first=gates[0];
if (!first) return 0;
int device=first->device,D=first->I,I=first->O,total=0;
GroupDesc host[64];
for(int c=0;c<count;c++){
ColiCudaTensor *g=gates[c],*u=ups[c],*d=downs[c];
if(!g||!u||!d||rows[c]<1||g->device!=device||u->device!=device||d->device!=device||
g->I!=D||u->I!=D||g->O!=I||u->O!=I||d->I!=I||d->O!=D) return 0;
host[c]={g->weights,u->weights,d->weights,g->scales,u->scales,d->scales,
g->fmt,u->fmt,d->fmt,rows[c],total};
total+=rows[c];
}
if(total>8) return 0; /* decode-scale only */
DeviceContext *ctx=find_ctx(device); if(!ctx||ctx->group_pending||!select_ctx(ctx)) return 0;
size_t xb=(size_t)total*D*sizeof(float), ib=(size_t)total*I*sizeof(float);
if(!reserve(&ctx->x,&ctx->x_cap,xb)||!reserve(&ctx->y,&ctx->y_cap,xb)||
!reserve(&ctx->gate,&ctx->gate_cap,ib)||!reserve(&ctx->up,&ctx->up_cap,ib)||
!reserve_pinned(&ctx->host_x,&ctx->host_x_cap,xb)||
!reserve_pinned(&ctx->host_y,&ctx->host_y_cap,xb)) return 0;
std::memcpy(ctx->host_x,x,xb);
if(!cuda_ok(cudaMemcpyAsync(ctx->x,ctx->host_x,xb,cudaMemcpyHostToDevice,ctx->stream),
"expert group issue upload")) return 0;
for(int c=0;c<count;c++){
int r=rows[c];
float *g16=ctx->gate+(size_t)host[c].offset*I,*u16=ctx->up+(size_t)host[c].offset*I;
float *x16=ctx->x+(size_t)host[c].offset*D,*y16=ctx->y+(size_t)host[c].offset*D;
quant_matmul<<<dim3((unsigned)I,(unsigned)r),256,0,ctx->stream>>>(g16,x16,
host[c].g,host[c].gs,host[c].gf,r,D,I,row_bytes(host[c].gf,D),0,1);
quant_matmul<<<dim3((unsigned)I,(unsigned)r),256,0,ctx->stream>>>(u16,x16,
host[c].u,host[c].us,host[c].uf,r,D,I,row_bytes(host[c].uf,D),0,1);
silu_mul<<<(unsigned)(((size_t)r*I+255)/256),256,0,ctx->stream>>>(g16,u16,(size_t)r*I);
quant_matmul<<<dim3((unsigned)D,(unsigned)r),256,0,ctx->stream>>>(y16,g16,
host[c].d,host[c].ds,host[c].df,r,I,D,row_bytes(host[c].df,I),0,1);
}
if(!cuda_ok(cudaGetLastError(),"expert group issue launch")||
!cuda_ok(cudaMemcpyAsync(ctx->host_y,ctx->y,xb,cudaMemcpyDeviceToHost,ctx->stream),
"expert group issue download")) return 0;
ctx->group_pending=1; ctx->group_pending_bytes=xb;
{ std::lock_guard<std::mutex> lock(g_group_stats_mu);
g_group_calls++; g_group_experts+=(uint64_t)count; g_group_rows+=(uint64_t)total; }
return 1;
}
extern "C" const float *coli_cuda_expert_group_take(int device) {
DeviceContext *ctx=find_ctx(device);
if(!ctx||!ctx->group_pending) return nullptr;
ctx->group_pending=0;
if(!select_ctx(ctx)) return nullptr;
if(!cuda_ok(cudaStreamSynchronize(ctx->stream),"expert group take")) return nullptr;
return ctx->host_y;
}
extern "C" int coli_cuda_attention_absorb(ColiCudaTensor *w,float *ctx,const float *q,
const float *latent,const float *rope,int H,int Q,
int R,int V,int K,int T,float scale){
if (fault_injected()) return 0;
if(!w||!ctx||!q||!latent||!rope||H<1||Q<1||R<1||V<1||K<1||K>512||T<1||T>4096||
w->I!=K||w->O!=H*(Q+V))return 0;
DeviceContext *dc=find_ctx(w->device);if(!select_ctx(dc))return 0;
@@ -773,7 +980,7 @@ extern "C" int coli_cuda_attention_absorb(ColiCudaTensor *w,float *ctx,const flo
!cuda_ok(cudaMemcpyAsync(dc->ar,rope,rb,cudaMemcpyHostToDevice,dc->stream),"attention rope upload"))return 0;
size_t shared=(size_t)(2*K+T)*sizeof(float);
attention_absorb_kernel<<<H,256,shared,dc->stream>>>(dc->ac,dc->aq,dc->al,dc->ar,w->weights,w->scales,
w->fmt,H,Q,R,V,K,T,scale);
w->fmt,H,Q,R,V,K,T,scale,w->gs,w->ng);
if(!cuda_ok(cudaGetLastError(),"attention absorb launch")||
!cuda_ok(cudaMemcpyAsync(ctx,dc->ac,cb,cudaMemcpyDeviceToHost,dc->stream),"attention context download")||
!cuda_ok(cudaStreamSynchronize(dc->stream),"attention synchronize"))return 0;
@@ -796,13 +1003,13 @@ static int attention_absorb_batch_run(ColiCudaTensor *w,ColiCudaTensor *proj,flo
!cuda_ok(cudaMemcpyAsync(dc->ar,rope,rb,cudaMemcpyHostToDevice,dc->stream),"attention batch rope upload"))return 0;
size_t shared=(size_t)(2*K+T+256)*sizeof(float);
attention_absorb_batch_kernel<<<dim3(H,S),256,shared,dc->stream>>>(dc->ac,dc->aq,dc->al,
dc->ar,w->weights,w->scales,w->fmt,S,H,Q,R,V,K,T,scale);
dc->ar,w->weights,w->scales,w->fmt,S,H,Q,R,V,K,T,scale,w->gs,w->ng);
if(!cuda_ok(cudaGetLastError(),"attention batch launch"))return 0;
const float *src=dc->ac;size_t ob=cb;
if(proj){
ob=(size_t)S*proj->O*sizeof(float);if(!reserve(&dc->y,&dc->y_cap,ob))return 0;
quant_matmul<<<dim3(proj->O,S),256,0,dc->stream>>>(dc->y,dc->ac,proj->weights,
proj->scales,proj->fmt,S,proj->I,proj->O,row_bytes(proj->fmt,proj->I));
proj->scales,proj->fmt,S,proj->I,proj->O,row_bytes(proj->fmt,proj->I),proj->gs,proj->ng);
if(!cuda_ok(cudaGetLastError(),"attention o_proj launch"))return 0;src=dc->y;
}
if(!cuda_ok(cudaMemcpyAsync(out,src,ob,cudaMemcpyDeviceToHost,dc->stream),
@@ -814,12 +1021,14 @@ static int attention_absorb_batch_run(ColiCudaTensor *w,ColiCudaTensor *proj,flo
extern "C" int coli_cuda_attention_absorb_batch(ColiCudaTensor *w,float *ctx,const float *q,
const float *latent,const float *rope,int S,int H,int Q,int R,int V,int K,int T,
float scale){
if (fault_injected()) return 0;
return attention_absorb_batch_run(w,nullptr,ctx,q,latent,rope,S,H,Q,R,V,K,T,scale);
}
extern "C" int coli_cuda_attention_project_batch(ColiCudaTensor *w,ColiCudaTensor *proj,
float *out,const float *q,const float *latent,const float *rope,int S,int H,int Q,
int R,int V,int K,int T,float scale){
if (fault_injected()) return 0;
return attention_absorb_batch_run(w,proj,out,q,latent,rope,S,H,Q,R,V,K,T,scale);
}
@@ -900,7 +1109,7 @@ extern "C" int coli_cuda_attention_project_ragged(ColiCudaTensor *w,ColiCudaTens
attention_absorb_ragged_kernel<<<dim3(H,S),256,shared,dc->stream>>>(dc->ac,dc->aq,ddl,ddr,
dn,w->weights,w->scales,w->fmt,S,H,Q,R,V,K,T,scale);
quant_matmul<<<dim3(proj->O,S),256,0,dc->stream>>>(dc->y,dc->ac,proj->weights,
proj->scales,proj->fmt,S,proj->I,proj->O,row_bytes(proj->fmt,proj->I));
proj->scales,proj->fmt,S,proj->I,proj->O,row_bytes(proj->fmt,proj->I),proj->gs,proj->ng);
return cuda_ok(cudaGetLastError(),"ragged attention launch")&&
cuda_ok(cudaMemcpyAsync(out,dc->y,ob,cudaMemcpyDeviceToHost,dc->stream),"ragged output download")&&
cuda_ok(cudaStreamSynchronize(dc->stream),"ragged attention synchronize");
@@ -911,7 +1120,8 @@ extern "C" void coli_cuda_tensor_free(ColiCudaTensor *tensor) {
DeviceContext *ctx = find_ctx(tensor->device);
if (ctx) select_ctx(ctx);
if (tensor->tracked && ctx) {
size_t bytes = tensor->weight_bytes + (tensor->fmt ? (size_t)tensor->O * sizeof(float) : 0);
int ng = tensor->ng > 0 ? tensor->ng : 1;
size_t bytes = tensor->weight_bytes + (tensor->fmt ? (size_t)tensor->O * ng * sizeof(float) : 0);
if (ctx->tensor_count) ctx->tensor_count--;
if (ctx->tensor_bytes >= bytes) ctx->tensor_bytes -= bytes;
}
@@ -925,7 +1135,9 @@ extern "C" void coli_cuda_tensor_free(ColiCudaTensor *tensor) {
}
extern "C" size_t coli_cuda_tensor_bytes(const ColiCudaTensor *tensor) {
return tensor ? tensor->weight_bytes + (tensor->fmt ? (size_t)tensor->O * sizeof(float) : 0) : 0;
if (!tensor) return 0;
int ng = tensor->ng > 0 ? tensor->ng : 1;
return tensor->weight_bytes + (tensor->fmt ? (size_t)tensor->O * ng * sizeof(float) : 0);
}
extern "C" int coli_cuda_tensor_device(const ColiCudaTensor *tensor) {
@@ -986,7 +1198,7 @@ __global__ static void pipe_rows_add(float *x,const float *partial,const int *ro
* per layer (78 x ~10 alloc/richiesta erano puro churn). */
extern "C" float *coli_cuda_pipe_scratch(int device,int slot,size_t bytes){
DeviceContext *ctx=find_ctx(device);
if(slot<0||slot>=24||!select_ctx(ctx)) return NULL;
if(slot<0||slot>=27||!select_ctx(ctx)) return NULL;
if(!reserve(&ctx->pipe_buf[slot],&ctx->pipe_cap[slot],bytes)) return NULL;
return ctx->pipe_buf[slot];
}
@@ -1010,6 +1222,7 @@ extern "C" int coli_cuda_pipe_download(int device,const void *src,void *dst,size
}
extern "C" int coli_cuda_pipe_rmsnorm(int device,float *y_dev,const float *x_dev,
const float *w_dev,int S,int D,float eps){
if (fault_injected()) return 0;
DeviceContext *ctx=find_ctx(device);
if(S<1||D<1||!select_ctx(ctx)) return 0;
pipe_rmsnorm_rows<<<S,256>>>(y_dev,x_dev,w_dev,D,eps,D,D);
@@ -1018,6 +1231,7 @@ extern "C" int coli_cuda_pipe_rmsnorm(int device,float *y_dev,const float *x_dev
extern "C" int coli_cuda_pipe_rmsnorm_s(int device,float *y_dev,const float *x_dev,
const float *w_dev,int S,int D,float eps,
int xstride,int ystride){
if (fault_injected()) return 0;
DeviceContext *ctx=find_ctx(device);
if(S<1||D<1||xstride<D||ystride<D||!select_ctx(ctx)) return 0;
pipe_rmsnorm_rows<<<S,256>>>(y_dev,x_dev,w_dev,D,eps,xstride,ystride);
@@ -1026,6 +1240,7 @@ extern "C" int coli_cuda_pipe_rmsnorm_s(int device,float *y_dev,const float *x_d
extern "C" int coli_cuda_pipe_rope(int device,float *v_dev,const int *pos_dev,
int rows,int stride,int offset,int R,int heads,
float theta){
if (fault_injected()) return 0;
DeviceContext *ctx=find_ctx(device);
if(rows<1||R<2||R>256||heads<1||!select_ctx(ctx)) return 0;
pipe_rope_rows<<<rows,128>>>(v_dev,pos_dev,0,stride,offset,R,heads,theta);
@@ -1033,11 +1248,88 @@ extern "C" int coli_cuda_pipe_rope(int device,float *v_dev,const int *pos_dev,
}
extern "C" int coli_cuda_pipe_rope_base(int device,float *v_dev,int pos_base,int rows,
int stride,int offset,int R,int heads,float theta){
if (fault_injected()) return 0;
DeviceContext *ctx=find_ctx(device);
if(rows<1||R<2||R>256||heads<1||!select_ctx(ctx)) return 0;
pipe_rope_rows<<<rows,128>>>(v_dev,NULL,pos_base,stride,offset,R,heads,theta);
return cuda_ok(cudaGetLastError(),"pipe rope base");
}
/* ---- device router (#431 PR-A) -------------------------------------------
* Router for one decode row, entirely on the layer's home device: logits GEMV
* (E x D, tiny) + sigmoid, bias-augmented top-K selection, route-level TOPP
* truncation, norm_topk and routed_scale — a float-faithful clone of moe()'s
* plain routing path (colibri.c FASE A). Selection runs single-thread so the
* argmax order, tie-breaking (strict >, lowest index wins) and weight math
* match the CPU reference exactly; only the dot/expf rounding can differ,
* which is the documented kernel-family divergence class (#100/#163).
* Results are packed [idx[K] | w[K] | keff] in one scratch buffer and read
* back with a single tiny D2H. */
__global__ void pipe_router_logits(const float *__restrict__ x,
const float *__restrict__ W,
const float *__restrict__ bias,
int D, float *logit, float *choice){
int e = blockIdx.x;
const float *w = W + (size_t)e*D;
float acc = 0.f;
for(int i=threadIdx.x; i<D; i+=blockDim.x) acc += x[i]*w[i];
__shared__ float sh[128];
sh[threadIdx.x]=acc; __syncthreads();
for(int s=blockDim.x>>1; s>0; s>>=1){
if(threadIdx.x<s) sh[threadIdx.x]+=sh[threadIdx.x+s];
__syncthreads();
}
if(!threadIdx.x){
float lg = 1.f/(1.f+expf(-sh[0]));
logit[e]=lg; choice[e]=lg+bias[e];
}
}
__global__ void pipe_router_select(const float *__restrict__ logit,
const float *__restrict__ choice, int E,
int Ksel, float topp, int norm_topk,
float routed_scale, char *out){
if(threadIdx.x||blockIdx.x) return;
int *idx = (int*)out;
float *w = (float*)(out + Ksel*sizeof(int));
int *keff= (int*)(out + Ksel*(sizeof(int)+sizeof(float)));
for(int kk=0;kk<Ksel;kk++){
int best=-1; float bv=-1e30f;
for(int e=0;e<E;e++){ int tk=0; for(int j=0;j<kk;j++) if(idx[j]==e){tk=1;break;}
if(!tk && choice[e]>bv){bv=choice[e];best=e;} }
idx[kk]=best; w[kk]=logit[best];
}
int Ke=Ksel;
if(topp>0.f && topp<1.f){
for(int a=1;a<Ksel;a++){ int ii=idx[a]; float ww=w[a]; int b=a-1;
while(b>=0 && w[b]<ww){ w[b+1]=w[b]; idx[b+1]=idx[b]; b--; } w[b+1]=ww; idx[b+1]=ii; }
float tot=1e-20f; for(int kk=0;kk<Ksel;kk++) tot+=w[kk];
float cum=0.f; for(int kk=0;kk<Ksel;kk++){ cum+=w[kk]; if(cum>=topp*tot){ Ke=kk+1; break; } }
}
if(norm_topk){ float sm=0.f; for(int kk=0;kk<Ke;kk++) sm+=w[kk]; sm+=1e-20f;
for(int kk=0;kk<Ke;kk++) w[kk]/=sm; }
for(int kk=0;kk<Ke;kk++) w[kk]*=routed_scale;
*keff=Ke;
}
extern "C" int coli_cuda_pipe_router(int device,const float *x_dev,
const void *rw_dev,const void *rb_dev,int D,int E,int Ksel,
float topp,int norm_topk,float routed_scale,
int *idx_host,float *w_host,int *keff_host){
DeviceContext *ctx=find_ctx(device);
if(!x_dev||!rw_dev||!rb_dev||D<1||E<1||E>4096||Ksel<1||Ksel>64||!select_ctx(ctx)) return 0;
size_t pack=(size_t)Ksel*(sizeof(int)+sizeof(float))+sizeof(int);
float *logit=coli_cuda_pipe_scratch(device,22,(size_t)E*sizeof(float));
float *chc =coli_cuda_pipe_scratch(device,23,(size_t)E*sizeof(float));
char *out =(char*)coli_cuda_pipe_scratch(device,24,pack);
if(!logit||!chc||!out) return 0;
pipe_router_logits<<<E,128>>>(x_dev,(const float*)rw_dev,(const float*)rb_dev,D,logit,chc);
pipe_router_select<<<1,1>>>(logit,chc,E,Ksel,topp,norm_topk,routed_scale,out);
if(!cuda_ok(cudaGetLastError(),"pipe router launch")) return 0;
char buf[64*(sizeof(int)+sizeof(float))+sizeof(int)];
if(!cuda_ok(cudaMemcpy(buf,out,pack,cudaMemcpyDeviceToHost),"pipe router readback")) return 0;
memcpy(idx_host,buf,(size_t)Ksel*sizeof(int));
memcpy(w_host,buf+Ksel*sizeof(int),(size_t)Ksel*sizeof(float));
memcpy(keff_host,buf+Ksel*(sizeof(int)+sizeof(float)),sizeof(int));
return 1;
}
extern "C" int coli_cuda_pipe_copy2d(int device,float *dst,int dpitch,const float *src,
int spitch,int width,int height){
DeviceContext *ctx=find_ctx(device); if(!select_ctx(ctx)) return 0;
@@ -1050,6 +1342,7 @@ extern "C" int coli_cuda_pipe_copy2d(int device,float *dst,int dpitch,const floa
extern "C" int coli_cuda_attention_project_batch_dev(ColiCudaTensor *w,ColiCudaTensor *proj,
float *out,const float *q_dev,const float *latent_dev,const float *rope_dev,
int S,int H,int Q,int R,int V,int K,int T,float scale){
if (fault_injected()) return 0;
if(!w||!proj||!out||!q_dev||!latent_dev||!rope_dev||S<1||H<1||Q<1||R<1||V<1||
K<1||K>512||T<S||T>8192||w->I!=K||w->O!=H*(Q+V)||
proj->device!=w->device||proj->I!=H*V)return 0;
@@ -1058,12 +1351,12 @@ extern "C" int coli_cuda_attention_project_batch_dev(ColiCudaTensor *w,ColiCudaT
if(!reserve(&dc->ac,&dc->ac_cap,cb))return 0;
size_t shared=(size_t)(2*K+T+256)*sizeof(float);
attention_absorb_batch_kernel<<<dim3(H,S),256,shared,dc->stream>>>(dc->ac,q_dev,latent_dev,
rope_dev,w->weights,w->scales,w->fmt,S,H,Q,R,V,K,T,scale);
rope_dev,w->weights,w->scales,w->fmt,S,H,Q,R,V,K,T,scale,w->gs,w->ng);
if(!cuda_ok(cudaGetLastError(),"pipe attention launch"))return 0;
size_t ob=(size_t)S*proj->O*sizeof(float);
if(!reserve(&dc->y,&dc->y_cap,ob))return 0;
quant_matmul<<<dim3(proj->O,S),256,0,dc->stream>>>(dc->y,dc->ac,proj->weights,
proj->scales,proj->fmt,S,proj->I,proj->O,row_bytes(proj->fmt,proj->I));
proj->scales,proj->fmt,S,proj->I,proj->O,row_bytes(proj->fmt,proj->I),proj->gs,proj->ng);
if(!cuda_ok(cudaGetLastError(),"pipe o_proj launch"))return 0;
if(!cuda_ok(cudaMemcpyAsync(out,dc->y,ob,cudaMemcpyDeviceToHost,dc->stream),"pipe attention download")||
!cuda_ok(cudaStreamSynchronize(dc->stream),"pipe attention sync"))return 0;
@@ -1071,17 +1364,20 @@ extern "C" int coli_cuda_attention_project_batch_dev(ColiCudaTensor *w,ColiCudaT
}
extern "C" int coli_cuda_pipe_silu_mul(int device,float *gate_dev,const float *up_dev,
size_t n){
if (fault_injected()) return 0;
DeviceContext *ctx=find_ctx(device); if(!n||!select_ctx(ctx)) return 0;
silu_mul<<<(unsigned)((n+255)/256),256>>>(gate_dev,up_dev,n);
return cuda_ok(cudaGetLastError(),"pipe silu mul");
}
extern "C" int coli_cuda_pipe_add(int device,float *x_dev,const float *t_dev,size_t n){
if (fault_injected()) return 0;
DeviceContext *ctx=find_ctx(device); if(!n||!select_ctx(ctx)) return 0;
pipe_add_n<<<(unsigned)((n+255)/256),256>>>(x_dev,t_dev,n);
return cuda_ok(cudaGetLastError(),"pipe add");
}
extern "C" int coli_cuda_pipe_rows_add(int device,float *x_dev,const float *partial_dev,
const int *rows_dev,int nrows,int D){
if (fault_injected()) return 0;
DeviceContext *ctx=find_ctx(device); if(nrows<1||D<1||!select_ctx(ctx)) return 0;
pipe_rows_add<<<nrows,256>>>(x_dev,partial_dev,rows_dev,D);
return cuda_ok(cudaGetLastError(),"pipe rows add");
@@ -1090,11 +1386,12 @@ extern "C" int coli_cuda_pipe_rows_add(int device,float *x_dev,const float *part
* coli_cuda_matmul, zero host transfers. */
extern "C" int coli_cuda_pipe_gemm(ColiCudaTensor *t,float *y_dev,const float *x_dev,
int S){
if (fault_injected()) return 0;
if(!t||S<1) return 0;
DeviceContext *ctx=find_ctx(t->device); if(!select_ctx(ctx)) return 0;
dim3 grid((unsigned)t->O,(unsigned)S);
quant_matmul<<<grid,256>>>(y_dev,x_dev,t->weights,t->scales,t->fmt,S,t->I,t->O,
row_bytes(t->fmt,t->I));
row_bytes(t->fmt,t->I),t->gs,t->ng);
return cuda_ok(cudaGetLastError(),"pipe gemm");
}
/* copia diretta scheda->scheda (P2P se disponibile, altrimenti staging driver) */
@@ -1109,6 +1406,7 @@ extern "C" int coli_cuda_pipe_peer_copy(int dst_dev,float *dst,int src_dev,
extern "C" int coli_cuda_attention_project_batch_dev_out(ColiCudaTensor *w,ColiCudaTensor *proj,
float *out_dev,const float *q_dev,const float *latent_dev,const float *rope_dev,
int S,int H,int Q,int R,int V,int K,int T,float scale){
if (fault_injected()) return 0;
if(!w||!proj||!out_dev||!q_dev||!latent_dev||!rope_dev||S<1||H<1||Q<1||R<1||V<1||
K<1||K>512||T<S||T>8192||w->I!=K||w->O!=H*(Q+V)||
proj->device!=w->device||proj->I!=H*V)return 0;
@@ -1117,10 +1415,10 @@ extern "C" int coli_cuda_attention_project_batch_dev_out(ColiCudaTensor *w,ColiC
if(!reserve(&dc->ac,&dc->ac_cap,cb))return 0;
size_t shared=(size_t)(2*K+T+256)*sizeof(float);
attention_absorb_batch_kernel<<<dim3(H,S),256,shared,dc->stream>>>(dc->ac,q_dev,latent_dev,
rope_dev,w->weights,w->scales,w->fmt,S,H,Q,R,V,K,T,scale);
rope_dev,w->weights,w->scales,w->fmt,S,H,Q,R,V,K,T,scale,w->gs,w->ng);
if(!cuda_ok(cudaGetLastError(),"pipe attention launch (dev out)"))return 0;
quant_matmul<<<dim3(proj->O,S),256,0,dc->stream>>>(out_dev,dc->ac,proj->weights,
proj->scales,proj->fmt,S,proj->I,proj->O,row_bytes(proj->fmt,proj->I));
proj->scales,proj->fmt,S,proj->I,proj->O,row_bytes(proj->fmt,proj->I),proj->gs,proj->ng);
if(!cuda_ok(cudaGetLastError(),"pipe o_proj launch (dev out)"))return 0;
return cuda_ok(cudaStreamSynchronize(dc->stream),"pipe attention sync (dev out)");
}
@@ -1130,12 +1428,13 @@ extern "C" int coli_cuda_attention_project_batch_dev_out(ColiCudaTensor *w,ColiC
extern "C" int coli_cuda_attention_absorb_batch_dev(ColiCudaTensor *w,float *ctx_dev,
const float *q_dev,const float *latent_dev,const float *rope_dev,
int S,int H,int Q,int R,int V,int K,int T,float scale){
if (fault_injected()) return 0;
if(!w||!ctx_dev||!q_dev||!latent_dev||!rope_dev||S<1||H<1||Q<1||R<1||V<1||
K<1||K>512||T<S||T>8192||w->I!=K||w->O!=H*(Q+V))return 0;
DeviceContext *dc=find_ctx(w->device);if(!select_ctx(dc))return 0;
size_t shared=(size_t)(2*K+T+256)*sizeof(float);
attention_absorb_batch_kernel<<<dim3(H,S),256,shared,dc->stream>>>(ctx_dev,q_dev,latent_dev,
rope_dev,w->weights,w->scales,w->fmt,S,H,Q,R,V,K,T,scale);
rope_dev,w->weights,w->scales,w->fmt,S,H,Q,R,V,K,T,scale,w->gs,w->ng);
if(!cuda_ok(cudaGetLastError(),"pipe shard attention launch"))return 0;
return cuda_ok(cudaStreamSynchronize(dc->stream),"pipe shard attention sync");
}
@@ -1144,6 +1443,7 @@ extern "C" int coli_cuda_attention_absorb_batch_dev(ColiCudaTensor *w,float *ctx
extern "C" int coli_cuda_attention_absorb_kvdev(ColiCudaTensor *w,float *ctx,const float *q,
const float *latent_dev,const float *rope_dev,int H,int Q,int R,int V,int K,int T,
float scale){
if (fault_injected()) return 0;
if(!w||!ctx||!q||!latent_dev||!rope_dev||H<1||Q<1||R<1||V<1||K<1||K>512||T<1||T>8192||
w->I!=K||w->O!=H*(Q+V))return 0;
DeviceContext *dc=find_ctx(w->device);if(!select_ctx(dc))return 0;
@@ -1152,7 +1452,7 @@ extern "C" int coli_cuda_attention_absorb_kvdev(ColiCudaTensor *w,float *ctx,con
if(!cuda_ok(cudaMemcpyAsync(dc->aq,q,qb,cudaMemcpyHostToDevice,dc->stream),"kvdev q upload"))return 0;
size_t shared=(size_t)(2*K+T+256)*sizeof(float);
attention_absorb_batch_kernel<<<dim3(H,1),256,shared,dc->stream>>>(dc->ac,dc->aq,latent_dev,
rope_dev,w->weights,w->scales,w->fmt,1,H,Q,R,V,K,T,scale);
rope_dev,w->weights,w->scales,w->fmt,1,H,Q,R,V,K,T,scale,w->gs,w->ng);
if(!cuda_ok(cudaGetLastError(),"kvdev absorb launch")||
!cuda_ok(cudaMemcpyAsync(ctx,dc->ac,cb,cudaMemcpyDeviceToHost,dc->stream),"kvdev ctx download")||
!cuda_ok(cudaStreamSynchronize(dc->stream),"kvdev absorb sync"))return 0;
c/backend_cuda.h
+21 -3
View File
@@ -36,20 +36,24 @@ COLI_CUDA_DLLEXPORT void coli_cuda_group_stats(uint64_t *calls, uint64_t *expert
double *h2d_ms, double *kernel_ms, double *d2h_ms);
/* Upload without executing, so capacity failures happen during model startup. */
COLI_CUDA_DLLEXPORT int coli_cuda_tensor_upload_g(ColiCudaTensor **tensor,
const void *weights, const float *scales,
int fmt, int I, int O, int device, int gs);
COLI_CUDA_DLLEXPORT int coli_cuda_tensor_upload(ColiCudaTensor **tensor,
const void *weights, const float *scales,
int fmt, int I, int O, int device);
/*
* y[S,O] = x[S,I] @ W[O,I]^T.
* fmt matches QT in glm.c: 0=f32, 1=int8, 2=int4, 3=int2.
* The first successful call uploads W and its row scales; later calls reuse it.
* fmt matches QT in glm.c: 0=f32, 1=int8, 2=int4, 3=int2, 4=grouped int4.
* gs is the group size for fmt=4 (0 for all other formats).
* The first successful call uploads W and its scales; later calls reuse it.
* Returns 1 on success and 0 when CUDA is not initialized or the format is invalid.
*/
COLI_CUDA_DLLEXPORT int coli_cuda_matmul(ColiCudaTensor **tensor,
float *y, const float *x,
const void *weights, const float *scales,
int fmt, int S, int I, int O, int device);
int fmt, int S, int I, int O, int device, int gs);
/* Fused expert pipeline: y = down(silu(gate(x)) * up(x)). All three tensors
* must already be resident on one device. Activations cross PCIe once in
@@ -67,6 +71,16 @@ COLI_CUDA_DLLEXPORT int coli_cuda_shared_mlp_w4a16(ColiCudaTensor *gate, ColiCud
/* Packed group of same-shaped experts. Inputs and outputs contain sum(rows)
* consecutive [D] rows in call order. */
/* Async issue/take split of the group call below (Inc.4): issue launches on the
* device stream and returns; take syncs and returns the pinned result rows (valid
* until the next issue on that device). Small totals only (<=8 rows); one
* outstanding issue per device. */
COLI_CUDA_DLLEXPORT int coli_cuda_expert_group_issue(ColiCudaTensor *const *gates,
ColiCudaTensor *const *ups,
ColiCudaTensor *const *downs,
const int *rows, int count, const float *x);
COLI_CUDA_DLLEXPORT const float *coli_cuda_expert_group_take(int device);
COLI_CUDA_DLLEXPORT int coli_cuda_expert_group(ColiCudaTensor *const *gates,
ColiCudaTensor *const *ups,
ColiCudaTensor *const *downs,
@@ -126,6 +140,10 @@ COLI_CUDA_DLLEXPORT int coli_cuda_pipe_rmsnorm_s(int device,float *y_dev,const f
int xstride,int ystride);
COLI_CUDA_DLLEXPORT int coli_cuda_pipe_rope_base(int device,float *v_dev,int pos_base,int rows,
int stride,int offset,int R,int heads,float theta);
COLI_CUDA_DLLEXPORT int coli_cuda_pipe_router(int device,const float *x_dev,
const void *rw_dev,const void *rb_dev,int D,int E,int Ksel,
float topp,int norm_topk,float routed_scale,
int *idx_host,float *w_host,int *keff_host);
COLI_CUDA_DLLEXPORT int coli_cuda_pipe_copy2d(int device,float *dst,int dpitch,const float *src,
int spitch,int width,int height);
COLI_CUDA_DLLEXPORT int coli_cuda_attention_project_batch_dev(ColiCudaTensor *kv_b,ColiCudaTensor *o_proj,
c/backend_loader.c
+41 -3
View File
@@ -41,14 +41,20 @@ typedef int (*fn_expert_mlp)(ColiCudaTensor *gate, ColiCudaTensor *up
typedef int (*fn_expert_group)(ColiCudaTensor *const *gates, ColiCudaTensor *const *ups,
ColiCudaTensor *const *downs, const int *rows, int count,
float *y, const float *x);
typedef int (*fn_expert_group_issue)(ColiCudaTensor *const *gates,
ColiCudaTensor *const *ups,
ColiCudaTensor *const *downs,
const int *rows, int count, const float *x);
typedef const float * (*fn_expert_group_take)(int device);
typedef int (*fn_attention_absorb)(ColiCudaTensor *kv_b, float *ctx, const float *q,
const float *latent, const float *rope, int H, int Q,
int R, int V, int K, int T, float attention_scale);
typedef int (*fn_tensor_upload)(ColiCudaTensor **tensor, const void *weights,
const float *scales, int fmt, int I, int O, int device);
typedef int (*fn_tensor_upload_g)(ColiCudaTensor **tensor, const void *weights, const float *scales, int fmt, int I, int O, int device, int gs);
typedef int (*fn_matmul)(ColiCudaTensor **tensor, float *y, const float *x,
const void *weights, const float *scales,
int fmt, int S, int I, int O, int device);
int fmt, int S, int I, int O, int device, int gs);
typedef void (*fn_tensor_free)(ColiCudaTensor *tensor);
typedef size_t (*fn_tensor_bytes)(const ColiCudaTensor *tensor);
typedef int (*fn_tensor_device)(const ColiCudaTensor *tensor);
@@ -76,6 +82,7 @@ typedef int (*fn_pipe_gemm)(ColiCudaTensor *t,float *y_dev,const float *x_dev,in
typedef int (*fn_pipe_peer_copy)(int dst_dev,float *dst,int src_dev, const float *src,size_t bytes);
typedef int (*fn_pipe_rmsnorm)(int device,float *y_dev,const float *x_dev, const float *w_dev,int S,int D,float eps);
typedef int (*fn_pipe_rmsnorm_s)(int device,float *y_dev,const float *x_dev, const float *w_dev,int S,int D,float eps, int xstride,int ystride);
typedef int (*fn_pipe_router)(int device,const float *x_dev,const void *rw_dev,const void *rb_dev,int D,int E,int Ksel,float topp,int norm_topk,float routed_scale,int *idx_host,float *w_host,int *keff_host);
typedef int (*fn_pipe_rope)(int device,float *v_dev,const int *pos_dev,int rows, int stride,int offset,int R,int heads,float theta);
typedef int (*fn_pipe_rope_base)(int device,float *v_dev,int pos_base,int rows, int stride,int offset,int R,int heads,float theta);
typedef int (*fn_pipe_rows_add)(int device,float *x_dev,const float *partial_dev, const int *rows_dev,int nrows,int D);
@@ -100,8 +107,11 @@ static struct {
fn_group_stats group_stats;
fn_expert_mlp expert_mlp;
fn_expert_group expert_group;
fn_expert_group_issue expert_group_issue;
fn_expert_group_take expert_group_take;
fn_attention_absorb attention_absorb;
fn_tensor_upload tensor_upload;
fn_tensor_upload_g tensor_upload_g;
fn_matmul matmul;
fn_tensor_free tensor_free;
fn_tensor_bytes tensor_bytes;
@@ -123,6 +133,7 @@ static struct {
fn_pipe_peer_copy pipe_peer_copy;
fn_pipe_rmsnorm pipe_rmsnorm;
fn_pipe_rmsnorm_s pipe_rmsnorm_s;
fn_pipe_router pipe_router;
fn_pipe_rope pipe_rope;
fn_pipe_rope_base pipe_rope_base;
fn_pipe_rows_add pipe_rows_add;
@@ -194,8 +205,11 @@ static int coli_cuda_load(void){
RESOLVE(group_stats, fn_group_stats)
RESOLVE(expert_mlp, fn_expert_mlp)
RESOLVE(expert_group, fn_expert_group)
RESOLVE(expert_group_issue, fn_expert_group_issue)
RESOLVE(expert_group_take, fn_expert_group_take)
RESOLVE(attention_absorb, fn_attention_absorb)
RESOLVE(tensor_upload, fn_tensor_upload)
RESOLVE(tensor_upload_g, fn_tensor_upload_g)
RESOLVE(matmul, fn_matmul)
RESOLVE(tensor_free, fn_tensor_free)
RESOLVE(tensor_bytes, fn_tensor_bytes)
@@ -217,6 +231,7 @@ static int coli_cuda_load(void){
RESOLVE(pipe_peer_copy, fn_pipe_peer_copy)
RESOLVE(pipe_rmsnorm, fn_pipe_rmsnorm)
RESOLVE(pipe_rmsnorm_s, fn_pipe_rmsnorm_s)
RESOLVE(pipe_router, fn_pipe_router)
RESOLVE(pipe_rope, fn_pipe_rope)
RESOLVE(pipe_rope_base, fn_pipe_rope_base)
RESOLVE(pipe_rows_add, fn_pipe_rows_add)
@@ -289,6 +304,19 @@ int coli_cuda_expert_group(ColiCudaTensor *const *gates, ColiCudaTensor *const *
return g_cuda.expert_group(gates, ups, downs, rows, count, y, x);
}
int coli_cuda_expert_group_issue(ColiCudaTensor *const *gates,
ColiCudaTensor *const *ups,
ColiCudaTensor *const *downs,
const int *rows, int count, const float *x){
if(!g_cuda.available) return 0;
return g_cuda.expert_group_issue(gates, ups, downs, rows, count, x);
}
const float *coli_cuda_expert_group_take(int device){
if(!g_cuda.available) return NULL;
return g_cuda.expert_group_take(device);
}
int coli_cuda_attention_absorb(ColiCudaTensor *kv_b, float *ctx, const float *q,
const float *latent, const float *rope, int H, int Q,
int R, int V, int K, int T, float attention_scale){
@@ -302,11 +330,16 @@ int coli_cuda_tensor_upload(ColiCudaTensor **tensor, const void *weights,
return g_cuda.tensor_upload(tensor, weights, scales, fmt, I, O, device);
}
int coli_cuda_tensor_upload_g(ColiCudaTensor **tensor, const void *weights, const float *scales, int fmt, int I, int O, int device, int gs){
if(!g_cuda.available || !g_cuda.tensor_upload_g){ return 0; }
return g_cuda.tensor_upload_g(tensor, weights, scales, fmt, I, O, device, gs);
}
int coli_cuda_matmul(ColiCudaTensor **tensor, float *y, const float *x,
const void *weights, const float *scales,
int fmt, int S, int I, int O, int device){
int fmt, int S, int I, int O, int device, int gs){
if(!g_cuda.available) return 0;
return g_cuda.matmul(tensor, y, x, weights, scales, fmt, S, I, O, device);
return g_cuda.matmul(tensor, y, x, weights, scales, fmt, S, I, O, device, gs);
}
void coli_cuda_tensor_free(ColiCudaTensor *tensor){
@@ -407,6 +440,11 @@ int coli_cuda_pipe_rmsnorm(int device,float *y_dev,const float *x_dev, const flo
return g_cuda.pipe_rmsnorm(device, y_dev, x_dev, w_dev, S, D, eps);
}
int coli_cuda_pipe_router(int device,const float *x_dev,const void *rw_dev,const void *rb_dev,int D,int E,int Ksel,float topp,int norm_topk,float routed_scale,int *idx_host,float *w_host,int *keff_host){
if(!g_cuda.available || !g_cuda.pipe_router){ return 0; }
return g_cuda.pipe_router(device, x_dev, rw_dev, rb_dev, D, E, Ksel, topp, norm_topk, routed_scale, idx_host, w_host, keff_host);
}
int coli_cuda_pipe_rmsnorm_s(int device,float *y_dev,const float *x_dev, const float *w_dev,int S,int D,float eps, int xstride,int ystride){
if(!g_cuda.available){ return 0; }
return g_cuda.pipe_rmsnorm_s(device, y_dev, x_dev, w_dev, S, D, eps, xstride, ystride);
c/backend_metal.h
+21
View File
@@ -84,6 +84,23 @@ int coli_metal_layer_decode(float *x,
int coli_metal_gemm(float *y, const float *x, const void *weights, const float *scales,
int fmt, int S, int I, int O); /* large-batch sync GEMM; 0 -> CPU */
/* Parallel top-8 expert selection (r_top8_par): run ONE top-8 selection kernel standalone
* on host arrays — par=0 the serial r_top8, par=1 the parallel exact-match replica gated
* in the engine by COLI_RTOP8 (default ON; COLI_RTOP8=0 opts out to the serial kernel).
* Exists so the metal-test suite (and any battery probe) can prove serial/parallel
* equivalence on the ENGINE build's own compiled shaders, not just in the bench tool.
* sig[S*E], bias[E], idx[S*K], w[S*K], keff[S].
* Expert-count generality: the parallel kernel's blocked-lane design (ch[8]/32-lane
* threadgroup) is validated correct for arbitrary E<=256, including non-multiples of the
* 32-lane width and small E (see metal-test's E=24/E=168/E=256 cases — 168 is the REAP
* expert-pruned package width from #428/#426). For E>256 (out of contract) this function
* transparently falls back to the serial kernel even when par=1 is requested, and the
* same automatic fallback is wired into the engine dispatch site — "par" is a request,
* never a guarantee, so no caller can reach the unguarded parallel path out of contract.
* Returns 1 on success, 0 if Metal is unavailable. */
int coli_metal_rtop8(int par, const float *sig, const float *bias, int S, int E, int K,
int Ksel, float topp, int normk, float rscale,
int *idx, float *w, int *keff);
void coli_metal_attn_counts(uint64_t *ok, double *wall, double *kernel);
void coli_metal_attn_lat(double *ksched, double *gsched);
int coli_metal_attn_decode(const float *x,
@@ -98,6 +115,10 @@ int coli_metal_attn_decode(const float *x,
void coli_metal_moe_counts(uint64_t *ok, uint64_t *fb, uint64_t *experts);
void coli_metal_moe_times(double *setup, double *gpu, double *scatter);
double coli_metal_moe_kernel_time(void);
/* E5 (COLI_METAL_RESSET=1): returns 1 when the queue-attached residency set is active and
* writes the cumulative seconds moe_submit spent committing pending set adds -- a cost that
* sits OUTSIDE the setup/gpu/scatter breakdown above. Returns 0 (and writes 0) when off. */
int coli_metal_resset_stats(double *flush_s);
/*
* Batched routed-expert SwiGLU for one MoE block, in ONE command buffer.
c/backend_metal.mm
+265 -12
View File
@@ -219,6 +219,63 @@ kernel void r_top8(device const float* sig [[buffer(0)]], device const float* bi
if(normk){ float sm=0; for(int kk=0;kk<Ke;kk++) sm+=ww[kk]; sm+=1e-20f; for(int kk=0;kk<Ke;kk++) ww[kk]/=sm; }
for(int kk=0;kk<Ke;kk++) ww[kk]*=rscale;
}
// parallel replica of r_top8's selection on ONE SIMDGROUP per row instead of one serial
// thread (bench/kernels @ 27bfe83: serial r_top8 measured 0.465 ms/layer, ~55% of the
// layer CB; this replica ~93x faster with exactly matching output). EXACT-MATCH is the
// contract: each lane owns ceil(E/32) contiguous experts (blocked) and keeps a taken
// bitmask; per selection step: lane-local strict-'>' ascending max (lowest index wins
// within a lane, matching the serial ascending scan), then a shuffle-down argmax
// reduction where ties prefer the LOWER index — together exactly the serial kernel's
// first-max-wins order. The topp/normk/rscale tail is the serial code verbatim on lane 0
// (same ops, same order => bitwise-identical results; metal-test enforces this with
// memcmp). Contract: E<=256 (ch[8]/taken mask sizing: ceil(E/32)<=8) — the defensive
// return below makes an out-of-contract dispatch a visible no-op (idx/w/keff untouched),
// never an OOB write; both call sites (coli_metal_layer_decode's dispatch and the
// standalone coli_metal_rtop8 runner) additionally gate on E<=256 in host code before
// selecting this pipeline at all, so the return here is defense-in-depth, not the only
// guard. Sentinel-per-lane design (ch[j]=-1e30f for e>=E) makes non-multiple-of-32 E
// and small E correct without special-casing — validated for E=24, E=168 (REAP
// expert-pruned packages, see the upstream feature-request thread) and E=256 by metal-test.
// ASSUMES SIMD width 32 (shuffle offsets 16..1, 32-thread threadgroup per row): enforced
// at init — coli_metal_init clears g_rtop8_width_ok (and therefore both call sites' use
// of this pipeline) if threadExecutionWidth != 32.
kernel void r_top8_par(device const float* sig [[buffer(0)]], device const float* bias [[buffer(1)]],
device int* idx [[buffer(2)]], device float* w [[buffer(3)]],
device int* keff [[buffer(4)]], constant int& E [[buffer(5)]],
constant int& K [[buffer(6)]], constant int& Ksel [[buffer(7)]],
constant float& topp [[buffer(8)]], constant int& normk [[buffer(9)]],
constant float& rscale [[buffer(10)]],
uint s [[threadgroup_position_in_grid]],
uint slane [[thread_index_in_simdgroup]]) {
if(E>256) return;
device const float* sg=sig+(long)s*E;
device int* id_=idx+(long)s*K; device float* ww=w+(long)s*K;
int per=(E+31)/32, base=(int)slane*per;
float ch[8]; uint taken=0u;
for(int j=0;j<per;j++){ int e=base+j; ch[j]=(e<E)?sg[e]+bias[e]:-1e30f; }
for(int kk=0;kk<Ksel;kk++){
float bv=-1e30f; int bi=0x7FFFFFFF;
for(int j=0;j<per;j++) if(!(taken&(1u<<j)) && ch[j]>bv){ bv=ch[j]; bi=base+j; }
for(uint off=16;off>0;off>>=1){
float ov=simd_shuffle_down(bv,off); int oi=simd_shuffle_down(bi,off);
if(ov>bv || (ov==bv && oi<bi)){ bv=ov; bi=oi; }
}
bv=simd_broadcast(bv,0); bi=simd_broadcast(bi,0);
if(bi>=base && bi<base+per) taken|=1u<<(bi-base);
if(slane==0){ id_[kk]=bi; ww[kk]=sg[bi]; }
}
if(slane!=0) return;
int Ke=Ksel;
if(topp>0.0f && topp<1.0f){
for(int a=1;a<Ksel;a++){ int ii=id_[a]; float wv=ww[a]; int b=a-1;
while(b>=0 && ww[b]<wv){ ww[b+1]=ww[b]; id_[b+1]=id_[b]; b--; } ww[b+1]=wv; id_[b+1]=ii; }
float tot=1e-20f; for(int kk=0;kk<Ksel;kk++) tot+=ww[kk];
float cum=0; for(int kk=0;kk<Ksel;kk++){ cum+=ww[kk]; if(cum>=topp*tot){ Ke=kk+1; break; } }
}
keff[s]=Ke;
if(normk){ float sm=0; for(int kk=0;kk<Ke;kk++) sm+=ww[kk]; sm+=1e-20f; for(int kk=0;kk<Ke;kk++) ww[kk]/=sm; }
for(int kk=0;kk<Ke;kk++) ww[kk]*=rscale;
}
)METAL";
struct ColiMetalTensor {
@@ -231,7 +288,15 @@ static id<MTLDevice> g_dev;
static id<MTLCommandQueue> g_queue;
static id<MTLComputePipelineState> g_gemv, g_moe_gemv, g_moe_silu;
static id<MTLComputePipelineState> g_a_rms, g_a_rope, g_a_copy, g_a_qabs, g_a_score, g_a_smax, g_a_clat, g_a_ctx;
static id<MTLComputePipelineState> g_a_add, g_r_router, g_r_top8;
static id<MTLComputePipelineState> g_a_add, g_r_router, g_r_top8, g_r_top8p;
static int g_rtop8_par = 1; // COLI_RTOP8 (default ON); COLI_RTOP8=0 opts out to the
// serial kernel — see coli_metal_init.
static int g_rtop8_width_ok = 1; // hardware fact, independent of the policy gate above:
// false if this device's threadExecutionWidth != 32.
// Consulted by BOTH the engine dispatch site and the
// standalone coli_metal_rtop8 runner, so no caller can
// reach r_top8_par's 32-lane reduction on an unsafe
// device even by explicitly requesting par=1.
static size_t g_tensor_count, g_tensor_bytes;
static uint64_t g_moe_ok, g_moe_fb, g_moe_experts; // GPU blocks / CPU-fallback blocks / experts on GPU
static double g_t_setup, g_t_gpu, g_t_scatter, g_t_kernel; // per-block time breakdown (seconds)
@@ -258,6 +323,73 @@ extern "C" void coli_metal_attn_lat(double *ksched, double *gsched){
struct Slab { void *base; size_t len; id<MTLBuffer> buf; };
static std::vector<Slab> g_slabs;
static std::mutex g_slab_mtx; // expert_load registers slabs from parallel OpenMP threads
// ---- E5 experiment: COLI_METAL_RESSET=1 -- one persistent MTLResidencySet attached to
// g_queue (macOS 15+) replaces moe_submit's per-command-buffer useResource: loop over
// resolved expert weight/scale slabs. Allocation is untouched (same newBufferWithBytesNoCopy
// wrap as stock); only residency bookkeeping moves off the dispatch hot path -- see
// SUMMARY.md for why skipping useResource: there is safe (read-only, indirectly-referenced
// buffers only; residency sets don't do hazard tracking, but nothing here relied on it).
// g_resset_obj is a bare `id` (holds id<MTLResidencySet>) so the global's declared type
// carries no availability annotation -- the protocol name only appears inside
// @available(macOS 15.0, *) guards below, keeping -Wunguarded-availability clean.
static id g_resset_obj;
static bool g_resset_enabled; // COLI_METAL_RESSET=1, macOS 15+, and creation succeeded
static bool g_resset_dirty; // addAllocation: calls pending commit; g_resset_mtx-guarded
// Set mutations + dirty flag get their OWN mutex, never held together with g_slab_mtx: no
// live Metal call may run under the slab lock the parallel OMP loader threads contend on
// (E4's audit round 2 found exactly that shape -- mutex over a live Metal call -- as the
// leading suspect for its +12s expert-disk regression). g_slab_mtx keeps guarding g_slabs
// bookkeeping only, exactly as on stock.
static std::mutex g_resset_mtx;
static double g_t_resset_flush; // sec committing pending adds in moe_submit (gate on only)
// Add a just-wrapped buffer to the set; commit deferred (an OMP loader burst batches into
// one commit at the next moe_submit instead of one per slab). Called by coli_metal_register
// after it drops g_slab_mtx but before it returns -- and the engine cannot dispatch an
// expert before the load that registers its slab returns, so any slab a given moe_submit
// can resolve() was added (and marked dirty) under g_resset_mtx strictly before that
// moe_submit's resset_flush() acquired the same mutex: the flush covers it. The slab-table
// ordering itself (register-before-resolve) is unchanged and stays under g_slab_mtx.
// Cost lands in the caller's existing expert-load accounting (t_ewait window in colibri.c);
// no separate counter for the add/remove side.
static void resset_add(id<MTLBuffer> b) {
if (!g_resset_enabled) return;
std::lock_guard<std::mutex> lk(g_resset_mtx);
if (@available(macOS 15.0, *)) { [(id<MTLResidencySet>)g_resset_obj addAllocation:b]; g_resset_dirty = true; }
}
// Remove + commit immediately, NOT deferred: the caller frees the underlying host memory
// right after coli_metal_unregister returns, so the removal must be applied before that --
// an uncommitted-but-still-resident allocation pointing at freed memory is a use-after-free
// risk the GPU could act on. Also runs outside g_slab_mtx (see g_resset_mtx above).
static void resset_remove(id<MTLBuffer> b) {
if (!g_resset_enabled) return;
std::lock_guard<std::mutex> lk(g_resset_mtx);
if (@available(macOS 15.0, *)) {
id<MTLResidencySet> rs = (id<MTLResidencySet>)g_resset_obj;
[rs removeAllocation:b]; [rs commit];
}
g_resset_dirty = false; // commit above also flushes any pending adds
}
// Flush pending adds before moe_submit relies on the set alone for residency -- the only
// caller that skips per-buffer useResource: (see moe_submit below). Takes g_resset_mtx
// only, never g_slab_mtx; the happens-before argument lives at resset_add above.
static void resset_flush() {
if (!g_resset_enabled) return;
std::lock_guard<std::mutex> lk(g_resset_mtx);
if (!g_resset_dirty) return;
if (@available(macOS 15.0, *)) { [(id<MTLResidencySet>)g_resset_obj commit]; }
g_resset_dirty = false;
}
// Harness visibility for the flush cost, which sits OUTSIDE the moe_times setup/gpu
// breakdown (timed around resset_flush in moe_submit, before ts_start). Returns whether
// the set is active so colibri.c prints the METAL-RESSET line only when the gate is on --
// stock output stays byte-identical.
extern "C" int coli_metal_resset_stats(double *flush_s) {
if (flush_s) *flush_s = g_t_resset_flush;
return g_resset_enabled ? 1 : 0;
}
// Persistent scratch buffers (grow-only) for the MoE pipeline.
static id<MTLBuffer> g_gg, g_uu, g_hh, g_xg; static size_t g_gg_cap, g_uu_cap, g_hh_cap, g_xg_cap;
static id<MTLBuffer> ensure(id<MTLBuffer> b, size_t *cap, size_t need) {
@@ -284,6 +416,8 @@ extern "C" int coli_metal_init(void) {
if (g_dev) return 1;
if (getenv("COLI_METAL_UNTRACKED") && atoi(getenv("COLI_METAL_UNTRACKED")))
g_res_opts = MTLResourceStorageModeShared | MTLResourceHazardTrackingModeUntracked;
{ const char *e = getenv("COLI_RTOP8"); // default ON; COLI_RTOP8=0 opts out
if (e && atoi(e) == 0) g_rtop8_par = 0; }
@autoreleasepool {
g_dev = MTLCreateSystemDefaultDevice();
if (!g_dev) return 0;
@@ -299,11 +433,45 @@ extern "C" int coli_metal_init(void) {
auto P=[&](const char*n){ return [g_dev newComputePipelineStateWithFunction:[lib newFunctionWithName:@(n)] error:&err]; };
g_a_rms=P("a_rmsnorm"); g_a_rope=P("a_rope"); g_a_copy=P("a_copy");
g_a_qabs=P("a_qabs"); g_a_score=P("a_score"); g_a_smax=P("a_smax"); g_a_clat=P("a_clat"); g_a_ctx=P("a_ctx");
g_a_add=P("a_add"); g_r_router=P("r_router"); g_r_top8=P("r_top8");
if(!g_a_add||!g_r_router||!g_r_top8){ fprintf(stderr,"[metal] tail pipelines failed\n"); g_dev=nil; return 0; }
g_a_add=P("a_add"); g_r_router=P("r_router"); g_r_top8=P("r_top8"); g_r_top8p=P("r_top8_par");
if(!g_a_add||!g_r_router||!g_r_top8||!g_r_top8p){ fprintf(stderr,"[metal] tail pipelines failed\n"); g_dev=nil; return 0; }
// r_top8_par's reduction hardcodes SIMD width 32 (shuffle-down offsets 16..1, one
// 32-thread threadgroup per row). True on all Apple Silicon shipped to date, but a
// non-32-width device would reduce wrongly AND race multiple lane-0 writers, so this
// is a hard safety fact (g_rtop8_width_ok), not just a policy default: it gates BOTH
// the engine dispatch site and the standalone coli_metal_rtop8 runner (degrade-to-safe,
// same pattern as the pool/ring fallbacks elsewhere) — no caller can opt back into an
// unsafe reduction on such a device, even by explicitly requesting par=1.
if ([g_r_top8p threadExecutionWidth] != 32) {
g_rtop8_width_ok = 0;
if (g_rtop8_par)
fprintf(stderr, "[metal] COLI_RTOP8 parallel top-8 disabled: threadExecutionWidth=%lu "
"!= 32 (r_top8_par's reduction assumes 32-lane simdgroups) — serial "
"r_top8 in use\n", (unsigned long)[g_r_top8p threadExecutionWidth]);
g_rtop8_par = 0;
}
if (!g_gemv || !g_moe_gemv || !g_moe_silu || !g_a_rms || !g_a_rope || !g_a_copy ||
!g_a_qabs || !g_a_score || !g_a_smax || !g_a_clat || !g_a_ctx) {
fprintf(stderr, "[metal] pipeline failed\n"); g_dev = nil; return 0; }
// E5 experiment: COLI_METAL_RESSET=1 -- see g_resset_obj comment above.
if (getenv("COLI_METAL_RESSET") && atoi(getenv("COLI_METAL_RESSET"))) {
if (@available(macOS 15.0, *)) {
MTLResidencySetDescriptor *rd = [MTLResidencySetDescriptor new];
rd.initialCapacity = 4096; // hint only (internal array presize), not a hard limit
NSError *rerr = nil;
id<MTLResidencySet> rs = [g_dev newResidencySetWithDescriptor:rd error:&rerr];
if (rs) {
[g_queue addResidencySet:rs];
g_resset_obj = rs; g_resset_enabled = true;
fprintf(stderr, "[METAL] residency-set: on (macOS 15+, moe_submit skips per-buffer useResource:)\n");
} else {
fprintf(stderr, "[METAL] residency-set create failed: %s -- stock per-CB residency path\n",
rerr ? [[rerr localizedDescription] UTF8String] : "?");
}
} else {
fprintf(stderr, "[METAL] COLI_METAL_RESSET=1 requested but OS < macOS 15 -- stock per-CB residency path\n");
}
}
}
return 1;
}
@@ -313,13 +481,29 @@ extern "C" void coli_metal_register(void *base, size_t len) {
id<MTLBuffer> b = [g_dev newBufferWithBytesNoCopy:base length:len
options:g_res_opts deallocator:nil];
if (!b) return;
std::lock_guard<std::mutex> lk(g_slab_mtx); // called from parallel expert_load threads
for (auto &s : g_slabs) if (s.base == base) { s.len = len; s.buf = b; return; }
g_slabs.push_back({base, len, b});
id<MTLBuffer> old = nil; // E5: replaced wrapper on re-register of a live base (defensive)
{
std::lock_guard<std::mutex> lk(g_slab_mtx); // called from parallel expert_load threads
bool found = false;
for (auto &s : g_slabs) if (s.base == base) { old = s.buf; s.len = len; s.buf = b; found = true; break; }
if (!found) g_slabs.push_back({base, len, b});
}
// E5, outside g_slab_mtx (no Metal call under the slab lock), before returning. Invariant
// defended: set membership mirrors g_slabs exactly -- a re-register of a live base must
// drop the replaced wrapper from the set (ARC releases our reference, but the set retains
// it and keeps its pages resident forever) before adding the new one. No in-tree caller
// re-registers a live base today; defensive.
if (old && old != b) resset_remove(old);
if (old != b) resset_add(b);
}
extern "C" void coli_metal_unregister(void *base) {
std::lock_guard<std::mutex> lk(g_slab_mtx);
for (size_t i=0;i<g_slabs.size();i++) if (g_slabs[i].base==base) { g_slabs[i].buf=nil; g_slabs.erase(g_slabs.begin()+i); return; }
id<MTLBuffer> b = nil;
{
std::lock_guard<std::mutex> lk(g_slab_mtx);
for (size_t i=0;i<g_slabs.size();i++) if (g_slabs[i].base==base) {
b = g_slabs[i].buf; g_slabs[i].buf=nil; g_slabs.erase(g_slabs.begin()+i); break; }
}
if (b) resset_remove(b); // E5: outside g_slab_mtx; commits before the caller frees base
}
// Resolve a host pointer inside a registered slab to (buffer, gpuAddress). Returns nil if unknown.
static id<MTLBuffer> resolve(const void *p, uint64_t *addr) {
@@ -357,7 +541,14 @@ extern "C" void coli_metal_spin_start(void) {
}
extern "C" void coli_metal_spin_stop(void) { g_spin_run.store(false); }
extern "C" void coli_metal_shutdown(void) { coli_metal_spin_stop(); g_gemv=nil; g_queue=nil; g_dev=nil; g_tensor_count=g_tensor_bytes=0; }
extern "C" void coli_metal_shutdown(void) {
coli_metal_spin_stop();
if (g_resset_enabled) {
if (@available(macOS 15.0, *)) { [g_queue removeResidencySet:(id<MTLResidencySet>)g_resset_obj]; }
}
g_resset_obj=nil; g_resset_enabled=false; g_resset_dirty=false;
g_gemv=nil; g_queue=nil; g_dev=nil; g_tensor_count=g_tensor_bytes=0;
}
extern "C" int coli_metal_available(void) { return g_dev != nil; }
extern "C" void coli_metal_stats(size_t *c, size_t *b) { if(c)*c=g_tensor_count; if(b)*b=g_tensor_bytes; }
extern "C" int coli_metal_mem_info(size_t *used, size_t *total) {
@@ -597,12 +788,22 @@ extern "C" int coli_metal_layer_decode(float *x,
// 5) silu(gate)*up + exact top-K select
[e setComputePipelineState:g_moe_silu]; [e setBuffer:ash1_ offset:0 atIndex:0]; [e setBuffer:ash2_ offset:0 atIndex:1];
[e dispatchThreads:MTLSizeMake((size_t)S*SI,1,1) threadsPerThreadgroup:MTLSizeMake(256,1,1)];
{ [e setComputePipelineState:g_r_top8];
{ // COLI_RTOP8 (default ON) swaps the serial 1-thread-per-row select for the exact-
// match 1-simdgroup-per-row replica (same buffers/args; only pipeline+grid change).
// E<=256 is required by r_top8_par's ch[8]/32-lane blocking contract; this call
// site's E is always 256 today (layer_forward_rows' own architecture-shape gate in
// colibri.c requires c->n_experts==256 to reach coli_metal_layer_decode at all —
// see PR body "Scope statement") but the check is kept here too, defense-in-depth,
// so a future relaxation of that gate (e.g. to admit REAP-pruned E=168 models into
// the fused path) degrades safely to the serial kernel instead of mis-dispatching.
int use_par = g_rtop8_par && g_rtop8_width_ok && E<=256;
[e setComputePipelineState:use_par?g_r_top8p:g_r_top8];
[e setBuffer:asig_ offset:0 atIndex:0]; [e setBuffer:rbB offset:rboff atIndex:1];
[e setBuffer:aidx_ offset:0 atIndex:2]; [e setBuffer:aw_ offset:0 atIndex:3]; [e setBuffer:akeff_ offset:0 atIndex:4];
[e setBytes:&E length:4 atIndex:5]; [e setBytes:&K length:4 atIndex:6]; [e setBytes:&Ksel length:4 atIndex:7];
[e setBytes:&topp length:4 atIndex:8]; [e setBytes:&normk length:4 atIndex:9]; [e setBytes:&rscale length:4 atIndex:10];
[e dispatchThreads:MTLSizeMake(S,1,1) threadsPerThreadgroup:MTLSizeMake(S,1,1)]; }
if(use_par) [e dispatchThreadgroups:MTLSizeMake(S,1,1) threadsPerThreadgroup:MTLSizeMake(32,1,1)];
else [e dispatchThreads:MTLSizeMake(S,1,1) threadsPerThreadgroup:MTLSizeMake(S,1,1)]; }
BAR();
// 6) shared down
bind_gemv(e,shd_w,shd_s,shd_fmt,SI,AH,ash1_,ashout_,S);
@@ -651,6 +852,44 @@ extern "C" int coli_metal_gemm(float *y, const float *x, const void *wp, const f
return 1;
}
// Standalone single-kernel runner for the top-8 select (see backend_metal.h). Fresh
// shared buffers per call (a test/probe path, not a hot path); grids exactly as the
// engine dispatch site: serial = S threads of one S-wide threadgroup, parallel = S
// threadgroups x 32 (one simdgroup per row). "par" is a REQUEST, not a guarantee: same
// E<=256 and SIMD-width-32 host-side checks as the engine dispatch site gate the actual
// pipeline choice, so a caller (including metal-test itself) can never reach the parallel
// kernel out of contract by asking for it — par=1 with E>256, or on a non-32-wide device,
// transparently runs the serial kernel instead and still returns 1 (success).
extern "C" int coli_metal_rtop8(int par, const float *sig, const float *bias, int S, int E, int K,
int Ksel, float topp, int normk, float rscale,
int *idx, float *w, int *keff) {
if (!g_dev || S < 1 || E < 1 || K < 1 || Ksel < 1 || Ksel > K) return 0;
int use_par = par && g_r_top8p && g_rtop8_width_ok && E<=256;
@autoreleasepool {
id<MTLBuffer> bs=[g_dev newBufferWithBytes:sig length:(size_t)S*E*4 options:MTLResourceStorageModeShared];
id<MTLBuffer> bb=[g_dev newBufferWithBytes:bias length:(size_t)E*4 options:MTLResourceStorageModeShared];
id<MTLBuffer> bi=[g_dev newBufferWithLength:(size_t)S*K*4 options:MTLResourceStorageModeShared];
id<MTLBuffer> bw=[g_dev newBufferWithLength:(size_t)S*K*4 options:MTLResourceStorageModeShared];
id<MTLBuffer> bk=[g_dev newBufferWithLength:(size_t)S*4 options:MTLResourceStorageModeShared];
if(!bs||!bb||!bi||!bw||!bk) return 0;
memset(bi.contents,0xFF,(size_t)S*K*4); // poison: untouched slots stay visible
id<MTLCommandBuffer> cb=[g_queue commandBuffer]; id<MTLComputeCommandEncoder> e=[cb computeCommandEncoder];
[e setComputePipelineState:use_par?g_r_top8p:g_r_top8];
[e setBuffer:bs offset:0 atIndex:0]; [e setBuffer:bb offset:0 atIndex:1];
[e setBuffer:bi offset:0 atIndex:2]; [e setBuffer:bw offset:0 atIndex:3]; [e setBuffer:bk offset:0 atIndex:4];
[e setBytes:&E length:4 atIndex:5]; [e setBytes:&K length:4 atIndex:6]; [e setBytes:&Ksel length:4 atIndex:7];
[e setBytes:&topp length:4 atIndex:8]; [e setBytes:&normk length:4 atIndex:9]; [e setBytes:&rscale length:4 atIndex:10];
if(use_par) [e dispatchThreadgroups:MTLSizeMake((NSUInteger)S,1,1) threadsPerThreadgroup:MTLSizeMake(32,1,1)];
else [e dispatchThreads:MTLSizeMake((NSUInteger)S,1,1) threadsPerThreadgroup:MTLSizeMake((NSUInteger)S,1,1)];
[e endEncoding]; [cb commit]; [cb waitUntilCompleted];
if(cb.status==MTLCommandBufferStatusError){ fprintf(stderr,"[metal] rtop8 cmdbuf error\n"); return 0; }
memcpy(idx,bi.contents,(size_t)S*K*4);
memcpy(w,bw.contents,(size_t)S*K*4);
memcpy(keff,bk.contents,(size_t)S*4);
}
return 1;
}
extern "C" void coli_metal_tensor_free(ColiMetalTensor *t) {
if (!t) return;
g_tensor_count--; g_tensor_bytes -= t->wbytes;
@@ -668,6 +907,9 @@ static id<MTLCommandBuffer> moe_submit(int nb, int D, int Iinter, int fmt,
const float *xg, const int *xoff, const int *nr, int R,
id<MTLBuffer> xg_buf, id<MTLBuffer> gg_buf, id<MTLBuffer> uu_buf, id<MTLBuffer> hh_buf) {
if (!g_dev || (fmt != 1 && fmt != 2)) return nil;
if (g_resset_enabled) { // E5: commit any pending slab adds before we may skip useResource:
double t0 = mnow(); resset_flush(); g_t_resset_flush += mnow() - t0; // METAL-RESSET line
}
double ts_start = mnow();
std::vector<uint64_t> ag(nb),au(nb),ad(nb),sgv(nb),suv(nb),sdv(nb);
std::vector<id<MTLBuffer>> use; use.reserve(nb*2);
@@ -689,7 +931,18 @@ static id<MTLCommandBuffer> moe_submit(int nb, int D, int Iinter, int fmt,
memcpy([xg_buf contents], xg, (size_t)R*D*4);
id<MTLCommandBuffer> cb=[g_queue commandBuffer]; id<MTLComputeCommandEncoder> e=[cb computeCommandEncoder];
for(auto&b:use) [e useResource:b usage:MTLResourceUsageRead];
// E5 (COLI_METAL_RESSET=1): the queue-attached MTLResidencySet already guarantees these
// buffers are resident, so skip the per-buffer declaration whose count scales with LRU
// cache size (mechanism history v5). Residency sets don't do hazard tracking (Apple docs),
// but none was load-bearing here: every buffer in `use` is MTLResourceUsageRead-only and
// referenced only indirectly (moe_gemv dereferences waddr[]/saddr[] baked into bag/bsg's
// contents), so there's no GPU-side write to serialize against; the one real hazard -- a
// slab unregistered+freed+reused while an async in-flight CB still reads it -- is a
// CPU-write race outside Metal's hazard tracking either way, held by the engine's own slot
// lifecycle, not by useResource:. See SUMMARY.md UNCERTAINTIES.
if (!g_resset_enabled) {
for(auto&b:use) [e useResource:b usage:MTLResourceUsageRead];
}
auto gemv=[&](id<MTLBuffer> wa,id<MTLBuffer> sa,id<MTLBuffer> xin,id<MTLBuffer> y,int O,int K,int Kin){
int NT=R*O;
[e setComputePipelineState:g_moe_gemv];
c/coli
+37
View File
@@ -240,6 +240,43 @@ def env_for(a):
gpu=f" · VRAM {format_bytes(vt['budget_bytes'])}" if has_cuda and vt["devices"] else " · CPU"
print(f" {C.dim}[PLAN] RAM {format_bytes(rt['budget_bytes'])} · cap {rt['cache_slots_per_layer']}/layer{gpu}{C.r}",file=sys.stderr)
else:
# Windows: a bare `coli chat` (no --gpu/--vram/--auto-tier) used to ALWAYS
# run CPU-only, even on a CUDA build with a GPU present — cuda_binary()
# returned False on Windows (see above), and nothing set COLI_CUDA without
# an explicit flag. Now that detection works, auto-enable the GPU when one
# is detected so `coli chat` Just Works. Scoped to Windows: Linux already
# has working detection + the explicit-flag UX, and changing bare-chat
# semantics there is out of scope. Falls back to CPU with a warning if
# nvidia-smi is missing (discover_gpus can't size VRAM without it).
if (sys.platform == "win32" and a.gpu is None and not a.vram):
if cuda_binary():
from resource_plan import discover_gpus, build_plan, environment_for_plan, format_bytes
gpus = discover_gpus()
if gpus:
e["COLI_CUDA"]="1"
e.setdefault("COLI_GPUS", ",".join(str(g["index"]) for g in gpus))
# Reuse the planner so the expert-tier VRAM budget is the real
# free VRAM minus the 2 GB reserve — not a guess. Same machinery
# as --auto-tier, just without requiring the user to pass it.
ram,ctx,devices,vram_req = resource_request(a, e)
try:
plan=build_plan(a.model,ram,ctx,devices,vram_req,policy=a.policy)
e.update(environment_for_plan(plan,e,cuda_enabled=True))
vt=plan["tiers"]["vram"]
names=",".join(g["name"].strip() for g in gpus)
print(f" {C.dim}[GPU] auto-enabled CUDA · {names} · "
f"{format_bytes(vt['budget_bytes'])} expert tier{C.r}", file=sys.stderr)
except (OSError,ValueError,json.JSONDecodeError) as error:
# Plan failed (e.g. model dir unreadable): don't block the
# run, just leave the unsized COLI_CUDA=1 and let the engine
# pick its own budget. Engine handles a missing budget.
print(f" {C.yel}[GPU] auto-enable: could not size VRAM ({error}); "
f"using engine default{C.r}", file=sys.stderr)
else:
print(f" {C.yel}[GPU] coli_cuda.dll present but nvidia-smi not found on PATH "
f"(cannot size VRAM); running CPU-only. Add nvidia-smi to PATH or pass "
f"--vram N to enable CUDA.{C.r}", file=sys.stderr)
# else: CPU build (no coli_cuda.dll) — stay silent, CPU is correct.
# --gpu/--vram SENZA --auto-tier: prima venivano ignorati in silenzio e il run
# partiva CPU-only senza alcun avviso — benchmark "GPU" pubblicati per errore (#121).
if a.gpu is not None:
c/colibri.c
+957 -136
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File diff suppressed because it is too large Load Diff
c/decode_batch.h
+17 -7
View File
@@ -13,22 +13,32 @@ static inline float *coli_kv_row(float *base, int position, int width)
}
typedef struct {
unsigned long long id, bytes;
unsigned long long id, bytes, gbytes;
int slot, max_tokens;
float temperature, top_p;
} ColiSubmit;
/* Parse the textual header. The payload is read separately using `bytes`, so
* it may contain newlines. Reject trailing fields to keep framing unambiguous. */
* it may contain newlines. Reject trailing fields to keep framing unambiguous.
* Optional 7th field `gbytes`: length of a per-request grammar (raw GBNF, or a
* JSON-Schema compiled engine-side) appended to the payload AFTER the prompt
* bytes. 6-field headers remain valid (gbytes = 0). */
static inline int coli_submit_parse(const char *line, ColiSubmit *s)
{
char tail;
if (!line || !s ||
sscanf(line, "SUBMIT %llu %d %llu %d %f %f %c", &s->id, &s->slot,
if (!line || !s) return 0;
s->gbytes = 0;
if (sscanf(line, "SUBMIT %llu %d %llu %d %f %f %llu %c", &s->id, &s->slot,
&s->bytes, &s->max_tokens, &s->temperature, &s->top_p,
&tail) != 6)
return 0;
return s->id > 0 && s->bytes <= (16u << 20) && s->slot >= 0 && s->max_tokens >= 1 &&
&s->gbytes, &tail) != 7) {
s->gbytes = 0;
if (sscanf(line, "SUBMIT %llu %d %llu %d %f %f %c", &s->id, &s->slot,
&s->bytes, &s->max_tokens, &s->temperature, &s->top_p,
&tail) != 6)
return 0;
}
return s->id > 0 && s->bytes <= (16u << 20) && s->gbytes <= (1u << 20) &&
s->slot >= 0 && s->max_tokens >= 1 &&
isfinite(s->temperature) && isfinite(s->top_p) &&
s->temperature >= 0 && s->temperature <= 2 &&
s->top_p > 0 && s->top_p <= 1;
c/openai_server.py
+85 -16
View File
@@ -370,10 +370,46 @@ def render_chat(messages, enable_thinking=False, reasoning_effort=None, tools=No
return "".join(prompt)
# Generic whitespace-tolerant JSON grammar for response_format {"type": "json_object"}.
# Draft-source semantics: positions with one legal byte draft; jws points just keep
# the walker alive through the model's own spacing (see docs/grammar-draft.md).
GENERIC_JSON_GBNF = (
'root ::= jws jval jws\n'
'jval ::= jobj | jarr | jstr | jnum | "true" | "false" | "null"\n'
'jobj ::= "{" jws ( jstr jws ":" jws jval jws ( "," jws jstr jws ":" jws jval jws )* )? "}"\n'
'jarr ::= "[" jws ( jval jws ( "," jws jval jws )* )? "]"\n'
'jstr ::= "\\"" jchar* "\\""\n'
'jchar ::= [^"\\\\\\x00-\\x1f] | "\\\\" ( ["\\\\/bfnrt] | "u" jhex jhex jhex jhex )\n'
'jhex ::= [0-9a-fA-F]\n'
'jnum ::= "-"? ( "0" | [1-9] [0-9]* ) ( "." [0-9]+ )? ( ( "e" | "E" ) ( "+" | "-" )? [0-9]+ )?\n'
'jws ::= ( " " | "\\t" | "\\n" | "\\r" )*\n'
)
def generation_options(body, limit):
if body.get("n", 1) != 1:
raise APIError(400, "Colibri currently supports `n=1` only.", "n", "unsupported_value")
# `tools`/`functions` are handled by render_chat (declaration) + parse_tool_calls (output).
# Validate tools/functions structure early so malformed input fails with a clear error.
tools_raw = body.get("tools") or body.get("functions")
if tools_raw is not None:
if not isinstance(tools_raw, list):
raise APIError(400, "`tools` must be a non-empty array.", "tools", "invalid_value")
if not tools_raw:
raise APIError(400, "`tools` must be a non-empty array.", "tools", "invalid_value")
for idx, tool in enumerate(tools_raw):
if not isinstance(tool, dict):
raise APIError(400, f"Each tool must be an object, got {type(tool).__name__} at index {idx}.",
f"tools.{idx}", "invalid_value")
fn = tool.get("function", tool) if isinstance(tool, dict) else {}
if not isinstance(fn, dict):
raise APIError(400, f"Tool function must be an object at index {idx}.",
f"tools.{idx}.function", "invalid_value")
if not fn.get("name"):
raise APIError(400, f"Each tool must have a `name` at index {idx}.",
f"tools.{idx}.function.name", "invalid_value")
if not isinstance(fn["name"], str):
raise APIError(400, f"Tool `name` must be a string at index {idx}.",
f"tools.{idx}.function.name", "invalid_value")
choice = body.get("tool_choice")
if choice is not None:
if isinstance(choice, str):
@@ -403,10 +439,38 @@ def generation_options(body, limit):
raise APIError(400, "Token penalties are not supported yet.", None, "unsupported_parameter")
if body.get("seed") is not None:
raise APIError(400, "Per-request seeds are not supported yet.", "seed", "unsupported_parameter")
# response_format -> optional per-request grammar for the engine's grammar-forced
# draft source (#70/#148). NEVER a sampling constraint: drafts are verified, so a
# schema the engine cannot compile degrades to "no speedup", not to an error and
# not to changed output. json_schema payloads are forwarded as-is (the engine
# compiles them via schema_gbnf.h); {"type": "gbnf"} is a raw-GBNF extension.
grammar = None
response_format = body.get("response_format")
if response_format not in (None, {"type": "text"}):
raise APIError(400, "Only the default text response format is supported.",
"response_format", "unsupported_parameter")
if response_format is not None and response_format != {"type": "text"}:
if not isinstance(response_format, dict) or "type" not in response_format:
raise APIError(400, "`response_format` must be an object with a `type`.",
"response_format", "invalid_value")
ftype = response_format["type"]
if ftype == "json_object":
grammar = GENERIC_JSON_GBNF
elif ftype == "json_schema":
schema = (response_format.get("json_schema") or {}).get("schema")
if not isinstance(schema, dict):
raise APIError(400, "`response_format.json_schema.schema` must be an object.",
"response_format", "invalid_value")
grammar = json.dumps(schema)
elif ftype == "gbnf":
grammar = response_format.get("grammar")
if not isinstance(grammar, str) or not grammar.strip():
raise APIError(400, "`response_format.grammar` must be a non-empty GBNF string.",
"response_format", "invalid_value")
else:
raise APIError(400, "`response_format.type` must be \"text\", \"json_object\", "
"\"json_schema\" or \"gbnf\".",
"response_format", "unsupported_value")
if grammar is not None and len(grammar.encode("utf-8")) > (1 << 20):
raise APIError(400, "`response_format` grammar/schema exceeds 1 MiB.",
"response_format", "invalid_value")
maximum = body.get("max_completion_tokens")
maximum_param = "max_completion_tokens"
@@ -433,7 +497,7 @@ def generation_options(body, limit):
if (isinstance(top_p, bool) or not isinstance(top_p, (int, float)) or
not math.isfinite(top_p) or not 0 < top_p <= 1):
raise APIError(400, "`top_p` must be greater than 0 and at most 1.", "top_p")
return maximum, float(temperature), float(top_p)
return maximum, float(temperature), float(top_p), grammar
def read_engine_turn(stream, sentinel, on_bytes):
@@ -597,12 +661,15 @@ class Engine:
self._fail_pending(error)
def generate(self, prompt, max_tokens, temperature, top_p, on_text, cache_slot=0,
cancelled=None):
cancelled=None, grammar=None):
if isinstance(cache_slot, bool) or not isinstance(cache_slot, int) or not 0 <= cache_slot < self.kv_slots:
raise APIError(400, "Invalid cache slot.", "cache_slot")
payload = prompt.encode("utf-8")
if b"\0" in payload:
raise APIError(400, "NUL bytes are not supported in prompts.", "messages")
gpayload = grammar.encode("utf-8") if grammar else b""
if b"\0" in gpayload:
raise APIError(400, "NUL bytes are not supported in grammars.", "response_format")
decoder = codecs.getincrementaldecoder("utf-8")("replace")
def decode(data):
@@ -622,12 +689,13 @@ class Engine:
self.next_request_id += 1
self.pending[request_id] = events
header = (f"SUBMIT {request_id} {cache_slot} {len(payload)} {max_tokens} "
f"{temperature:.8g} {top_p:.8g}\n").encode()
f"{temperature:.8g} {top_p:.8g}"
+ (f" {len(gpayload)}" if gpayload else "") + "\n").encode()
try:
with self.write_lock:
if self.process.poll() is not None:
raise RuntimeError("colibri engine is not running")
self.process.stdin.write(header + payload + b"\n")
self.process.stdin.write(header + payload + gpayload + b"\n")
self.process.stdin.flush()
except Exception:
with self.pending_lock:
@@ -863,7 +931,7 @@ class APIHandler(BaseHTTPRequestHandler):
except OSError:
pass
def generation(self, body, prompt, request_id, chat):
def generation(self, body, prompt, request_id, chat, tools=None, tool_choice=None):
# COLI_DEBUG tees the engine transaction to stderr: 1 = decoded output stream only,
# 2 = both sides (rendered prompt + output). render_chat already folds prior turns and
# tool results into `prompt`, so level 2 is the full conversation the engine saw.
@@ -874,9 +942,9 @@ class APIHandler(BaseHTTPRequestHandler):
if dbg >= 2:
sys.stderr.write(f"\n===== PROMPT [{request_id}] =====\n{prompt}\n===== OUTPUT [{request_id}] =====\n")
sys.stderr.flush()
maximum, temperature, top_p = generation_options(body, self.server.max_tokens)
tools = (body.get("tools") or body.get("functions") or None) if chat else None
if body.get("tool_choice") == "none":
maximum, temperature, top_p, grammar = generation_options(body, self.server.max_tokens)
# tools and tool_choice come from chat_completion() already processed/filtered
if chat and tool_choice == "none":
tools = None # client forbade tools: never surface tool_calls
cache_slot = body.get("cache_slot")
if (cache_slot is not None and
@@ -903,7 +971,7 @@ class APIHandler(BaseHTTPRequestHandler):
output = []
stats = self.server.engine.generate(
prompt, maximum, temperature, top_p, output.append, cache_slot,
self.client_disconnected)
self.client_disconnected, grammar=grammar)
text = "".join(output)
length_finish = "length" if stats["length_limited"] else "stop"
if chat and tools:
@@ -1010,7 +1078,7 @@ class APIHandler(BaseHTTPRequestHandler):
sp["buf"] = sp["buf"][flush:]
stats = self.server.engine.generate(
prompt, maximum, temperature, top_p, emit_tools, cache_slot,
lambda: not connected)
lambda: not connected, grammar=grammar)
if not sp["tool"] and sp["buf"]:
emit(sp["buf"]) # no tool call happened: flush held tail
_content, calls = parse_tool_calls("".join(raw), tools)
@@ -1027,7 +1095,7 @@ class APIHandler(BaseHTTPRequestHandler):
emit(chunk)
stats = self.server.engine.generate(
prompt, maximum, temperature, top_p, emit_plain, cache_slot,
lambda: not connected)
lambda: not connected, grammar=grammar)
finish = "length" if stats["length_limited"] else "stop"
ka_stop.set() # generation done: stop the keepalive pump
ka_thread.join(timeout=2)
@@ -1078,9 +1146,10 @@ class APIHandler(BaseHTTPRequestHandler):
if not isinstance(enable_thinking, bool):
raise APIError(400, "`enable_thinking` must be a boolean.", "enable_thinking")
tools = body.get("tools") or body.get("functions") or None
tool_choice = body.get("tool_choice")
prompt = render_chat(body.get("messages"), enable_thinking, reasoning_effort, tools,
body.get("tool_choice"))
self.generation(body, prompt, request_id, True)
tool_choice)
self.generation(body, prompt, request_id, True, tools, tool_choice)
def completion(self, body, request_id):
prompt = body.get("prompt")
c/quant.h
+131 -7
View File
@@ -268,6 +268,68 @@ static void matmul_i2(float *y, const float *x, const uint8_t *q2, const float *
y[(int64_t)s*O+o]=a*sc; } }
}
/* ---- int3-g64 (fmt=5): 3-bit weights with ONE f32 scale per 64-input group -
* Per group: 16B low plane (2 bits/val, int2 layout) + 8B high plane (1 bit/val),
* values in [-4,3] stored v+4. 3.5 bits/weight effective — the quality/size point
* the #132 OLMoE ablation measured BEATING per-row int4. */
#define I3_GROUP 64
#define I3_GBYTES 24 /* 16B low plane + 8B high plane per group */
static inline int64_t i3_groups(int I){ return ((int64_t)I + I3_GROUP - 1) / I3_GROUP; }
static inline int64_t i3_rowbytes(int I){ return i3_groups(I) * I3_GBYTES; }
/* Dequant-on-use with PER-GROUP scale. Exact f32 path only (no IDOT in v1: int8
* activations don't compose with per-group accumulation without a kernel
* restructure — follow-up). NEON: low plane = matmul_i2's unpack, high plane
* expanded via vtst on bit masks; x86 stays scalar for now (follow-up). */
static void matmul_i3(float *y, const float *x, const uint8_t *q3, const float *scale, int S, int I, int O){
int64_t ng=i3_groups(I), rb=i3_rowbytes(I);
#pragma omp parallel for schedule(static)
for(int o=0;o<O;o++){
const uint8_t *wrow=q3+(int64_t)o*rb;
const float *srow=scale+(int64_t)o*ng;
for(int s=0;s<S;s++){
const float *xs=x+(int64_t)s*I;
float acc=0;
for(int64_t g=0; g<ng; g++){
const uint8_t *lo=wrow+g*I3_GBYTES, *hi=lo+16;
int base=(int)(g*I3_GROUP), n = I-base < I3_GROUP ? I-base : I3_GROUP;
float a=0; int k=0;
#if defined(__ARM_NEON)
if(n==I3_GROUP){
const uint8x8_t m2v=vdup_n_u8(3); const int8x16_t b4q=vdupq_n_s8(4);
const uint8x16_t bitm={1,2,4,8,16,32,64,128,1,2,4,8,16,32,64,128};
const uint8x16_t fourq=vdupq_n_u8(4);
float32x4_t ac0=vdupq_n_f32(0), ac1=vdupq_n_f32(0);
for(;k+16<=I3_GROUP;k+=16){
uint32_t wd; memcpy(&wd, lo+(k>>2), 4); /* 4 bytes = 16 low-plane values */
uint8x8_t by=vreinterpret_u8_u32(vdup_n_u32(wd));
uint8x8x2_t z01=vzip_u8(vand_u8(by,m2v), vand_u8(vshr_n_u8(by,2),m2v));
uint8x8x2_t z23=vzip_u8(vand_u8(vshr_n_u8(by,4),m2v), vshr_n_u8(by,6));
uint16x4x2_t zz=vzip_u16(vreinterpret_u16_u8(z01.val[0]), vreinterpret_u16_u8(z23.val[0]));
uint8x16_t lov=vcombine_u8(vreinterpret_u8_u16(zz.val[0]), vreinterpret_u8_u16(zz.val[1]));
uint8x16_t hv=vcombine_u8(vdup_n_u8(hi[k>>3]), vdup_n_u8(hi[(k>>3)+1]));
uint8x16_t hb=vandq_u8(vtstq_u8(hv,bitm), fourq); /* 4 where high bit set */
int8x16_t wq=vsubq_s8(vreinterpretq_s8_u8(vaddq_u8(lov,hb)), b4q); /* [-4,3] in order */
int16x8_t w0=vmovl_s8(vget_low_s8(wq)), w1=vmovl_s8(vget_high_s8(wq));
ac0=vfmaq_f32(ac0, vld1q_f32(xs+base+k), vcvtq_f32_s32(vmovl_s16(vget_low_s16(w0))));
ac1=vfmaq_f32(ac1, vld1q_f32(xs+base+k+4), vcvtq_f32_s32(vmovl_s16(vget_high_s16(w0))));
ac0=vfmaq_f32(ac0, vld1q_f32(xs+base+k+8), vcvtq_f32_s32(vmovl_s16(vget_low_s16(w1))));
ac1=vfmaq_f32(ac1, vld1q_f32(xs+base+k+12), vcvtq_f32_s32(vmovl_s16(vget_high_s16(w1))));
}
a=vaddvq_f32(vaddq_f32(ac0,ac1));
}
#endif
for(;k<n;k++){
unsigned u=((lo[k>>2]>>((k&3)*2))&3) | (((hi[k>>3]>>(k&7))&1)<<2);
a += xs[base+k]*(float)((int)u-4);
}
acc += a*srow[g];
}
y[(int64_t)s*O+o]=acc;
}
}
}
/* ---- IDOT: integer dot kernels (int8-quantized activations) --------------- */
#if defined(__AVX512VNNI__) && defined(__AVX512BW__)
#define IDOT_KERNEL "avx512-vnni"
@@ -314,12 +376,27 @@ static inline int32_t dot_i8i8(const int8_t *w, const int8_t *x, int I){
}
sum=_mm512_reduce_add_epi32(acc);
#elif defined(__AVXVNNI__) && defined(__AVX2__)
__m128i acc=_mm_setzero_si128();
/* 4 accumulatori indipendenti (64 byte/iter): un solo acc incatena i vpdpbusd
* (latenza-bound ~5c). Somme intere associative -> bit-identico. Stessa struttura
* dei 4 accumulatori del ramo NEON piu' sotto.
* EN: four independent accumulators break the serial vpdpbusd->acc chain; integer
* adds are associative, so the result is bit-identical (mirrors the NEON path). */
__m128i a0=_mm_setzero_si128(),a1=_mm_setzero_si128(),a2=_mm_setzero_si128(),a3=_mm_setzero_si128();
for(;i+64<=I;i+=64){
__m128i w0=_mm_loadu_si128((const __m128i*)(w+i)), x0=_mm_loadu_si128((const __m128i*)(x+i));
__m128i w1=_mm_loadu_si128((const __m128i*)(w+i+16)), x1=_mm_loadu_si128((const __m128i*)(x+i+16));
__m128i w2=_mm_loadu_si128((const __m128i*)(w+i+32)), x2=_mm_loadu_si128((const __m128i*)(x+i+32));
__m128i w3=_mm_loadu_si128((const __m128i*)(w+i+48)), x3=_mm_loadu_si128((const __m128i*)(x+i+48));
a0=_mm_dpbusd_epi32(a0,_mm_abs_epi8(w0),_mm_sign_epi8(x0,w0));
a1=_mm_dpbusd_epi32(a1,_mm_abs_epi8(w1),_mm_sign_epi8(x1,w1));
a2=_mm_dpbusd_epi32(a2,_mm_abs_epi8(w2),_mm_sign_epi8(x2,w2));
a3=_mm_dpbusd_epi32(a3,_mm_abs_epi8(w3),_mm_sign_epi8(x3,w3));
}
__m128i acc=_mm_add_epi32(_mm_add_epi32(a0,a1),_mm_add_epi32(a2,a3));
for(;i+16<=I;i+=16){
__m128i wv=_mm_loadu_si128((const __m128i*)(w+i));
__m128i xv=_mm_loadu_si128((const __m128i*)(x+i));
__m128i xs=_mm_sign_epi8(xv,wv);
acc=_mm_dpbusd_epi32(acc,_mm_abs_epi8(wv),xs);
acc=_mm_dpbusd_epi32(acc,_mm_abs_epi8(wv),_mm_sign_epi8(xv,wv));
}
sum=hsum128_i32(acc);
#elif defined(__AVX2__)
@@ -390,13 +467,32 @@ static inline int32_t dot_i4i8(const uint8_t *w4, const int8_t *x, int I){
}
sum=_mm512_reduce_add_epi32(acc);
#elif defined(__AVXVNNI__) && defined(__AVX2__)
/* 4 accumulatori indipendenti (64 elementi = 32 byte packed/iter): un solo acc
* incatena i vpdpbusd (latenza-bound ~5c). Somme intere associative -> bit-identico.
* Stessa struttura dei 4 accumulatori del ramo NEON piu' sotto.
* EN: four independent accumulators break the serial vpdpbusd->acc chain; integer
* adds are associative, so the result is bit-identical (mirrors the NEON path). */
const __m128i m4=_mm_set1_epi8(0x0F); const __m128i b8=_mm_set1_epi8(8);
__m128i acc=_mm_setzero_si128();
for(;i+32<=I;i+=32){
__m128i a0=_mm_setzero_si128(),a1=_mm_setzero_si128(),a2=_mm_setzero_si128(),a3=_mm_setzero_si128();
for(;i+64<=I;i+=64){
__m128i by0=_mm_loadu_si128((const __m128i*)(w4+(i>>1))); /* elem i..i+31 */
__m128i by1=_mm_loadu_si128((const __m128i*)(w4+(i>>1)+16)); /* elem i+32..i+63 */
__m128i lo0=_mm_and_si128(by0,m4), hi0=_mm_and_si128(_mm_srli_epi16(by0,4),m4);
__m128i lo1=_mm_and_si128(by1,m4), hi1=_mm_and_si128(_mm_srli_epi16(by1,4),m4);
__m128i w0=_mm_sub_epi8(_mm_unpacklo_epi8(lo0,hi0),b8), w1=_mm_sub_epi8(_mm_unpackhi_epi8(lo0,hi0),b8);
__m128i w2=_mm_sub_epi8(_mm_unpacklo_epi8(lo1,hi1),b8), w3=_mm_sub_epi8(_mm_unpackhi_epi8(lo1,hi1),b8);
__m128i x0=_mm_loadu_si128((const __m128i*)(x+i)), x1=_mm_loadu_si128((const __m128i*)(x+i+16));
__m128i x2=_mm_loadu_si128((const __m128i*)(x+i+32)), x3=_mm_loadu_si128((const __m128i*)(x+i+48));
a0=_mm_dpbusd_epi32(a0,_mm_abs_epi8(w0),_mm_sign_epi8(x0,w0));
a1=_mm_dpbusd_epi32(a1,_mm_abs_epi8(w1),_mm_sign_epi8(x1,w1));
a2=_mm_dpbusd_epi32(a2,_mm_abs_epi8(w2),_mm_sign_epi8(x2,w2));
a3=_mm_dpbusd_epi32(a3,_mm_abs_epi8(w3),_mm_sign_epi8(x3,w3));
}
__m128i acc=_mm_add_epi32(_mm_add_epi32(a0,a1),_mm_add_epi32(a2,a3));
for(;i+32<=I;i+=32){ /* 32-nibble remainder: 2 dpbusd, same unpack */
__m128i by=_mm_loadu_si128((const __m128i*)(w4+(i>>1)));
__m128i lo=_mm_and_si128(by,m4), hi=_mm_and_si128(_mm_srli_epi16(by,4),m4);
__m128i n0=_mm_unpacklo_epi8(lo,hi), n1=_mm_unpackhi_epi8(lo,hi);
__m128i w0=_mm_sub_epi8(n0,b8), w1=_mm_sub_epi8(n1,b8);
__m128i w0=_mm_sub_epi8(_mm_unpacklo_epi8(lo,hi),b8), w1=_mm_sub_epi8(_mm_unpackhi_epi8(lo,hi),b8);
__m128i x0=_mm_loadu_si128((const __m128i*)(x+i));
__m128i x1=_mm_loadu_si128((const __m128i*)(x+i+16));
acc=_mm_dpbusd_epi32(acc,_mm_abs_epi8(w0),_mm_sign_epi8(x0,w0));
@@ -655,6 +751,34 @@ static void pack_int4(const float *w, uint8_t *q4, float *scale, int O, int I, i
}
}
}
/* quantize w[O,I] f32 -> int3-g64 (fmt=5): per 64-input group, symmetric absmax
* (qmax=3, clamp [-4,3], stored v+4), 16B low plane + 8B high plane, ONE f32 scale
* per group. Same math as tools/quant_ablation.py `_quant_last_dim(bits=3, group=64)`
* (#132), here with real bit packing. */
static void pack_int3_g64(const float *w, uint8_t *q3, float *scale, int O, int I){
int64_t ng=i3_groups(I), rb=i3_rowbytes(I);
#pragma omp parallel for schedule(static)
for(int o=0;o<O;o++){
const float *wr=w+(int64_t)o*I;
uint8_t *qr=q3+(int64_t)o*rb;
float *sr=scale+(int64_t)o*ng;
for(int64_t g=0; g<ng; g++){
int base=(int)(g*I3_GROUP), n = I-base < I3_GROUP ? I-base : I3_GROUP;
float amax=0;
for(int k=0;k<n;k++){ float a=fabsf(wr[base+k]); if(a>amax)amax=a; }
float s=amax/3.f; if(s<1e-8f)s=1e-8f; sr[g]=s;
uint8_t *lo=qr+g*I3_GBYTES, *hi=lo+16;
memset(lo,0,I3_GBYTES);
for(int k=0;k<n;k++){
int v=(int)lrintf(wr[base+k]/s); if(v>3)v=3; if(v<-4)v=-4;
unsigned u=(unsigned)(v+4); /* 0..7 */
lo[k>>2] |= (uint8_t)((u&3)<<((k&3)*2));
hi[k>>3] |= (uint8_t)(((u>>2)&1)<<(k&7));
}
}
}
}
static void pack_int2(const float *w, uint8_t *q2, float *scale, int O, int I, int bits){
int qmax=(1<<(bits-1))-1, rb=(I+3)/4;
#pragma omp parallel for schedule(static)
c/resource_plan.py
+181 -23
View File
@@ -166,14 +166,33 @@ def discover_gpus():
return devices
def _physical_cores_warn(message):
"""Visibility for a mis-detected core count: a silent "1" here becomes
OMP_NUM_THREADS=1 and pins the whole run to a single core (#325). Emit on
stderr so it surfaces in the [PLAN]/[OMP] stream without being swallowed."""
print(f"[plan] warning: {message}", file=sys.stderr)
def physical_cpu_count():
"""Number of physical CPU cores (not SMT siblings).
Per-expert matmul regions are tiny and back-to-back; two SMT siblings share
one AVX-512 unit and contend, so logical (SMT) counts over-subscribe and
hurt throughput. We want true physical cores. A silent 1 here propagates to
OMP_NUM_THREADS=1 and pins the run to one core (#325), so every fallback
must be visible, never just ``or 1``.
"""
if sys.platform == "win32":
# os.cpu_count() conta i processori logici (SMT): 2 thread/core saturano
# le unita' AVX-512 e peggiorano il matmul. Contiamo i core fisici veri
# con GetLogicalProcessorInformationEx(RelationProcessorCore).
# Contiamo i core fisici veri con GetLogicalProcessorInformationEx
# (RelationProcessorCore). Le firme vanno dichiarate: su Python a 64 bit
# una WinAPI non dichiarata ritorna c_int (32 bit) e riceve i puntatori
# come c_int di default, quindi il probe puo' fallire silenziosamente.
try:
import ctypes
k32 = ctypes.windll.kernel32
k32.GetLogicalProcessorInformationEx.argtypes = [
ctypes.c_uint, ctypes.c_void_p, ctypes.POINTER(ctypes.c_ulong)]
k32.GetLogicalProcessorInformationEx.restype = ctypes.c_int
need = ctypes.c_ulong(0)
k32.GetLogicalProcessorInformationEx(0, None, ctypes.byref(need))
buf = (ctypes.c_char * need.value)()
@@ -189,18 +208,69 @@ def physical_cpu_count():
off += size
if cores:
return cores
except (OSError, ValueError, AttributeError):
pass
_physical_cores_warn("GetLogicalProcessorInformationEx returned no cores")
except (OSError, ValueError, AttributeError) as error:
_physical_cores_warn(f"Windows core probe failed: {error}")
try:
# Ask lscpu for exactly core,socket and dedupe on (core, socket).
# Counting un-deduplicated rows would return logical threads (SMT),
# which was the original over-subscription bug. Empty fields ("-")
# mark an offline core/socket and fail int() -> skipped.
#
# Column layout robustness: `lscpu -p=<list>` emits *exactly* the
# requested columns (no CPU prefix), while bare `lscpu -p` prepends
# CPU. We requested two columns, but take the LAST TWO fields so the
# parser stays correct whether or not a CPU column is present
# (JustVugg review: the previous fields[1]/fields[2] indexing assumed
# a 3-column layout and regressed 2-column output to the logical
# count -- the opposite of the fix).
result = subprocess.run(["lscpu", "-p=core,socket"], text=True,
capture_output=True, check=True, timeout=5)
cores = {tuple(map(int, line.split(","))) for line in result.stdout.splitlines()
if line and not line.startswith("#")}
cores = set()
for line in result.stdout.splitlines():
if not line or line.startswith("#"):
continue
fields = line.split(",")
if len(fields) < 2:
continue
try:
core, socket = int(fields[-2]), int(fields[-1])
except ValueError:
continue # "-" for an offline core/socket
cores.add((core, socket))
if cores:
return len(cores)
except (OSError, ValueError, subprocess.SubprocessError):
pass
return os.cpu_count() or 1
except (OSError, ValueError, subprocess.SubprocessError) as error:
_physical_cores_warn(f"lscpu core probe failed: {error}")
logical = os.cpu_count()
if not logical:
_physical_cores_warn(
"could not detect any CPU cores; falling back to 1. "
"Set OMP_NUM_THREADS manually to fix single-core decode (#325).")
return 1
_physical_cores_warn(
f"physical-core probes unavailable; using {logical} logical CPUs "
f"(SMT may over-subscribe). Set OMP_NUM_THREADS to physical cores if slow.")
return logical
def _resolve_physical_cores(physical_cpus):
"""Coerce the build_plan() physical-core argument to a sane positive int.
A None/0/None-ish value reaching here means physical_cpu_count() already
warned; clamp to 1 (so the engine always gets a positive team size) but keep
that clamp visible rather than silently masking it as the old ``max(1, int())``
did (#325)."""
try:
count = int(physical_cpus or 0)
except (TypeError, ValueError):
count = 0
if count < 1:
_physical_cores_warn(
"physical core count resolved to 0; defaulting to 1. "
"Set OMP_NUM_THREADS to fix single-core decode (#325).")
return 1
return count
def cpu_socket_count():
@@ -219,6 +289,54 @@ def cpu_socket_count():
return 1
def _auto_tune(bottleneck_class, projected_hit, gpus, cpu_sockets, plan_has_metal):
"""Derive tuning knobs from the bottleneck classification."""
tune = {}
has_gpu = bool(gpus)
n_gpu = len(gpus)
# MTP: costs more than it saves when compute-bound (#389 measured 42% loss)
if bottleneck_class == "compute":
tune["DRAFT"] = {"value": "0",
"reason": "compute-bound: MTP batch overhead exceeds yield"}
elif bottleneck_class == "disk" and projected_hit < 0.90:
tune["DRAFT"] = {"value": "0",
"reason": "low hit rate: MTP widens expert union, adds disk reads"}
# otherwise leave DRAFT unset (engine default: auto)
# PIPE: resident pipeline mode depends on GPU count
if has_gpu and n_gpu == 1:
tune["COLI_CUDA_PIPE"] = {"value": "1",
"reason": "single GPU: S=1 pipeline gate"}
elif has_gpu and n_gpu > 1:
tune["COLI_CUDA_PIPE"] = {"value": "2",
"reason": "multi-GPU: residual stays on-device across layers"}
elif not has_gpu and bottleneck_class == "disk":
tune["PIPE"] = {"value": "1",
"reason": "overlap disk reads with resident expert compute"}
# NUMA: selective interleave for GPU hosts, blanket hint for CPU-only
if cpu_sockets > 1 and has_gpu:
tune["COLI_NUMA"] = {"value": "1",
"reason": "multi-socket + GPU: interleave expert slabs, protect DMA buffers"}
elif cpu_sockets > 1 and not has_gpu:
tune["COLI_NUMA"] = {"value": "1",
"reason": "multi-socket CPU-only: interleave expert slabs across nodes"}
tune["_numa_hint"] = "numactl --interleave=all may perform better on CPU-only hosts"
# OMP: kill hot-thread spin when GPU/Metal owns the power budget
if plan_has_metal:
tune["COLI_NO_OMP_TUNE"] = {"value": "1",
"reason": "Metal: OMP spin-wait steals GPU power budget"}
# PIN: fully resident if RAM allows and no GPU tier competes
if projected_hit >= 0.99 and not has_gpu:
tune["PIN_GB"] = {"value": "all",
"reason": "enough RAM for full expert residency"}
return tune
POLICIES = {
"quality": {"preserve_quantization": True, "preserve_router": True},
"balanced": {"preserve_quantization": True, "preserve_router": True},
@@ -290,19 +408,35 @@ def build_plan(model, ram_gb=0, context=4096, gpu_indices=None, vram_gb=0,
if cold_bytes:
warnings.append("cold expert misses may reach disk; normal decode speed depends on hit rate")
total_expert = info["expert_bytes"]
resident_expert = hot_bytes + warm_bytes
projected_hit = resident_expert / total_expert if total_expert else 1.0
if cold_bytes:
bottleneck = "disk expert misses"
elif warm_bytes:
bottleneck = "CPU expert compute and RAM bandwidth"
bottleneck_class = "disk"
elif warm_bytes and gpus:
bottleneck = "CPU expert tail and GPU compute"
bottleneck_class = "mixed"
elif projected_hit >= 0.99:
if gpus:
bottleneck = "GPU compute and interconnect"
else:
bottleneck = "CPU expert compute (fully resident)"
bottleneck_class = "compute"
else:
bottleneck = "GPU compute and interconnect"
bottleneck = "CPU expert compute and RAM bandwidth"
bottleneck_class = "memory"
tune = _auto_tune(bottleneck_class, projected_hit, gpus, cpu_sockets,
plan_has_metal=False)
return {
"version": 2,
"policy": {"name": policy, **POLICIES[policy],
"quality_preserving": policy != "experimental-fast"},
"model": {key: value for key, value in info.items() if key != "config"},
"cpu": {"physical_cores": max(1, int(physical_cpus)),
"cpu": {"physical_cores": _resolve_physical_cores(physical_cpus),
"sockets": max(1, int(cpu_sockets)),
"thread_policy": "physical-cores"},
"tiers": {
@@ -317,6 +451,9 @@ def build_plan(model, ram_gb=0, context=4096, gpu_indices=None, vram_gb=0,
"expert_capacity": vram_experts, "requires_host_backing": False},
},
"expected_bottleneck": bottleneck,
"bottleneck_class": bottleneck_class,
"projected_hit_rate": round(projected_hit, 4),
"tune": tune,
"decisions": [
{"target": "VRAM", "reason": "profile-ranked hot experts"},
{"target": "RAM", "reason": "warm experts execute on CPU without quality loss"},
@@ -331,15 +468,23 @@ def environment_for_plan(plan, env=None, cuda_enabled=True):
result = dict(env or {})
result.setdefault("COLI_POLICY", plan["policy"]["name"])
result.setdefault("OMP_NUM_THREADS", str(plan["cpu"]["physical_cores"]))
if sys.platform != "win32":
# la libgomp di MinGW non supporta l'affinity su Windows
# ("Affinity not supported on this configuration"): non impostarle li'.
result.setdefault("OMP_PROC_BIND", "spread")
result.setdefault("OMP_PLACES", "cores")
if sys.platform.startswith("linux") and plan["cpu"].get("sockets", 1) > 1:
# Selectively interleave large expert/dense slabs across memory controllers.
# Unlike blanket numactl interleave, this leaves CUDA staging buffers local.
result.setdefault("COLI_NUMA", "1")
# NOTE: we intentionally do NOT set OMP_PROC_BIND / OMP_PLACES here.
# The engine's own hot-thread tuning (glm.c main(), the COLI_OMP_TUNED
# self-exec) sets OMP_PROC_BIND=close with overwrite=0 -- it prefers
# packing the team onto adjacent cores for the tiny back-to-back per-expert
# matmuls. Pre-setting OMP_PROC_BIND=spread here ran first and won (the
# engine's overwrite=0 setenv could not override an already-set var), and
# spread + OMP_PLACES=cores collapsed the team to one CPU on some libgomp /
# multi-socket topologies (#325: --auto-tier pinned decode to 1 core on a
# 64-core box even with OMP_NUM_THREADS=64). Leaving affinity to the engine
# makes --auto-tier match the plain (working) path. A user who wants a
# specific policy can still set OMP_PROC_BIND/OMP_PLACES in the environment
# themselves -- setdefault above only covers OMP_NUM_THREADS.
tune = plan.get("tune", {})
for key, entry in tune.items():
if key.startswith("_"):
continue
result.setdefault(key, entry["value"])
if plan["policy"]["name"] == "balanced":
result.setdefault("REPIN", "64")
ram = plan["tiers"]["ram"]
@@ -386,5 +531,18 @@ def format_plan(plan):
else:
lines.append("VRAM no NVIDIA device detected · CPU path")
lines.append(f"limit {plan['expected_bottleneck']}")
hit = plan.get("projected_hit_rate", 0)
lines.append(f"hit {hit:.0%} projected expert residency")
tune = plan.get("tune", {})
if tune:
lines.append("")
lines.append("auto-tune:")
for key, entry in tune.items():
if key.startswith("_"):
continue
lines.append(f" {key}={entry['value']:12s} {entry['reason']}")
hint = tune.get("_numa_hint")
if hint:
lines.append(f" hint: {hint}")
lines.extend(f"warn {warning}" for warning in plan["warnings"])
return "\n".join(lines)
c/sample.h
+10
View File
@@ -116,6 +116,16 @@ static void stops_arm_tok(const Cfg *c, int tok_eos, Tok *T){
int nsp = 0;
if (T) for (int id = 0; id < T->n_ids && g_nstop < 64; id++)
if (T->id_special[id] && !is_stop(id)) { g_stop[g_nstop++] = id; nsp++; }
/* #401: in serve mode keep ONLY <|endoftext|>. Role markers <|user|>/<|observation|>
* (config stops + tokenizer special set) are boundaries the Python server owns; as
* hard stops they cut generation the moment the model opens a <tool_call> block,
* because int4 argmax noise picks a stop-token ID over the correct '<' token. */
if (getenv("SERVE") && tok_eos >= 0) {
int kept = 0;
for (int i = 0; i < g_nstop; i++) if (g_stop[i] == tok_eos) g_stop[kept++] = g_stop[i];
if (kept < g_nstop) fprintf(stderr, "[stop] serve mode: filtered %d non-EOS stop tokens (tool-call safety, #401)\n", g_nstop - kept);
g_nstop = kept; nsp = 0;
}
fprintf(stderr, "[stop] %d stop tokens:", g_nstop);
for (int i = 0; i < g_nstop; i++) fprintf(stderr, " %d", g_stop[i]);
if (nsp) fprintf(stderr, " (%d from the tokenizer's special set)", nsp);
c/tests/README_efficiency.md
+98
View File
@@ -0,0 +1,98 @@
# Efficiency suite — regression tests + optimization dossier
Two layers:
1. **`test_inefficiency.py`** — tiny-model *asserted* regression tests. Fast
(~0.15s/run), gate CI, catch breakage. Run as part of `make test`.
2. **`test_efficiency_report.py`** — an *opt-in optimization dossier* for a real
model. Runs every instrumentation flag, prints a 9-section report answering
*what is doing what, when, with what, is it inefficient, how to improve*.
Never fails CI (it's a report, not a gate).
## The dossier (what you run when optimizing)
```bash
# CPU-only (safe, fast to validate):
COLI_EFFICIENCY_MODEL=../glm52_i4_g64 make efficiency-report
# CUDA (dense + expert tiers — needs a CUDA build, see below):
COLI_EFFICIENCY_MODEL=../glm52_i4_g64 COLI_EFFICIENCY_CUDA=1 make efficiency-report
```
It turns ON every observability flag the engine supports — `PROF=1`,
`COLI_CUDA_PROFILE=1`, `CACHE_ROUTE=1` (auto-unlocks `route_agree`/`route_kl`),
`DISK_SPLIT=1`, `LOOKA=1` — so nothing the engine can tell you is left dark.
None of these change the computed output; they only add telemetry.
The 9 sections, and the question each answers:
| § | section | answers |
|---|---|---|
| 1 | PROVENANCE | what is running, on what CPU/backend, with what effective config |
| 2 | THROUGHPUT | tok/s + forward-latency p50/p90/p99/max (is the tail healthy?) |
| 3 | WHERE TIME GOES | the 5 PROFILE phases as % of decode + absolute seconds + verdict |
| 3a | ATTENTION BREAKDOWN | attention split into projection/RoPE, score-softmax-value, output |
| 4 | EXPERT CACHE | hit %, experts-loaded/token vs baseline topk |
| 5 | DISK I/O | GB fetched, MB/token, GB/s, read-service vs felt-wait, phase split |
| 5a | DISK-LOAD SPLIT | loads by decode phase (draft/absorb/verify) + MTP-vs-main bytes |
| 6 | ROUTING QUALITY | route_agree %, route_kl, cache swaps |
| 6a | ROUTING PREDICTABILITY | LOOKAHEAD recall per predictor (which prefetch wins) |
| 7 | SPECULATION | tokens/forward, MTP acceptance % |
| 8 | GPU TIERS | resident tensors, expert tier (count/GB/calls), H2D/kernel/D2H ms |
Every line that crosses an advisory threshold is marked `[FLAG]` with the
concrete lever to pull (raise RAM_GB, add PIN_GB, try DIRECT=1, lower CTX, …),
and all flags repeat in a summary at the end.
## Tunable thresholds
The `IS IT INEFFICIENT?` lines are advisory constants at the top of
`test_efficiency_report.py`:
| constant | default | meaning |
|---|---|---|
| `DISK_WAIT_DOMINANT` | 0.40 | >40% decode waiting on expert reads → I/O-bound |
| `LOW_HIT_RATE` | 0.30 | <30% cache hit → thrashing |
| `LOW_ROUTE_AGREE` | 0.80 | <80% routing overlap → prefetch guessing wrong |
| `HIGH_TAIL_RATIO` | 3.0 | p99 > 3× p50 → decode stalls |
| `LOW_MTP_ACCEPT` | 0.20 | <20% MTP acceptance → draft decoder is dead weight |
The tiny-model asserted floors live in `tools/efficiency.py` (`TINY_TOK_S_FLOOR`,
`MAX_DISK_WAIT_SHARE`, `MIN_CPU_CUDA_AGREEMENT`).
## The regression tests (what gates CI)
`test_inefficiency.py` runs on the bundled `glm_tiny` model and asserts:
- telemetry parses (no format drift)
- tiny tok/s ≥ floor (throughput regression)
- PROFILE phases present and non-negative (accounting sanity)
- disk-wait not dominant on a resident model (I/O-path regression)
- CPU determinism (two greedy runs agree)
- **CUDA** (skip unless CUDA built): init path, dense uploads VRAM, CPU-vs-CUDA
argmax agreement ≥ 70% (kernel-correctness guard)
```bash
make efficiency # tiny CPU tests
make efficiency-cuda # tiny CUDA tests (needs CUDA build)
```
## CUDA build prerequisite
The default `make glm.exe` builds **without** CUDA. The CUDA tests and the CUDA
dossier need a host built with `-DCOLI_CUDA` plus the runtime DLL:
```bash
make clean && make glm.exe CUDA_DLL=1 && make cuda-dll
```
`make efficiency-cuda` auto-skips with a clear message if the host is CPU-only
(it scans the binary for the "CPU-only" marker the engine embeds).
## Files
- `tools/efficiency.py` — shared harness: `parse_run()` (captures every
telemetry signal), `run_engine()`, thresholds. Reuses `PROFILE_RE`/`SPEED_RE`
from `tools/benchmark_cuda_fixture.py`.
- `tests/test_inefficiency.py` — tiny-model asserted tests (CPU + CUDA).
- `tests/test_efficiency_report.py` — the opt-in optimization dossier.
c/tests/bench_idot.c
+92
View File
@@ -0,0 +1,92 @@
/* Microbenchmark: old (single-accumulator) vs new (independent-accumulator) AVX-VNNI
* int8/int4 dot kernels (quant.h). NOT a unit test -- test_idot.c proves correctness.
* This measures the headline claim: breaking the serial vpdpbusd->acc chain lifts
* per-core kernel throughput, the same win the NEON path already took ("26->63 GB/s, 2.4x").
*
* It re-implements the OLD single-acc AVX-VNNI kernels inline and calls the REAL (new)
* ones via the include-colibri.c pattern -- one process, identical frozen inputs, warm
* caches. Reports median ns/call + GB/s-of-weights + new/old ratio. Measures the kernel's
* compute ceiling (warm caches), NOT end-to-end tok/s.
*
* Run: make tests/bench_idot ARCH=native && ./tests/bench_idot (not in TEST_BINS -- not a gate)
*/
#define main coli_glm_main_unused
#include "../colibri.c"
#undef main
#include <stdint.h>
#include <string.h>
static uint32_t rs=0x2545F491u;
static uint32_t xr(void){ rs^=rs<<13; rs^=rs>>17; rs^=rs<<5; return rs; }
/* ---- OLD kernels: verbatim copies of the pre-change __AVXVNNI__ branches (single acc) ---- */
#if defined(__AVXVNNI__) && defined(__AVX2__)
static int32_t dot_i8i8_old(const int8_t *w, const int8_t *x, int I){
int32_t sum=0; int i=0;
__m128i acc=_mm_setzero_si128();
for(;i+16<=I;i+=16){
__m128i wv=_mm_loadu_si128((const __m128i*)(w+i));
__m128i xv=_mm_loadu_si128((const __m128i*)(x+i));
__m128i xs=_mm_sign_epi8(xv,wv);
acc=_mm_dpbusd_epi32(acc,_mm_abs_epi8(wv),xs);
}
sum=hsum128_i32(acc);
for(;i<I;i++) sum+=(int32_t)w[i]*x[i];
return sum;
}
static int32_t dot_i4i8_old(const uint8_t *w4, const int8_t *x, int I){
int32_t sum=0; int i=0;
const __m128i m4=_mm_set1_epi8(0x0F); const __m128i b8=_mm_set1_epi8(8);
__m128i acc=_mm_setzero_si128();
for(;i+32<=I;i+=32){
__m128i by=_mm_loadu_si128((const __m128i*)(w4+(i>>1)));
__m128i lo=_mm_and_si128(by,m4), hi=_mm_and_si128(_mm_srli_epi16(by,4),m4);
__m128i n0=_mm_unpacklo_epi8(lo,hi), n1=_mm_unpackhi_epi8(lo,hi);
__m128i w0=_mm_sub_epi8(n0,b8), w1=_mm_sub_epi8(n1,b8);
__m128i x0=_mm_loadu_si128((const __m128i*)(x+i));
__m128i x1=_mm_loadu_si128((const __m128i*)(x+i+16));
acc=_mm_dpbusd_epi32(acc,_mm_abs_epi8(w0),_mm_sign_epi8(x0,w0));
acc=_mm_dpbusd_epi32(acc,_mm_abs_epi8(w1),_mm_sign_epi8(x1,w1));
}
sum=hsum128_i32(acc);
for(;i<I;i++){ uint8_t b=w4[i>>1]; int v=(i&1)?((int)(b>>4)-8):((int)(b&0xF)-8); sum+=v*x[i]; }
return sum;
}
#else
#error "bench_idot requires an AVX-VNNI build: make tests/bench_idot ARCH=native on an AVX-VNNI CPU"
#endif
#define I_DIM 6144
#define N_REPEAT 20000
static int cmp_d(const void*a,const void*b){ double x=*(const double*)a,y=*(const double*)b; return x<y?-1:x>y?1:0; }
int main(void){
static int8_t w8[I_DIM], x8[I_DIM]; static uint8_t w4[I_DIM/2];
for(int i=0;i<I_DIM;i++){ w8[i]=(int8_t)(xr()&0xFF); x8[i]=(int8_t)((int)(xr()%255)-127); }
for(int i=0;i<I_DIM/2;i++) w4[i]=(uint8_t)(xr()&0xFF);
/* correctness sanity: new must equal old (bit-exact) */
if(dot_i8i8(w8,x8,I_DIM)!=dot_i8i8_old(w8,x8,I_DIM)){ fprintf(stderr,"MISMATCH i8i8\n"); return 1; }
if(dot_i4i8(w4,x8,I_DIM)!=dot_i4i8_old(w4,x8,I_DIM)){ fprintf(stderr,"MISMATCH i4i8\n"); return 1; }
static double t[N_REPEAT]; volatile int32_t sink=0;
const char *names[4]={"i8i8 old","i8i8 new","i4i8 old","i4i8 new"};
double gbs[4];
for(int k=0;k<4;k++){
for(int wi=0;wi<200;wi++) sink+= (k==0)?dot_i8i8_old(w8,x8,I_DIM):(k==1)?dot_i8i8(w8,x8,I_DIM)
:(k==2)?dot_i4i8_old(w4,x8,I_DIM):dot_i4i8(w4,x8,I_DIM); /* warmup */
for(int r=0;r<N_REPEAT;r++){
double t0=now_s();
sink+= (k==0)?dot_i8i8_old(w8,x8,I_DIM):(k==1)?dot_i8i8(w8,x8,I_DIM)
:(k==2)?dot_i4i8_old(w4,x8,I_DIM):dot_i4i8(w4,x8,I_DIM);
t[r]=(now_s()-t0)*1e9;
}
qsort(t,N_REPEAT,sizeof(double),cmp_d);
double med=t[N_REPEAT/2];
double bytes=(k<2)?I_DIM:(double)I_DIM/2; /* weight bytes touched */
gbs[k]=bytes/med;
printf("%-9s %8.1f ns/call %6.2f GB/s\n", names[k], med, gbs[k]);
}
printf("ratio i8i8 new/old: %.2fx | ratio i4i8 new/old: %.2fx\n", gbs[1]/gbs[0], gbs[3]/gbs[2]);
(void)sink; return 0;
}
c/tests/bench_tensor_core.cu
+3 -3
View File
@@ -30,9 +30,9 @@ int main(){
for(int i=0;i<D;i++)ds[i]=0.006f+(i%7)*0.0002f;
for(size_t i=0;i<x.size();i++)x[i]=std::sin((float)(i+1)*0.013f)*2.f;
ColiCudaTensor *g=nullptr,*u=nullptr,*d=nullptr;
if(!coli_cuda_tensor_upload(&g,hidden.data(),hs.data(),2,D,I,device)||
!coli_cuda_tensor_upload(&u,hidden.data(),hs.data(),2,D,I,device)||
!coli_cuda_tensor_upload(&d,down.data(),ds.data(),2,I,D,device))return 2;
if(!coli_cuda_tensor_upload(&g,hidden.data(),hs.data(),2,D,I,device,0)||
!coli_cuda_tensor_upload(&u,hidden.data(),hs.data(),2,D,I,device,0)||
!coli_cuda_tensor_upload(&d,down.data(),ds.data(),2,I,D,device,0))return 2;
for(int rows: {1,2,4,8}){
double scalar=run(g,u,d,x.data(),a.data(),rows,3,0);
double packed=run(g,u,d,x.data(),b.data(),rows,3,1);
c/tests/test_backend_cuda.cu
+57 -11
View File
@@ -50,10 +50,56 @@ int main(int argc, char **argv) {
if (coli_cuda_tensor_upload(&t8, q8, s8, 1, 5, 2, d0)) return 1;
if (ndev > 1 && coli_cuda_tensor_upload(&t8, q8, s8, 1, 4, 2, d1)) return 1;
if (!coli_cuda_matmul(&t8, got, x, q8, s8, 1, 2, 4, 2, d0) || !close_enough(got, want8, 4)) return 1;
/* Cached tensor must stay callable without live host pointers
* (CUDA_RELEASE_HOST slots null theirs after upload) — including
* SUSTAINED reuse, not just the first call. */
for (int rep = 0; rep < 64; rep++)
if (!coli_cuda_matmul(&t8, got, x, nullptr, nullptr, 1, 2, 4, 2, d0) ||
!close_enough(got, want8, 4)) return 1;
/* A tensor uploaded from a TEMPORARY host buffer must survive the buffer
* being scribbled and freed (the release-host lifecycle). */
{
int8_t *tmpw = static_cast<int8_t *>(std::malloc(8));
float *tmps = static_cast<float *>(std::malloc(2 * sizeof(float)));
if (!tmpw || !tmps) return 2;
for (int i = 0; i < 8; i++) tmpw[i] = q8[i];
tmps[0] = s8[0]; tmps[1] = s8[1];
ColiCudaTensor *tt = nullptr;
if (!coli_cuda_tensor_upload(&tt, tmpw, tmps, 1, 4, 2, d0)) return 1;
for (int i = 0; i < 8; i++) tmpw[i] = 99;
std::free(tmpw); std::free(tmps);
if (!coli_cuda_matmul(&tt, got, x, nullptr, nullptr, 1, 2, 4, 2, d0) ||
!close_enough(got, want8, 4)) return 1;
coli_cuda_tensor_free(tt);
}
/* Upload failures must be graceful and must not corrupt accounting —
* and must not poison LATER healthy launches (sticky-error regression). */
{
size_t c0 = 0, b0 = 0, c1 = 0, b1 = 0;
coli_cuda_stats(-1, &c0, &b0);
ColiCudaTensor *bad = nullptr;
if (coli_cuda_tensor_upload(&bad, q8, s8, 1, 4, 2, 9999)) return 1;
if (coli_cuda_tensor_upload(&bad, q8, s8, 7, 4, 2, d0)) return 1;
if (coli_cuda_tensor_upload(&bad, q8, nullptr, 1, 4, 2, d0)) return 1;
if (coli_cuda_tensor_upload(&bad, nullptr, s8, 1, 4, 2, d0)) return 1;
if (coli_cuda_tensor_upload(&bad, q8, s8, 1, 1 << 20, 1 << 24, d0)) return 1; /* ~16 TB */
if (bad) return 1;
coli_cuda_stats(-1, &c1, &b1);
if (c0 != c1 || b0 != b1) return 1;
/* healthy launch immediately after the failed allocation */
if (!coli_cuda_matmul(&t8, got, x, nullptr, nullptr, 1, 2, 4, 2, d0) ||
!close_enough(got, want8, 4)) return 1;
}
/* Fault injection hook: on/off, restores cleanly. */
if (setenv("COLI_GPU_FAIL_AFTER", "0", 1)) return 2;
if (coli_cuda_matmul(&t8, got, x, nullptr, nullptr, 1, 2, 4, 2, d0)) return 1;
if (unsetenv("COLI_GPU_FAIL_AFTER")) return 2;
if (!coli_cuda_matmul(&t8, got, x, nullptr, nullptr, 1, 2, 4, 2, d0) ||
!close_enough(got, want8, 4)) return 1;
const int8_t q8b[8]={-1,-2,-3,-4, 1,-2,3,-4};
const float s8b[2]={1.f,.5f},want8b[4]={10.f,15.f,-3.f,-2.5f};
if(!coli_cuda_tensor_update(t8,q8b,s8b)||
!coli_cuda_matmul(&t8,got,x,q8b,s8b,1,2,4,2,d0)||
!coli_cuda_matmul(&t8,got,x,q8b,s8b,1,2,4,2,d0,0)||
!close_enough(got,want8b,4))return 1;
/* Rows [-8,-1,0,7] and [1,2,3,4], packed low nibble first. */
@@ -61,26 +107,26 @@ int main(int argc, char **argv) {
const float s4[2] = {1.0f, 0.25f};
const float want4[2] = {-34.0f, -2.5f};
ColiCudaTensor *t4 = nullptr;
if (!coli_cuda_matmul(&t4, got, x, q4, s4, 2, 1, 4, 2, d1) || !close_enough(got, want4, 2)) return 1;
if (!coli_cuda_matmul(&t4, got, x, q4, s4, 2, 1, 4, 2, d1, 0) || !close_enough(got, want4, 2)) return 1;
const uint8_t q2[2] = {0xe4, 0x1b};
const float s2[2] = {0.5f, 2.0f};
const float want2[2] = {-2.0f, 12.0f};
ColiCudaTensor *t2 = nullptr;
if (!coli_cuda_matmul(&t2, got, x, q2, s2, 3, 1, 4, 2, d1) || !close_enough(got, want2, 2)) return 1;
if (!coli_cuda_matmul(&t2, got, x, q2, s2, 3, 1, 4, 2, d1, 0) || !close_enough(got, want2, 2)) return 1;
const float wf[8] = {1, 0, -1, 2, 0.5f, 0.5f, 0.5f, 0.5f};
const float wantf[2] = {-10.0f, -1.0f};
ColiCudaTensor *tf = nullptr;
if (!coli_cuda_matmul(&tf, got, x, wf, nullptr, 0, 1, 4, 2, d0) || !close_enough(got, wantf, 2)) return 1;
if (!coli_cuda_matmul(&tf, got, x, wf, nullptr, 0, 1, 4, 2, d0, 0) || !close_enough(got, wantf, 2)) return 1;
const float eg[8] = {1,0,0,0, 0,1,0,0};
const float eu[8] = {1,0,0,0, 0,1,0,0};
const float ed[8] = {1,0, 0,1, 1,1, 1,-1};
ColiCudaTensor *tg=nullptr,*tu=nullptr,*td=nullptr;
if (!coli_cuda_tensor_upload(&tg,eg,nullptr,0,4,2,d0) ||
!coli_cuda_tensor_upload(&tu,eu,nullptr,0,4,2,d0) ||
!coli_cuda_tensor_upload(&td,ed,nullptr,0,2,4,d0)) return 1;
if (!coli_cuda_tensor_upload(&tg,eg,nullptr,0,4,2,d0,0) ||
!coli_cuda_tensor_upload(&tu,eu,nullptr,0,4,2,d0,0) ||
!coli_cuda_tensor_upload(&td,ed,nullptr,0,2,4,d0,0)) return 1;
float expert[8], want_expert[8];
for(int s=0;s<2;s++){
float a=x[s*4], b=x[s*4+1];
@@ -98,7 +144,7 @@ int main(int argc, char **argv) {
const float aw[16]={1,0,0,0, 0,1,0,0, 0,0,1,0, 0,0,0,1};
const float aq[4]={1,2,.5f,-.5f},al[12]={1,0,0,0, 0,1,0,0, 0,0,1,0};
const float ar[6]={1,0, 0,1, 1,1};float actx[2],aref[2];
ColiCudaTensor *at=nullptr;if(!coli_cuda_tensor_upload(&at,aw,nullptr,0,4,4,d0))return 1;
ColiCudaTensor *at=nullptr;if(!coli_cuda_tensor_upload(&at,aw,nullptr,0,4,4,d0,0))return 1;
float score[3];for(int t=0;t<3;t++)score[t]=aq[0]*al[t*4]+aq[1]*al[t*4+1]+aq[2]*ar[t*2]+aq[3]*ar[t*2+1];
float mx=score[0],z=0;for(int t=1;t<3;t++)mx=score[t]>mx?score[t]:mx;
for(int t=0;t<3;t++){score[t]=std::exp(score[t]-mx);z+=score[t];}for(int t=0;t<3;t++)score[t]/=z;
@@ -117,9 +163,9 @@ int main(int argc, char **argv) {
for(int i=0;i<32;i++)ws4[i]=0.01f+(i%5)*0.002f;
for(int i=0;i<64;i++)gx4[i]=std::sin((float)(i+1)*0.17f)*2.f;
ColiCudaTensor *g4=nullptr,*u4=nullptr,*d4=nullptr;
if(!coli_cuda_tensor_upload(&g4,w4,ws4,2,32,32,d0)||
!coli_cuda_tensor_upload(&u4,w4,ws4,2,32,32,d0)||
!coli_cuda_tensor_upload(&d4,w4,ws4,2,32,32,d0))return 1;
if(!coli_cuda_tensor_upload(&g4,w4,ws4,2,32,32,d0,0)||
!coli_cuda_tensor_upload(&u4,w4,ws4,2,32,32,d0,0)||
!coli_cuda_tensor_upload(&d4,w4,ws4,2,32,32,d0,0))return 1;
ColiCudaTensor *gg4[2]={g4,g4},*ug4[2]={u4,u4},*dg4[2]={d4,d4};
if(!coli_cuda_expert_group(gg4,ug4,dg4,group_rows,2,scalar4,gx4))return 1;
setenv("COLI_CUDA_TC_INT4","1",1);
c/tests/test_backend_metal.mm
+88
View File
@@ -177,6 +177,68 @@ static int run_attn(int S, int pos_base, const char* name){
return pass?0:1;
}
// serial r_top8 vs parallel r_top8_par on the ENGINE build's own compiled shaders — the
// exact-match contract (same indices, same order, same weights bitwise, same keff)
// enforced with memcmp, per adversarial input family. `mode` selects the input
// construction; see the inventory at the call sites in main(). E is a parameter (not
// hardcoded 256) so the same helper drives both the original E=256 fuzz and the
// expert-count-generality cases (E=24 <32-lane-width, E=168 REAP-pruned, E=200
// lane-straddling boundary, E=257 out-of-contract auto-serial-fallback proof).
static int run_rtop8(int mode, int S, int E, float topp, int normk, float rscale, const char *name) {
const int K=8, Ksel=8;
std::vector<float> sig((size_t)S*E), bias(E);
srand(4242+mode*17+S+E);
for (int e=0;e<E;e++) bias[e]=((rand()%2001)-1000)/1000.f;
for (int s=0;s<S;s++) for (int e=0;e<E;e++) {
float *v=&sig[(size_t)s*E+e];
switch (mode) {
case 0: *v=(float)(rand()%10000)/10000.f; break; // generic sigmoid-like
case 1: *v=0.5f; break; // ALL EQUAL: pure tie-break test
case 2: *v=(float)((e/2)%8)/8.f; break; // massed duplicates (paired+cyclic ties)
case 3: *v=(e%2)?1e-40f:2e-40f; break; // denormal logits (flush behavior must match)
case 4: *v=(float)(rand()%3)/2.f; break; // 3-level ties across the whole row
// boundary-forcing: elevate the LAST 4 valid experts (E-4..E-1) to near-max choice
// so they are guaranteed in the top-8. For an E whose per-lane block size doesn't
// divide E evenly, E-1's lane straddles the E boundary (real indices below E,
// sentinel -1e30f at/above E in the SAME ch[] block) -- e.g. E=200: per=ceil(200/
// 32)=7, lane 28 owns indices 196..202, of which 196-199 are real and 200-202 are
// sentinel. Forcing selection onto 196-199 exercises exactly that lane's per-index
// e<E boundary check, rather than hoping random data happens to land there.
case 5: *v=(e>=E-4)?1.0f:(float)(rand()%10000)/10000.f; break;
default: *v=(float)(rand()%10000)/10000.f; break;
}
}
if (mode==1) for (int e=0;e<E;e++) bias[e]=0.25f; // choice fully tied too
if (mode==3) for (int e=0;e<E;e++) bias[e]=(e%3)?3e-40f:-3e-40f; // denormal bias as well
if (mode==5) for (int e=E-4;e<E;e++) bias[e]=1.0f; // combined choice = 2.0, max possible
std::vector<int> is((size_t)S*K), ip((size_t)S*K); std::vector<float> ws((size_t)S*K), wp((size_t)S*K);
std::vector<int> ks(S), kp(S);
if (!coli_metal_rtop8(0,sig.data(),bias.data(),S,E,K,Ksel,topp,normk,rscale,is.data(),ws.data(),ks.data()) ||
!coli_metal_rtop8(1,sig.data(),bias.data(),S,E,K,Ksel,topp,normk,rscale,ip.data(),wp.data(),kp.data())) {
printf(" %-34s FAIL (rtop8 runner returned 0)\n", name); return 1; }
int ok = memcmp(is.data(),ip.data(),(size_t)S*K*4)==0 &&
memcmp(ws.data(),wp.data(),(size_t)S*K*4)==0 && // bitwise: same ops, same order
memcmp(ks.data(),kp.data(),(size_t)S*4)==0;
if (mode==5 && ok) {
// Don't just trust the input design -- confirm the straddling lane's valid segment
// (E-4..E-1) was actually selected, in EVERY row, so this case can't silently
// degrade into an unrelated pass if the input construction above ever changes.
for (int s=0;s<S;s++) { int seen=0;
for (int k=0;k<K;k++) if (ip[(size_t)s*K+k]>=E-4 && ip[(size_t)s*K+k]<E) seen++;
if (seen<4) { printf(" %-34s *** boundary segment not exercised (row %d saw %d/4) -- test setup bug\n", name, s, seen); return 1; }
}
}
if (!ok) {
printf(" %-34s *** MISMATCH\n", name);
for (int s=0;s<S;s++){ printf(" row %d keff %d/%d:",s,ks[s],kp[s]);
for(int k=0;k<K;k++) printf(" [%d]%d/%d %.6g/%.6g",k,is[s*K+k],ip[s*K+k],ws[s*K+k],wp[s*K+k]);
printf("\n"); }
return 1;
}
printf(" %-34s ok (serial==parallel bitwise, S=%d E=%d)\n", name, S, E);
return 0;
}
int main(void) {
if (!coli_metal_init()) { printf("Metal unavailable (skipping)\n"); return 0; }
printf("Metal backend kernel tests:\n");
@@ -215,6 +277,32 @@ int main(void) {
fail |= run_attn(1, 37, "attn S=1 pos=37");
fail |= run_attn(4, 12, "attn S=4 pos=12 (MTP)");
fail |= run_attn(3, 0, "attn S=3 pos=0");
printf("Metal top-8 select serial-vs-parallel tests (exact-match contract, E=256):\n");
fail |= run_rtop8(0, 1, 256, 0.0f, 1, 1.0f, "top8 generic S=1");
fail |= run_rtop8(0, 4, 256, 0.0f, 1, 1.0f, "top8 generic S=4");
fail |= run_rtop8(1, 1, 256, 0.0f, 1, 1.0f, "top8 ALL-EQUAL ties");
fail |= run_rtop8(2, 4, 256, 0.0f, 1, 1.0f, "top8 massed dup ties S=4");
fail |= run_rtop8(4, 2, 256, 0.0f, 0, 2.5f, "top8 3-level ties rscale");
fail |= run_rtop8(3, 1, 256, 0.0f, 1, 1.0f, "top8 denormal logits");
fail |= run_rtop8(0, 1, 256, 0.01f, 1, 1.0f, "top8 topp=0.01 (Ke=1 edge)");
fail |= run_rtop8(2, 1, 256, 0.6f, 1, 1.0f, "top8 topp=0.6 tied weights");
fail |= run_rtop8(0, 4, 256, 0.999f,1, 1.75f, "top8 topp=0.999 S=4");
fail |= run_rtop8(1, 2, 256, 0.5f, 0, 1.0f, "top8 topp on ALL-EQUAL");
printf("Metal top-8 select expert-count-generality tests (E!=256, REAP/#428 motivated):\n");
fail |= run_rtop8(0, 1, 168, 0.0f, 1, 1.0f, "top8 E=168 (REAP) generic S=1");
fail |= run_rtop8(2, 4, 168, 0.0f, 1, 1.0f, "top8 E=168 (REAP) massed dup ties S=4");
fail |= run_rtop8(0, 1, 24, 0.0f, 1, 1.0f, "top8 E=24 (<32 lane width) generic");
fail |= run_rtop8(1, 1, 24, 0.0f, 1, 1.0f, "top8 E=24 (<32 lane width) ALL-EQUAL ties");
// E=200: per-lane block size ceil(200/32)=7, and 200 is NOT a multiple of 7, so lane 28
// (indices 196..202) straddles the boundary -- 196-199 real, 200-202 sentinel -1e30f in
// the SAME ch[] block. E=24 and E=168 above both happen to divide evenly by their own
// per (24/1, 168/6), so no case before this one exercised a lane whose ch[] mixes real
// and sentinel indices. mode 5 deterministically forces indices 196-199 into the top-8
// (see run_rtop8) and asserts they were actually selected, rather than hoping random
// data lands there -- proving by TEST what the per-index `e<E` check was proven by
// reading (both kernels agree bitwise on a selection that requires that check to fire).
fail |= run_rtop8(5, 4, 200, 0.0f, 1, 1.0f, "top8 E=200 (lane straddles E boundary)");
fail |= run_rtop8(0, 1, 257, 0.0f, 1, 1.0f, "top8 E=257 (>256, auto-serial-fallback)");
printf(fail? "metal backend tests: FAILED\n" : "metal backend tests: ok\n");
coli_metal_shutdown();
return fail;
c/tests/test_decode_batch.c
+6
View File
@@ -44,6 +44,12 @@ static void test_submit_header(void)
assert(coli_submit_parse("SUBMIT 1 0 16777216 3 1 1", &sub));
assert(!coli_submit_parse("SUBMIT 1 0 16777217 3 1 1", &sub));
assert(!coli_submit_parse("SUBMIT 1 0 2 3 1 1 trailing", &sub));
/* optional 7th field: per-request grammar length (0 when absent) */
assert(coli_submit_parse("SUBMIT 42 3 17 64 0.7 0.95", &sub) && sub.gbytes == 0);
assert(coli_submit_parse("SUBMIT 42 3 17 64 0.7 0.95 512", &sub) && sub.gbytes == 512);
assert(coli_submit_parse("SUBMIT 42 3 17 64 0.7 0.95 1048576", &sub));
assert(!coli_submit_parse("SUBMIT 42 3 17 64 0.7 0.95 1048577", &sub));
assert(!coli_submit_parse("SUBMIT 42 3 17 64 0.7 0.95 512 extra", &sub));
}
int main(void)
c/tests/test_efficiency_report.py
+315
View File
@@ -0,0 +1,315 @@
#!/usr/bin/env python3
"""Exhaustive optimization dossier for a colibri engine run.
This is NOT a pass/fail test. It runs the engine with every instrumentation flag
on (PROF, COLI_CUDA_PROFILE, CACHE_ROUTE, DISK_SPLIT, LOOKA) and prints a section-
by-section report answering, for each subsystem:
WHAT is doing it — which phase/kernel/tier
WHEN it is doing it — how much of decode wall-time it owns
WITH WHAT — the config/weights/tier it used
IS IT INEFFICIENT? — a verdict, with the threshold
HOW TO IMPROVE — the concrete knob, named
Activation (opt-in only — NOT in `make test`):
COLI_EFFICIENCY_MODEL=<model_dir> python tests/test_efficiency_report.py
Optional env:
COLI_EFFICIENCY_CUDA=1 also exercise the CUDA dense/expert tiers
COLI_EFFICIENCY_NGEN=N decode tokens (default 24)
COLI_EFFICIENCY_RAM_GB=N RAM budget (default 28)
COLI_EFFICIENCY_VRAM_GB=N CUDA expert-tier budget GB (default 4)
COLI_EFFICIENCY_PROMPT=... prompt (default: a code-gen prompt)
Exit code is always 0 (it's a dossier, not a gate). Lines marked FLAG point at
the most likely lever to move tok/s for the observed bottleneck.
"""
import os
import sys
import time
from pathlib import Path
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
from tools.efficiency import run_engine, disk_wait_share # noqa: E402
# --- advisory thresholds (the "IS IT INEFFICIENT?" lines) ---
DISK_WAIT_DOMINANT = 0.40 # >40% decode waiting on expert reads -> I/O-bound
LOW_HIT_RATE = 0.30 # <30% cache hit -> thrashing (cap too small)
LOW_ROUTE_AGREE = 0.80 # <80% routing overlap -> prefetch is guessing wrong
HIGH_TAIL_RATIO = 3.0 # p99 > 3x p50 -> decode stalls (I/O hiccups / KV grow)
LOW_MTP_ACCEPT = 0.20 # <20% MTP acceptance -> draft decoder is dead weight
VRAM_WASTE_CALLS = 0 # experts pinned in VRAM but 0 calls served
def _flag(ok): return "OK " if ok else "FLAG"
def _bar(frac, width=24):
"""A simple ASCII bar for share visualization."""
n = max(0, min(width, round(frac * width)))
return "#" * n + "." * (width - n)
def _line(label, value, flag=None, note=""):
tag = f" [{flag}]" if flag else ""
print(f" {label:<22} {value}{tag} {note}" if note else f" {label:<22} {value}{tag}")
def main() -> int:
model = os.environ.get("COLI_EFFICIENCY_MODEL")
if not model:
print(__doc__)
print("\nNot activated: set COLI_EFFICIENCY_MODEL=<model_dir> to run.")
return 0
model = str(Path(model).resolve())
if not Path(model).is_dir():
print(f"ERROR: {model} is not a directory", file=sys.stderr)
return 0
ngen = int(os.environ.get("COLI_EFFICIENCY_NGEN", "24"))
ram_gb = os.environ.get("COLI_EFFICIENCY_RAM_GB", "28")
vram_gb = os.environ.get("COLI_EFFICIENCY_VRAM_GB", "4")
prompt = os.environ.get(
"COLI_EFFICIENCY_PROMPT",
"Write a Python function that computes the factorial of a number. "
"Include error handling and a docstring.")
use_cuda = os.environ.get("COLI_EFFICIENCY_CUDA") == "1"
# Turn ON every instrumentation flag so the dossier has maximum detail.
# These are all observability toggles (PROF/COLI_CUDA_PROFILE/CACHE_ROUTE/
# DISK_SPLIT/LOOKA); none change the computed output.
overlay = dict(
NGEN=str(ngen), TEMP="0", RAM_GB=ram_gb, PROMPT=prompt,
PROF="1", CACHE_ROUTE="1", DISK_SPLIT="1", LOOKA="1", ROUTE_AGREE="1",
)
if use_cuda:
overlay.update(COLI_CUDA="1", COLI_GPU="0", CUDA_DENSE="1",
COLI_CUDA_PROFILE="1", CUDA_EXPERT_GB=vram_gb)
print("=" * 78)
print(f"OPTIMIZATION DOSSIER — {Path(model).name}")
print(f" mode : {'CUDA (dense+expert tiers)' if use_cuda else 'CPU-only'} "
f"ngen : {ngen} ram : {ram_gb} GB" +
(f" vram : {vram_gb} GB" if use_cuda else ""))
print("=" * 78)
t0 = time.time()
t, proc = run_engine(overlay, snap=model, timeout=3600.0)
wall = time.time() - t0
flags = [] # collected FLAG lines for the summary
print(f"\n[0] RUN")
_line("wall clock", f"{wall:.0f}s")
_line("exit code", proc.returncode,
None if proc.returncode == 0 else "FLAG",
"" if proc.returncode == 0 else "non-zero exit")
if proc.returncode != 0:
print(" stderr tail:")
for ln in proc.stderr.strip().splitlines()[-8:]:
print(f" {ln}")
return 0
# ---------------------------------------------------------------- [1] WHO ----
print(f"\n[1] PROVENANCE — what is running, on what, with what config")
if t.get("machine"):
m = t["machine"]
_line("CPU", m["cpu"])
_line("cores / omp", f"{m['cores']} cores")
_line("backend", m["backend"])
if t.get("load"):
ld = t["load"]
_line("model load time", f"{ld['load_s']:.2f}s")
_line("resident dense", f"{ld['resident_dense_mb']:.1f} MB")
_line("layers / experts", f"{ld['layers']} layers, {ld['experts']} experts")
_line("MTP", f"{ld['mtp_status']} (draft={ld['draft']})")
if t.get("config_str"):
_line("resolved config", t["config_str"])
print(" (this is the EFFECTIVE config after auto-budgeting — not your env verbatim)")
# ---------------------------------------------------------------- [2] SPEED --
print(f"\n[2] THROUGHPUT — is it fast, is the tail healthy")
if t.get("tok_s") is not None:
_line("tok/s", f"{t['tok_s']:.3f}")
else:
flags.append("throughput line missing — engine output format may have changed")
if t.get("latency"):
la = t["latency"]
_line("decode forwards", f"{int(la['forwards'])}")
_line("p50 / p90", f"{la['p50_ms']:.1f} / {la['p90_ms']:.1f} ms")
_line("p99 / max", f"{la['p99_ms']:.1f} / {la['max_ms']:.1f} ms")
tail_ok = la["p99_ms"] <= HIGH_TAIL_RATIO * la["p50_ms"]
_line("tail ratio (p99/p50)", f"{la['p99_ms']/max(la['p50_ms'],1e-9):.2f}x",
_flag(tail_ok),
"high tail = decode stalls (I/O hiccups, KV growth, re-pin)")
if not tail_ok:
flags.append(f"tail latency p99={la['p99_ms']:.1f}ms >> p50={la['p50_ms']:.1f}ms "
"(look for REPIN swaps or disk stalls)")
# ---------------------------------------------------------------- [3] TIME ---
print(f"\n[3] WHERE TIME GOES — what is doing it, when (share of decode)")
ts = t.get("time_shares")
prof = t.get("profile")
if ts:
order = [("io", "expert-disk I/O", DISK_WAIT_DOMINANT, "the cache is too small / disk is slow"),
("matmul", "expert matmul", 0.40, "compute-bound; more cores or a GPU expert tier"),
("attention", "attention", 0.35, "context length is the cost; lower CTX"),
("head", "lm_head", 0.10, "vocab projection; unusual to dominate"),
("other", "other", 0.30, "scheduling / KV bookkeeping overhead")]
for key, name, thresh, lever in order:
f = ts[key]
ok = f < thresh
_line(name, f"{f:5.0%} {_bar(f)}", _flag(ok),
"" if ok else f"->{lever}")
if not ok:
flags.append(f"{name} dominates ({f:.0%}) -> {lever}")
if t.get("verdict"):
print(f" engine verdict : {t['verdict']}")
elif prof:
print(" (no [PROF] time shares — set PROF=1 for phase percentages)")
if prof:
print(" absolute seconds :")
for k in ("disk", "expert_matmul", "attention", "lm_head", "other"):
_line(k, f"{prof[k]:.3f}s")
# attention sub-breakdown: how is attention being read
ab = t.get("attn_breakdown")
if ab:
print(f"\n[3a] ATTENTION BREAKDOWN — how the attention phase is spent")
atot = sum(ab.values()) or 1.0
for k, label in (("proj_rope", "projection + RoPE"),
("score_sm_value", "score-softmax-value"),
("out_proj", "output projection")):
_line(label, f"{ab[k]:.3f}s ({ab[k]/atot:.0%} of attn)")
# ---------------------------------------------------------------- [4] CACHE --
print(f"\n[4] EXPERT CACHE — is the cache efficient")
hit = t.get("hit_pct")
if hit is not None:
ok = hit >= LOW_HIT_RATE * 100
_line("hit rate", f"{hit:.1f}%", _flag(ok),
"" if ok else "<30% = thrashing; raise RAM_GB or cap")
if not ok:
flags.append(f"cache hit {hit:.1f}% is low -> raise RAM_GB (or cap), add PIN_GB")
el = t.get("experts_loaded")
if el:
per_tok = el["per_tok"]
_line("experts loaded/token", f"{per_tok:.1f}")
_line(" per-layer", f"{el['per_layer']:.2f} across {el['n_sparse_layers']} sparse layers")
_line(" baseline", f"topk={el['baseline_topk']} active experts/token")
base_topk = el["baseline_topk"]
if base_topk > 0 and per_tok > 2 * base_topk:
flags.append(f"loading {per_tok:.0f} experts/token vs topk={base_topk} "
"-> redundant I/O; cache is re-fetching evicted experts")
# ---------------------------------------------------------------- [5] DISK ---
print(f"\n[5] DISK I/O — is I/O the bottleneck, and where")
eio = t.get("expert_io")
if eio:
_line("total fetched", f"{eio['gb_fetched']:.3f} GB")
_line("per token", f"{eio['mb_per_tok']:.1f} MB/token")
_line("disk throughput", f"{eio['gb_per_s']:.2f} GB/s over the run")
_line("read service", f"{eio['read_service_s']:.2f}s (on I/O threads)")
_line("felt wait", f"{eio['felt_wait_s']:.2f}s (stall compute felt)")
if eio["felt_wait_s"] > eio["read_service_s"] * 0.5 and eio["read_service_s"] > 0:
flags.append("felt wait is a large fraction of read service -> PIPE=1 may not be "
"overlapping fully, or DIRECT=1 on NVMe")
ds = t.get("disk_split")
if ds:
print(f"\n[5a] DISK-LOAD SPLIT — which decode phase reads the bytes")
_line("draft phase", f"{ds['draft']} loads")
_line("absorb phase", f"{ds['absorb']} loads")
_line("verify/main", f"{ds['verify_main']} loads")
_line("MTP-layer bytes", f"{ds['mtp_loads']} loads, {ds['mtp_gb']:.2f} GB")
_line("main-layer bytes", f"{ds['main_loads']} loads, {ds['main_gb']:.2f} GB")
if ds.get("mtp_bytes_pct") is not None:
_line("MTP share of bytes", f"{ds['mtp_bytes_pct']:.1f}%")
share = disk_wait_share(t)
if share is not None:
ok = share < DISK_WAIT_DOMINANT
_line("disk-wait share", f"{share:.0%}", _flag(ok),
"" if ok else "I/O-bound (see levers in [3])")
# ---------------------------------------------------------------- [6] ROUTE --
print(f"\n[6] ROUTING QUALITY — is the router / prefetch accurate")
ra = t.get("route_agree")
if ra:
ok = ra["agree_pct"] >= LOW_ROUTE_AGREE * 100
_line("route_agree", f"{ra['agree_pct']:.1f}% overlap with true top-K",
_flag(ok),
"" if ok else "prefetch is guessing wrong; CACHE_ROUTE params may need tuning")
_line("route_kl", f"{ra['kl']:.4f} mean KL (lower = closer to true routing)")
if not ok:
flags.append(f"route_agree {ra['agree_pct']:.1f}% low -> tune ROUTE_J/M/P, "
"or prefetch is hurting more than helping")
sw = t.get("swap")
if sw:
_line("cache swaps", f"{sw['swaps']}/{sw['slots']} ({sw['pct']:.1f}%)",
None, "high swap = churn between turns")
la = t.get("lookahead")
if la:
print(f"\n[6a] ROUTING PREDICTABILITY — recall of true experts in predicted top-8")
print(" (which predictor should drive prefetch? highest recall wins)")
for row in la:
_line(row["predictor"][:34], f"{row['pct']:5.1f}% ({row['hit']}/{row['tot']})")
# ---------------------------------------------------------------- [7] SPEC ---
print(f"\n[7] SPECULATION — is the draft decoder pulling weight")
sp = t.get("speculation")
if sp:
_line("tokens/forward", f"{sp['tok_per_fw']:.2f} (>1.0 means speculation helps)")
_line("forwards/tokens", f"{sp['forwards']} forwards for {sp['tokens']} tokens")
# Speculation helps only if acceptance is high enough that tok/forward > 1.
# tok_per_fw already == 1.0 when nothing verifies, so judge by acceptance.
ok = sp["mtp_accept_pct"] >= LOW_MTP_ACCEPT * 100
_line("MTP acceptance", f"{sp['mtp_accept_pct']:.0f}%", _flag(ok),
"" if ok else "<20% -> drafts rarely verify; DRAFT=0 may be faster")
if not ok:
flags.append(f"MTP acceptance {sp['mtp_accept_pct']:.0f}% low -> "
"drafts cost more I/O than they save; try DRAFT=0")
# ---------------------------------------------------------------- [8] GPU ----
print(f"\n[8] GPU TIERS — is the GPU actually used")
cuda = t.get("cuda") or {}
if not cuda.get("enabled"):
print(" (CUDA not enabled — CPU-only run)")
else:
if cuda.get("resident_tensors") is not None:
_line("resident dense tensors", f"{cuda['resident_tensors']} tensors, "
f"{cuda['resident_gb']:.2f} GB")
if cuda.get("expert_count") is not None:
waste = cuda["calls_served"] <= VRAM_WASTE_CALLS
_line("expert tier", f"{cuda['expert_count']} experts pinned "
f"({cuda['expert_gb']:.2f} GB)", _flag(not waste))
_line(" calls served", f"{cuda['calls_served']} from VRAM",
_flag(not waste),
"" if not waste else "WASTE: pinned but never routed -> lower CUDA_EXPERT_GB")
if waste:
flags.append("VRAM expert tier has 0 calls served -> experts pinned but unused; "
"PIN stats may not match this workload")
if cuda.get("groups"):
g = cuda["groups"]
_line("expert groups", f"{g['calls']} calls, {g['experts']} experts, "
f"{g['rows']} rows ({g['experts_per_call']:.1f} experts/call)")
if cuda.get("groups_timing"):
gt = cuda["groups_timing"]
_line("GPU timing", f"H2D {gt['h2d_ms']:.1f} ms | kernel {gt['kernel_ms']:.1f} ms | "
f"D2H {gt['d2h_ms']:.1f} ms")
if gt["h2d_ms"] + gt["d2h_ms"] > gt["kernel_ms"]:
flags.append("CUDA H2D+D2H > kernel time -> transfer-bound; "
"consider larger expert tier to keep weights resident")
# ---------------------------------------------------------------- summary ----
print("\n" + "=" * 78)
if flags:
print(f" {len(flags)} FLAG(s) — the most likely levers to move tok/s:")
for f in flags:
print(f" - {f}")
else:
print(" no flags — every measured subsystem is within advisory thresholds.")
print("=" * 78)
return 0
if __name__ == "__main__":
raise SystemExit(main())
c/tests/test_env_defaults.py
+84 -2
View File
@@ -32,9 +32,16 @@ def args(**over):
class EnvDefaultsTest(unittest.TestCase):
def env_for_with(self, environ, platform):
def env_for_with(self, environ, platform, cuda=False):
"""Run env_for on a bare-chat args() under a faked env + platform.
cuda=False by default so the existing default-I/O tests stay
deterministic: the Windows auto-enable branch calls cuda_binary() and
(if True) discover_gpus(), both of which reach the real machine — faking
False keeps these tests independent of the host's GPU."""
with mock.patch.dict(os.environ, environ, clear=True), \
mock.patch.object(sys, "platform", platform):
mock.patch.object(sys, "platform", platform), \
mock.patch.object(coli, "cuda_binary", return_value=cuda):
return coli.env_for(args())
def test_win32_sets_measured_defaults(self):
@@ -64,5 +71,80 @@ class EnvDefaultsTest(unittest.TestCase):
self.assertNotIn(k, e)
class CudaAutoEnableTest(unittest.TestCase):
"""Windows bare `coli chat` (no --gpu/--vram/--auto-tier) used to ALWAYS run
CPU-only even on a CUDA build with a GPU present. env_for now auto-enables
CUDA on win32 when cuda_binary() is True and a GPU is discoverable; falls
back to CPU with a warning if nvidia-smi (discover_gpus) is missing; stays
silent on a CPU build; and never touches the Linux path."""
def _env_for(self, platform, cuda, gpus, plan=None):
# Patch discover_gpus / build_plan / environment_for_plan at the
# resource_plan module (env_for imports them lazily on each call, so the
# patches are live when those imports run). Stubbing the planner keeps
# the test independent of a real model dir (args().model == "X").
import resource_plan
a = args()
GPB = 1024 ** 3
if plan is None:
plan = {"tiers": {"ram": {"budget_bytes": 16 * GPB, "cache_slots_per_layer": 4},
"vram": {"budget_bytes": int(8.0 * GPB), "devices": gpus}}}
def fake_environment_for_plan(p, env, cuda_enabled=True):
# Mirror the real contract: size CUDA_EXPERT_GB from the plan's VRAM
# budget (this is the value env_for propagates into the engine env).
r = dict(env)
if cuda_enabled and p["tiers"]["vram"]["devices"] and p["tiers"]["vram"]["budget_bytes"] > 0:
r["CUDA_EXPERT_GB"] = f"{p['tiers']['vram']['budget_bytes'] / GPB:.3f}"
return r
with mock.patch.dict(os.environ, {}, clear=True), \
mock.patch.object(sys, "platform", platform), \
mock.patch.object(coli, "cuda_binary", return_value=cuda), \
mock.patch.object(resource_plan, "discover_gpus", return_value=gpus), \
mock.patch.object(resource_plan, "build_plan", return_value=plan), \
mock.patch.object(resource_plan, "environment_for_plan",
side_effect=fake_environment_for_plan):
return coli.env_for(a)
def _fake_gpu(self, index=0, name="NVIDIA GeForce RTX 5070 Ti",
total_mib=16384, free_mib=15000):
return {"index": index, "name": name,
"total_bytes": total_mib * 1024 * 1024,
"free_bytes": free_mib * 1024 * 1024}
def test_win32_auto_enables_cuda_when_gpu_present(self):
e = self._env_for("win32", cuda=True, gpus=[self._fake_gpu()])
self.assertEqual(e["COLI_CUDA"], "1")
self.assertEqual(e["COLI_GPUS"], "0")
# VRAM budget is sized from free VRAM by build_plan (real minus reserve),
# so it must be present and positive — never a guess or zero.
self.assertIn("CUDA_EXPERT_GB", e)
self.assertGreater(float(e["CUDA_EXPERT_GB"]), 0.0)
# Dense offload is an explicit opt-in (matches --auto-tier): not set here.
self.assertNotIn("CUDA_DENSE", e)
def test_win32_falls_back_to_cpu_when_nvidia_smi_missing(self):
# coli_cuda.dll present (cuda=True) but nvidia-smi absent (no GPUs found)
# -> warn + CPU-only, never crash, never set COLI_CUDA.
e = self._env_for("win32", cuda=True, gpus=[])
self.assertNotIn("COLI_CUDA", e)
self.assertNotIn("COLI_GPUS", e)
self.assertNotIn("CUDA_EXPERT_GB", e)
def test_win32_cpu_build_stays_silent(self):
# No coli_cuda.dll (cuda=False) -> CPU build, nothing GPU-related emitted.
e = self._env_for("win32", cuda=False, gpus=[self._fake_gpu()])
self.assertNotIn("COLI_CUDA", e)
self.assertNotIn("COLI_GPUS", e)
def test_linux_bare_chat_not_auto_enabled(self):
# The auto-enable is scoped to win32: a Linux bare chat with a GPU
# present must NOT turn CUDA on (Linux keeps the explicit-flag UX).
e = self._env_for("linux", cuda=True, gpus=[self._fake_gpu()])
self.assertNotIn("COLI_CUDA", e)
self.assertNotIn("CUDA_EXPERT_GB", e)
if __name__ == "__main__":
unittest.main()
c/tests/test_grouped_g4_cuda.cu
+108
View File
@@ -0,0 +1,108 @@
/* Grouped-int4 (fmt=4) CUDA kernel oracle (#334).
*
* Feeds random offset-binary nibble weights + [O, ng] group scales through
* grouped_hidden_g4_dual / grouped_down_g4 and checks against a CPU reference
* that replicates matmul_i4_grouped's semantics (value = nibble - 8, per-group
* partial dot x scale). Covers gs=64, a non-divisible tail group, and a
* per-row (gs=0) member riding in the same launch — the fmt=2-compat case.
*
* The device buffers get the same XOR 0x88 offset->signed conversion the
* upload path applies, so the kernels are exercised exactly as deployed.
*
* Build: nvcc -O2 -std=c++17 -arch=native tests/test_grouped_g4_cuda.cu -o tests/test_grouped_g4
*/
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <cmath>
#include <cuda_runtime.h>
#include "../backend_cuda.cu"
static void cpu_gemv_g4(const uint8_t *q,const float *sc,int K,int O,int gs,
const float *x,float *y){
int rb=(K+1)/2, ng=gs>0?(K+gs-1)/gs:1, egs=gs>0?gs:K;
for(int o=0;o<O;o++){
const uint8_t *row=q+(size_t)o*rb; const float *scl=sc+(size_t)o*ng;
double a=0;
for(int g=0; g*egs<K; g++){
int base=g*egs, glen=egs; if(base+glen>K) glen=K-base;
double p=0;
for(int i=base;i<base+glen;i++){
uint8_t v=row[i>>1]; int n=(i&1)?(v>>4):(v&15);
p+=(double)x[i]*(n-8);
}
a+=p*scl[g];
}
y[o]=(float)a;
}
}
int main(void){
srand(7);
const int D=200, I=96, gs=64; /* tail group: 200 % 64 = 8 */
const int COUNT=3; /* expert 0,1: fmt4 gs=64; expert 2: per-row (gs=0) */
const int rbD=(D+1)/2, rbI=(I+1)/2;
const int ngD=(D+gs-1)/gs, ngI=(I+gs-1)/gs;
int trials=50, bad=0;
for(int t=0;t<trials;t++){
GroupDesc host[COUNT]; float *xs; cudaMallocManaged(&xs,(size_t)COUNT*D*4);
float *gate,*up,*y; cudaMallocManaged(&gate,(size_t)COUNT*I*4);
cudaMallocManaged(&up,(size_t)COUNT*I*4); cudaMallocManaged(&y,(size_t)COUNT*D*4);
uint8_t *qg[COUNT],*qu[COUNT],*qd[COUNT]; float *sg[COUNT],*su[COUNT],*sd[COUNT];
uint8_t *hg[COUNT],*hu[COUNT],*hd[COUNT]; float *hgs[COUNT],*hus[COUNT],*hds[COUNT];
for(int c=0;c<COUNT;c++){
int cgs = c==2 ? 0 : gs;
int cngD = cgs? ngD:1, cngI = cgs? ngI:1;
hg[c]=(uint8_t*)malloc((size_t)I*rbD); hu[c]=(uint8_t*)malloc((size_t)I*rbD);
hd[c]=(uint8_t*)malloc((size_t)D*rbI);
hgs[c]=(float*)malloc((size_t)I*cngD*4); hus[c]=(float*)malloc((size_t)I*cngD*4);
hds[c]=(float*)malloc((size_t)D*cngI*4);
for(size_t i=0;i<(size_t)I*rbD;i++){ hg[c][i]=rand()&255; hu[c][i]=rand()&255; }
for(size_t i=0;i<(size_t)D*rbI;i++) hd[c][i]=rand()&255;
for(size_t i=0;i<(size_t)I*cngD;i++){ hgs[c][i]=.01f+.05f*(rand()/(float)RAND_MAX);
hus[c][i]=.01f+.05f*(rand()/(float)RAND_MAX); }
for(size_t i=0;i<(size_t)D*cngI;i++) hds[c][i]=.01f+.05f*(rand()/(float)RAND_MAX);
cudaMalloc(&qg[c],(size_t)I*rbD); cudaMalloc(&qu[c],(size_t)I*rbD); cudaMalloc(&qd[c],(size_t)D*rbI);
cudaMalloc(&sg[c],(size_t)I*cngD*4); cudaMalloc(&su[c],(size_t)I*cngD*4); cudaMalloc(&sd[c],(size_t)D*cngI*4);
cudaMemcpy(qg[c],hg[c],(size_t)I*rbD,cudaMemcpyHostToDevice);
cudaMemcpy(qu[c],hu[c],(size_t)I*rbD,cudaMemcpyHostToDevice);
cudaMemcpy(qd[c],hd[c],(size_t)D*rbI,cudaMemcpyHostToDevice);
offset_to_signed_s4<<<64,256>>>(qg[c],(size_t)I*rbD);
offset_to_signed_s4<<<64,256>>>(qu[c],(size_t)I*rbD);
offset_to_signed_s4<<<64,256>>>(qd[c],(size_t)D*rbI);
cudaMemcpy(sg[c],hgs[c],(size_t)I*cngD*4,cudaMemcpyHostToDevice);
cudaMemcpy(su[c],hus[c],(size_t)I*cngD*4,cudaMemcpyHostToDevice);
cudaMemcpy(sd[c],hds[c],(size_t)D*cngI*4,cudaMemcpyHostToDevice);
host[c]={qg[c],qu[c],qd[c],sg[c],su[c],sd[c],4,4,4,1,c,cgs,cgs,cgs};
}
for(size_t i=0;i<(size_t)COUNT*D;i++) xs[i]=(rand()/(float)RAND_MAX-.5f)*2.f;
GroupDesc *ddesc; cudaMalloc(&ddesc,sizeof(host));
cudaMemcpy(ddesc,host,sizeof(host),cudaMemcpyHostToDevice);
dim3 hgd((unsigned)I,1,(unsigned)COUNT),ogd((unsigned)D,1,(unsigned)COUNT);
grouped_hidden_g4_dual<<<hgd,256>>>(gate,up,xs,ddesc,I,D);
grouped_down_g4<<<ogd,256>>>(y,gate,ddesc,D,I);
if(cudaDeviceSynchronize()!=cudaSuccess){ printf("FAIL cuda\n"); return 1; }
for(int c=0;c<COUNT;c++){
int cgs=c==2?0:gs;
float rg[512],ru[512],ry[512];
cpu_gemv_g4(hg[c],hgs[c],D,I,cgs,xs+(size_t)c*D,rg);
cpu_gemv_g4(hu[c],hus[c],D,I,cgs,xs+(size_t)c*D,ru);
for(int o=0;o<I;o++){
if(fabsf(gate[(size_t)c*I+o]-rg[o])>1e-3f*(fabsf(rg[o])+1e-3f)||
fabsf(up[(size_t)c*I+o]-ru[o])>1e-3f*(fabsf(ru[o])+1e-3f)) bad++;
}
cpu_gemv_g4(hd[c],hds[c],I,D,cgs,(float*)gate+(size_t)c*I,ry);
for(int o=0;o<D;o++)
if(fabsf(y[(size_t)c*D+o]-ry[o])>1e-3f*(fabsf(ry[o])+1e-3f)) bad++;
}
for(int c=0;c<COUNT;c++){ cudaFree(qg[c]);cudaFree(qu[c]);cudaFree(qd[c]);
cudaFree(sg[c]);cudaFree(su[c]);cudaFree(sd[c]);
free(hg[c]);free(hu[c]);free(hd[c]);free(hgs[c]);free(hus[c]);free(hds[c]); }
cudaFree(ddesc);cudaFree(xs);cudaFree(gate);cudaFree(up);cudaFree(y);
}
printf("grouped-g4 oracle: %d trials x %d experts (gs=64 + tail + per-row member), %d mismatches\n",
trials,COUNT,bad);
if(bad){ printf("FAIL\n"); return 1; }
printf("OK\n"); return 0;
}
c/tests/test_inefficiency.py
+257
View File
@@ -0,0 +1,257 @@
"""Inefficiency / regression tests for the colibri engine (tiny model, asserted).
These run against the bundled glm_tiny model (~0.6 MB resident, ~0.1s/run) and
gate CI: a regression here means something broke. They run on a plain CPU-only
`glm.exe` build; the CUDA_* tests auto-skip if the engine wasn't built with
CUDA_DLL=1 (see tests/README_efficiency.md for the build command).
The signals under test, and the inefficiency each catches:
- tok/s floor : a throughput regression (broken build / bad config)
- profile phases sum : telemetry accounting bug (other balloons)
- disk-wait not dominant: a tiny resident model should never be I/O-bound
- CPU determinism : greedy decode is reproducible (no stray RNG/threading)
- CUDA init path : COLI_CUDA=1 initializes and does not silently exit 2
- CUDA dense uses VRAM : CUDA_DENSE=1 actually uploads tensors (no silent CPU fallback)
- CPU vs CUDA TF-match : identical weights+inputs → identical argmax (kernel bug guard)
"""
import os
import shutil
import unittest
from pathlib import Path
from tools.efficiency import (
parse_run, run_engine, disk_wait_share, tf_agreement,
TINY_TOK_S_FLOOR, MAX_DISK_WAIT_SHARE, MIN_CPU_CUDA_AGREEMENT,
)
HERE = Path(__file__).resolve().parent
C_DIR = HERE.parent
ENGINE = C_DIR / "glm.exe"
TINY = C_DIR / "glm_tiny"
def _engine_present() -> bool:
"""True iff BOTH the built engine AND the tiny fixture are available.
These tests need glm.exe (a build artifact) AND glm_tiny/ (a generated
fixture, gitignored). CI runs `make check` = "dependency-free tests, no
model downloads" (workflow .github/workflows/check.yml, by design #140), so
neither is present there and these tests must SKIP rather than fail. They
run locally after `make glm.exe` (glm_tiny ships alongside the source, or
is regenerated by tools/make_glm_oracle.py).
"""
return ENGINE.exists() and (TINY / "config.json").exists()
def _skip_reason() -> str:
"""Name exactly which prerequisite is missing, so the skip is actionable."""
if not ENGINE.exists():
return "glm.exe not built (run: make glm.exe)"
if not (TINY / "config.json").exists():
return "glm_tiny fixture absent (gitignored; ship it locally or run tools/make_glm_oracle.py)"
return ""
def _cuda_available() -> bool:
"""True iff the engine binary has the CUDA loader compiled in AND the DLL is present.
The host is built with -DCOLI_CUDA only when CUDA_DLL=1 (Makefile). A binary
built without it embeds the string "this binary is CPU-only; rebuild" and
exits 2 on any CUDA env var — so we detect the CPU-only build by scanning
the binary for that marker (avoids a slow ldd/strings on every import; we
only read enough to find it). On Windows the DLL is also required
(backend_loader.c loads it at runtime); on Linux it's direct-linked.
"""
if not _engine_present():
return False
try:
# Read once; the marker is near the read-only string table. 256 KB is
# plenty for this string and avoids loading a 1 MB binary into memory.
with open(ENGINE, "rb") as f:
blob = f.read(2 * 1024 * 1024)
if b"this binary is CPU-only" in blob:
return False # CPU-only build: COLI_CUDA=1 would exit 2.
except OSError:
pass
# Windows: DLL required at runtime. Linux: direct-linked (no DLL).
if os.name == "nt":
return (C_DIR / "coli_cuda.dll").exists()
import subprocess
try:
out = subprocess.run(["ldd", str(ENGINE)], capture_output=True, text=True)
return "libcudart" in out.stdout
except (FileNotFoundError, OSError):
return False
@unittest.skipUnless(_engine_present(), _skip_reason() or "glm.exe + glm_tiny required")
class TinyEfficiencyTest(unittest.TestCase):
"""Asserted regression tests on the resident tiny model. Gates CI."""
def _run(self, **overlay):
return run_engine(overlay, engine=str(ENGINE), snap=str(TINY))[0]
# -- telemetry contract ---------------------------------------------------
def test_telemetry_parses(self):
"""A REPLAY run must emit the throughput + PROFILE lines the suite keys on.
If this fails, either the engine changed its output format (update the
parsers in tools/efficiency.py) or the run crashed early."""
t = self._run(REPLAY="1", TEMP="0", NGEN="4")
self.assertEqual(t["returncode"], 0, f"engine exited non-zero:\n{t['stderr']}")
self.assertIn("tok_s", t["parsed"], f"missing tok/s line:\n{t['stderr']}")
self.assertIn("profile", t["parsed"], f"missing PROFILE line:\n{t['stderr']}")
self.assertIn("hit_pct", t["parsed"], f"missing expert hit line:\n{t['stderr']}")
self.assertIsNotNone(t["tok_s"])
self.assertIsNotNone(t["profile"])
# -- throughput floor -----------------------------------------------------
def test_tiny_tok_s_floor(self):
"""Tiny decode must beat TINY_TOK_S_FLOOR.
The tiny model is fully resident and runs ~200 tok/s; the 20 tok/s
default floor is a 10x margin that catches broken builds or a pathological
config cascade (the cap=1 trap from ISSUE_new_model_resource_regression.md)
without flapping on machine noise."""
t = self._run(REPLAY="1", TEMP="0", NGEN="8")
self.assertEqual(t["returncode"], 0, f"engine exited non-zero:\n{t['stderr']}")
self.assertGreaterEqual(
t["tok_s"], TINY_TOK_S_FLOOR,
f"tok/s {t['tok_s']:.1f} below floor {TINY_TOK_S_FLOOR} "
f"(regression, or a config cascade starving the cache)",
)
# -- accounting sanity ----------------------------------------------------
def test_profile_phases_present_and_nonneg(self):
"""Every PROFILE phase must be present and non-negative.
`other` can go slightly negative from timer overhead (the engine allows
it), but a large negative means the timers are double-counting."""
t = self._run(REPLAY="1", TEMP="0", NGEN="4")
p = t["profile"]
for phase in ("disk", "expert_matmul", "attention", "lm_head"):
self.assertGreaterEqual(p[phase], -0.01, f"{phase} went negative: {p}")
# 'other' is a residual; allow a small negative from timer overlap.
self.assertGreaterEqual(p["other"], -0.05, f"other too negative (double-count): {p}")
# -- disk-wait not dominant on a resident model ---------------------------
def test_disk_wait_not_dominant(self):
"""A fully-resident tiny model must NOT be I/O-bound.
Everything fits in RAM; the expert-disk wait share should be ~0. If it
exceeds MAX_DISK_WAIT_SHARE, the cache/I/O path regressed — on a real
model this same regression would make decode I/O-bound (the exact
failure mode the [PROF] verdict flags)."""
t = self._run(REPLAY="1", TEMP="0", NGEN="8", PROF="1")
self.assertIn("time_shares", t["parsed"], f"missing [PROF] time shares:\n{t['stderr']}")
share = disk_wait_share(t)
self.assertIsNotNone(share)
self.assertLess(
share, MAX_DISK_WAIT_SHARE,
f"expert-I/O share {share:.0%} on a resident model — I/O path regressed",
)
# -- determinism ----------------------------------------------------------
def test_cpu_vs_cpu_determinism(self):
"""Two greedy REPLAY runs with the same seed produce identical telemetry.
TEMP=0 = greedy (no sampling), so tok/s and hit-rate must be reproducible.
A drift here means non-determinism crept into the decode path (stray
threading, uninitialized state) — which on a real model would make A/B
comparisons meaningless."""
a = self._run(REPLAY="1", TEMP="0", NGEN="8", SEED="1")
b = self._run(REPLAY="1", TEMP="0", NGEN="8", SEED="1")
self.assertEqual(a["returncode"], 0)
self.assertEqual(b["returncode"], 0)
self.assertEqual(a["hit_pct"], b["hit_pct"], "greedy hit-rate drifted between runs")
# tok/s within 25% — exact equality is too strict across scheduler noise.
self.assertLess(abs(a["tok_s"] - b["tok_s"]) / max(a["tok_s"], b["tok_s"]), 0.25)
@unittest.skipUnless(_cuda_available(),
_skip_reason() or "CUDA build not present (run: make clean && make glm.exe CUDA_DLL=1 && make cuda-dll)")
class TinyCudaEfficiencyTest(unittest.TestCase):
"""CUDA-path regression tests on the tiny model. Skip unless CUDA built.
glm_tiny is small and fully resident, so CUDA here is fast and exercises the
real GPU code path (init, dense upload, kernel correctness) without the
long load time or memory pressure of the full model. These guard the
silent-failure modes that are otherwise invisible:
- COLI_CUDA=1 silently falling back to CPU (loader/DLL missing)
- CUDA_DENSE=1 uploading nothing
- a CUDA kernel producing different argmax than CPU on identical inputs
"""
def _run(self, **overlay):
return run_engine(overlay, engine=str(ENGINE), snap=str(TINY))[0]
def test_cuda_init_path(self):
"""COLI_CUDA=1 must initialize the device and NOT exit 2.
Exit 2 is the engine's "requested backend is unavailable" path
(glm.c: g_cuda_enabled check). A clean init prints the [CUDA] device
banner to stderr. If this fails, the DLL is broken or the loader can't
resolve symbols (ABI drift between backend_cuda.h and the dll)."""
t = self._run(COLI_CUDA="1", COLI_GPU="0", REPLAY="1", TEMP="0", NGEN="4")
self.assertNotEqual(t["returncode"], 2,
f"engine refused CUDA backend:\n{t['stderr']}")
self.assertTrue(t["cuda"]["enabled"],
f"no [CUDA] device banner on stderr:\n{t['stderr']}")
def test_cuda_dense_uses_vram(self):
"""CUDA_DENSE=1 must actually upload dense tensors to VRAM.
The minimal GPU-exercising config (per backend_loader.c analysis):
COLI_CUDA=1 + CUDA_DENSE=1. Without CUDA_DENSE the dense path stays on
CPU and [CUDA] resident set reports 0 tensors — a silent no-op. This
catches that regression: after the run, resident_tensors > 0."""
t = self._run(COLI_CUDA="1", COLI_GPU="0", CUDA_DENSE="1",
REPLAY="1", TEMP="0", NGEN="4")
self.assertEqual(t["returncode"], 0, f"engine exited non-zero:\n{t['stderr']}")
rt = t["cuda"]["resident_tensors"]
self.assertIsNotNone(rt, f"no [CUDA] resident set line:\n{t['stderr']}")
self.assertGreater(rt, 0,
f"CUDA_DENSE=1 but {rt} tensors resident — silent CPU fallback")
def test_cpu_vs_cuda_tf_match(self):
"""CPU and CUDA teacher-forcing must agree on most positions (DIRECTLY).
Both paths prefill the SAME oracle sequence on the SAME weights, so their
argmaxes should match position-for-position — but not exactly: the two
backends accumulate dot-products in different orders (x86 SIMD vs CUDA
kernel), so a few near-tied logits flip. That divergence is expected
numeric behavior, not a kernel bug. A *catastrophic* kernel regression
(wrong GEMM, wrong scale, wrong fmt) would collapse agreement toward
random (~1/vocab = ~4%); the MIN_CPU_CUDA_AGREEMENT floor (default 70%)
catches that while tolerating harmless drift.
We compare CPU-vs-CUDA *directly* (not via the oracle match-counts),
because both backends differ from the oracle at different positions and
the summary line can't tell "CPU≠CUDA" from "CPU≠oracle"."""
import json
ref = json.loads((C_DIR / "ref_glm.json").read_text())
oracle = ref["tf_pred"]
cpu = self._run(TF="1", TEMP="0")
cuda = self._run(COLI_CUDA="1", COLI_GPU="0", CUDA_DENSE="1", TF="1", TEMP="0")
self.assertEqual(cpu["returncode"], 0, f"CPU TF run failed:\n{cpu['stderr']}")
self.assertEqual(cuda["returncode"], 0, f"CUDA TF run failed:\n{cuda['stderr']}")
self.assertIn("tf_mismatches", cpu["parsed"], "CPU run missing per-position mismatches")
self.assertIn("tf_mismatches", cuda["parsed"], "CUDA run missing per-position mismatches")
agree, diff = tf_agreement(cpu, cuda, oracle)
self.assertGreaterEqual(
agree, MIN_CPU_CUDA_AGREEMENT,
f"CPU-vs-CUDA argmax agreement {agree:.0%} below floor "
f"{MIN_CPU_CUDA_AGREEMENT:.0%} — CUDA kernel likely regressed. "
f"Differing positions: {diff[:10]}{'...' if len(diff)>10 else ''}",
)
if __name__ == "__main__":
unittest.main()
c/tests/test_int3.c
+147
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/* int3-g64 (fmt=5) tests: pack layout, dequant round-trip vs plain-C reference,
* matmul_i3 (NEON + scalar tail) vs reference dequant-matmul, per-row helpers,
* the .qs-size format tag, and the quality claim in miniature (per-group int3
* beats per-row int4 on rows with outliers — the #132 result this format ships). */
#define main coli_glm_main_unused
#include "../colibri.c"
#undef main
#include <stdio.h>
#include <string.h>
#include <math.h>
static int fails = 0;
#define CHECK(c) do{ if(!(c)){ printf("FAIL %s:%d: %s\n", __FILE__, __LINE__, #c); fails++; } }while(0)
static uint64_t rng = 0x9E3779B97F4A7C15ull;
static float rndf(void){ rng ^= rng << 13; rng ^= rng >> 7; rng ^= rng << 17;
return ((int64_t)(rng & 0xFFFFF) - 0x80000) / (float)0x80000; }
/* reference: quantize like pack_int3_g64 but keep dequantized f32 (mirrors
* quant_ablation._quant_last_dim(bits=3, group=64)) */
static void ref_i3_dequant(const float *w, float *dq, int O, int I){
int64_t ng=i3_groups(I);
for(int o=0;o<O;o++) for(int64_t g=0;g<ng;g++){
int base=(int)(g*I3_GROUP), n=I-base<I3_GROUP?I-base:I3_GROUP;
float amax=0; for(int k=0;k<n;k++){ float a=fabsf(w[(int64_t)o*I+base+k]); if(a>amax)amax=a; }
float s=amax/3.f; if(s<1e-8f)s=1e-8f;
for(int k=0;k<n;k++){
int v=(int)lrintf(w[(int64_t)o*I+base+k]/s); if(v>3)v=3; if(v<-4)v=-4;
dq[(int64_t)o*I+base+k]=(float)v*s;
}
}
}
static void unpack_i3(const uint8_t *q3, const float *s, float *dq, int O, int I){
int64_t ng=i3_groups(I), rb=i3_rowbytes(I);
for(int o=0;o<O;o++) for(int64_t g=0;g<ng;g++){
const uint8_t *lo=q3+(int64_t)o*rb+g*I3_GBYTES, *hi=lo+16;
int base=(int)(g*I3_GROUP), n=I-base<I3_GROUP?I-base:I3_GROUP;
for(int k=0;k<n;k++){
unsigned u=((lo[k>>2]>>((k&3)*2))&3)|(((hi[k>>3]>>(k&7))&1)<<2);
dq[(int64_t)o*I+base+k]=(float)((int)u-4)*s[(int64_t)o*ng+g];
}
}
}
int main(void){
const int Is[]={64,128,192,100,65,7168}; /* incl. short tail groups and one real GLM dim */
enum { O=7, MAXI=7168 };
static float w[(int64_t)O*MAXI], dq_ref[(int64_t)O*MAXI], dq_pk[(int64_t)O*MAXI];
static float x[4*MAXI], y_ref[4*O], y_ker[4*O];
static uint8_t q3[(int64_t)O*(MAXI/64+1)*24];
static float sc[(int64_t)O*(MAXI/64+1)];
for(unsigned c=0;c<sizeof Is/sizeof *Is;c++){
int I=Is[c];
for(int64_t i=0;i<(int64_t)O*I;i++) w[i]=rndf()*0.05f;
w[3]=1.7f; w[(int64_t)2*I+5]=-2.2f; /* outliers */
/* 1. pack -> unpack == reference quantize-dequantize, bit for bit */
pack_int3_g64(w, q3, sc, O, I);
ref_i3_dequant(w, dq_ref, O, I);
unpack_i3(q3, sc, dq_pk, O, I);
int bad=0;
for(int64_t i=0;i<(int64_t)O*I;i++) if(dq_pk[i]!=dq_ref[i]) bad++;
CHECK(bad==0);
/* 2. matmul_i3 == matmul over the dequantized reference (fp tolerance:
* NEON fma order differs from the scalar reference loop) */
for(int S=1;S<=4;S+=3){
for(int64_t i=0;i<(int64_t)S*I;i++) x[i]=rndf();
matmul_i3(y_ker, x, q3, sc, S, I, O);
for(int s=0;s<S;s++) for(int o=0;o<O;o++){
double a=0; for(int i=0;i<I;i++) a+=(double)dq_ref[(int64_t)o*I+i]*x[(int64_t)s*I+i];
y_ref[s*O+o]=(float)a;
}
for(int i=0;i<S*O;i++){
float d=fabsf(y_ker[i]-y_ref[i]), m=fabsf(y_ref[i])>1?fabsf(y_ref[i]):1;
if(d/m>2e-4f){ CHECK(!"matmul_i3 mismatch"); break; }
}
}
/* 3. QT plumbing: qt_alloc(bits=3) -> qt_fill -> matmul_qt & qt_bytes & helpers */
QT t; qt_alloc(&t, O, I, 3);
CHECK(t.fmt==5);
qt_fill(&t, w, 3);
CHECK(qt_bytes(&t)==(int64_t)O*i3_rowbytes(I)+(int64_t)O*i3_groups(I)*4);
matmul_qt(y_ker, x, &t, 1);
for(int o=0;o<O;o++){
double a=0; for(int i=0;i<I;i++) a+=(double)dq_ref[(int64_t)o*I+i]*x[i];
float d=fabsf(y_ker[o]-(float)a), m=fabsf((float)a)>1?fabsf((float)a):1;
CHECK(d/m<=2e-4f);
}
float acc[MAXI]; memset(acc,0,I*sizeof(float));
qt_addrow(&t, 2, 0.5f, acc);
for(int i=0;i<I;i++) CHECK(fabsf(acc[i]-0.5f*dq_ref[(int64_t)2*I+i])<=1e-6f);
float yr[3];
qt_matvec_rows(&t, 1, 3, x, yr);
for(int j=0;j<3;j++){
double a=0; for(int i=0;i<I;i++) a+=(double)dq_ref[(int64_t)(1+j)*I+i]*x[i];
float d=fabsf(yr[j]-(float)a), m=fabsf((float)a)>1?fabsf((float)a):1;
CHECK(d/m<=2e-4f);
}
/* 4. format resolution through the #413 gate: fmt=5 is tagged by its distinct
* WEIGHT byte count. int3-g64 and grouped-int4-at-gs=64 carry the SAME scale
* cardinality O*ceil(I/64), so the pair (weight bytes, scale bytes) must
* disambiguate: same scales, int4 weights -> fmt=4/gs=64; int3 weights -> fmt=5.
* Only well-posed for I > 256: below that, O row scales legitimately match a
* 1-group grouped layout too (detect_group_size probes gs up to 256), so
* per-row vs grouped is not distinguishable from byte counts alone. */
if(I>256){ int gs=-1;
int64_t ns_g64=(int64_t)O*i3_groups(I)*4, ns_row=(int64_t)O*4;
CHECK(qt_resolve_fmt("t.i3", O, I, (int64_t)O*i3_rowbytes(I), ns_g64, &gs)==5);
CHECK(gs==0);
CHECK(qt_resolve_fmt("t.i8", O, I, (int64_t)O*I, ns_row, &gs)==1);
CHECK(qt_resolve_fmt("t.i4", O, I, (int64_t)O*((I+1)/2), ns_row, &gs)==2);
CHECK(qt_resolve_fmt("t.i4g", O, I, (int64_t)O*((I+1)/2), ns_g64, &gs)==4);
CHECK(gs==64);
CHECK(qt_resolve_fmt("t.i2", O, I, (int64_t)O*((I+3)/4), ns_row, &gs)==3); }
free(t.q4); free(t.s);
}
/* 5. quality in miniature: on rows with outliers, per-group int3 must beat
* per-row int4 on reconstruction RMS (the #132 finding this format ships). */
{
int I=1024;
for(int64_t i=0;i<(int64_t)O*I;i++) w[i]=rndf()*0.02f;
for(int o=0;o<O;o++) w[(int64_t)o*I+(o*37)%I]=1.5f; /* one outlier per row */
ref_i3_dequant(w, dq_ref, O, I);
QT t4; qt_alloc(&t4, O, I, 4); qt_fill(&t4, w, 4);
double e3=0, e4=0;
for(int o=0;o<O;o++) for(int i=0;i<I;i++){
float w4; { const uint8_t *q=t4.q4+(int64_t)o*((I+1)/2); uint8_t b=q[i>>1];
int v=(i&1)?((int)(b>>4)-8):((int)(b&0xF)-8); w4=(float)v*t4.s[o]; }
double d3=w[(int64_t)o*I+i]-dq_ref[(int64_t)o*I+i], d4=w[(int64_t)o*I+i]-w4;
e3+=d3*d3; e4+=d4*d4;
}
CHECK(e3 < e4);
printf(" outlier-rows RMS: int3-g64 %.3e < int4-row %.3e (ratio %.2f)\n",
sqrt(e3/((double)O*I)), sqrt(e4/((double)O*I)), sqrt(e4/e3));
free(t4.q4); free(t4.s);
}
if(fails){ printf("int3-g64 tests: %d FAILED\n", fails); return 1; }
printf("int3-g64 tests: ok\n");
return 0;
}
c/tests/test_int3_convert.py
+70
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"""quant_int3_g64 (tools/convert_fp8_to_int4.py): pack layout + round-trip.
Decodes the packed bytes with an independent NumPy decoder implementing the
fmt=5 spec (16B low plane / 8B high plane per 64-group, v+4, per-group f32
scale) and checks the dequantized result equals the reference
quantize-dequantize (same math as quant_ablation._quant_last_dim(3, 64)).
The C side of the same layout is covered by tests/test_int3.c.
"""
import os, sys, unittest
try:
import numpy as np
except ImportError:
raise unittest.SkipTest("numpy not installed")
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "tools"))
from convert_fp8_to_int4 import quant_int3_g64
def decode(packed, scales, O, I, group=64):
ng = (I + group - 1) // group
b = packed.reshape(O, ng, 24)
lo, hi = b[:, :, :16], b[:, :, 16:]
k = np.arange(group)
lov = (lo[:, :, k >> 2] >> ((k & 3) * 2)[None, None, :]) & 3
hiv = (hi[:, :, k >> 3] >> (k & 7)[None, None, :]) & 1
v = (lov | (hiv << 2)).astype(np.int64) - 4
dq = v.astype(np.float64) * scales.reshape(O, ng, 1).astype(np.float64)
return dq.reshape(O, ng * group)[:, :I]
def reference(w, group=64):
"""same math as quant_int3_g64 (which works in f32), replayed exactly, then
dequantized in f64 so it matches decode() bit for bit"""
O, I = w.shape
ng = (I + group - 1) // group
pad = ng * group - I
wp = np.pad(w, ((0, 0), (0, pad))) if pad else w
g = wp.reshape(O, ng, group)
s = np.maximum(np.abs(g).max(axis=2, keepdims=True) / 3.0, 1e-8).astype(np.float32)
q = np.clip(np.rint(g / s), -4, 3).astype(np.int64)
return (q.astype(np.float64) * s.astype(np.float64)).reshape(O, ng * group)[:, :I]
class Int3ConvertTest(unittest.TestCase):
def test_round_trip(self):
rng = np.random.default_rng(7)
for I in (64, 128, 100, 65, 7168):
w = (rng.standard_normal((5, I)) * 0.05).astype(np.float32)
w[0, 3] = 1.7; w[2, min(5, I - 1)] = -2.2
packed, scales = quant_int3_g64(w)
ng = (I + 63) // 64
self.assertEqual(packed.size, 5 * ng * 24)
self.assertEqual(scales.size, 5 * ng)
np.testing.assert_allclose(decode(packed, scales, 5, I),
reference(w), rtol=0, atol=0)
def test_outliers_beat_row_int4(self):
rng = np.random.default_rng(11)
w = (rng.standard_normal((8, 1024)) * 0.02).astype(np.float32)
for o in range(8): w[o, (o * 37) % 1024] = 1.5
packed, scales = quant_int3_g64(w)
e3 = float(((decode(packed, scales, 8, 1024) - w) ** 2).mean())
s4 = np.maximum(np.abs(w).max(axis=1, keepdims=True) / 7.0, 1e-8)
w4 = np.clip(np.rint(w / s4), -8, 7) * s4
e4 = float(((w4 - w) ** 2).mean())
self.assertLess(e3, e4)
if __name__ == "__main__":
unittest.main()
c/tests/test_int3_load.c
+103
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@@ -0,0 +1,103 @@
/* Loader-seam test for fmt=5: writes a real .safetensors file containing an
* int3-g64 tensor (U8 payload + per-GROUP .qs) next to an int4 control tensor
* (per-row .qs), indexes it with st_init, loads both through qt_from_disk, and
* checks the byte-count/.qs-size format inference picks fmt=5 vs fmt=2 correctly
* and the loaded weights dequantize identically to pack_int3_g64's output. */
#define main coli_glm_main_unused
#include "../colibri.c"
#undef main
#include <stdio.h>
#include <string.h>
#include <sys/stat.h>
#include <unistd.h>
static int fails = 0;
#define CHECK(c) do{ if(!(c)){ printf("FAIL %s:%d: %s\n", __FILE__, __LINE__, #c); fails++; } }while(0)
static uint64_t rng = 0xA5A5A5A55A5A5A5Aull;
static float rndf(void){ rng ^= rng << 13; rng ^= rng >> 7; rng ^= rng << 17;
return ((int64_t)(rng & 0xFFFFF) - 0x80000) / (float)0x80000; }
static void deq4(const QT *t, float *dq){
for(int o=0;o<t->O;o++) for(int i=0;i<t->I;i++){
if(t->fmt==5){
int64_t g=i/I3_GROUP; const uint8_t *lo=t->q4+(int64_t)o*i3_rowbytes(t->I)+g*I3_GBYTES, *hi=lo+16;
int k=i%I3_GROUP;
unsigned u=((lo[k>>2]>>((k&3)*2))&3)|(((hi[k>>3]>>(k&7))&1)<<2);
dq[(int64_t)o*t->I+i]=(float)((int)u-4)*t->s[(int64_t)o*i3_groups(t->I)+g];
} else { /* fmt2 */
uint8_t b=t->q4[(int64_t)o*((t->I+1)/2)+(i>>1)];
int v=(i&1)?((int)(b>>4)-8):((int)(b&0xF)-8);
dq[(int64_t)o*t->I+i]=(float)v*t->s[o];
}
}
}
int main(void){
enum { O=5, I=320 }; /* 5 groups per row; I > 256 so the per-row int4
* control stays fmt=2 (detect_group_size probes
* gs up to 256: any smaller I would make O row
* scales match a legitimate 1-group layout) */
int64_t ng=i3_groups(I), rb=i3_rowbytes(I);
static float w[O*I];
for(int i=0;i<O*I;i++) w[i]=rndf()*0.05f;
w[7]=1.9f;
static uint8_t q3[O*(I/64)*24]; static float s3[O*(I/64)];
pack_int3_g64(w, q3, s3, O, I);
static uint8_t q4b[O*((I+1)/2)]; static float s4[O];
pack_int4(w, q4b, s4, O, I, 4);
/* write a minimal single-shard safetensors file */
const char *dir="tests/tmp_int3_snap";
#ifdef _WIN32
mkdir(dir);
#else
mkdir(dir, 0755);
#endif
char path[256]; snprintf(path,sizeof path,"%s/model.safetensors",dir);
int64_t nb3=(int64_t)O*rb, ns3=(int64_t)O*ng*4, nb4=(int64_t)O*((I+1)/2), ns4=(int64_t)O*4;
char hdr[1024];
int hl=snprintf(hdr,sizeof hdr,
"{\"w3\":{\"dtype\":\"U8\",\"shape\":[%lld],\"data_offsets\":[0,%lld]},"
"\"w3.qs\":{\"dtype\":\"F32\",\"shape\":[%lld],\"data_offsets\":[%lld,%lld]},"
"\"w4\":{\"dtype\":\"U8\",\"shape\":[%lld],\"data_offsets\":[%lld,%lld]},"
"\"w4.qs\":{\"dtype\":\"F32\",\"shape\":[%lld],\"data_offsets\":[%lld,%lld]}}",
(long long)nb3,(long long)nb3,
(long long)(O*ng),(long long)nb3,(long long)(nb3+ns3),
(long long)nb4,(long long)(nb3+ns3),(long long)(nb3+ns3+nb4),
(long long)O,(long long)(nb3+ns3+nb4),(long long)(nb3+ns3+nb4+ns4));
FILE *f=fopen(path,"wb");
if(!f){ printf("FAIL: cannot create %s (run from c/, like tools/run_tests.py does)\n", path); return 1; }
uint64_t hlen=(uint64_t)hl;
fwrite(&hlen,8,1,f); fwrite(hdr,1,hl,f);
fwrite(q3,1,(size_t)nb3,f); fwrite(s3,1,(size_t)ns3,f);
fwrite(q4b,1,(size_t)nb4,f); fwrite(s4,1,(size_t)ns4,f);
fclose(f);
static Model gm; /* only gm.S is used by qt_from_disk */
st_init(&gm.S, dir);
QT t3; memset(&t3,0,sizeof t3);
qt_from_disk(&gm,"w3",O,I,8,0,&t3);
CHECK(t3.fmt==5);
static float dq_load[O*I], dq_ref[O*I];
deq4(&t3,dq_load);
QT tr={.fmt=5,.q4=q3,.s=s3,.O=O,.I=I};
deq4(&tr,dq_ref);
CHECK(memcmp(dq_load,dq_ref,sizeof dq_ref)==0);
QT t4; memset(&t4,0,sizeof t4);
qt_from_disk(&gm,"w4",O,I,8,0,&t4);
CHECK(t4.fmt==2); /* control: row-scale int4 still detected */
deq4(&t4,dq_load);
QT tr4={.fmt=2,.q4=q4b,.s=s4,.O=O,.I=I};
deq4(&tr4,dq_ref);
CHECK(memcmp(dq_load,dq_ref,sizeof dq_ref)==0);
unlink(path); rmdir(dir);
if(fails){ printf("int3 loader tests: %d FAILED\n", fails); return 1; }
printf("int3 loader tests: ok\n");
return 0;
}
c/tests/test_iq3_pack.py
+96
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"""fmt=5 codec oracle (#452 ladder step 2).
Checks the properties the container, the converter and the decode kernels all
depend on: exact byte budget, deterministic encode, decode agreeing with a
straight-from-the-spec reader, sign parity closure, and reconstruction quality
matching the ablation that chose this scheme (#453).
"""
import os
import sys
import unittest
# The runtime path is dependency-free by design and CI keeps it that way, so the
# offline-tooling tests skip rather than fail where numpy is absent.
np = None
P = None
try:
import numpy as np
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "tools"))
import iq3_pack as P
except ImportError: # pragma: no cover - exercised only on dependency-free CI
pass
def ref_decode(packed, K):
"""Independent reader written straight from the layout comment — deliberately
naive and loop-based, so a shared bug in the vectorized path shows up."""
g = P.grid()
nsb = K // P.QK
rows = packed.reshape(-1, nsb * P.BLOCK_BYTES)
out = np.zeros((len(rows), K), dtype=np.float32)
for r in range(len(rows)):
for sb in range(nsb):
base = sb * P.BLOCK_BYTES
d = float(rows[r, base + 96:base + 98].copy().view(np.float16)[0])
for ib in range(P.QK // P.SUB):
word = int(np.ascontiguousarray(
rows[r, base + 64 + ib * 4:base + 64 + ib * 4 + 4]).view(np.uint32)[0])
db = d * (0.5 + ((word >> 28) & 0xF)) * 0.5
for l in range(4):
seven = (word >> (7 * l)) & 0x7F
bits = [(seven >> j) & 1 for j in range(7)]
bits.append(sum(bits) & 1) # odd parity closes the 8th
idx = int(rows[r, base + ib * 8 + l * 2 + 0])
idx2 = int(rows[r, base + ib * 8 + l * 2 + 1])
mags = list(g[idx]) + list(g[idx2])
for j in range(8):
pos = sb * P.QK + ib * P.SUB + l * 8 + j
out[r, pos] = mags[j] * db * (-1.0 if bits[j] else 1.0)
return out
@unittest.skipIf(P is None, "numpy not available (offline-tooling test)")
class TestIq3Pack(unittest.TestCase):
def setUp(self):
np.random.seed(4242)
self.x = (np.random.randn(6, 1024) * 0.05).astype(np.float32)
def test_byte_budget(self):
self.assertEqual(P.BLOCK_BYTES, 98)
self.assertAlmostEqual(P.bpw(), 3.0625, places=6)
packed = P.encode(self.x)
self.assertEqual(packed.shape, (6, 1024 // P.QK * 98))
self.assertEqual(packed.dtype, np.uint8)
def test_encode_is_deterministic(self):
self.assertTrue(np.array_equal(P.encode(self.x), P.encode(self.x)))
def test_decode_matches_spec_reader(self):
packed = P.encode(self.x)
fast = P.decode(packed, 1024)
slow = ref_decode(packed, 1024)
self.assertTrue(np.allclose(fast, slow, rtol=1e-6, atol=1e-8),
f"max |Δ| = {np.abs(fast - slow).max()}")
def test_sign_parity_closes(self):
"""Every 8-weight block must have an even number of negatives — that is
what lets the 8th sign be derived instead of stored."""
y = P.decode(P.encode(self.x), 1024)
neg = (y < 0).reshape(-1, 8).sum(-1)
self.assertTrue(np.all(neg % 2 == 0), "a block stored odd negatives")
def test_reconstruction_quality(self):
y = P.decode(P.encode(self.x), 1024)
rel = np.sqrt(((y - self.x) ** 2).mean()) / np.sqrt((self.x ** 2).mean())
# the torch model that won the #453 A/B measures ~0.195 on this input class
self.assertLess(rel, 0.25, f"rel-RMSE {rel:.4f} — worse than the chosen scheme")
self.assertGreater(rel, 0.05, f"rel-RMSE {rel:.4f} — implausibly good, check the test")
def test_shape_guard(self):
with self.assertRaises(ValueError):
P.encode(np.zeros((2, 300), dtype=np.float32))
if __name__ == "__main__":
unittest.main()
c/tests/test_openai_server.py
+30 -6
View File
@@ -20,8 +20,8 @@ class FakeEngine:
self.calls = []
def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0,
cancelled=None):
self.calls.append((prompt, maximum, temperature, top_p, cache_slot))
cancelled=None, grammar=None):
self.calls.append((prompt, maximum, temperature, top_p, cache_slot, grammar))
on_text("Hé")
on_text("llo")
return {"prompt_tokens": 7, "completion_tokens": 2, "length_limited": False}
@@ -34,7 +34,7 @@ class BlockingEngine(FakeEngine):
self.release = threading.Event()
def generate(self, prompt, maximum, temperature, top_p, on_text, cache_slot=0,
cancelled=None):
cancelled=None, grammar=None):
self.entered.set()
self.release.wait(2)
return super().generate(prompt, maximum, temperature, top_p, on_text, cache_slot,
@@ -71,11 +71,11 @@ class TemplateTest(unittest.TestCase):
def test_validates_generation_limits(self):
self.assertEqual(generation_options({"max_tokens": 4, "temperature": 0, "top_p": 1}, 8),
(4, 0.0, 1.0))
(4, 0.0, 1.0, None))
# max_tokens above the server cap is clamped, not rejected (#260): OpenAI
# clients default to large values; erroring breaks them.
self.assertEqual(generation_options({"max_tokens": 9, "temperature": 0, "top_p": 1}, 8),
(8, 0.0, 1.0))
(8, 0.0, 1.0, None))
# non-positive / non-int max_tokens is still a hard error
with self.assertRaises(APIError):
generation_options({"max_tokens": 0}, 8)
@@ -84,7 +84,31 @@ class TemplateTest(unittest.TestCase):
with self.assertRaises(APIError):
generation_options({"top_p": math.inf}, 8)
self.assertEqual(generation_options({"temperature": None, "top_p": None}, 8),
(8, 0.7, 0.9))
(8, 0.7, 0.9, None))
# response_format -> grammar plumbing (draft source, never a constraint)
opts = generation_options({"max_tokens": 4, "response_format": {"type": "json_object"}}, 8)
self.assertIn("root ::=", opts[3])
schema = {"type": "object", "properties": {"a": {"type": "string"}}, "required": ["a"]}
opts = generation_options({"max_tokens": 4, "response_format":
{"type": "json_schema", "json_schema": {"schema": schema}}}, 8)
self.assertEqual(json.loads(opts[3]), schema)
opts = generation_options({"max_tokens": 4, "response_format":
{"type": "gbnf", "grammar": 'root ::= "x"'}}, 8)
self.assertEqual(opts[3], 'root ::= "x"')
with self.assertRaises(APIError):
generation_options({"response_format": {"type": "yaml"}}, 8)
with self.assertRaises(APIError):
generation_options({"response_format": {"type": "json_schema", "json_schema": {}}}, 8)
with self.assertRaises(APIError): # non-dict response_format
generation_options({"response_format": "json"}, 8)
with self.assertRaises(APIError): # empty gbnf
generation_options({"response_format": {"type": "gbnf", "grammar": " "}}, 8)
with self.assertRaises(APIError): # oversized grammar (> 1 MiB pre-check)
generation_options({"response_format": {"type": "gbnf", "grammar": "x" * ((1 << 20) + 1)}}, 8)
# malformed GBNF passes the gateway by design: the ENGINE fail-softs it
# (draft source only — bad grammar costs the speedup, never the request)
opts = generation_options({"response_format": {"type": "gbnf", "grammar": "not a grammar ::="}}, 8)
self.assertEqual(opts[3], "not a grammar ::=")
class ProtocolTest(unittest.TestCase):
c/tests/test_pipe_block.c
+169
View File
@@ -0,0 +1,169 @@
/* COLI_PIPE_BLOCK: the pipe pool's condvar waiter must be observably
* equivalent to the sched_yield spin it replaces — same bytes land in the
* same ws[] slots, and no interleaving loses a wakeup (the worker RELEASE-
* stores ready[] BEFORE taking mx to broadcast; the waiter re-checks under
* the lock, so a flag set between its fast-path check and the wait cannot
* be missed). Both waiters are exercised against the same on-disk fixture,
* alternating parked waits (wait issued before the load finishes) with
* fast-path waits (load already done), across enough generations to cycle
* the pool's gen-tagged cursor.
*
* Also pins the PIPE_WORKERS => PIPE implication table: fires ONLY when
* PIPE is unset in the env AND the platform default left the pipe off AND
* PIPE_WORKERS parses positive (PIPE_WORKERS=0/empty/negative must NOT
* silently enable a clamped 1-worker pipe). */
#include <errno.h>
#include <fcntl.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>
#define main coli_glm_main_unused
#include "../colibri.c"
#undef main
static int fail(const char *s){ fprintf(stderr,"FAIL: %s\n",s); return 1; }
enum { NE=8, LAYER=1 }; /* experts 0..NE-1 on one MoE layer */
/* per-expert file image: [gate 12][up 12][down 12][gate.qs 12][up.qs 12][down.qs 16] */
enum { WB=12, QS_G=12, QS_U=12, QS_D=16, ESZ=3*WB+QS_G+QS_U+QS_D };
static unsigned char wbyte(int e,int j){ return (unsigned char)(e*31+j+1); }
static float scale(int e,int i){ return (float)(e*8+i)+0.5f; }
#define TMPF "test_pipe_block.tmp"
static int write_fixture(void){
FILE *w=fopen(TMPF,"wb"); if(!w) return fail("create temp");
for(int e=0;e<NE;e++){
unsigned char img[ESZ];
for(int j=0;j<3*WB;j++) img[j]=wbyte(e,j);
float sc[(QS_G+QS_U+QS_D)/4];
for(int i=0;i<(int)(sizeof(sc)/sizeof(sc[0]));i++) sc[i]=scale(e,i);
memcpy(img+3*WB,sc,sizeof(sc));
if(fwrite(img,1,ESZ,w)!=ESZ){ fclose(w); return fail("expert fixture write"); }
}
fclose(w);
return 0;
}
static int build_fixture(Model *m,int fd){
m->c.hidden=4; m->c.moe_inter=3; m->ebits=8;
m->S.n=NE*6; m->S.cap=NE*6; m->S.t=calloc(NE*6,sizeof(st_tensor));
if(!m->S.t) return fail("tensor metadata allocation");
const char *proj[3]={"gate_proj","up_proj","down_proj"};
int sbytes[3]={QS_G,QS_U,QS_D};
for(int e=0;e<NE;e++){
int64_t wo=(int64_t)e*ESZ, so=wo+3*WB;
for(int k=0;k<3;k++){
char name[300];
snprintf(name,sizeof(name),"model.layers.%d.mlp.experts.%d.%s.weight",LAYER,e,proj[k]);
m->S.t[e*6+k]=(st_tensor){strdup(name),fd,wo,WB,3,WB}; wo+=WB;
size_t n=strlen(name); memcpy(name+n,".qs",4);
m->S.t[e*6+3+k]=(st_tensor){strdup(name),fd,so,sbytes[k],2,sbytes[k]/4}; so+=sbytes[k];
}
}
return 0;
}
static int check_slot(ESlot *s,int e){
if(s->eid!=e || s->g.fmt!=1 || s->u.fmt!=1 || s->d.fmt!=1){
fprintf(stderr," slot: eid=%d (want %d) fmt g/u/d=%d/%d/%d (want 1/1/1)\n",
s->eid,e,s->g.fmt,s->u.fmt,s->d.fmt);
return 1;
}
const unsigned char *g=(const unsigned char*)s->g.q8,
*u=(const unsigned char*)s->u.q8,
*d=(const unsigned char*)s->d.q8; /* q8 is int8_t; compare raw bytes */
for(int j=0;j<WB;j++)
if(g[j]!=wbyte(e,j) || u[j]!=wbyte(e,WB+j) || d[j]!=wbyte(e,2*WB+j)){
fprintf(stderr," slot e=%d weight byte %d: g=%d/%d u=%d/%d d=%d/%d (got/want)\n",e,j,
g[j],wbyte(e,j),u[j],wbyte(e,WB+j),d[j],wbyte(e,2*WB+j));
return 1;
}
for(int i=0;i<3;i++)
if(s->g.s[i]!=scale(e,i) || s->u.s[i]!=scale(e,3+i)){
fprintf(stderr," slot e=%d scale %d: g=%g/%g u=%g/%g (got/want)\n",e,i,
(double)s->g.s[i],(double)scale(e,i),(double)s->u.s[i],(double)scale(e,3+i));
return 1;
}
for(int i=0;i<4;i++)
if(s->d.s[i]!=scale(e,6+i)){
fprintf(stderr," slot e=%d scale %d: d=%g/%g (got/want)\n",e,i,
(double)s->d.s[i],(double)scale(e,6+i));
return 1;
}
return 0;
}
static int run_generations(Model *m,int block,int gens){
g_pipe_block=block;
for(int gen=0;gen<gens;gen++){
int eids[NE];
for(int q=0;q<NE;q++) eids[q]=(gen*3+q)%NE; /* deterministic shuffle across gens */
pipe_dispatch(m,LAYER,eids,NE);
if(gen%4==0) usleep(300); /* let loads finish → fast-path wait */
for(int i=0;i<NE;i++){
/* odd gens wait on the LAST-dispatched slot first: with jobs this
* small, in-order waits mostly find ready already set — reverse
* order is what actually parks the waiter on the condvar. */
int q=(gen&1)?NE-1-i:i;
pipe_wait(q);
if(!atomic_load_explicit(&g_pp.ready[q],memory_order_acquire))
return fail(block?"blocking wait returned before ready":"spin wait returned before ready");
if(check_slot(&m->ws[q],eids[q])) return fail(block?"slot contents (block)":"slot contents (spin)");
}
}
return 0;
}
static int test_implication_table(void){
struct { const char *pipe_env,*pw_env; int pipe_now,want; } T[]={
{NULL,"4",0,1}, /* pool sized, pipe off, PIPE unset → imply */
{NULL,"16",0,1},
{NULL,"0",0,0}, /* PIPE_WORKERS=0 must NOT enable a clamped pipe */
{NULL,"",0,0},
{NULL,"-3",0,0},
{"0","4",0,0}, /* explicit PIPE=0 always wins */
{"1","4",1,0}, /* explicit PIPE=1: nothing to imply */
{NULL,"4",1,0}, /* platform default already ON (win32) */
{NULL,NULL,0,0},
};
for(size_t i=0;i<sizeof(T)/sizeof(T[0]);i++)
if(pipe_workers_imply_pipe(T[i].pipe_env,T[i].pw_env,T[i].pipe_now)!=T[i].want){
fprintf(stderr,"FAIL: implication row %zu (PIPE=%s PIPE_WORKERS=%s pipe_now=%d)\n",
i,T[i].pipe_env?T[i].pipe_env:"<unset>",T[i].pw_env?T[i].pw_env:"<unset>",T[i].pipe_now);
return 1;
}
return 0;
}
int main(void){
if(test_implication_table()) return 1;
/* Relative to the CWD, like test_compat_direct's TMPF — NOT "/tmp/...":
* the windows job builds native .exe files and "/tmp" is not a Windows
* path. fwrite then reopen read-only: Windows compat has pread, not pwrite. */
if(write_fixture()) return 1;
int fd=open(TMPF,COMPAT_O_RDONLY);
if(fd<0) return fail("open temp");
static Model m; /* zeroed: buffered pread path, no mmap/cuda */
if(build_fixture(&m,fd)){ close(fd); remove(TMPF); return 1; }
g_pipe=1; g_pipe_nw=4;
pipe_init(&m);
/* spin waiter first (control), then the condvar waiter under the same
* dispatch pattern; 200 generations each cycles the gen-tagged cursor
* and alternates parked/fast-path waits. */
if(run_generations(&m,0,200) || run_generations(&m,1,200)){ close(fd); remove(TMPF); return 1; }
for(int q=0;q<NE;q++){ compat_aligned_free(m.ws[q].slab); free(m.ws[q].fslab); }
for(int i=0;i<m.S.n;i++) free(m.S.t[i].name);
free(m.S.t);
close(fd);
remove(TMPF);
puts("test_pipe_block: ok");
return 0;
}
c/tests/test_pipe_cuda.cu
+1 -1
View File
@@ -119,7 +119,7 @@ int main(void){
for(int i=0;i<O;i++) sc[i]=0.01f+0.001f*(i%7);
for(size_t i=0;i<(size_t)S*K;i++) x[i]=rndf();
ColiCudaTensor *t=NULL;
ok&=coli_cuda_matmul(&t,ref,x,w4,sc,2,S,K,O,0); /* host path = reference */
ok&=coli_cuda_matmul(&t,ref,x,w4,sc,2,S,K,O,0,0); /* host path = reference */
float *xd=(float*)coli_cuda_pipe_alloc(0,(size_t)S*K*4);
float *yd=(float*)coli_cuda_pipe_alloc(0,(size_t)S*O*4);
ok&=coli_cuda_pipe_upload(0,xd,x,(size_t)S*K*4);
c/tests/test_resource_plan.py
+225 -7
View File
@@ -5,6 +5,7 @@ import sys
import tempfile
import unittest
from pathlib import Path
from unittest import mock
from resource_plan import (
GB,
@@ -14,6 +15,7 @@ from resource_plan import (
environment_for_plan,
format_plan,
memory_available,
physical_cpu_count,
)
@@ -87,6 +89,79 @@ class ResourcePlanTest(unittest.TestCase):
self.assertIn("clamped", plan["warnings"][0])
self.assertIn("0:test-gpu", format_plan(plan))
def test_auto_tier_thread_count_uses_physical_cores(self):
# End-to-end for #325: build_plan + environment_for_plan must export the
# physical (not logical SMT) core count as OMP_NUM_THREADS. The original
# suite passed physical_cpus=24 explicitly, so it never exercised the
# real physical_cpu_count() probe whose single-core failure pinned decode.
def lscpu(stdout):
return subprocess.CompletedProcess(args=[], returncode=0,
stdout=stdout, stderr="")
# 1 socket, 12 cores, 2 SMT siblings -> 24 threads, 12 physical cores.
# The parser must return 12 physical cores under BOTH lscpu layouts:
# - 2-col: `lscpu -p=core,socket` emits exactly [core,socket] (this is
# what the probe actually requests; the previous fields[1]/[2]
# indexing skipped every line here and fell through to the
# logical count -> the regression JustVugg caught).
# - 3-col: bare `lscpu -p` prepends a CPU column -> [cpu,core,socket].
# Taking the last two fields is correct in both cases.
layouts = {
"2-col (-p=core,socket)": (
"# core,socket\n" +
"\n".join(f"{core},0" for core in range(12) for _ in range(2))),
"3-col (bare -p, CPU prefix)": (
"# CPU,Core,Socket\n" +
"\n".join(f"{cpu},{core},0" for core in range(12) for cpu in range(2))),
}
for label, blob in layouts.items():
with mock.patch("resource_plan.subprocess.run",
return_value=lscpu(blob)), \
mock.patch.object(sys, "platform", "linux"):
plan = build_plan(self.model, available_memory=16 * GB,
available_disk=1, gpus=[])
env = environment_for_plan(plan)
self.assertEqual(plan["cpu"]["physical_cores"], 12, label)
self.assertEqual(env["OMP_NUM_THREADS"], "12", label)
def test_plan_does_not_set_omp_affinity_vars(self):
# The real #325 regression: --auto-tier set OMP_PROC_BIND=spread +
# OMP_PLACES=cores, which ran before the engine's overwrite=0 setenv and
# so won, collapsing the OpenMP team to one CPU on the reporter's 64-core
# Linux box even though OMP_NUM_THREADS was correct. The plan must leave
# affinity to the engine's own hot-thread tuning (which prefers 'close').
plan = build_plan(self.model, available_memory=16 * GB,
available_disk=1, gpus=[], physical_cpus=64)
env = environment_for_plan(plan)
self.assertEqual(env["OMP_NUM_THREADS"], "64")
self.assertNotIn("OMP_PROC_BIND", env)
self.assertNotIn("OMP_PLACES", env)
def test_plan_conserves_budget_and_experts_above_256gb(self):
# Regression for #325's reporter: a 512 GB machine loading the whole
# model into RAM. Verify the budget math stays exact at large RAM sizes
# (no integer truncation, no over-allocation, no experts lost between
# tiers). Checked at 256/512/800 GB to bracket the reporter's box.
for ram_gb in (256, 512, 800):
plan = build_plan(self.model, ram_gb=ram_gb, available_disk=1,
gpus=[], physical_cpus=64)
ram = plan["tiers"]["ram"]
# RAM budget never over-allocated: dense + runtime + cache <= budget.
allocated = (ram["dense_bytes"] + ram["runtime_bytes"]
+ ram["expert_cache_bytes"])
self.assertLessEqual(allocated, ram["budget_bytes"],
f"over-allocated RAM at {ram_gb} GB")
# Every expert byte is accounted for exactly once across the tiers.
tiers = plan["tiers"]
tiered = (tiers["vram"]["hot_expert_bytes"]
+ ram["warm_expert_bytes"]
+ tiers["disk"]["cold_expert_bytes"])
self.assertEqual(tiered, plan["model"]["expert_bytes"],
f"expert bytes lost/duplicated at {ram_gb} GB")
# A positive RAM budget yields a non-negative cache and a sensible cap.
self.assertGreaterEqual(ram["expert_cache_bytes"], 0)
self.assertGreaterEqual(ram["cache_slots_per_layer"], 0)
def test_filters_requested_devices(self):
gpus = [{"index": 0, "name": "a", "total_bytes": 8 * GB, "free_bytes": 8 * GB}]
plan = build_plan(self.model, available_memory=16 * GB, available_disk=1,
@@ -117,13 +192,13 @@ class ResourcePlanTest(unittest.TestCase):
self.assertEqual(env["COLI_CUDA"], "1")
self.assertEqual(env["COLI_GPUS"], "1")
self.assertEqual(env["OMP_NUM_THREADS"], str(plan["cpu"]["physical_cores"]))
if sys.platform == "win32":
# MinGW libgomp: niente affinity su Windows, le chiavi non vanno emesse
self.assertNotIn("OMP_PROC_BIND", env)
self.assertNotIn("OMP_PLACES", env)
else:
self.assertEqual(env["OMP_PROC_BIND"], "spread")
self.assertEqual(env["OMP_PLACES"], "cores")
# The plan must NOT set OMP_PROC_BIND / OMP_PLACES on any platform:
# the engine's own hot-thread tuning owns affinity (it prefers
# OMP_PROC_BIND=close for the back-to-back per-expert matmuls). Setting
# spread + cores here ran before the engine's overwrite=0 setenv and so
# won, collapsing the team to one CPU on some libgomp topologies (#325).
self.assertNotIn("OMP_PROC_BIND", env)
self.assertNotIn("OMP_PLACES", env)
self.assertEqual(env["PIN_GB"], env["CUDA_EXPERT_GB"])
explicit_threads = environment_for_plan(plan, {"OMP_NUM_THREADS": "7",
@@ -140,6 +215,81 @@ class ResourcePlanTest(unittest.TestCase):
plan = build_plan(self.model, available_memory=16 * GB, available_disk=1,
gpus=[], physical_cpus=8, cpu_sockets=1)
self.assertNotIn("COLI_NUMA", environment_for_plan(plan))
def test_auto_tune_mtp_off_when_compute_bound(self):
# Tiny model with 64 GB RAM and no GPU: all experts fit in RAM with no
# warm tier, so the plan classifies as compute-bound.
plan = build_plan(self.model, ram_gb=64, available_memory=64 * GB,
available_disk=100 * GB, gpus=[], physical_cpus=24,
cpu_sockets=2)
# With such a small model fully in RAM and no GPU, bottleneck is compute
self.assertEqual(plan["bottleneck_class"], "compute")
self.assertIn("DRAFT", plan["tune"])
self.assertEqual(plan["tune"]["DRAFT"]["value"], "0")
env = environment_for_plan(plan)
self.assertEqual(env["DRAFT"], "0")
explicit = environment_for_plan(plan, {"DRAFT": "3"})
self.assertEqual(explicit["DRAFT"], "3")
def test_auto_tune_mtp_off_when_disk_low_hit(self):
# Use a model large enough that 8 GB RAM can't hold all experts.
big = tempfile.TemporaryDirectory()
bigmodel = Path(big.name)
(bigmodel / "config.json").write_text(json.dumps({
"num_hidden_layers": 2, "n_routed_experts": 4,
"kv_lora_rank": 4, "qk_rope_head_dim": 2,
"qk_nope_head_dim": 3, "v_head_dim": 5, "num_attention_heads": 2,
}))
expert_size = 3 * GB # each expert 3 GB → 12 GB total, won't fit in 8 GB budget
write_shard(bigmodel / "out-00000.safetensors", [
("model.embed_tokens.weight", 100),
("model.layers.0.self_attn.q_a_proj.weight", 200),
])
for i in range(4):
write_shard(bigmodel / f"out-{i+1:05d}.safetensors", [
(f"model.layers.1.mlp.experts.{i}.gate_proj.weight", expert_size),
])
plan = build_plan(bigmodel, ram_gb=0, available_memory=4 * GB,
available_disk=100 * GB, gpus=[], physical_cpus=8,
cpu_sockets=1)
big.cleanup()
self.assertEqual(plan["bottleneck_class"], "disk")
self.assertLess(plan["projected_hit_rate"], 0.90)
self.assertEqual(plan["tune"]["DRAFT"]["value"], "0")
def test_auto_tune_pipe_multi_gpu(self):
gpus = [
{"index": 0, "name": "a", "total_bytes": 32 * GB, "free_bytes": 30 * GB},
{"index": 1, "name": "b", "total_bytes": 32 * GB, "free_bytes": 30 * GB},
]
plan = build_plan(self.model, ram_gb=16, available_memory=32 * GB,
available_disk=1, gpus=gpus, cpu_sockets=2)
self.assertEqual(plan["tune"]["COLI_CUDA_PIPE"]["value"], "2")
env = environment_for_plan(plan)
self.assertEqual(env["COLI_CUDA_PIPE"], "2")
def test_auto_tune_pipe_single_gpu(self):
gpus = [{"index": 0, "name": "a", "total_bytes": 12 * GB, "free_bytes": 10 * GB}]
plan = build_plan(self.model, ram_gb=16, available_memory=32 * GB,
available_disk=1, gpus=gpus, cpu_sockets=1)
self.assertEqual(plan["tune"]["COLI_CUDA_PIPE"]["value"], "1")
def test_auto_tune_numa_hint_for_cpu_only(self):
plan = build_plan(self.model, ram_gb=64, available_memory=64 * GB,
available_disk=1, gpus=[], physical_cpus=64, cpu_sockets=2)
self.assertIn("_numa_hint", plan["tune"])
self.assertIn("numactl", plan["tune"]["_numa_hint"])
self.assertIn("auto-tune", format_plan(plan))
def test_format_plan_shows_tune_and_hit_rate(self):
plan = build_plan(self.model, ram_gb=64, available_memory=64 * GB,
available_disk=100 * GB, gpus=[], physical_cpus=24,
cpu_sockets=1)
text = format_plan(plan)
self.assertIn("hit", text)
self.assertIn("auto-tune", text)
self.assertIn("DRAFT", text)
def test_cpu_binary_does_not_apply_gpu_tier(self):
plan = build_plan(self.model, available_memory=16 * GB, available_disk=1,
gpus=[{"index": 0, "name": "a", "total_bytes": 8 * GB,
@@ -178,5 +328,73 @@ class ResourcePlanTest(unittest.TestCase):
self.assertIn("expected_bottleneck", plan)
class PhysicalCpuCountTest(unittest.TestCase):
"""Regression for #325: --auto-tier pinned decode to one core because
physical_cpu_count() silently returned 1.
Two root causes this locks down:
1. lscpu -p prepends a CPU column, so `-p=core,socket` emits
CPU,Core,Socket; counting rows counted logical SMT siblings.
2. any probe failure fell through to ``os.cpu_count() or 1`` and the
``or 1`` could pin a constrained/cgroup'd box to a single core.
"""
def _lscpu(self, stdout):
return subprocess.CompletedProcess(args=[], returncode=0,
stdout=stdout, stderr="")
def _lscpu_topology(self, sockets, cores_per_socket, threads_per_core):
# Real lscpu shape: socket-local core IDs repeat across sockets; the
# CPU column (always prepended) is a unique logical-CPU index.
rows, cpu = [], 0
for sock in range(sockets):
for core in range(cores_per_socket):
for _ in range(threads_per_core):
rows.append(f"{cpu},{core},{sock}")
cpu += 1
return "# CPU,Core,Socket\n" + "\n".join(rows)
def test_counts_physical_cores_not_smt_threads(self):
blob = self._lscpu_topology(sockets=2, cores_per_socket=16, threads_per_core=2)
with mock.patch("resource_plan.subprocess.run", return_value=self._lscpu(blob)), \
mock.patch.object(sys, "platform", "linux"):
self.assertEqual(physical_cpu_count(), 32)
def test_single_socket_no_smt(self):
blob = self._lscpu_topology(sockets=1, cores_per_socket=8, threads_per_core=1)
with mock.patch("resource_plan.subprocess.run", return_value=self._lscpu(blob)), \
mock.patch.object(sys, "platform", "linux"):
self.assertEqual(physical_cpu_count(), 8)
def test_skips_offline_core_socket_fields(self):
# VMs / large NUMA boxes emit "-" for offline core or socket IDs; that
# used to raise ValueError, discard the whole parse, and fall through
# to the single-core fallback.
blob = "# CPU,Core,Socket\n0,0,0\n1,-,0\n2,1,0\n3,1,0\n"
with mock.patch("resource_plan.subprocess.run", return_value=self._lscpu(blob)), \
mock.patch.object(sys, "platform", "linux"):
self.assertEqual(physical_cpu_count(), 2)
def test_lscpu_missing_falls_back_to_logical_not_silent_one(self):
# The bug: lscpu absent -> os.cpu_count() or 1. On a constrained box
# os.cpu_count() can be 1. We still must never silently pick 1 without
# a warning, and when logical cores exist they must be used.
import os
with mock.patch("resource_plan.subprocess.run", side_effect=FileNotFoundError), \
mock.patch.object(sys, "platform", "linux"), \
mock.patch("resource_plan.os.cpu_count", return_value=16), \
mock.patch("sys.stderr"):
self.assertEqual(physical_cpu_count(), 16)
def test_zero_logical_cores_warns_and_returns_one(self):
# The genuine degenerate case: no probe works and os.cpu_count() is
# None/1. Must return 1 (engine needs a positive team size) but warn.
with mock.patch("resource_plan.subprocess.run", side_effect=FileNotFoundError), \
mock.patch.object(sys, "platform", "linux"), \
mock.patch("resource_plan.os.cpu_count", return_value=None), \
mock.patch("sys.stderr"):
self.assertEqual(physical_cpu_count(), 1)
if __name__ == "__main__":
unittest.main()
c/tests/test_router_cuda.cu
+79
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@@ -0,0 +1,79 @@
/* Device-router kernel oracle (#431 PR-A).
*
* Feeds random activations/router weights through pipe_router_logits +
* pipe_router_select and checks against a CPU reference that replicates
* moe()'s plain routing path verbatim (sigmoid -> bias-augmented top-K by
* `choice`, weights from raw `logit`, route-level TOPP truncation, norm_topk,
* routed_scale). The dot/expf rounding may differ from libm at ~1e-6 rel, so
* a handful of near-tie index flips across trials is tolerated; the weight
* math itself must agree to 1e-4 rel on matching selections.
*
* Build: nvcc -O2 -std=c++17 -arch=native tests/test_router_cuda.cu -o tests/test_router_cuda
*/
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <cmath>
#include <cuda_runtime.h>
/* pull in the kernel definitions (same idiom as the CPU tests' #include "../colibri.c") */
#include "../backend_cuda.cu"
static void cpu_ref(const float *x,const float *W,const float *bias,int D,int E,
int Ksel,float topp,int norm_topk,float rscale,
int *idx,float *w,int *keff){
float *logit=(float*)malloc(E*sizeof(float)),*choice=(float*)malloc(E*sizeof(float));
for(int e=0;e<E;e++){ double a=0; const float *r=W+(size_t)e*D;
for(int i=0;i<D;i++) a+=(double)x[i]*r[i];
float lg=1.f/(1.f+expf(-(float)a)); logit[e]=lg; choice[e]=lg+bias[e]; }
for(int kk=0;kk<Ksel;kk++){ int best=-1; float bv=-1e30f;
for(int e=0;e<E;e++){ int tk=0; for(int j=0;j<kk;j++) if(idx[j]==e){tk=1;break;}
if(!tk && choice[e]>bv){bv=choice[e];best=e;} }
idx[kk]=best; w[kk]=logit[best]; }
int Ke=Ksel;
if(topp>0.f && topp<1.f){
for(int a=1;a<Ksel;a++){ int ii=idx[a]; float ww=w[a]; int b=a-1;
while(b>=0 && w[b]<ww){ w[b+1]=w[b]; idx[b+1]=idx[b]; b--; } w[b+1]=ww; idx[b+1]=ii; }
float tot=1e-20f; for(int kk=0;kk<Ksel;kk++) tot+=w[kk];
float cum=0; for(int kk=0;kk<Ksel;kk++){ cum+=w[kk]; if(cum>=topp*tot){ Ke=kk+1; break; } } }
if(norm_topk){ float sm=0; for(int kk=0;kk<Ke;kk++) sm+=w[kk]; sm+=1e-20f;
for(int kk=0;kk<Ke;kk++) w[kk]/=sm; }
for(int kk=0;kk<Ke;kk++) w[kk]*=rscale;
*keff=Ke; free(logit); free(choice);
}
int main(void){
const int D=6144,E=256,K=8,TRIALS=200;
srand(42);
float *x,*W,*b; cudaMallocManaged(&x,D*4); cudaMallocManaged(&W,(size_t)E*D*4);
cudaMallocManaged(&b,E*4);
float *lg,*ch; char *out;
cudaMalloc(&lg,E*4); cudaMalloc(&ch,E*4); cudaMalloc(&out,K*8+4);
int flips=0, bad=0;
for(int t=0;t<TRIALS;t++){
float topp = (t%3==1)?0.7f:0.f;
int nt = (t%2);
float rs = 1.0f+(t%5)*0.25f;
for(int i=0;i<D;i++) x[i]=(rand()/(float)RAND_MAX-.5f)*2.f;
for(size_t i=0;i<(size_t)E*D;i++) W[i]=(rand()/(float)RAND_MAX-.5f)*.06f;
for(int e=0;e<E;e++) b[e]=(rand()/(float)RAND_MAX-.5f)*.02f;
pipe_router_logits<<<E,128>>>(x,W,b,D,lg,ch);
pipe_router_select<<<1,1>>>(lg,ch,E,K,topp,nt,rs,out);
char pack[K*8+4];
if(cudaMemcpy(pack,out,sizeof(pack),cudaMemcpyDeviceToHost)!=cudaSuccess){
printf("FAIL cuda\n"); return 1; }
int gidx[K],gkeff; float gw[K];
memcpy(gidx,pack,K*4); memcpy(gw,pack+K*4,K*4); memcpy(&gkeff,pack+K*8,4);
int ridx[K],rkeff; float rw[K];
cpu_ref(x,W,b,D,E,K,topp,nt,rs,ridx,rw,&rkeff);
int mism=0; for(int k2=0;k2<K;k2++) if(gidx[k2]!=ridx[k2]) mism++;
if(mism||gkeff!=rkeff){ flips++; continue; } /* near-tie flip: counted, tolerated */
for(int k2=0;k2<gkeff;k2++){
float ref=rw[k2], d=fabsf(gw[k2]-ref);
if(d>1e-4f*(fabsf(ref)+1e-6f)+1e-6f){ bad++; break; }
}
}
printf("router oracle: %d trials, %d near-tie flips, %d weight mismatches\n",TRIALS,flips,bad);
if(flips>4||bad){ printf("FAIL\n"); return 1; }
printf("OK\n"); return 0;
}
c/tests/test_st_pread
BIN
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c/tests/test_tok_o200k.c
+64
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@@ -0,0 +1,64 @@
/* o200k pre-tokenizer validation against HF-tokenizers-generated expectations.
* Self-contained for the test-c harness: loads tests/tok_o200k_tiny.json (a
* synthetic byte-level BPE whose Split regex is the o200k pattern — a few KB,
* no model download) and scores tests/tok_o200k_cases.txt, whose expected ids
* were produced by HF `tokenizers` on the same file. Guards the case-aware
* letter matcher, contractions, digit groups, the [\r\n/]* punctuation tail,
* whitespace branches, and added-token atomicity; round-trips every case.
* The cl100k path is untouched by construction (dispatch requires \p{Lu} in
* the tokenizer's own Split pattern) and stays covered by the GLM oracle. */
#define _GNU_SOURCE
#include "../tok.h"
int main(void) {
Tok T;
tok_load(&T, "tests/tok_o200k_tiny.json");
if (!T.o200k) { fprintf(stderr, "test_tok_o200k: o200k pattern not detected\n"); return 1; }
FILE *f = fopen("tests/tok_o200k_cases.txt", "rb");
if (!f) { perror("tests/tok_o200k_cases.txt"); return 1; }
/* fgets, not getline: MinGW's UCRT lacks getline and this must run on
* the windows job. Case lines are short; 8 KB is generous. */
char line[8192];
int pass = 0, tot = 0, dpass = 0;
while (fgets(line, sizeof(line), f)) {
size_t nr = strlen(line);
while (nr > 0 && (line[nr-1] == '\n' || line[nr-1] == '\r')) line[--nr] = 0;
if (nr == 0) continue;
char *tab = strchr(line, '\t'); if (!tab) continue;
*tab = 0;
const char *text = line, *idstr = tab + 1;
char tbuf[4096]; int tn = 0;
for (const char *q = text; *q && tn < 4095; q++) {
if (q[0]=='\\' && q[1]=='n') { tbuf[tn++]='\n'; q++; }
else if (q[0]=='\\' && q[1]=='t') { tbuf[tn++]='\t'; q++; }
else if (q[0]=='\\' && q[1]=='r') { tbuf[tn++]='\r'; q++; }
else if (q[0]=='\\' && q[1]=='\\') { tbuf[tn++]='\\'; q++; }
else tbuf[tn++] = *q;
}
tbuf[tn] = 0;
int exp[512], ne = 0;
for (const char *q = idstr; *q; ) {
while (*q == ',' || *q == ' ') q++;
if (!*q) break;
exp[ne++] = atoi(q);
while (*q && *q != ',') q++;
}
int got[512]; int ng = tok_encode(&T, tbuf, tn, got, 512);
int ok = (ng == ne);
for (int i = 0; i < ng && ok; i++) ok = (got[i] == exp[i]);
tot++; if (ok) pass++;
char dec[8192]; int dn = tok_decode(&T, got, ng, dec, 8191);
int drt = (dn == tn) && !memcmp(dec, tbuf, tn);
if (drt) dpass++;
if (!ok || !drt) {
fprintf(stderr, "MISMATCH text=%s\n exp(%d):", text, ne);
for (int i = 0; i < ne; i++) fprintf(stderr, " %d", exp[i]);
fprintf(stderr, "\n got(%d):", ng);
for (int i = 0; i < ng; i++) fprintf(stderr, " %d", got[i]);
fprintf(stderr, "\n decode_ok=%d\n", drt);
}
}
fclose(f);
printf("test_tok_o200k: ENCODE %d/%d DECODE %d/%d\n", pass, tot, dpass, tot);
return (pass == tot && dpass == tot) ? 0 : 2;
}
c/tests/tok_o200k_cases.txt
+40
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@@ -0,0 +1,40 @@
hello world 259,32,119,111,114,263
HelloWorld 72,101,257,111,262
XMLHttpRequest 88,77,76,72,116,116,112,82,101,113,117,101,115,116
helloWORLDhello 259,87,79,82,76,68,259
dog's 100,111,103,270
DON'T 68,79,78,39,84
don't 100,111,110,39,116
I'll've 73,39,257,39,118,101
O'Brien 79,39,66,114,105,101,110
the theatre 265,32,116,256,97,116,114,101
12345 268,52,53
a1b22c333d4444 97,49,98,50,50,99,51,51,51,100,52,52,52,52
3.14 51,46,49,52
http://x.com/a/b 104,116,116,112,58,47,47,120,46,99,111,109,47,97,47,98
path/to/file 112,97,264,47,116,111,47,102,105,108,101
a//b///c 97,47,47,98,47,47,47,99
!!\r\n//x 33,33,13,10,47,47,120
one\ntwo\r\nthree 111,110,101,10,116,119,111,13,10,264,114,101,101
\n x 32,32,10,32,32,120
32,32,32
a b c 97,32,32,98,32,32,32,99
tab\there 116,269,9,256,114,101
Café 67,97,102,101,204,129
naiveBayes 110,97,105,118,101,66,97,121,101,115
Éclair 195,137,99,108,97,105,114
北京大学 229,140,151,228,186,172,229,164,167,229,173,166
ΑΒαβ 206,145,206,146,206,177,206,178
Иван 208,152,208,178,208,176,208,189
ẞßscharf 225,186,158,195,159,115,99,104,97,114,102
i̇stanbul 105,204,135,115,116,97,110,98,117,108
hello<|endoftext|>world 259,274,119,111,114,263
<|message_user|>hi 275,104,105
mixedCASEand123 109,105,120,101,100,67,65,83,69,97,110,100,268
's 270
's 32,39,115
A 65
aB 97,66
Ab 65,98
AB 65,66
ab 269
c/tests/tok_o200k_tiny.json
+1
View File
@@ -0,0 +1 @@
{"version": "1.0", "truncation": null, "padding": null, "added_tokens": [{"id": 274, "content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true}, {"id": 275, "content": "<|message_user|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": false, "special": true}], "normalizer": null, "pre_tokenizer": {"type": "Sequence", "pretokenizers": [{"type": "Split", "pattern": {"Regex": "[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"}, "behavior": "Isolated", "invert": false}, {"type": "ByteLevel", "add_prefix_space": false, "trim_offsets": true, "use_regex": false}]}, "post_processor": null, "decoder": {"type": "ByteLevel", "add_prefix_space": true, "trim_offsets": true, "use_regex": true}, "model": {"type": "BPE", "dropout": null, "unk_token": null, "continuing_subword_prefix": null, "end_of_word_suffix": null, "fuse_unk": false, "byte_fallback": false, "ignore_merges": true, "vocab": {"Ā": 0, "ā": 1, "Ă": 2, "ă": 3, "Ą": 4, "ą": 5, "Ć": 6, "ć": 7, "Ĉ": 8, "ĉ": 9, "Ċ": 10, "ċ": 11, "Č": 12, "č": 13, "Ď": 14, "ď": 15, "Đ": 16, "đ": 17, "Ē": 18, "ē": 19, "Ĕ": 20, "ĕ": 21, "Ė": 22, "ė": 23, "Ę": 24, "ę": 25, "Ě": 26, "ě": 27, "Ĝ": 28, "ĝ": 29, "Ğ": 30, "ğ": 31, "Ġ": 32, "!": 33, "\"": 34, "#": 35, "$": 36, "%": 37, "&": 38, "'": 39, "(": 40, ")": 41, "*": 42, "+": 43, ",": 44, "-": 45, ".": 46, "/": 47, "0": 48, "1": 49, "2": 50, "3": 51, "4": 52, "5": 53, "6": 54, "7": 55, "8": 56, "9": 57, ":": 58, ";": 59, "<": 60, "=": 61, ">": 62, "?": 63, "@": 64, "A": 65, "B": 66, "C": 67, "D": 68, "E": 69, "F": 70, "G": 71, "H": 72, "I": 73, "J": 74, "K": 75, "L": 76, "M": 77, "N": 78, "O": 79, "P": 80, "Q": 81, "R": 82, "S": 83, "T": 84, "U": 85, "V": 86, "W": 87, "X": 88, "Y": 89, "Z": 90, "[": 91, "\\": 92, "]": 93, "^": 94, "_": 95, "`": 96, "a": 97, "b": 98, "c": 99, "d": 100, "e": 101, "f": 102, "g": 103, "h": 104, "i": 105, "j": 106, "k": 107, "l": 108, "m": 109, "n": 110, "o": 111, "p": 112, "q": 113, "r": 114, "s": 115, "t": 116, "u": 117, "v": 118, "w": 119, "x": 120, "y": 121, "z": 122, "{": 123, "|": 124, "}": 125, "~": 126, "ġ": 127, "Ģ": 128, "ģ": 129, "Ĥ": 130, "ĥ": 131, "Ħ": 132, "ħ": 133, "Ĩ": 134, "ĩ": 135, "Ī": 136, "ī": 137, "Ĭ": 138, "ĭ": 139, "Į": 140, "į": 141, "İ": 142, "ı": 143, "IJ": 144, "ij": 145, "Ĵ": 146, "ĵ": 147, "Ķ": 148, "ķ": 149, "ĸ": 150, "Ĺ": 151, "ĺ": 152, "Ļ": 153, "ļ": 154, "Ľ": 155, "ľ": 156, "Ŀ": 157, "ŀ": 158, "Ł": 159, "ł": 160, "¡": 161, "¢": 162, "£": 163, "¤": 164, "¥": 165, "¦": 166, "§": 167, "¨": 168, "©": 169, "ª": 170, "«": 171, "¬": 172, "Ń": 173, "®": 174, "¯": 175, "°": 176, "±": 177, "²": 178, "³": 179, "´": 180, "µ": 181, "¶": 182, "·": 183, "¸": 184, "¹": 185, "º": 186, "»": 187, "¼": 188, "½": 189, "¾": 190, "¿": 191, "À": 192, "Á": 193, "Â": 194, "Ã": 195, "Ä": 196, "Å": 197, "Æ": 198, "Ç": 199, "È": 200, "É": 201, "Ê": 202, "Ë": 203, "Ì": 204, "Í": 205, "Î": 206, "Ï": 207, "Ð": 208, "Ñ": 209, "Ò": 210, "Ó": 211, "Ô": 212, "Õ": 213, "Ö": 214, "×": 215, "Ø": 216, "Ù": 217, "Ú": 218, "Û": 219, "Ü": 220, "Ý": 221, "Þ": 222, "ß": 223, "à": 224, "á": 225, "â": 226, "ã": 227, "ä": 228, "å": 229, "æ": 230, "ç": 231, "è": 232, "é": 233, "ê": 234, "ë": 235, "ì": 236, "í": 237, "î": 238, "ï": 239, "ð": 240, "ñ": 241, "ò": 242, "ó": 243, "ô": 244, "õ": 245, "ö": 246, "÷": 247, "ø": 248, "ù": 249, "ú": 250, "û": 251, "ü": 252, "ý": 253, "þ": 254, "ÿ": 255, "he": 256, "ll": 257, "hell": 258, "hello": 259, "Wo": 260, "Wor": 261, "World": 262, "ld": 263, "th": 264, "the": 265, "Ġthe": 266, "12": 267, "123": 268, "ab": 269, "'s": 270, "Ġa": 271, "./": 272, "ĊĊ": 273}, "merges": [["h", "e"], ["l", "l"], ["he", "ll"], ["hell", "o"], ["W", "o"], ["Wo", "r"], ["Wor", "ld"], ["l", "d"], ["t", "h"], ["th", "e"], ["Ġ", "the"], ["1", "2"], ["12", "3"], ["a", "b"], ["'", "s"], ["Ġ", "a"], [".", "/"], ["Ċ", "Ċ"]]}}
c/tok.h
+115 -1
View File
@@ -19,6 +19,7 @@
#include <limits.h>
#include "json.h"
#include "tok_unicode.h"
#include "tok_unicode_o200k.h"
/* ---------- hash map (chiavi binarie con lunghezza) ---------- */
typedef struct { const char *k; int klen; int v; int used; } ment;
@@ -50,6 +51,7 @@ typedef struct {
Special *sp; int nsp; /* added tokens, ordinati per lunghezza decrescente */
uint32_t byte2cp[256]; int byte2cp_len[256]; char byte2str[256][3];
int16_t cp2byte[1024];
int o200k; /* pre_tokenizer regex family: 0 = cl100k (GLM), 1 = o200k (Inkling) */
} Tok;
/* ---------- UTF-8 ---------- */
@@ -144,6 +146,17 @@ static void tok_load(Tok *T, const char *path){
}
qsort(T->sp,T->nsp,sizeof(Special),cmp_sp_len); /* match piu' lungo per primo */
}
/* pre_tokenizer family: the o200k Split regex is recognizable by its
* case-category classes (\p{Lu}...) which cl100k does not use */
jval *pt=json_get(root,"pre_tokenizer");
if(pt){
jval *ps=json_get(pt,"pretokenizers");
if(ps&&ps->t==J_ARR) for(int i=0;i<ps->len;i++){
jval *pat=json_get(ps->kids[i],"pattern");
jval *rx=pat?json_get(pat,"Regex"):NULL;
if(rx&&rx->t==J_STR&&strstr(rx->str,"\\p{Lu}")) T->o200k=1;
}
}
/* arena/buf restano allocati: le stringhe (j_dup) sono malloc indipendenti e ci servono vive */
(void)arena;
}
@@ -241,6 +254,104 @@ static void pretok_chunk(Tok *T, const unsigned char *p, int a, int b, int *out,
free(cp); free(off);
}
/* ---------- pre-tokenizer o200k (Inkling / GPT-4o family) ----------
* Split regex:
* A: [^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]*[\p{Ll}\p{Lm}\p{Lo}\p{M}]+(?i:'s|'t|'re|'ve|'m|'ll|'d)?
* B: [^\r\n\p{L}\p{N}]?[\p{Lu}\p{Lt}\p{Lm}\p{Lo}\p{M}]+[\p{Ll}\p{Lm}\p{Lo}\p{M}]*(?i:'s|'t|'re|'ve|'m|'ll|'d)?
* C: \p{N}{1,3} D: ' ?[^\s\p{L}\p{N}]+[\r\n/]*' E: \s*[\r\n]+ F: \s+(?!\S) G: \s+
* S1 = Lu|Lt|Lm|Lo|M, S2 = Ll|Lm|Lo|M. The letter matcher below replays the
* regex engine's backtracking order exactly: A with greedy optional prefix and
* maximally-greedy S1* given back until S2+ can take >=1 char, then B. */
#define O2_S1(c) (is_U(c)||is_X(c))
#define O2_S2(c) (is_X(c)||(is_L(c)&&!is_U(c)))
static uint32_t o2_low(uint32_t c){ return (c>='A'&&c<='Z')?c+32:c; }
static int o2_contraction(const uint32_t *cp, int n, int k){
if(k<n && cp[k]=='\'' && k+1<n){
uint32_t d=o2_low(cp[k+1]);
if(k+2<n){ uint32_t e=o2_low(cp[k+2]);
if((d=='r'&&e=='e')||(d=='v'&&e=='e')||(d=='l'&&e=='l')) return k+3; }
if(d=='s'||d=='t'||d=='m'||d=='d') return k+2;
}
return k;
}
/* end (cp index) of branch A|B match at i, or -1 */
static int o2_letters(const uint32_t *cp, int n, int i){
/* branch A, prefix greedy (taken first), then without prefix */
for(int pfx=1; pfx>=0; pfx--){
int j0=i;
if(pfx){
uint32_t c=cp[i];
if(c=='\r'||c=='\n'||is_L(c)||is_N(c)||i+1>=n) continue;
j0=i+1;
}
int m1=j0; while(m1<n && O2_S1(cp[m1])) m1++;
for(int s=m1; s>=j0; s--){
if(s<n && O2_S2(cp[s])){
int k=s+1; while(k<n && O2_S2(cp[k])) k++;
return o2_contraction(cp,n,k);
}
}
}
/* branch B */
for(int pfx=1; pfx>=0; pfx--){
int j0=i;
if(pfx){
uint32_t c=cp[i];
if(c=='\r'||c=='\n'||is_L(c)||is_N(c)||i+1>=n) continue;
j0=i+1;
}
int m1=j0; while(m1<n && O2_S1(cp[m1])) m1++;
if(m1>j0){
int k=m1; while(k<n && O2_S2(cp[k])) k++;
return o2_contraction(cp,n,k);
}
}
return -1;
}
static void pretok_chunk_o200k(Tok *T, const unsigned char *p, int a, int b, int *out, int *no, int max){
int nb=b-a; if(nb<=0) return;
uint32_t *cp=malloc((nb+1)*sizeof(uint32_t)); int *off=malloc((nb+2)*sizeof(int)); int n=0;
for(int i=a;i<b;){ uint32_t c; int k=u8_next(p,b,i,&c); off[n]=i; cp[n]=c; n++; i+=k; }
off[n]=b;
#define ISNL(c) ((c)=='\r'||(c)=='\n')
int i=0;
while(i<n){
int start=i; uint32_t c=cp[i];
/* A|B: letter runs with case-aware split + optional contraction */
{
int e=o2_letters(cp,n,i);
if(e>i){ i=e; bpe_piece(T,p,off[start],off[i],out,no,max); continue; }
}
/* C: \p{N}{1,3} */
if(is_N(c)){ int j=i,k=0; while(j<n && is_N(cp[j]) && k<3){ j++; k++; } i=j; bpe_piece(T,p,off[start],off[i],out,no,max); continue; }
/* D: ' ?[^\s\p{L}\p{N}]+[\r\n/]*' */
{
int j=i;
if(c==' ' && j+1<n && !is_S(cp[j+1]) && !is_L(cp[j+1]) && !is_N(cp[j+1])) j++;
if(j<n && !is_S(cp[j]) && !is_L(cp[j]) && !is_N(cp[j])){
while(j<n && !is_S(cp[j]) && !is_L(cp[j]) && !is_N(cp[j])) j++;
while(j<n && (ISNL(cp[j]) || cp[j]=='/')) j++;
i=j; bpe_piece(T,p,off[start],off[i],out,no,max); continue;
}
}
/* E: \s*[\r\n]+ F: \s+(?!\S) G: \s+ (same as cl100k) */
{
int r=i; while(r<n && is_S(cp[r])) r++;
if(r>i){ int last=-1; for(int j=i;j<r;j++) if(ISNL(cp[j])) last=j;
if(last>=0){ i=last+1; bpe_piece(T,p,off[start],off[i],out,no,max); continue; }
int end = (r<n) ? r-1 : r;
if(end<=i) end=i+1;
i=end; bpe_piece(T,p,off[start],off[i],out,no,max); continue;
}
}
i++;
bpe_piece(T,p,off[start],off[i],out,no,max);
}
#undef ISNL
free(cp); free(off);
}
/* ---------- encode: testo -> id (split sugli added token, poi pretok+BPE) ---------- */
static int tok_encode(Tok *T, const char *text, int len, int *out, int max){
const unsigned char *p=(const unsigned char*)text; int no=0; int i=0;
@@ -254,7 +365,10 @@ static int tok_encode(Tok *T, const char *text, int len, int *out, int max){
}
}
int chunk_end = (hitpos<0) ? len : hitpos;
if(chunk_end>i) pretok_chunk(T,p,i,chunk_end,out,&no,max);
if(chunk_end>i){
if(T->o200k) pretok_chunk_o200k(T,p,i,chunk_end,out,&no,max);
else pretok_chunk(T,p,i,chunk_end,out,&no,max);
}
if(hitpos<0) break;
if(no<max) out[no++]=hitid;
i=hitpos+hitlen;
c/tok_unicode_o200k.h
+228
View File
@@ -0,0 +1,228 @@
/* Unicode range tables for the o200k pre-tokenizer (Inkling, GPT-4o family).
* Generated from Python unicodedata (see tools/ in git history):
* uni_U = Lu + Lt (uppercase/titlecase letters)
* uni_X = Lm + Lo + M (caseless letters and combining marks; members of
* BOTH bracket classes in the o200k regex)
* Lookup via the same uni_in() binary search as tok_unicode.h. */
#ifndef TOK_UNICODE_O200K_H
#define TOK_UNICODE_O200K_H
#include <stdint.h>
static const uint32_t uni_U[][2] = {
{0x41,0x5A},{0xC0,0xD6},{0xD8,0xDE},{0x100,0x100},{0x102,0x102},{0x104,0x104},
{0x106,0x106},{0x108,0x108},{0x10A,0x10A},{0x10C,0x10C},{0x10E,0x10E},{0x110,0x110},
{0x112,0x112},{0x114,0x114},{0x116,0x116},{0x118,0x118},{0x11A,0x11A},{0x11C,0x11C},
{0x11E,0x11E},{0x120,0x120},{0x122,0x122},{0x124,0x124},{0x126,0x126},{0x128,0x128},
{0x12A,0x12A},{0x12C,0x12C},{0x12E,0x12E},{0x130,0x130},{0x132,0x132},{0x134,0x134},
{0x136,0x136},{0x139,0x139},{0x13B,0x13B},{0x13D,0x13D},{0x13F,0x13F},{0x141,0x141},
{0x143,0x143},{0x145,0x145},{0x147,0x147},{0x14A,0x14A},{0x14C,0x14C},{0x14E,0x14E},
{0x150,0x150},{0x152,0x152},{0x154,0x154},{0x156,0x156},{0x158,0x158},{0x15A,0x15A},
{0x15C,0x15C},{0x15E,0x15E},{0x160,0x160},{0x162,0x162},{0x164,0x164},{0x166,0x166},
{0x168,0x168},{0x16A,0x16A},{0x16C,0x16C},{0x16E,0x16E},{0x170,0x170},{0x172,0x172},
{0x174,0x174},{0x176,0x176},{0x178,0x179},{0x17B,0x17B},{0x17D,0x17D},{0x181,0x182},
{0x184,0x184},{0x186,0x187},{0x189,0x18B},{0x18E,0x191},{0x193,0x194},{0x196,0x198},
{0x19C,0x19D},{0x19F,0x1A0},{0x1A2,0x1A2},{0x1A4,0x1A4},{0x1A6,0x1A7},{0x1A9,0x1A9},
{0x1AC,0x1AC},{0x1AE,0x1AF},{0x1B1,0x1B3},{0x1B5,0x1B5},{0x1B7,0x1B8},{0x1BC,0x1BC},
{0x1C4,0x1C5},{0x1C7,0x1C8},{0x1CA,0x1CB},{0x1CD,0x1CD},{0x1CF,0x1CF},{0x1D1,0x1D1},
{0x1D3,0x1D3},{0x1D5,0x1D5},{0x1D7,0x1D7},{0x1D9,0x1D9},{0x1DB,0x1DB},{0x1DE,0x1DE},
{0x1E0,0x1E0},{0x1E2,0x1E2},{0x1E4,0x1E4},{0x1E6,0x1E6},{0x1E8,0x1E8},{0x1EA,0x1EA},
{0x1EC,0x1EC},{0x1EE,0x1EE},{0x1F1,0x1F2},{0x1F4,0x1F4},{0x1F6,0x1F8},{0x1FA,0x1FA},
{0x1FC,0x1FC},{0x1FE,0x1FE},{0x200,0x200},{0x202,0x202},{0x204,0x204},{0x206,0x206},
{0x208,0x208},{0x20A,0x20A},{0x20C,0x20C},{0x20E,0x20E},{0x210,0x210},{0x212,0x212},
{0x214,0x214},{0x216,0x216},{0x218,0x218},{0x21A,0x21A},{0x21C,0x21C},{0x21E,0x21E},
{0x220,0x220},{0x222,0x222},{0x224,0x224},{0x226,0x226},{0x228,0x228},{0x22A,0x22A},
{0x22C,0x22C},{0x22E,0x22E},{0x230,0x230},{0x232,0x232},{0x23A,0x23B},{0x23D,0x23E},
{0x241,0x241},{0x243,0x246},{0x248,0x248},{0x24A,0x24A},{0x24C,0x24C},{0x24E,0x24E},
{0x370,0x370},{0x372,0x372},{0x376,0x376},{0x37F,0x37F},{0x386,0x386},{0x388,0x38A},
{0x38C,0x38C},{0x38E,0x38F},{0x391,0x3A1},{0x3A3,0x3AB},{0x3CF,0x3CF},{0x3D2,0x3D4},
{0x3D8,0x3D8},{0x3DA,0x3DA},{0x3DC,0x3DC},{0x3DE,0x3DE},{0x3E0,0x3E0},{0x3E2,0x3E2},
{0x3E4,0x3E4},{0x3E6,0x3E6},{0x3E8,0x3E8},{0x3EA,0x3EA},{0x3EC,0x3EC},{0x3EE,0x3EE},
{0x3F4,0x3F4},{0x3F7,0x3F7},{0x3F9,0x3FA},{0x3FD,0x42F},{0x460,0x460},{0x462,0x462},
{0x464,0x464},{0x466,0x466},{0x468,0x468},{0x46A,0x46A},{0x46C,0x46C},{0x46E,0x46E},
{0x470,0x470},{0x472,0x472},{0x474,0x474},{0x476,0x476},{0x478,0x478},{0x47A,0x47A},
{0x47C,0x47C},{0x47E,0x47E},{0x480,0x480},{0x48A,0x48A},{0x48C,0x48C},{0x48E,0x48E},
{0x490,0x490},{0x492,0x492},{0x494,0x494},{0x496,0x496},{0x498,0x498},{0x49A,0x49A},
{0x49C,0x49C},{0x49E,0x49E},{0x4A0,0x4A0},{0x4A2,0x4A2},{0x4A4,0x4A4},{0x4A6,0x4A6},
{0x4A8,0x4A8},{0x4AA,0x4AA},{0x4AC,0x4AC},{0x4AE,0x4AE},{0x4B0,0x4B0},{0x4B2,0x4B2},
{0x4B4,0x4B4},{0x4B6,0x4B6},{0x4B8,0x4B8},{0x4BA,0x4BA},{0x4BC,0x4BC},{0x4BE,0x4BE},
{0x4C0,0x4C1},{0x4C3,0x4C3},{0x4C5,0x4C5},{0x4C7,0x4C7},{0x4C9,0x4C9},{0x4CB,0x4CB},
{0x4CD,0x4CD},{0x4D0,0x4D0},{0x4D2,0x4D2},{0x4D4,0x4D4},{0x4D6,0x4D6},{0x4D8,0x4D8},
{0x4DA,0x4DA},{0x4DC,0x4DC},{0x4DE,0x4DE},{0x4E0,0x4E0},{0x4E2,0x4E2},{0x4E4,0x4E4},
{0x4E6,0x4E6},{0x4E8,0x4E8},{0x4EA,0x4EA},{0x4EC,0x4EC},{0x4EE,0x4EE},{0x4F0,0x4F0},
{0x4F2,0x4F2},{0x4F4,0x4F4},{0x4F6,0x4F6},{0x4F8,0x4F8},{0x4FA,0x4FA},{0x4FC,0x4FC},
{0x4FE,0x4FE},{0x500,0x500},{0x502,0x502},{0x504,0x504},{0x506,0x506},{0x508,0x508},
{0x50A,0x50A},{0x50C,0x50C},{0x50E,0x50E},{0x510,0x510},{0x512,0x512},{0x514,0x514},
{0x516,0x516},{0x518,0x518},{0x51A,0x51A},{0x51C,0x51C},{0x51E,0x51E},{0x520,0x520},
{0x522,0x522},{0x524,0x524},{0x526,0x526},{0x528,0x528},{0x52A,0x52A},{0x52C,0x52C},
{0x52E,0x52E},{0x531,0x556},{0x10A0,0x10C5},{0x10C7,0x10C7},{0x10CD,0x10CD},{0x13A0,0x13F5},
{0x1C90,0x1CBA},{0x1CBD,0x1CBF},{0x1E00,0x1E00},{0x1E02,0x1E02},{0x1E04,0x1E04},{0x1E06,0x1E06},
{0x1E08,0x1E08},{0x1E0A,0x1E0A},{0x1E0C,0x1E0C},{0x1E0E,0x1E0E},{0x1E10,0x1E10},{0x1E12,0x1E12},
{0x1E14,0x1E14},{0x1E16,0x1E16},{0x1E18,0x1E18},{0x1E1A,0x1E1A},{0x1E1C,0x1E1C},{0x1E1E,0x1E1E},
{0x1E20,0x1E20},{0x1E22,0x1E22},{0x1E24,0x1E24},{0x1E26,0x1E26},{0x1E28,0x1E28},{0x1E2A,0x1E2A},
{0x1E2C,0x1E2C},{0x1E2E,0x1E2E},{0x1E30,0x1E30},{0x1E32,0x1E32},{0x1E34,0x1E34},{0x1E36,0x1E36},
{0x1E38,0x1E38},{0x1E3A,0x1E3A},{0x1E3C,0x1E3C},{0x1E3E,0x1E3E},{0x1E40,0x1E40},{0x1E42,0x1E42},
{0x1E44,0x1E44},{0x1E46,0x1E46},{0x1E48,0x1E48},{0x1E4A,0x1E4A},{0x1E4C,0x1E4C},{0x1E4E,0x1E4E},
{0x1E50,0x1E50},{0x1E52,0x1E52},{0x1E54,0x1E54},{0x1E56,0x1E56},{0x1E58,0x1E58},{0x1E5A,0x1E5A},
{0x1E5C,0x1E5C},{0x1E5E,0x1E5E},{0x1E60,0x1E60},{0x1E62,0x1E62},{0x1E64,0x1E64},{0x1E66,0x1E66},
{0x1E68,0x1E68},{0x1E6A,0x1E6A},{0x1E6C,0x1E6C},{0x1E6E,0x1E6E},{0x1E70,0x1E70},{0x1E72,0x1E72},
{0x1E74,0x1E74},{0x1E76,0x1E76},{0x1E78,0x1E78},{0x1E7A,0x1E7A},{0x1E7C,0x1E7C},{0x1E7E,0x1E7E},
{0x1E80,0x1E80},{0x1E82,0x1E82},{0x1E84,0x1E84},{0x1E86,0x1E86},{0x1E88,0x1E88},{0x1E8A,0x1E8A},
{0x1E8C,0x1E8C},{0x1E8E,0x1E8E},{0x1E90,0x1E90},{0x1E92,0x1E92},{0x1E94,0x1E94},{0x1E9E,0x1E9E},
{0x1EA0,0x1EA0},{0x1EA2,0x1EA2},{0x1EA4,0x1EA4},{0x1EA6,0x1EA6},{0x1EA8,0x1EA8},{0x1EAA,0x1EAA},
{0x1EAC,0x1EAC},{0x1EAE,0x1EAE},{0x1EB0,0x1EB0},{0x1EB2,0x1EB2},{0x1EB4,0x1EB4},{0x1EB6,0x1EB6},
{0x1EB8,0x1EB8},{0x1EBA,0x1EBA},{0x1EBC,0x1EBC},{0x1EBE,0x1EBE},{0x1EC0,0x1EC0},{0x1EC2,0x1EC2},
{0x1EC4,0x1EC4},{0x1EC6,0x1EC6},{0x1EC8,0x1EC8},{0x1ECA,0x1ECA},{0x1ECC,0x1ECC},{0x1ECE,0x1ECE},
{0x1ED0,0x1ED0},{0x1ED2,0x1ED2},{0x1ED4,0x1ED4},{0x1ED6,0x1ED6},{0x1ED8,0x1ED8},{0x1EDA,0x1EDA},
{0x1EDC,0x1EDC},{0x1EDE,0x1EDE},{0x1EE0,0x1EE0},{0x1EE2,0x1EE2},{0x1EE4,0x1EE4},{0x1EE6,0x1EE6},
{0x1EE8,0x1EE8},{0x1EEA,0x1EEA},{0x1EEC,0x1EEC},{0x1EEE,0x1EEE},{0x1EF0,0x1EF0},{0x1EF2,0x1EF2},
{0x1EF4,0x1EF4},{0x1EF6,0x1EF6},{0x1EF8,0x1EF8},{0x1EFA,0x1EFA},{0x1EFC,0x1EFC},{0x1EFE,0x1EFE},
{0x1F08,0x1F0F},{0x1F18,0x1F1D},{0x1F28,0x1F2F},{0x1F38,0x1F3F},{0x1F48,0x1F4D},{0x1F59,0x1F59},
{0x1F5B,0x1F5B},{0x1F5D,0x1F5D},{0x1F5F,0x1F5F},{0x1F68,0x1F6F},{0x1F88,0x1F8F},{0x1F98,0x1F9F},
{0x1FA8,0x1FAF},{0x1FB8,0x1FBC},{0x1FC8,0x1FCC},{0x1FD8,0x1FDB},{0x1FE8,0x1FEC},{0x1FF8,0x1FFC},
{0x2102,0x2102},{0x2107,0x2107},{0x210B,0x210D},{0x2110,0x2112},{0x2115,0x2115},{0x2119,0x211D},
{0x2124,0x2124},{0x2126,0x2126},{0x2128,0x2128},{0x212A,0x212D},{0x2130,0x2133},{0x213E,0x213F},
{0x2145,0x2145},{0x2183,0x2183},{0x2C00,0x2C2E},{0x2C60,0x2C60},{0x2C62,0x2C64},{0x2C67,0x2C67},
{0x2C69,0x2C69},{0x2C6B,0x2C6B},{0x2C6D,0x2C70},{0x2C72,0x2C72},{0x2C75,0x2C75},{0x2C7E,0x2C80},
{0x2C82,0x2C82},{0x2C84,0x2C84},{0x2C86,0x2C86},{0x2C88,0x2C88},{0x2C8A,0x2C8A},{0x2C8C,0x2C8C},
{0x2C8E,0x2C8E},{0x2C90,0x2C90},{0x2C92,0x2C92},{0x2C94,0x2C94},{0x2C96,0x2C96},{0x2C98,0x2C98},
{0x2C9A,0x2C9A},{0x2C9C,0x2C9C},{0x2C9E,0x2C9E},{0x2CA0,0x2CA0},{0x2CA2,0x2CA2},{0x2CA4,0x2CA4},
{0x2CA6,0x2CA6},{0x2CA8,0x2CA8},{0x2CAA,0x2CAA},{0x2CAC,0x2CAC},{0x2CAE,0x2CAE},{0x2CB0,0x2CB0},
{0x2CB2,0x2CB2},{0x2CB4,0x2CB4},{0x2CB6,0x2CB6},{0x2CB8,0x2CB8},{0x2CBA,0x2CBA},{0x2CBC,0x2CBC},
{0x2CBE,0x2CBE},{0x2CC0,0x2CC0},{0x2CC2,0x2CC2},{0x2CC4,0x2CC4},{0x2CC6,0x2CC6},{0x2CC8,0x2CC8},
{0x2CCA,0x2CCA},{0x2CCC,0x2CCC},{0x2CCE,0x2CCE},{0x2CD0,0x2CD0},{0x2CD2,0x2CD2},{0x2CD4,0x2CD4},
{0x2CD6,0x2CD6},{0x2CD8,0x2CD8},{0x2CDA,0x2CDA},{0x2CDC,0x2CDC},{0x2CDE,0x2CDE},{0x2CE0,0x2CE0},
{0x2CE2,0x2CE2},{0x2CEB,0x2CEB},{0x2CED,0x2CED},{0x2CF2,0x2CF2},{0xA640,0xA640},{0xA642,0xA642},
{0xA644,0xA644},{0xA646,0xA646},{0xA648,0xA648},{0xA64A,0xA64A},{0xA64C,0xA64C},{0xA64E,0xA64E},
{0xA650,0xA650},{0xA652,0xA652},{0xA654,0xA654},{0xA656,0xA656},{0xA658,0xA658},{0xA65A,0xA65A},
{0xA65C,0xA65C},{0xA65E,0xA65E},{0xA660,0xA660},{0xA662,0xA662},{0xA664,0xA664},{0xA666,0xA666},
{0xA668,0xA668},{0xA66A,0xA66A},{0xA66C,0xA66C},{0xA680,0xA680},{0xA682,0xA682},{0xA684,0xA684},
{0xA686,0xA686},{0xA688,0xA688},{0xA68A,0xA68A},{0xA68C,0xA68C},{0xA68E,0xA68E},{0xA690,0xA690},
{0xA692,0xA692},{0xA694,0xA694},{0xA696,0xA696},{0xA698,0xA698},{0xA69A,0xA69A},{0xA722,0xA722},
{0xA724,0xA724},{0xA726,0xA726},{0xA728,0xA728},{0xA72A,0xA72A},{0xA72C,0xA72C},{0xA72E,0xA72E},
{0xA732,0xA732},{0xA734,0xA734},{0xA736,0xA736},{0xA738,0xA738},{0xA73A,0xA73A},{0xA73C,0xA73C},
{0xA73E,0xA73E},{0xA740,0xA740},{0xA742,0xA742},{0xA744,0xA744},{0xA746,0xA746},{0xA748,0xA748},
{0xA74A,0xA74A},{0xA74C,0xA74C},{0xA74E,0xA74E},{0xA750,0xA750},{0xA752,0xA752},{0xA754,0xA754},
{0xA756,0xA756},{0xA758,0xA758},{0xA75A,0xA75A},{0xA75C,0xA75C},{0xA75E,0xA75E},{0xA760,0xA760},
{0xA762,0xA762},{0xA764,0xA764},{0xA766,0xA766},{0xA768,0xA768},{0xA76A,0xA76A},{0xA76C,0xA76C},
{0xA76E,0xA76E},{0xA779,0xA779},{0xA77B,0xA77B},{0xA77D,0xA77E},{0xA780,0xA780},{0xA782,0xA782},
{0xA784,0xA784},{0xA786,0xA786},{0xA78B,0xA78B},{0xA78D,0xA78D},{0xA790,0xA790},{0xA792,0xA792},
{0xA796,0xA796},{0xA798,0xA798},{0xA79A,0xA79A},{0xA79C,0xA79C},{0xA79E,0xA79E},{0xA7A0,0xA7A0},
{0xA7A2,0xA7A2},{0xA7A4,0xA7A4},{0xA7A6,0xA7A6},{0xA7A8,0xA7A8},{0xA7AA,0xA7AE},{0xA7B0,0xA7B4},
{0xA7B6,0xA7B6},{0xA7B8,0xA7B8},{0xA7BA,0xA7BA},{0xA7BC,0xA7BC},{0xA7BE,0xA7BE},{0xA7C2,0xA7C2},
{0xA7C4,0xA7C7},{0xA7C9,0xA7C9},{0xA7F5,0xA7F5},{0xFF21,0xFF3A},{0x10400,0x10427},{0x104B0,0x104D3},
{0x10C80,0x10CB2},{0x118A0,0x118BF},{0x16E40,0x16E5F},{0x1D400,0x1D419},{0x1D434,0x1D44D},{0x1D468,0x1D481},
{0x1D49C,0x1D49C},{0x1D49E,0x1D49F},{0x1D4A2,0x1D4A2},{0x1D4A5,0x1D4A6},{0x1D4A9,0x1D4AC},{0x1D4AE,0x1D4B5},
{0x1D4D0,0x1D4E9},{0x1D504,0x1D505},{0x1D507,0x1D50A},{0x1D50D,0x1D514},{0x1D516,0x1D51C},{0x1D538,0x1D539},
{0x1D53B,0x1D53E},{0x1D540,0x1D544},{0x1D546,0x1D546},{0x1D54A,0x1D550},{0x1D56C,0x1D585},{0x1D5A0,0x1D5B9},
{0x1D5D4,0x1D5ED},{0x1D608,0x1D621},{0x1D63C,0x1D655},{0x1D670,0x1D689},{0x1D6A8,0x1D6C0},{0x1D6E2,0x1D6FA},
{0x1D71C,0x1D734},{0x1D756,0x1D76E},{0x1D790,0x1D7A8},{0x1D7CA,0x1D7CA},{0x1E900,0x1E921},
};
static const int uni_U_n = 641;
static const uint32_t uni_X[][2] = {
{0xAA,0xAA},{0xBA,0xBA},{0x1BB,0x1BB},{0x1C0,0x1C3},{0x294,0x294},{0x2B0,0x2C1},
{0x2C6,0x2D1},{0x2E0,0x2E4},{0x2EC,0x2EC},{0x2EE,0x2EE},{0x300,0x36F},{0x374,0x374},
{0x37A,0x37A},{0x483,0x489},{0x559,0x559},{0x591,0x5BD},{0x5BF,0x5BF},{0x5C1,0x5C2},
{0x5C4,0x5C5},{0x5C7,0x5C7},{0x5D0,0x5EA},{0x5EF,0x5F2},{0x610,0x61A},{0x620,0x65F},
{0x66E,0x6D3},{0x6D5,0x6DC},{0x6DF,0x6E8},{0x6EA,0x6EF},{0x6FA,0x6FC},{0x6FF,0x6FF},
{0x710,0x74A},{0x74D,0x7B1},{0x7CA,0x7F5},{0x7FA,0x7FA},{0x7FD,0x7FD},{0x800,0x82D},
{0x840,0x85B},{0x860,0x86A},{0x8A0,0x8B4},{0x8B6,0x8C7},{0x8D3,0x8E1},{0x8E3,0x963},
{0x971,0x983},{0x985,0x98C},{0x98F,0x990},{0x993,0x9A8},{0x9AA,0x9B0},{0x9B2,0x9B2},
{0x9B6,0x9B9},{0x9BC,0x9C4},{0x9C7,0x9C8},{0x9CB,0x9CE},{0x9D7,0x9D7},{0x9DC,0x9DD},
{0x9DF,0x9E3},{0x9F0,0x9F1},{0x9FC,0x9FC},{0x9FE,0x9FE},{0xA01,0xA03},{0xA05,0xA0A},
{0xA0F,0xA10},{0xA13,0xA28},{0xA2A,0xA30},{0xA32,0xA33},{0xA35,0xA36},{0xA38,0xA39},
{0xA3C,0xA3C},{0xA3E,0xA42},{0xA47,0xA48},{0xA4B,0xA4D},{0xA51,0xA51},{0xA59,0xA5C},
{0xA5E,0xA5E},{0xA70,0xA75},{0xA81,0xA83},{0xA85,0xA8D},{0xA8F,0xA91},{0xA93,0xAA8},
{0xAAA,0xAB0},{0xAB2,0xAB3},{0xAB5,0xAB9},{0xABC,0xAC5},{0xAC7,0xAC9},{0xACB,0xACD},
{0xAD0,0xAD0},{0xAE0,0xAE3},{0xAF9,0xAFF},{0xB01,0xB03},{0xB05,0xB0C},{0xB0F,0xB10},
{0xB13,0xB28},{0xB2A,0xB30},{0xB32,0xB33},{0xB35,0xB39},{0xB3C,0xB44},{0xB47,0xB48},
{0xB4B,0xB4D},{0xB55,0xB57},{0xB5C,0xB5D},{0xB5F,0xB63},{0xB71,0xB71},{0xB82,0xB83},
{0xB85,0xB8A},{0xB8E,0xB90},{0xB92,0xB95},{0xB99,0xB9A},{0xB9C,0xB9C},{0xB9E,0xB9F},
{0xBA3,0xBA4},{0xBA8,0xBAA},{0xBAE,0xBB9},{0xBBE,0xBC2},{0xBC6,0xBC8},{0xBCA,0xBCD},
{0xBD0,0xBD0},{0xBD7,0xBD7},{0xC00,0xC0C},{0xC0E,0xC10},{0xC12,0xC28},{0xC2A,0xC39},
{0xC3D,0xC44},{0xC46,0xC48},{0xC4A,0xC4D},{0xC55,0xC56},{0xC58,0xC5A},{0xC60,0xC63},
{0xC80,0xC83},{0xC85,0xC8C},{0xC8E,0xC90},{0xC92,0xCA8},{0xCAA,0xCB3},{0xCB5,0xCB9},
{0xCBC,0xCC4},{0xCC6,0xCC8},{0xCCA,0xCCD},{0xCD5,0xCD6},{0xCDE,0xCDE},{0xCE0,0xCE3},
{0xCF1,0xCF2},{0xD00,0xD0C},{0xD0E,0xD10},{0xD12,0xD44},{0xD46,0xD48},{0xD4A,0xD4E},
{0xD54,0xD57},{0xD5F,0xD63},{0xD7A,0xD7F},{0xD81,0xD83},{0xD85,0xD96},{0xD9A,0xDB1},
{0xDB3,0xDBB},{0xDBD,0xDBD},{0xDC0,0xDC6},{0xDCA,0xDCA},{0xDCF,0xDD4},{0xDD6,0xDD6},
{0xDD8,0xDDF},{0xDF2,0xDF3},{0xE01,0xE3A},{0xE40,0xE4E},{0xE81,0xE82},{0xE84,0xE84},
{0xE86,0xE8A},{0xE8C,0xEA3},{0xEA5,0xEA5},{0xEA7,0xEBD},{0xEC0,0xEC4},{0xEC6,0xEC6},
{0xEC8,0xECD},{0xEDC,0xEDF},{0xF00,0xF00},{0xF18,0xF19},{0xF35,0xF35},{0xF37,0xF37},
{0xF39,0xF39},{0xF3E,0xF47},{0xF49,0xF6C},{0xF71,0xF84},{0xF86,0xF97},{0xF99,0xFBC},
{0xFC6,0xFC6},{0x1000,0x103F},{0x1050,0x108F},{0x109A,0x109D},{0x10FC,0x10FC},{0x1100,0x1248},
{0x124A,0x124D},{0x1250,0x1256},{0x1258,0x1258},{0x125A,0x125D},{0x1260,0x1288},{0x128A,0x128D},
{0x1290,0x12B0},{0x12B2,0x12B5},{0x12B8,0x12BE},{0x12C0,0x12C0},{0x12C2,0x12C5},{0x12C8,0x12D6},
{0x12D8,0x1310},{0x1312,0x1315},{0x1318,0x135A},{0x135D,0x135F},{0x1380,0x138F},{0x1401,0x166C},
{0x166F,0x167F},{0x1681,0x169A},{0x16A0,0x16EA},{0x16F1,0x16F8},{0x1700,0x170C},{0x170E,0x1714},
{0x1720,0x1734},{0x1740,0x1753},{0x1760,0x176C},{0x176E,0x1770},{0x1772,0x1773},{0x1780,0x17D3},
{0x17D7,0x17D7},{0x17DC,0x17DD},{0x180B,0x180D},{0x1820,0x1878},{0x1880,0x18AA},{0x18B0,0x18F5},
{0x1900,0x191E},{0x1920,0x192B},{0x1930,0x193B},{0x1950,0x196D},{0x1970,0x1974},{0x1980,0x19AB},
{0x19B0,0x19C9},{0x1A00,0x1A1B},{0x1A20,0x1A5E},{0x1A60,0x1A7C},{0x1A7F,0x1A7F},{0x1AA7,0x1AA7},
{0x1AB0,0x1AC0},{0x1B00,0x1B4B},{0x1B6B,0x1B73},{0x1B80,0x1BAF},{0x1BBA,0x1BF3},{0x1C00,0x1C37},
{0x1C4D,0x1C4F},{0x1C5A,0x1C7D},{0x1CD0,0x1CD2},{0x1CD4,0x1CFA},{0x1D2C,0x1D6A},{0x1D78,0x1D78},
{0x1D9B,0x1DF9},{0x1DFB,0x1DFF},{0x2071,0x2071},{0x207F,0x207F},{0x2090,0x209C},{0x20D0,0x20F0},
{0x2135,0x2138},{0x2C7C,0x2C7D},{0x2CEF,0x2CF1},{0x2D30,0x2D67},{0x2D6F,0x2D6F},{0x2D7F,0x2D96},
{0x2DA0,0x2DA6},{0x2DA8,0x2DAE},{0x2DB0,0x2DB6},{0x2DB8,0x2DBE},{0x2DC0,0x2DC6},{0x2DC8,0x2DCE},
{0x2DD0,0x2DD6},{0x2DD8,0x2DDE},{0x2DE0,0x2DFF},{0x2E2F,0x2E2F},{0x3005,0x3006},{0x302A,0x302F},
{0x3031,0x3035},{0x303B,0x303C},{0x3041,0x3096},{0x3099,0x309A},{0x309D,0x309F},{0x30A1,0x30FA},
{0x30FC,0x30FF},{0x3105,0x312F},{0x3131,0x318E},{0x31A0,0x31BF},{0x31F0,0x31FF},{0x3400,0x4DBF},
{0x4E00,0x9FFC},{0xA000,0xA48C},{0xA4D0,0xA4FD},{0xA500,0xA60C},{0xA610,0xA61F},{0xA62A,0xA62B},
{0xA66E,0xA672},{0xA674,0xA67D},{0xA67F,0xA67F},{0xA69C,0xA6E5},{0xA6F0,0xA6F1},{0xA717,0xA71F},
{0xA770,0xA770},{0xA788,0xA788},{0xA78F,0xA78F},{0xA7F7,0xA7F9},{0xA7FB,0xA827},{0xA82C,0xA82C},
{0xA840,0xA873},{0xA880,0xA8C5},{0xA8E0,0xA8F7},{0xA8FB,0xA8FB},{0xA8FD,0xA8FF},{0xA90A,0xA92D},
{0xA930,0xA953},{0xA960,0xA97C},{0xA980,0xA9C0},{0xA9CF,0xA9CF},{0xA9E0,0xA9EF},{0xA9FA,0xA9FE},
{0xAA00,0xAA36},{0xAA40,0xAA4D},{0xAA60,0xAA76},{0xAA7A,0xAAC2},{0xAADB,0xAADD},{0xAAE0,0xAAEF},
{0xAAF2,0xAAF6},{0xAB01,0xAB06},{0xAB09,0xAB0E},{0xAB11,0xAB16},{0xAB20,0xAB26},{0xAB28,0xAB2E},
{0xAB5C,0xAB5F},{0xAB69,0xAB69},{0xABC0,0xABEA},{0xABEC,0xABED},{0xAC00,0xD7A3},{0xD7B0,0xD7C6},
{0xD7CB,0xD7FB},{0xF900,0xFA6D},{0xFA70,0xFAD9},{0xFB1D,0xFB28},{0xFB2A,0xFB36},{0xFB38,0xFB3C},
{0xFB3E,0xFB3E},{0xFB40,0xFB41},{0xFB43,0xFB44},{0xFB46,0xFBB1},{0xFBD3,0xFD3D},{0xFD50,0xFD8F},
{0xFD92,0xFDC7},{0xFDF0,0xFDFB},{0xFE00,0xFE0F},{0xFE20,0xFE2F},{0xFE70,0xFE74},{0xFE76,0xFEFC},
{0xFF66,0xFFBE},{0xFFC2,0xFFC7},{0xFFCA,0xFFCF},{0xFFD2,0xFFD7},{0xFFDA,0xFFDC},{0x10000,0x1000B},
{0x1000D,0x10026},{0x10028,0x1003A},{0x1003C,0x1003D},{0x1003F,0x1004D},{0x10050,0x1005D},{0x10080,0x100FA},
{0x101FD,0x101FD},{0x10280,0x1029C},{0x102A0,0x102D0},{0x102E0,0x102E0},{0x10300,0x1031F},{0x1032D,0x10340},
{0x10342,0x10349},{0x10350,0x1037A},{0x10380,0x1039D},{0x103A0,0x103C3},{0x103C8,0x103CF},{0x10450,0x1049D},
{0x10500,0x10527},{0x10530,0x10563},{0x10600,0x10736},{0x10740,0x10755},{0x10760,0x10767},{0x10800,0x10805},
{0x10808,0x10808},{0x1080A,0x10835},{0x10837,0x10838},{0x1083C,0x1083C},{0x1083F,0x10855},{0x10860,0x10876},
{0x10880,0x1089E},{0x108E0,0x108F2},{0x108F4,0x108F5},{0x10900,0x10915},{0x10920,0x10939},{0x10980,0x109B7},
{0x109BE,0x109BF},{0x10A00,0x10A03},{0x10A05,0x10A06},{0x10A0C,0x10A13},{0x10A15,0x10A17},{0x10A19,0x10A35},
{0x10A38,0x10A3A},{0x10A3F,0x10A3F},{0x10A60,0x10A7C},{0x10A80,0x10A9C},{0x10AC0,0x10AC7},{0x10AC9,0x10AE6},
{0x10B00,0x10B35},{0x10B40,0x10B55},{0x10B60,0x10B72},{0x10B80,0x10B91},{0x10C00,0x10C48},{0x10D00,0x10D27},
{0x10E80,0x10EA9},{0x10EAB,0x10EAC},{0x10EB0,0x10EB1},{0x10F00,0x10F1C},{0x10F27,0x10F27},{0x10F30,0x10F50},
{0x10FB0,0x10FC4},{0x10FE0,0x10FF6},{0x11000,0x11046},{0x1107F,0x110BA},{0x110D0,0x110E8},{0x11100,0x11134},
{0x11144,0x11147},{0x11150,0x11173},{0x11176,0x11176},{0x11180,0x111C4},{0x111C9,0x111CC},{0x111CE,0x111CF},
{0x111DA,0x111DA},{0x111DC,0x111DC},{0x11200,0x11211},{0x11213,0x11237},{0x1123E,0x1123E},{0x11280,0x11286},
{0x11288,0x11288},{0x1128A,0x1128D},{0x1128F,0x1129D},{0x1129F,0x112A8},{0x112B0,0x112EA},{0x11300,0x11303},
{0x11305,0x1130C},{0x1130F,0x11310},{0x11313,0x11328},{0x1132A,0x11330},{0x11332,0x11333},{0x11335,0x11339},
{0x1133B,0x11344},{0x11347,0x11348},{0x1134B,0x1134D},{0x11350,0x11350},{0x11357,0x11357},{0x1135D,0x11363},
{0x11366,0x1136C},{0x11370,0x11374},{0x11400,0x1144A},{0x1145E,0x11461},{0x11480,0x114C5},{0x114C7,0x114C7},
{0x11580,0x115B5},{0x115B8,0x115C0},{0x115D8,0x115DD},{0x11600,0x11640},{0x11644,0x11644},{0x11680,0x116B8},
{0x11700,0x1171A},{0x1171D,0x1172B},{0x11800,0x1183A},{0x118FF,0x11906},{0x11909,0x11909},{0x1190C,0x11913},
{0x11915,0x11916},{0x11918,0x11935},{0x11937,0x11938},{0x1193B,0x11943},{0x119A0,0x119A7},{0x119AA,0x119D7},
{0x119DA,0x119E1},{0x119E3,0x119E4},{0x11A00,0x11A3E},{0x11A47,0x11A47},{0x11A50,0x11A99},{0x11A9D,0x11A9D},
{0x11AC0,0x11AF8},{0x11C00,0x11C08},{0x11C0A,0x11C36},{0x11C38,0x11C40},{0x11C72,0x11C8F},{0x11C92,0x11CA7},
{0x11CA9,0x11CB6},{0x11D00,0x11D06},{0x11D08,0x11D09},{0x11D0B,0x11D36},{0x11D3A,0x11D3A},{0x11D3C,0x11D3D},
{0x11D3F,0x11D47},{0x11D60,0x11D65},{0x11D67,0x11D68},{0x11D6A,0x11D8E},{0x11D90,0x11D91},{0x11D93,0x11D98},
{0x11EE0,0x11EF6},{0x11FB0,0x11FB0},{0x12000,0x12399},{0x12480,0x12543},{0x13000,0x1342E},{0x14400,0x14646},
{0x16800,0x16A38},{0x16A40,0x16A5E},{0x16AD0,0x16AED},{0x16AF0,0x16AF4},{0x16B00,0x16B36},{0x16B40,0x16B43},
{0x16B63,0x16B77},{0x16B7D,0x16B8F},{0x16F00,0x16F4A},{0x16F4F,0x16F87},{0x16F8F,0x16F9F},{0x16FE0,0x16FE1},
{0x16FE3,0x16FE4},{0x16FF0,0x16FF1},{0x17000,0x187F7},{0x18800,0x18CD5},{0x18D00,0x18D08},{0x1B000,0x1B11E},
{0x1B150,0x1B152},{0x1B164,0x1B167},{0x1B170,0x1B2FB},{0x1BC00,0x1BC6A},{0x1BC70,0x1BC7C},{0x1BC80,0x1BC88},
{0x1BC90,0x1BC99},{0x1BC9D,0x1BC9E},{0x1D165,0x1D169},{0x1D16D,0x1D172},{0x1D17B,0x1D182},{0x1D185,0x1D18B},
{0x1D1AA,0x1D1AD},{0x1D242,0x1D244},{0x1DA00,0x1DA36},{0x1DA3B,0x1DA6C},{0x1DA75,0x1DA75},{0x1DA84,0x1DA84},
{0x1DA9B,0x1DA9F},{0x1DAA1,0x1DAAF},{0x1E000,0x1E006},{0x1E008,0x1E018},{0x1E01B,0x1E021},{0x1E023,0x1E024},
{0x1E026,0x1E02A},{0x1E100,0x1E12C},{0x1E130,0x1E13D},{0x1E14E,0x1E14E},{0x1E2C0,0x1E2EF},{0x1E800,0x1E8C4},
{0x1E8D0,0x1E8D6},{0x1E944,0x1E94B},{0x1EE00,0x1EE03},{0x1EE05,0x1EE1F},{0x1EE21,0x1EE22},{0x1EE24,0x1EE24},
{0x1EE27,0x1EE27},{0x1EE29,0x1EE32},{0x1EE34,0x1EE37},{0x1EE39,0x1EE39},{0x1EE3B,0x1EE3B},{0x1EE42,0x1EE42},
{0x1EE47,0x1EE47},{0x1EE49,0x1EE49},{0x1EE4B,0x1EE4B},{0x1EE4D,0x1EE4F},{0x1EE51,0x1EE52},{0x1EE54,0x1EE54},
{0x1EE57,0x1EE57},{0x1EE59,0x1EE59},{0x1EE5B,0x1EE5B},{0x1EE5D,0x1EE5D},{0x1EE5F,0x1EE5F},{0x1EE61,0x1EE62},
{0x1EE64,0x1EE64},{0x1EE67,0x1EE6A},{0x1EE6C,0x1EE72},{0x1EE74,0x1EE77},{0x1EE79,0x1EE7C},{0x1EE7E,0x1EE7E},
{0x1EE80,0x1EE89},{0x1EE8B,0x1EE9B},{0x1EEA1,0x1EEA3},{0x1EEA5,0x1EEA9},{0x1EEAB,0x1EEBB},{0x20000,0x2A6DD},
{0x2A700,0x2B734},{0x2B740,0x2B81D},{0x2B820,0x2CEA1},{0x2CEB0,0x2EBE0},{0x2F800,0x2FA1D},{0x30000,0x3134A},
{0xE0100,0xE01EF},
};
static const int uni_X_n = 595;
static inline int is_U(uint32_t c){ return uni_in(uni_U,uni_U_n,c); }
static inline int is_X(uint32_t c){ return uni_in(uni_X,uni_X_n,c); }
#endif
c/tools/convert_fp8_to_int4.py
+90 -2
View File
@@ -83,6 +83,29 @@ def quant_int4_grouped(w, bits, gs=128):
s_flat = s[:, :, 0].astype(np.float32).reshape(-1)
return out.reshape(-1), s_flat
def quant_int3_g64(w, bits=3, group=64): # -> (qbytes U8 [O*ceil(I/64)*24], scales f32 [O*ceil(I/64)])
"""int3 with PER-GROUP scales (fmt=5 in colibri.c): per 64-input group, symmetric absmax
(qmax=3, clamp [-4,3], stored v+4), packed as 16B low plane (2 bits/val, int2 layout)
+ 8B high plane (1 bit/val). Same math as quant_ablation._quant_last_dim(bits=3,
group=64) (#132), here with real packing. 3.5 bits/weight effective."""
O, I = w.shape
ng = (I + group - 1) // group
pad = ng * group - I
wp = np.pad(w, ((0, 0), (0, pad))) if pad else w
g = wp.reshape(O, ng, group)
amax = np.abs(g).max(axis=2, keepdims=True)
s = np.maximum(amax / 3.0, 1e-8)
q = (np.clip(np.rint(g / s), -4, 3).astype(np.int32) + 4).astype(np.uint8) # 0..7
if pad: q[:, -1, group - pad:] = 4 # pad packs as 0 after -4
lo = np.zeros((O, ng, 16), np.uint8)
for k in range(4):
lo |= ((q[:, :, k::4] & 3) << (k * 2)).astype(np.uint8)
hi = np.zeros((O, ng, 8), np.uint8)
for b in range(8):
hi |= (((q[:, :, b::8] >> 2) & 1) << b).astype(np.uint8)
out = np.concatenate([lo, hi], axis=2) # [O, ng, 24]
return out.reshape(-1), s[:, :, 0].astype(np.float32).reshape(-1)
def quant_int2(w, bits): # -> (qbytes U8 [O*ceil(I/4)], scale f32 [O]); 4/byte
O, I = w.shape
qmax = (1 << (bits - 1)) - 1 # bits=2 -> qmax=1, valori [-2,1]
@@ -214,6 +237,14 @@ def dequant(f, name, keys):
return (w * sc).numpy()
return f.get_tensor(name).to(torch.float32).numpy()
# Per-projection bit overrides for ROUTED experts (gate_proj/up_proj/down_proj), set from
# --up-bits/--gate-bits/--down-bits in main(). Empty = uniform xbits. Motivated by the
# measured result that up_proj tolerates int3-g64 at ~zero quality cost while int2 craters
# (OLMoE ablation, PR #168 comment): up-only int3 drops ~8% of expert bytes for free.
# NB: the resume manifests (check_or_record_params and the --indir progress file) already
# record dict(PROJ_BITS) — this global is the definition those sites depend on.
PROJ_BITS = {}
def convert_shard(path, out_dict, n_layers, ebits, io_bits, xbits,
keep_mtp=False, keep_idx=False, group_size=0, bits_map=None):
from safetensors import safe_open
@@ -235,9 +266,16 @@ def convert_shard(path, out_dict, n_layers, ebits, io_bits, xbits,
# Any unknown kind that fell through classify as "q"
if bits_map and kind not in bits_map and kind not in ("io", "x", "sh", "o", "kvb", "attn", "dmlp"):
bits = ebits
# Per-projection override for routed experts, applied on top of the type-level bits.
if kind == "x" and PROJ_BITS: # e.g. up_proj -> 3 (int3-g64) while gate/down stay 4
for proj, pb in PROJ_BITS.items():
if f".{proj}.weight" in name: bits = pb; break
if w.ndim != 2: # es. bias 1D non previsto come 'q' -> tienilo f32
out_dict[name] = w.astype(np.float32); continue
if group_size > 0 and bits <= 4:
if bits == 3:
# int3-g64 (fmt=5): inherently group-64, distinct from grouped-int4.
q, s = quant_int3_g64(w)
elif group_size > 0 and bits <= 4:
q, s = quant_int4_grouped(w, bits, group_size)
else:
q, s = (quant_int2(w, bits) if bits <= 2 else
@@ -247,6 +285,32 @@ def convert_shard(path, out_dict, n_layers, ebits, io_bits, xbits,
def free_gb(p): return shutil.disk_usage(p).free / 1e9
def check_or_record_params(outdir, prefix, params):
"""#383-class guard, mirrored onto the --repo download loops from the --indir
path's resume manifest (below): a resumed run with DIFFERENT conversion
parameters (bits, group size, PROJ_BITS, ...) must not silently mix bit-widths
across shards in the same outdir -- the #355 failure mode (a second pass with
changed flags overwriting/interleaving with a finished container in silence).
Unlike the --indir manifest this doesn't need to track per-shard completion:
the --repo loops already do that via out-NNNNN.safetensors existence, since
shard index maps directly to output filename there. Only whether the params
used SO FAR match this run's needs checking. Returns False (caller should
abort) on a mismatch, True otherwise; records params on first use."""
path = os.path.join(outdir, f".{prefix}params.json")
if os.path.exists(path):
try: prev = json.loads(open(path).read())
except (OSError, ValueError): prev = None
if prev is not None and prev != params:
print(f"ERROR: {path} records a conversion with {prev};\n"
f" this run uses {params}. Refusing to mix conversions in the "
f"same outdir — use a fresh --outdir (or delete {path} and the "
f"{prefix}*.safetensors shards to redo).")
return False
tmp = path + ".tmp"
with open(tmp, "w") as f: json.dump(params, f, indent=1) # atomic write, same reasoning as the --indir manifest
os.replace(tmp, path)
return True
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--repo", default=None)
@@ -269,6 +333,13 @@ def main():
help="bits for dense MLP (first 3 layers). Default=ebits")
ap.add_argument("--group-size", type=int, default=0, # 0 = per-row (backward compat); 128 = group-scaled
help="group size for int4 scales: 0=per-row (default), 128=one scale per 128 elements (much better quality)")
# Per-projection bit overrides for routed experts (orthogonal to the type-level flags above).
ap.add_argument("--up-bits", type=int, default=None,
help="bits for up_proj in routed experts (e.g. 3 = int3-g64). Default=xbits")
ap.add_argument("--gate-bits", type=int, default=None,
help="bits for gate_proj in routed experts. Default=xbits")
ap.add_argument("--down-bits", type=int, default=None,
help="bits for down_proj in routed experts. Default=xbits")
ap.add_argument("--n-layers", type=int, default=78)
ap.add_argument("--min-free-gb", type=float, default=20.0)
ap.add_argument("--selftest", action="store_true")
@@ -298,6 +369,10 @@ def main():
"embedding half -> MTP acceptance ~0% (issue #8). Use the default --ebits 8, "
"or add --group-size 128 for group-scaled int4.")
if a.xbits is None: a.xbits = a.ebits
for proj, val in (("gate_proj", a.gate_bits), ("up_proj", a.up_bits), ("down_proj", a.down_bits)):
if val is not None: PROJ_BITS[proj] = val
if PROJ_BITS:
print(f"[per-projection expert bits] {PROJ_BITS} (others -> xbits={a.xbits})")
# Build per-type bits map. If a type-specific arg is set, use it; otherwise the
# converter falls back to ebits for that type.
@@ -440,7 +515,8 @@ def main():
# EN: resume skips only what matches, and different parameters on the same
# EN: outdir are refused instead of mixing containers (the #355 failure mode).
params = {"ebits": a.ebits, "io_bits": a.io_bits, "xbits": a.xbits,
"group_size": a.group_size, "n_layers": a.n_layers, "bits_map": bits_map}
"group_size": a.group_size, "n_layers": a.n_layers, "bits_map": bits_map,
"proj_bits": dict(PROJ_BITS)}
prog_path = os.path.join(a.outdir, f".{prefix}progress.json")
prog = {}
if os.path.exists(prog_path):
@@ -718,6 +794,10 @@ def main():
except Exception: pass
tmp = os.path.join(a.outdir, "_inflight"); os.makedirs(tmp, exist_ok=True)
if a.mtp:
params = {"ebits": a.ebits, "io_bits": a.io_bits, "xbits": a.xbits,
"group_size": a.group_size, "n_layers": a.n_layers, "bits_map": bits_map,
"proj_bits": dict(PROJ_BITS)}
if not check_or_record_params(a.outdir, "out-mtp-", params): return
import urllib.request
idx = json.loads(urllib.request.urlopen(
f"https://huggingface.co/{a.repo}/resolve/main/model.safetensors.index.json", timeout=30).read())["weight_map"]
@@ -737,6 +817,10 @@ def main():
print(f" -> {os.path.basename(outp)} ({os.path.getsize(outp)/1e9:.2f} GB, {len(out)} tensors)", flush=True)
shutil.rmtree(tmp, ignore_errors=True); print("[MTP] DONE."); return
if a.indexer:
params = {"ebits": a.ebits, "io_bits": a.io_bits, "xbits": a.xbits,
"group_size": a.group_size, "n_layers": a.n_layers, "bits_map": bits_map,
"proj_bits": dict(PROJ_BITS)}
if not check_or_record_params(a.outdir, "out-idx-", params): return
import urllib.request
idx = json.loads(urllib.request.urlopen(
f"https://huggingface.co/{a.repo}/resolve/main/model.safetensors.index.json", timeout=30).read())["weight_map"]
@@ -756,6 +840,10 @@ def main():
if os.path.isfile(blob): os.remove(blob)
print(f" -> {os.path.basename(outp)} ({len(out)} tensors)", flush=True)
shutil.rmtree(tmp, ignore_errors=True); print("[IDX] DONE."); return
params = {"ebits": a.ebits, "io_bits": a.io_bits, "xbits": a.xbits,
"group_size": a.group_size, "n_layers": a.n_layers, "bits_map": bits_map,
"proj_bits": dict(PROJ_BITS)}
if not check_or_record_params(a.outdir, "out-", params): return
for i, sh in enumerate(shards):
if free_gb(a.outdir) < a.min_free_gb:
print(f"STOP: free space is below {a.min_free_gb} GB. Free space and rerun to resume."); break
c/tools/diag_harness.py
+704
View File
@@ -0,0 +1,704 @@
#!/usr/bin/env python3
"""
diag_harness.py — Comprehensive model diagnostic harness for the colibri GLM-5.2 engine.
Runs a full campaign of tests against any model snapshot:
Phase 0 (system): startup telemetry — GPU, RAM, cache cap, load time, MTP, idot kernel
Phase 1 (smoke): correctness — curated prompts, coherence checks, corruption detection
Phase 2 (diagnostic): deep x-ray — full PROFILE breakdown, routing, MTP, disk-split, CUDA tier
Phase 3 (quality): benchmark accuracy — hellaswag/arc_challenge/mmlu via eval_glm.py SCORE
Phase 4 (throughput): tok/s with and without MTP speculation
Phase 5 (report): structured JSON + human-readable Markdown summary
Usage:
python tools/diag_harness.py --snap /path/to/model --phase all
python tools/diag_harness.py --snap /path/to/model --phase smoke --ngen 64
python tools/diag_harness.py --snap /path/to/model --phase quality --quality-limit 200
Output goes to --out (default ./diag_results/<timestamp>/). Each phase writes a raw log
(<phase>_<run>.log) and all metrics are collected into report.json + report.md.
Design notes:
- stdout and stderr are captured separately via subprocess.PIPE. The engine streams
generated text to stdout (interleaved with prompt + PROFILE stats), but TOKENS=1 dumps
clean token-id lists to stderr — that is the primary text-capture path.
- Every regex is anchored to the exact printf format strings in glm.c (verified against
profile_print line 3853, run_text line 3948, the banner line 5299, etc.).
- Subprocess calls have a hard timeout (default 600s) and are killed cleanly on expiry.
- A single phase can be run standalone; results accumulate in the output dir.
"""
import os, sys, re, json, time, argparse, subprocess, signal, traceback
from datetime import datetime
from pathlib import Path
# ---------------------------------------------------------------------------
# PROMPT SUITE — curated across categories. "expect" is a case-insensitive
# substring checked against the generated text (None = coherence-only check).
# ---------------------------------------------------------------------------
PROMPTS = [
{"id":"fact_capital", "cat":"factual", "prompt":"The capital of France is",
"expect":"Paris", "note":"basic world knowledge"},
{"id":"fact_boiling", "cat":"factual", "prompt":"What is the boiling point of water in Celsius?",
"expect":"100", "note":"basic science"},
{"id":"fact_planet", "cat":"factual", "prompt":"What planet is closest to the Sun?",
"expect":"Mercury","note":"astronomy fact"},
{"id":"math_mult", "cat":"math", "prompt":"What is 15 times 12?",
"expect":"180", "note":"2-digit multiplication"},
{"id":"math_add", "cat":"math", "prompt":"What is 847 plus 153?",
"expect":"1000", "note":"3-digit addition"},
{"id":"reason_train", "cat":"reasoning", "prompt":"If a train travels 60 mph for 2.5 hours, how far does it go?",
"expect":"150", "note":"rate-time-distance"},
{"id":"code_factorial","cat":"code", "prompt":"Write a Python function that computes the factorial of a number.",
"expect":"def", "note":"code generation"},
{"id":"explain_nn", "cat":"explanation","prompt":"Explain what a neural network is in one sentence.",
"expect":None, "note":"coherence check"},
{"id":"creative_story","cat":"creative", "prompt":"Write a one-sentence story about a lighthouse.",
"expect":None, "note":"creative coherence"},
{"id":"edge_hello", "cat":"edge", "prompt":"Hello, how are you today?",
"expect":None, "note":"conversational opener"},
{"id":"edge_the", "cat":"edge", "prompt":"The weather today is",
"expect":None, "note":"simple continuation"},
{"id":"edge_repeat", "cat":"edge", "prompt":"The quick brown fox jumps over the lazy dog. The quick brown fox",
"expect":None, "note":"repetition-bait"},
]
# ---------------------------------------------------------------------------
# METRIC EXTRACTION — each parser is (name, compiled_regex, group_index, cast).
# Regexes match the EXACT printf format strings in glm.c.
# ---------------------------------------------------------------------------
def _f1(g): return float(g)
def _i1(g): return int(g)
# Build parsers as (regex, lambda(match)->value) for clarity
RX = {
# stdout: run_text summary line (line 3948)
"decode_toks": (re.compile(r"decode (\d+) tokens in"), lambda m: int(m.group(1))),
"decode_secs": (re.compile(r"decode \d+ tokens in ([\d.]+)s"), lambda m: float(m.group(1))),
"decode_tps": (re.compile(r"decode \d+ tokens in [\d.]+s \(([\d.]+) tok/s\)"), lambda m: float(m.group(1))),
"prefill_toks": (re.compile(r"prefill (\d+) tokens in"), lambda m: int(m.group(1))),
"prefill_secs": (re.compile(r"prefill \d+ tokens in ([\d.]+)s"), lambda m: float(m.group(1))),
"hit_rate": (re.compile(r"expert hit rate ([\d.]+)%"), lambda m: float(m.group(1))),
"rss_gb": (re.compile(r"RSS ([\d.]+) GB"), lambda m: float(m.group(1))),
"experts_per_tok":(re.compile(r"experts loaded/token: ([\d.]+)"), lambda m: float(m.group(1))),
"mtp_accept": (re.compile(r"MTP acceptance ([\d.]+)%"), lambda m: float(m.group(1))),
"mtp_acc_cnt": (re.compile(r"MTP acceptance \d+% \((\d+)/(\d+)\)"), lambda m: (int(m.group(1)), int(m.group(2)))),
"spec_tok_per_fw":(re.compile(r"speculation: ([\d.]+) tokens/forward"),lambda m: float(m.group(1))),
# stdout: PROFILE lines (profile_print, line 3853-3864)
"prof_expert_disk": (re.compile(r"expert-disk ([\d.]+)s service"), lambda m: float(m.group(1))),
"prof_expert_wait": (re.compile(r"service / ([\d.]+)s wait"), lambda m: float(m.group(1))),
"prof_expert_mm": (re.compile(r"expert-matmul ([\d.]+)s"), lambda m: float(m.group(1))),
"prof_attention": (re.compile(r"\| attention ([\d.]+)s"), lambda m: float(m.group(1))),
"prof_kvb": (re.compile(r"including kvb ([\d.]+)s"), lambda m: float(m.group(1))),
"prof_lm_head": (re.compile(r"lm_head ([\d.]+)s"), lambda m: float(m.group(1))),
"prof_other": (re.compile(r"\| other ([\d.]+)s"), lambda m: float(m.group(1))),
# stdout: banner (line 5299)
"load_secs": (re.compile(r"loaded in ([\d.]+)s"), lambda m: float(m.group(1))),
"resident_mb": (re.compile(r"resident dense: ([\d.]+) MB"), lambda m: float(m.group(1))),
"mtp_status": (re.compile(r"MTP (ACTIVE|absent)"), lambda m: m.group(1)),
"n_layers": (re.compile(r"layers=(\d+) experts=(\d+)"), lambda m: int(m.group(1))),
"n_experts": (re.compile(r"layers=(\d+) experts=(\d+)"), lambda m: int(m.group(2))),
# stdout: banner idot kernel
"idot_kernel": (re.compile(r"idot: (\S+) =="), lambda m: m.group(1)),
"cache_cap": (re.compile(r"cache=(\d+) experts/layer"), lambda m: int(m.group(1))),
# stderr: RAM_GB (line 5086-5112)
"ram_budget": (re.compile(r"\[RAM_GB=([\d.]+)"), lambda m: float(m.group(1))),
"cap_lowered": (re.compile(r"cap lowered (\d+)->(\d+)"), lambda m: (int(m.group(1)), int(m.group(2)))),
"cap_raised": (re.compile(r"cap raised (\d+)->(\d+)"), lambda m: (int(m.group(1)), int(m.group(2)))),
"cap_ok": (re.compile(r"cap=(\d+) ok"), lambda m: int(m.group(1))),
# stderr: CUDA (backend_cuda.cu:389)
"cuda_device": (re.compile(r"\[CUDA\] device (\d+): (.*?), ([\d.]+) GB VRAM, sm_(\d)(\d)"),
lambda m: {"id":int(m.group(1)),"name":m.group(2).strip(),"vram_gb":float(m.group(3)),
"sm":f"{m.group(4)}.{m.group(5)}"}),
"cuda_mode": (re.compile(r"\[CUDA\] mode: (.+)"), lambda m: m.group(1)),
"cuda_tier": (re.compile(r"CUDA expert tier: (\d+) resident experts \(([\d.]+) GB\)"),
lambda m: {"resident":int(m.group(1)),"vram_gb":float(m.group(2))}),
# stderr: TOKENS dump (line 4010-4012)
"tokens_dump": (re.compile(r"^\[TOKENS\] (\d+) generated:(.*)$", re.MULTILINE),
lambda m: [int(x) for x in m.group(2).split()]),
# stderr: per-16-token progress (emit_stream line 3742)
"progress_tps": (re.compile(r"t=(\d+)\s+RSS ([\d.]+) GB\s+hit ([\d.]+)%\s+([\d.]+) tok/s\s+([\d.]+) tok/fw"),
lambda m: {"tok":int(m.group(1)),"rss":float(m.group(2)),"hit":float(m.group(3)),
"tps":float(m.group(4)),"tpf":float(m.group(5))}),
# stderr: DSA, USAGE, KV startup lines
"usage_loaded": (re.compile(r"\[USAGE\].*?(\d+) selections"), lambda m: int(m.group(1))),
"kv_slots": (re.compile(r"\[KV\].*?(\d+) context slots"), lambda m: int(m.group(1))),
}
def extract_metrics(stdout: str, stderr: str) -> dict:
"""Parse all metrics from the engine's stdout+stderr output."""
text_out = stdout or ""
text_err = stderr or ""
metrics = {}
# For most parsers we search BOTH streams (engine is inconsistent about which
# channel a given line lands on). Some are stream-specific (noted below).
combined = text_out + "\n" + text_err
for name, (rx, fn) in RX.items():
m = rx.search(combined)
if m:
try: metrics[name] = fn(m)
except (ValueError, IndexError): pass
# TOKENS dump is stderr-only and may appear once; grab it explicitly
m = RX["tokens_dump"][0].search(text_err)
if m:
try: metrics["tokens_dump"] = RX["tokens_dump"][1](m)
except: pass
# Multiple CUDA devices — collect all
cuda_devs = []
for m in re.finditer(r"\[CUDA\] device (\d+): (.*?), ([\d.]+) GB VRAM, sm_(\d)(\d)", text_err):
cuda_devs.append({"id":int(m.group(1)),"name":m.group(2).strip(),
"vram_gb":float(m.group(3)),"sm":f"{m.group(4)}.{m.group(5)}"})
if cuda_devs: metrics["cuda_devices"] = cuda_devs
# Multiple progress checkpoints — collect the full curve
progress = []
for m in RX["progress_tps"][0].finditer(text_err):
try:
progress.append(RX["progress_tps"][1](m))
except: pass
if progress: metrics["progress_curve"] = progress
# Multiple PROFILE lines — there are two (prefill + decode). Keep both.
prof_lines = [(i, line) for i, line in enumerate(text_out.splitlines()) if line.startswith("PROFILE:")]
for idx, (line_no, line) in enumerate(prof_lines):
label = "prefill_profile" if idx == 0 else "decode_profile"
p = {}
for pname, (rx, fn) in RX.items():
if not pname.startswith("prof_"): continue
mm = rx.search(line)
if mm:
try: p[pname] = fn(mm)
except: pass
if p: metrics[label] = p
return metrics
# ---------------------------------------------------------------------------
# ENGINE RUNNER — subprocess wrapper with timeout, signal handling, logging.
# ---------------------------------------------------------------------------
class EngineRunner:
def __init__(self, glm_path, snap, out_dir, default_env=None, timeout=600):
self.glm = str(glm_path)
self.snap = str(snap)
self.out_dir = Path(out_dir)
self.out_dir.mkdir(parents=True, exist_ok=True)
self.default_env = default_env or {}
self.timeout = timeout
self.default_cap = 75 # production default (matches bench_full.sh)
def run_prompt(self, prompt, ngen=64, env_extra=None, log_name=None, cap=None):
"""Run the engine in PROMPT mode and return (stdout, stderr, returncode, elapsed)."""
env = dict(os.environ, SNAP=self.snap, PROMPT=prompt, NGEN=str(ngen))
env.update(self.default_env)
if env_extra: env.update(env_extra)
env["TOKENS"] = "1" # always capture token ids for reliable text extraction
cmd = [self.glm, str(cap if cap is not None else self.default_cap)]
return self._exec(cmd, env, log_name)
def run_score(self, score_file, cap=None, env_extra=None, log_name=None):
"""Run the engine in SCORE (log-likelihood) mode."""
env = dict(os.environ, SNAP=self.snap, SCORE=score_file)
env.update(self.default_env)
if env_extra: env.update(env_extra)
cmd = [self.glm, str(cap if cap is not None else self.default_cap)]
return self._exec(cmd, env, log_name)
def _exec(self, cmd, env, log_name):
t0 = time.time()
log_path = self.out_dir / (log_name or f"run_{int(t0)}.log")
try:
proc = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
text=True, encoding="utf-8", errors="replace")
try:
stdout, stderr = proc.communicate(timeout=self.timeout)
rc = proc.returncode
except subprocess.TimeoutExpired:
proc.kill()
stdout, stderr = proc.communicate()
rc = -1
stderr = (stderr or "") + f"\n[TIMEOUT after {self.timeout}s]\n"
except Exception as e:
stdout, stderr, rc = "", f"[EXCEPTION] {e}\n{traceback.format_exc()}", -2
elapsed = time.time() - t0
# Write raw log (both streams, clearly delimited)
with open(log_path, "w", encoding="utf-8") as f:
f.write(f"=== CMD: {' '.join(cmd)}\n=== ELAPSED: {elapsed:.1f}s\n=== RC: {rc}\n\n")
f.write("--- STDOUT ---\n"); f.write(stdout or ""); f.write("\n")
f.write("--- STDERR ---\n"); f.write(stderr or ""); f.write("\n")
return stdout or "", stderr or "", rc, elapsed
# ---------------------------------------------------------------------------
# TEXT RECOVERY — decode the [TOKENS] ids back to text using the tokenizer.
# Falls back to stdout extraction if tokenizer/tokens unavailable.
# ---------------------------------------------------------------------------
def recover_text(stdout, stderr, prompt, tokenizer=None):
"""Extract generated text. Primary: TOKENS dump decoded. Fallback: stdout parse."""
# Method 1: decode TOKENS ids via tokenizer
if tokenizer:
m = re.search(r"^\[TOKENS\] \d+ generated:(.*)$", stderr, re.MULTILINE)
if m:
ids = [int(x) for x in m.group(1).split()]
if ids:
try:
return tokenizer.decode(ids), "tokens"
except Exception: pass
# Method 2: stdout — text is between the prompt string and "PROFILO" or "\n---"
text = stdout or ""
# Find the prompt in stdout, take everything after it up to PROFILO
idx = text.find(prompt)
if idx >= 0:
after = text[idx + len(prompt):]
cut = after.find("PROFILO")
if cut >= 0:
return after[:cut].strip(), "stdout"
cut = after.find("\n---")
if cut >= 0:
return after[:cut].strip(), "stdout"
return "", "none"
# ---------------------------------------------------------------------------
# CORRUPTION / COHERENCE CHECKS
# ---------------------------------------------------------------------------
def check_repetition(token_ids):
"""Detect degenerate repetition: same token 3+ consecutive times."""
if not token_ids or len(token_ids) < 6:
return False, 0
max_run = 1; cur = 1
for i in range(1, len(token_ids)):
if token_ids[i] == token_ids[i-1]: cur += 1; max_run = max(max_run, cur)
else: cur = 1
return max_run >= 3, max_run
def check_expected(text, expect):
"""Check if expected substring appears case-insensitively in the first 200 chars."""
if not expect or not text: return None
return expect.lower() in text[:200].lower()
# ---------------------------------------------------------------------------
# PHASES
# ---------------------------------------------------------------------------
class DiagnosticHarness:
def __init__(self, args):
self.args = args
self.snap = args.snap
self.glm = args.glm
ts = datetime.now().strftime("%Y%m%d_%H%M%S")
self.out_dir = Path(args.out or f"./diag_results/{ts}")
self.out_dir.mkdir(parents=True, exist_ok=True)
# Base env for all runs
base_env = {"TEMP": "0", "PIPE": "1", "PIPE_WORKERS": "8", "DIRECT": "1"}
if args.ram: base_env["RAM_GB"] = str(args.ram)
if args.cuda:
base_env.update({
"COLI_CUDA": "1",
"COLI_GPU": str(args.gpu) if args.gpu is not None else "0",
"CUDA_DENSE": "1",
"COLI_CUDA_ATTN": "1",
"COLI_CUDA_PIPE": "2",
"COLI_CUDA_PIPE_S_MIN": "1",
})
self.runner = EngineRunner(self.glm, self.snap, self.out_dir, base_env, timeout=args.timeout)
self.runner.default_cap = args.cap
# Load tokenizer for text recovery
self.tokenizer = None
tok_path = os.path.join(self.snap, "tokenizer.json")
if os.path.exists(tok_path):
try:
from tokenizers import Tokenizer
self.tokenizer = Tokenizer.from_file(tok_path)
except Exception as e:
print(f"[warn] could not load tokenizer: {e}", file=sys.stderr)
self.results = {"meta": {"snap": self.snap, "glm": self.glm,
"timestamp": ts, "args": vars(args)},
"phases": {}}
def phase_system(self):
"""Phase 0: minimal run to capture startup telemetry."""
print("\n" + "="*60 + "\nPHASE 0: SYSTEM PROBE\n" + "="*60)
stdout, stderr, rc, elapsed = self.runner.run_prompt(
"Hello", ngen=1, log_name="system_probe.log")
metrics = extract_metrics(stdout, stderr)
result = {
"rc": rc, "elapsed": elapsed,
"load_secs": metrics.get("load_secs"),
"resident_mb": metrics.get("resident_mb"),
"n_layers": metrics.get("n_layers"),
"n_experts": metrics.get("n_experts"),
"mtp_status": metrics.get("mtp_status"),
"idot_kernel": metrics.get("idot_kernel"),
"cache_cap_requested": metrics.get("cache_cap"),
"ram_budget_gb": metrics.get("ram_budget"),
"cap_lowered": metrics.get("cap_lowered"),
"cap_raised": metrics.get("cap_raised"),
"cap_final": metrics.get("cap_ok"),
"cuda_devices": metrics.get("cuda_devices", []),
"cuda_mode": metrics.get("cuda_mode"),
}
# Print summary
print(f" load time: {result['load_secs']:.2f}s" if result['load_secs'] else " load time: FAILED")
print(f" resident: {result['resident_mb']:.0f} MB" if result['resident_mb'] else "")
print(f" layers/exp: {result['n_layers']}/{result['n_experts']}" if result['n_layers'] else "")
print(f" MTP: {result['mtp_status']}")
print(f" idot kernel: {result['idot_kernel']}")
print(f" RAM budget: {result['ram_budget_gb']} GB" if result['ram_budget_gb'] else " RAM budget: auto")
if result['cap_lowered']:
print(f" cache cap: {result['cap_lowered'][0]} -> {result['cap_lowered'][1]} (RAM-lowered)")
elif result['cap_final']:
print(f" cache cap: {result['cap_final']} (ok)")
else:
print(f" cache cap: {result['cache_cap_requested']}")
for d in result['cuda_devices']:
print(f" GPU {d['id']}: {d['name']}, {d['vram_gb']:.1f} GB, sm_{d['sm']}")
if rc != 0 and rc != -1:
print(f" WARNING: engine returned rc={rc}")
self.results["phases"]["system"] = result
return result
def phase_smoke(self):
"""Phase 1: correctness smoke test across curated prompts."""
print("\n" + "="*60 + "\nPHASE 1: CORRECTNESS SMOKE TEST\n" + "="*60)
ngen = self.args.ngen
prompt_results = []
pass_count = 0; total = len(PROMPTS)
for p in PROMPTS:
stdout, stderr, rc, elapsed = self.runner.run_prompt(
p["prompt"], ngen=ngen, log_name=f"smoke_{p['id']}.log")
metrics = extract_metrics(stdout, stderr)
token_ids = metrics.get("tokens_dump", [])
text, method = recover_text(stdout, stderr, p["prompt"], self.tokenizer)
is_rep, max_run = check_repetition(token_ids)
expect_ok = check_expected(text, p.get("expect"))
# Verdict: pass if text is non-empty, no severe repetition, and (if factual) expected found
has_text = bool(text.strip())
ok = has_text and not is_rep
if p.get("expect") and expect_ok is False: ok = False
if ok: pass_count += 1
# Diagnose failure mode for reporting
if not has_text:
fail_reason = "no tokens generated (immediate EOS)"
elif is_rep:
fail_reason = f"repetition loop (max run={max_run})"
elif p.get("expect") and expect_ok is False:
fail_reason = f"expected '{p['expect']}' not found"
else:
fail_reason = ""
prompt_results.append({
"id": p["id"], "cat": p["cat"], "prompt": p["prompt"],
"expect": p.get("expect"), "generated": text[:300],
"expect_match": expect_ok, "repetition": is_rep, "max_run": max_run,
"toks_generated": len(token_ids), "decode_tps": metrics.get("decode_tps"),
"hit_rate": metrics.get("hit_rate"), "rc": rc, "elapsed": elapsed,
"text_method": method, "pass": ok, "fail_reason": fail_reason,
})
status = "PASS" if ok else "FAIL"
extra = f" expect={'Y' if expect_ok else ('N' if expect_ok is False else '-')}" if p.get("expect") else ""
tps = f" {metrics.get('decode_tps',0):.2f}t/s" if metrics.get("decode_tps") else ""
print(f" [{status}] {p['id']:<22} ({p['cat']:<11}) rep={'Y' if is_rep else 'N'}{extra}{tps}")
if text:
preview = text.replace("\n", " ")[:80]
print(f" -> {preview}")
elif rc != 0:
print(f" -> [engine rc={rc}]")
else:
print(f" -> [{fail_reason}]")
result = {"prompts": prompt_results, "pass": pass_count, "total": total,
"pass_rate": 100.0 * pass_count / total if total else 0}
print(f"\n SMOKE SUMMARY: {pass_count}/{total} passed ({result['pass_rate']:.0f}%)")
self.results["phases"]["smoke"] = result
return result
def phase_diagnostic(self):
"""Phase 2: single deep-instrumented run — full PROFILE + routing + MTP."""
print("\n" + "="*60 + "\nPHASE 2: FULL SYSTEM DIAGNOSTIC\n" + "="*60)
diag_env = {
"LOOKA": "1", "DISK_SPLIT": "1", "ROUTE_AGREE": "1",
}
if self.args.cuda:
diag_env["COLI_CUDA_PROFILE"] = "1"
prompt = ("Write a short paragraph explaining how photosynthesis works. "
"Include the roles of sunlight, water, and carbon dioxide.")
stdout, stderr, rc, elapsed = self.runner.run_prompt(
prompt, ngen=self.args.ngen, env_extra=diag_env, log_name="diagnostic.log")
metrics = extract_metrics(stdout, stderr)
text, _ = recover_text(stdout, stderr, prompt, self.tokenizer)
result = {
"rc": rc, "elapsed": elapsed,
"generated_text": text[:500],
"prefill_profile": metrics.get("prefill_profile", {}),
"decode_profile": metrics.get("decode_profile", {}),
"decode_tps": metrics.get("decode_tps"),
"prefill_secs": metrics.get("prefill_secs"),
"hit_rate": metrics.get("hit_rate"),
"rss_gb": metrics.get("rss_gb"),
"experts_per_tok": metrics.get("experts_per_tok"),
"mtp_accept": metrics.get("mtp_accept"),
"mtp_counts": metrics.get("mtp_acc_cnt"),
"spec_tok_per_fw": metrics.get("spec_tok_per_fw"),
"cuda_tier": metrics.get("cuda_tier"),
"progress_curve": metrics.get("progress_curve", []),
}
# Print the PROFILE breakdown
dp = result["decode_profile"]
print(f"\n DECODE TIMING BREAKDOWN (per-bucket, seconds):")
print(f" expert-disk: {dp.get('prof_expert_disk', '?'):>8}")
print(f" expert-matmul: {dp.get('prof_expert_mm', '?'):>8}")
print(f" attention: {dp.get('prof_attention', '?'):>8}")
print(f" lm_head: {dp.get('prof_lm_head', '?'):>8}")
print(f" other: {dp.get('prof_other', '?'):>8}")
print(f"\n PERFORMANCE:")
print(f" prefill: {result['prefill_secs']:.2f}s" if result['prefill_secs'] else " prefill: ?")
print(f" decode: {result['decode_tps']:.3f} tok/s" if result['decode_tps'] else " decode: ?")
print(f" hit rate: {result['hit_rate']:.1f}%" if result.get('hit_rate') is not None else " hit rate: ?")
print(f" RSS: {result['rss_gb']:.1f} GB" if result.get('rss_gb') else " RSS: ?")
print(f" exp/tok: {result['experts_per_tok']:.1f}" if result.get('experts_per_tok') else " exp/tok: ?")
print(f" MTP: {result['mtp_accept']:.0f}% accept" if result.get('mtp_accept') is not None else " MTP: ?")
if result.get('cuda_tier'):
ct = result['cuda_tier']
print(f" CUDA tier: {ct['resident']} experts, {ct['vram_gb']:.1f} GB VRAM")
# Show generated text preview
if text:
print(f"\n GENERATED TEXT (first 200 chars):")
print(f" {text[:200].replace(chr(10), ' ')}")
else:
print(f"\n GENERATED TEXT: [none recovered]")
self.results["phases"]["diagnostic"] = result
return result
def phase_quality(self):
"""Phase 3: benchmark accuracy via eval_glm.py SCORE mode."""
print("\n" + "="*60 + "\nPHASE 3: QUALITY BENCHMARKS\n" + "="*60)
eval_script = os.path.join(os.path.dirname(__file__), "eval_glm.py")
bench_dir = os.path.join(os.path.dirname(os.path.dirname(__file__)), "bench")
if not os.path.exists(eval_script):
print(f" [SKIP] eval_glm.py not found at {eval_script}")
self.results["phases"]["quality"] = {"error": "eval_glm.py not found"}
return None
tasks = ["hellaswag", "arc_challenge", "mmlu"]
missing = [t for t in tasks if not os.path.exists(os.path.join(bench_dir, f"{t}.jsonl"))]
if missing:
print(f" [SKIP] benchmark data missing: {missing}")
print(f" run: python tools/fetch_benchmarks.py --out {bench_dir}")
self.results["phases"]["quality"] = {"error": f"missing benchmark data: {missing}"}
return None
limit = self.args.quality_limit
py = sys.executable
cmd = [py, eval_script, "--snap", self.snap, "--glm", self.glm,
"--data", bench_dir, "--tasks", ",".join(tasks), "--limit", str(limit)]
env = dict(os.environ)
if self.args.ram: env["RAM_GB"] = str(self.args.ram)
print(f" Running eval_glm.py (tasks={tasks}, n={limit})...")
print(f" This takes ~{limit*3*4/0.05:.0f}s at 0.05 tok/s (worst case)...")
t0 = time.time()
log_path = self.out_dir / "quality_eval.log"
try:
with open(log_path, "w", encoding="utf-8") as logf:
proc = subprocess.run(cmd, env=env, capture_output=True, text=True, timeout=self.args.timeout*3,
encoding="utf-8", errors="replace")
logf.write(proc.stdout); logf.write("\n--- STDERR ---\n"); logf.write(proc.stderr)
elapsed = time.time() - t0
# Parse the acc/acc_norm table from eval_glm.py output
scores = {}
for line in proc.stdout.splitlines():
# lines like: "hellaswag 40 45.0% 50.0%"
m = re.match(r"(\w+)\s+(\d+)\s+([\d.]+)%\s+([\d.]+)%", line.strip())
if m:
scores[m.group(1)] = {"n": int(m.group(2)), "acc": float(m.group(3)),
"acc_norm": float(m.group(4))}
mean_m = re.search(r"MEAN acc_norm:\s*([\d.]+)%", proc.stdout)
result = {"scores": scores, "mean_acc_norm": float(mean_m.group(1)) if mean_m else None,
"elapsed": elapsed, "rc": proc.returncode}
print(f" Completed in {elapsed:.0f}s\n")
print(f" {'task':<18} {'n':>4} {'acc':>7} {'acc_norm':>9}")
for t, s in scores.items():
print(f" {t:<18} {s['n']:>4} {s['acc']:>6.1f}% {s['acc_norm']:>8.1f}%")
if result["mean_acc_norm"] is not None:
print(f"\n MEAN acc_norm: {result['mean_acc_norm']:.1f}%")
except subprocess.TimeoutExpired:
result = {"error": f"eval timed out after {self.args.timeout*3}s"}
print(f" [TIMEOUT] eval_glm.py exceeded {self.args.timeout*3}s")
except Exception as e:
result = {"error": str(e)}
print(f" [ERROR] {e}")
self.results["phases"]["quality"] = result
return result
def phase_throughput(self):
"""Phase 4: tok/s comparison — MTP on vs off."""
print("\n" + "="*60 + "\nPHASE 4: THROUGHPUT BENCHMARK\n" + "="*60)
prompt = "Summarize the plot of Romeo and Juliet in three sentences."
ngen = self.args.ngen
results = {}
# Run 1: with MTP (default)
print(f" [1/2] MTP ON (draft=3)...")
stdout, stderr, rc, t = self.runner.run_prompt(
prompt, ngen=ngen, log_name="throughput_mtp_on.log")
m_on = extract_metrics(stdout, stderr)
results["mtp_on"] = {"tps": m_on.get("decode_tps"), "hit": m_on.get("hit_rate"),
"mtp_accept": m_on.get("mtp_accept"), "elapsed": t}
print(f" {m_on.get('decode_tps',0):.3f} tok/s | hit {m_on.get('hit_rate',0):.1f}% | "
f"MTP {m_on.get('mtp_accept',0):.0f}%")
# Run 2: MTP off
print(f" [2/2] MTP OFF (MTP=0)...")
stdout, stderr, rc, t = self.runner.run_prompt(
prompt, ngen=ngen, env_extra={"MTP": "0"}, log_name="throughput_mtp_off.log")
m_off = extract_metrics(stdout, stderr)
results["mtp_off"] = {"tps": m_off.get("decode_tps"), "hit": m_off.get("hit_rate"),
"elapsed": t}
print(f" {m_off.get('decode_tps',0):.3f} tok/s | hit {m_off.get('hit_rate',0):.1f}%")
# Compute speedup
if results["mtp_on"]["tps"] and results["mtp_off"]["tps"] and results["mtp_off"]["tps"] > 0:
sp = results["mtp_on"]["tps"] / results["mtp_off"]["tps"]
results["mtp_speedup"] = sp
print(f"\n MTP speedup: {sp:.2f}x ({'MTP helps' if sp > 1.05 else 'MTP hurts' if sp < 0.95 else 'no effect'})")
else:
print(f"\n MTP speedup: (insufficient data)")
self.results["phases"]["throughput"] = results
return results
def write_report(self):
"""Write report.json and report.md."""
ts = self.results["meta"]["timestamp"]
json_path = self.out_dir / "report.json"
md_path = self.out_dir / "report.md"
# JSON
with open(json_path, "w", encoding="utf-8") as f:
json.dump(self.results, f, indent=2, default=str)
# Markdown
lines = [
f"# Diagnostic Report — {ts}",
f"",
f"**Model:** `{self.snap}`",
f"**Engine:** `{self.glm}`",
f"**Date:** {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
f"",
]
phases = self.results["phases"]
# System
if "system" in phases:
s = phases["system"]
lines += ["## Phase 0: System Probe", ""]
lines += [f"- Load time: **{s.get('load_secs','?')}s**"]
lines += [f"- Layers/experts: {s.get('n_layers','?')}/{s.get('n_experts','?')}"]
lines += [f"- MTP: {s.get('mtp_status','?')}"]
lines += [f"- idot kernel: `{s.get('idot_kernel','?')}`"]
lines += [f"- RAM budget: {s.get('ram_budget_gb','auto')} GB"]
if s.get("cap_lowered"):
lines += [f"- Cache cap: {s['cap_lowered'][0]}→{s['cap_lowered'][1]} (RAM-lowered)"]
elif s.get("cap_final"):
lines += [f"- Cache cap: {s['cap_final']}"]
for d in s.get("cuda_devices", []):
lines += [f"- GPU {d['id']}: {d['name']}, {d['vram_gb']:.1f} GB, sm_{d['sm']}"]
lines.append("")
# Smoke
if "smoke" in phases:
sm = phases["smoke"]
lines += [f"## Phase 1: Correctness Smoke", "",
f"**{sm['pass']}/{sm['total']} prompts passed ({sm['pass_rate']:.0f}%)**", "",
"| ID | Category | Pass | Expect | Repetition | tok/s | Generated (first 80 chars) |",
"|---|---|---|---|---|---|---|"]
for p in sm["prompts"]:
gen = p["generated"][:80].replace("|", "\\|").replace("\n", " ") if p["generated"] else ""
exp = "Y" if p.get("expect_match") else ("N" if p.get("expect_match") is False else "-")
tps = f"{p.get('decode_tps',0):.2f}" if p.get("decode_tps") else "-"
lines.append(f"| {p['id']} | {p['cat']} | {'✅' if p['pass'] else '❌'} | {exp} | "
f"{'⚠️' if p['repetition'] else '-'} (run={p.get('max_run',0)}) | {tps} | {gen} |")
lines.append("")
# Diagnostic
if "diagnostic" in phases:
d = phases["diagnostic"]
lines += ["## Phase 2: Full System Diagnostic", ""]
dp = d.get("decode_profile", {})
lines += ["### Decode Timing Breakdown", "",
"| Bucket | Seconds |", "|---|---|"]
for k, label in [("prof_expert_disk","expert-disk"),("prof_expert_mm","expert-matmul"),
("prof_attention","attention"),("prof_lm_head","lm_head"),
("prof_other","other")]:
v = dp.get(k, "?")
lines.append(f"| {label} | {v} |")
lines += [f"", f"### Performance", f"- Decode: **{d.get('decode_tps','?')} tok/s**",
f"- Prefill: {d.get('prefill_secs','?')}s",
f"- Expert hit rate: {d.get('hit_rate','?')}%",
f"- RSS: {d.get('rss_gb','?')} GB",
f"- Experts/token: {d.get('experts_per_tok','?')}",
f"- MTP acceptance: {d.get('mtp_accept','?')}%", ""]
if d.get("generated_text"):
lines += [f"### Generated Text", f"```\n{d['generated_text'][:300]}\n```", ""]
# Quality
if "quality" in phases:
q = phases["quality"]
lines += ["## Phase 3: Quality Benchmarks", ""]
if "error" in q:
lines += [f"⚠️ {q['error']}", ""]
else:
lines += [f"**Mean acc_norm: {q.get('mean_acc_norm','?')}%**", "",
"| Task | n | acc | acc_norm |", "|---|---|---|---|"]
for t, s in q.get("scores", {}).items():
lines.append(f"| {t} | {s['n']} | {s['acc']:.1f}% | {s['acc_norm']:.1f}% |")
lines.append("")
# Throughput
if "throughput" in phases:
th = phases["throughput"]
lines += ["## Phase 4: Throughput", "",
"| Mode | tok/s | hit% | MTP accept |", "|---|---|---|---|"]
on = th.get("mtp_on", {}); off = th.get("mtp_off", {})
lines.append(f"| MTP ON | {on.get('tps','?')} | {on.get('hit','?')}% | {on.get('mtp_accept','?')}% |")
lines.append(f"| MTP OFF | {off.get('tps','?')} | {off.get('hit','?')}% | — |")
if th.get("mtp_speedup"):
lines.append(f"\n**MTP speedup: {th['mtp_speedup']:.2f}x**")
lines.append("")
with open(md_path, "w", encoding="utf-8") as f:
f.write("\n".join(lines))
print(f"\n{'='*60}")
print(f"Report written:")
print(f" JSON: {json_path}")
print(f" MD: {md_path}")
print(f" Logs: {self.out_dir}/*.log")
print(f"{'='*60}")
def run(self):
phase = self.args.phase
if phase == "all":
self.phase_system()
self.phase_smoke()
self.phase_diagnostic()
self.phase_quality()
self.phase_throughput()
elif phase == "system": self.phase_system()
elif phase == "smoke": self.phase_smoke()
elif phase == "diagnostic": self.phase_diagnostic()
elif phase == "quality": self.phase_quality()
elif phase == "throughput": self.phase_throughput()
else:
print(f"Unknown phase: {phase}", file=sys.stderr); sys.exit(1)
self.write_report()
def main():
ap = argparse.ArgumentParser(description="Comprehensive model diagnostic harness for colibri GLM-5.2")
ap.add_argument("--snap", required=True, help="model snapshot directory")
ap.add_argument("--glm", default=None, help="engine binary path (default: ./glm.exe or ./glm)")
ap.add_argument("--phase", default="all",
choices=["all","system","smoke","diagnostic","quality","throughput"],
help="which test phase to run")
ap.add_argument("--out", default=None, help="output directory (default: ./diag_results/<timestamp>)")
ap.add_argument("--ngen", type=int, default=64, help="generation length for smoke/throughput (default 64)")
ap.add_argument("--quality-limit", type=int, default=40, help="questions per benchmark task (default 40)")
ap.add_argument("--ram", type=float, default=0, help="RAM_GB override (0=auto)")
ap.add_argument("--cuda", action="store_true", help="enable COLI_CUDA GPU tier")
ap.add_argument("--gpu", type=int, default=None, help="GPU device ordinal (with --cuda)")
ap.add_argument("--cap", type=int, default=75, help="experts-per-layer cache cap (default 75)")
ap.add_argument("--timeout", type=int, default=600, help="per-run timeout in seconds (default 600)")
a = ap.parse_args()
# Auto-detect engine binary
if a.glm is None:
for cand in ["./glm.exe", "./glm", "../glm.exe", os.path.join(os.path.dirname(__file__), "..", "glm.exe")]:
if os.path.exists(cand): a.glm = os.path.abspath(cand); break
if a.glm is None:
print("ERROR: could not find glm/glm.exe. Specify with --glm", file=sys.stderr); sys.exit(1)
if not os.path.isdir(a.snap):
print(f"ERROR: snapshot dir does not exist: {a.snap}", file=sys.stderr); sys.exit(1)
harness = DiagnosticHarness(a)
harness.run()
if __name__ == "__main__":
main()
c/tools/efficiency.py
+458
View File
@@ -0,0 +1,458 @@
"""Efficiency / regression harness for the colibri engine.
The engine already emits rich telemetry (REPLAY tok/s, PROFILE phase timings,
[PROF] time shares + verdict, CUDA expert-tier utilization). Until now every
consumer of that telemetry — `benchmark_cuda_fixture.py`, `bench_full.sh`,
`bench_ux.sh` — has only *printed* it for a human to eyeball. This module turns
each signal into a parseable field so tests can assert on it.
Design:
- Reuses SPEED_RE / PROFILE_RE from tools.benchmark_cuda_fixture (no drift).
- parse_run() is pure: stdout+stderr in, dict out. Easy to unit-test against
captured strings (like the existing test_benchmark_cuda_fixture does).
- run_engine() is the subprocess wrapper. Captures stdout and stderr
separately, because the engine splits them: PROFILE/REPLAY/CUDA-tier go to
stdout, the [CUDA]/[PROF]/[prefill] banners go to stderr.
- Floor defaults are module constants (tunable in one place, not scattered).
No model file is required to import this module; only run_engine() invokes the
binary. parse_run() works on any captured text, so most test surface is covered
by string fixtures without spinning the engine at all.
"""
from __future__ import annotations
import os
import re
import subprocess
from pathlib import Path
from typing import Optional
# Reuse the validated regexes from the existing A/B benchmark harness so the
# PROFILE field order (disk, expert_matmul, attention, lm_head, other) and the
# tok/s capture stay identical. Drift here would silently break every consumer.
from tools.benchmark_cuda_fixture import SPEED_RE as _SPEED_RE_REPLAY, PROFILE_RE, PROFILE_KEYS
# SPEED_RE (from benchmark_cuda_fixture) matches the REPLAY-mode line only:
# "REPLAY decode: ... | 12.34 tok/s | ..."
# run_text / PROMPT mode uses a DIFFERENT format (glm.c:4682):
# "decode N tokens in X.XXs (12.34 tok/s) | expert hit rate ..."
# This alt regex catches the parenthesized form so the full-model report (which
# uses PROMPT mode) gets a real tok/s instead of reporting it missing.
SPEED_RE_TEXT = re.compile(r"decode \d+ tokens in [0-9.]+s \(([0-9.]+) tok/s\)")
def _first_speed(stdout: str):
"""Find tok/s in whichever run-mode format the engine used."""
for rx in (_SPEED_RE_REPLAY, SPEED_RE_TEXT):
m = rx.search(stdout)
if m:
return m
return None
# Public alias so existing imports keep working (tests reference SPEED_RE).
SPEED_RE = _SPEED_RE_REPLAY
# --- additional parsers (formats verified against glm.c printf strings) ---
# "expert hit rate 88.1%" (summary line) | "expert hit 95.0%" (REPLAY line)
HIT_RE = re.compile(r"expert hit(?:\s+rate)?\s+([0-9.]+)%")
# "[PROF] time shares: expert-I/O 3% | expert-matmul 12% | attention 56% | lm_head 2% | other 27%"
SHARES_RE = re.compile(
r"\[PROF\] time shares: expert-I/O\s+([0-9.]+)%\s*\|\s*expert-matmul\s+([0-9.]+)%\s*"
r"\|\s*attention\s+([0-9.]+)%\s*\|\s*lm_head\s+([0-9.]+)%\s*\|\s*other\s+([0-9.]+)%"
)
# "[PROF] verdict: I/O-bound — 60% of the time ..." (also compute-bound / attention-bound / balanced)
VERDICT_RE = re.compile(r"\[PROF\] verdict:\s*(I/O-bound|compute-bound|attention-bound|balanced)")
# "[PROF] expert I/O: ... hit 95.0% (76 hit / 4 load) | 4.0 loads/token"
LOADS_PER_TOK_RE = re.compile(r"\|\s*([0-9.]+)\s+loads/token")
# "CUDA expert tier: 111 resident experts (2.36 GB) | 5400 calls served from VRAM" (stdout)
CUDA_TIER_RE = re.compile(
r"CUDA expert tier:\s+(\d+)\s+resident experts\s+\(([0-9.]+)\s+GB\)\s*\|\s+(\d+)\s+calls served from VRAM"
)
# "[CUDA] device 0: NVIDIA ..., 14.4 GB VRAM, sm_120" (stderr, per device at init)
CUDA_DEVICE_RE = re.compile(r"\[CUDA\] device\s+\d+:")
# "[CUDA] resident set: 12 tensors, 0.45 GB VRAM" (stderr, cuda_stats_print)
CUDA_RESIDENT_RE = re.compile(
r"\[CUDA\] resident set:\s+(\d+)\s+tensors,\s+([0-9.]+)\s+GB\s+VRAM"
)
# "PREFILL (teacher-forcing) C vs oracle: 11/32 positions | 1700.4 pos/s" (TF=1 mode, stdout)
TF_MATCH_RE = re.compile(r"PREFILL \(teacher-forcing\).*:\s+(\d+)/(\d+)\s+positions")
# --- the six deeper signals (added after "are you gathering everything?" audit) ---
# "ATTENTION: projection/RoPE 0.050s | score-softmax-value 0.009s | output projection 0.011s"
# Sub-breakdown of the attention phase — answers "how is attention being read".
ATTN_BREAKDOWN_RE = re.compile(
r"ATTENTION: projection/RoPE\s+([0-9.]+)s\s*\|\s*score-softmax-value\s+([0-9.]+)s\s*"
r"\|\s*output projection\s+([0-9.]+)s"
)
# "[PROF] decode forwards: 20 | latency p50 7.3 ms | p90 8.0 ms | p99 8.1 ms | max 8.2 ms | 1.00 tok/forward"
# Per-forward tail latency — a p99 >> p50 means decode stalls (I/O hiccups, KV grow).
LATENCY_RE = re.compile(
r"\[PROF\] decode forwards:\s+(\d+)\s*\| latency p50\s+([0-9.]+)\s*ms\s*\| "
r"p90\s+([0-9.]+)\s*ms\s*\|\s*p99\s+([0-9.]+)\s*ms\s*\|\s*max\s+([0-9.]+)\s*ms"
)
# "[PROF] expert I/O: 0.004 GB fetched (0.2 MB/token, 0.03 GB/s over the run) | hit 95.0% ... |
# 4.0 loads/token | 0.0s read service / 0.0s felt wait"
# Absolute disk throughput + the felt-wait split (the [PROF] version, more detailed than PROFILE).
EXPERT_IO_RE = re.compile(
r"\[PROF\] expert I/O:\s+([0-9.]+)\s+GB fetched\s+\(([0-9.]+)\s+MB/token,\s*([0-9.]+)\s+GB/s"
r".*?\|\s*([0-9.]+)\s+loads/token\s*\|\s*([0-9.]+)s\s+read service\s*/\s*([0-9.]+)s\s+felt wait"
)
# "speculation: 1.05 tokens/forward (19 forwards per 20 tokens) | MTP acceptance 44% (7/16)"
# Draft efficiency — is the speculative decoder pulling weight or dead overhead?
SPECULATION_RE = re.compile(
r"speculation:\s+([0-9.]+)\s+tokens/forward\s+\((\d+)\s+forwards per\s+(\d+)\s+tokens\)"
r"\s*\|\s*MTP acceptance\s+([0-9.]+)%"
)
# "experts loaded/token: 450.0 (per-layer 56.25 across 8; baseline topk=8) | TOPK=0 TOPP=0.00"
# Fuller than loads_per_tok: includes the per-layer spread + the active topk/topp.
EXPERTS_LOADED_RE = re.compile(
r"experts loaded/token:\s+([0-9.]+)\s+\(per-layer\s+([0-9.]+)\s+across\s+(\d+);\s*baseline topk=(\d+)\)"
)
# "[PROF] machine: Intel(...) | 22 cores (22 omp threads) | RAM 34.1 GB total, 27.1 GB available | backend CUDA"
# Provenance — makes a report reproducible across machines/runs.
MACHINE_RE = re.compile(r"\[PROF\] machine:\s*(.*)\|\s*(\d+)\s+cores.*backend\s+(\S+)")
# "[PROF] config: RAM_GB=auto 23.9 CTX=4096 | expert cache cap 8/layer ... | DRAFT=0 PIPE=1 DIRECT=0 ..."
# Effective resolved config (after auto-budgeting). Answers "what config actually ran".
CONFIG_RE = re.compile(r"\[PROF\] config:\s*(.*)")
# "[ORACLE] mismatch pos=7 expected=197 got=22" (TF=1 mode, stderr, per position)
# Captures the engine's *actual* argmax at each position, so two backends can be
# compared DIRECTLY (independent of how each relates to the oracle).
TF_MISMATCH_RE = re.compile(r"\[ORACLE\] mismatch pos=(\d+) expected=\d+ got=(\d+)")
# --- routing-quality + disk-split + cuda-groups (the deep signals) ---
# summary suffix: " | swap 3.1% (12/384)" (CACHE_ROUTE inline)
SWAP_RE = re.compile(r"swap\s+([0-9.]+)%\s+\((\d+)/(\d+)\)")
# summary suffix: " | route_agree 85.0% | route_kl 0.0123" (CACHE_ROUTE/ROUTE_AGREE)
ROUTE_AGREE_RE = re.compile(r"route_agree\s+([0-9.]+)%\s*\|\s*route_kl\s+([0-9.]+)")
# "disk-load split: draft 8 + absorb 0 + verify/main 3 misses | MTP-layer 0 loads 0.00 GB |
# main-layers 11 loads 0.01 GB (MTP 0.0% of bytes)" (DISK_SPLIT=1)
DISK_SPLIT_RE = re.compile(
r"disk-load split: draft\s+(\d+)\s+\+\s+absorb\s+(\d+)\s+\+\s+verify/main\s+(\d+)\s+misses"
r"\s*\|\s*MTP-layer\s+(\d+)\s+loads\s+([0-9.]+)\s+GB\s*\|\s*main-layers\s+(\d+)\s+loads\s+([0-9.]+)\s+GB"
r"(?:\s+\(MTP\s+([0-9.]+)%\s+of bytes\))?"
)
# "[CUDA] expert groups: 120 call, 840 expert, 1200 righe (7.00 expert/call)"
CUDA_GROUPS_RE = re.compile(
r"\[CUDA\] expert groups:\s+(\d+)\s+call,\s+(\d+)\s+expert,\s+(\d+)\s+righe\s+\(([0-9.]+)\s+expert/call\)"
)
# "[CUDA] expert groups timing: H2D 12.3 ms | kernel 45.6 ms | D2H 7.8 ms" (COLI_CUDA_PROFILE=1)
CUDA_GROUPS_TIME_RE = re.compile(
r"\[CUDA\] expert groups timing: H2D\s+([0-9.]+)\s+ms\s*\|\s*kernel\s+([0-9.]+)\s+ms\s*\|\s*D2H\s+([0-9.]+)\s+ms"
)
# LOOKAHEAD recall block — 4 named rows. (name, pct, hit, tot)
LOOKAHEAD_RE = re.compile(
r"^\s*(.+?)\s+([0-9.]+)%\s+\((\d+)/(\d+)\)\s*$", re.MULTILINE
)
# "loaded in 0.02s | resident dense: 0.21 MB | layers=5 experts=8 | MTP absent (draft=0)"
LOAD_BANNER_RE = re.compile(
r"loaded in\s+([0-9.]+)s\s*\|\s*resident dense:\s+([0-9.]+)\s+MB\s*\|"
r"\s*layers=(\d+)\s+experts=(\d+)\s*\|\s*MTP\s+(\w+)\s+\(draft=(\d+)\)"
)
# --- tunable floors -----------------------------------------------------------
# These are deliberately generous so they catch *regressions* (broken builds,
# pathological configs, telemetry accounting bugs) without flapping on machine
# noise. The tiny model is fully resident at ~200 tok/s, so a 20 tok/s floor is
# a 10x margin. Tune per-host via env if needed (documented in README).
TINY_TOK_S_FLOOR = float(os.environ.get("COLI_TINY_TOK_S_FLOOR", "20.0"))
# On a fully-resident tiny model the expert-disk wait share should be tiny.
# If it exceeds this, something regressed in the I/O accounting or cache path.
MAX_DISK_WAIT_SHARE = float(os.environ.get("COLI_MAX_DISK_WAIT_SHARE", "0.20"))
# decode wall-time should be roughly the sum of PROFILE phases (other = residual).
PROFILE_SUM_TOLERANCE = float(os.environ.get("COLI_PROFILE_SUM_TOL", "0.05"))
# Minimum direct CPU-vs-CUDA argmax agreement on the tiny TF fixture. The two
# backends use different accumulation orders (SIMD dot vs CUDA kernel), so a
# few near-tied positions flip argmax — that's expected numeric divergence, not
# a kernel bug. Measured baseline ~84% (27/32) on this fixture; the 70% floor
# leaves headroom for machine noise while still catching a catastrophic kernel
# regression (e.g. a wrong GEMM would drop this to ~random = ~4%).
MIN_CPU_CUDA_AGREEMENT = float(os.environ.get("COLI_MIN_CPU_CUDA_AGREE", "0.70"))
def parse_run(stdout: str, stderr: str = "") -> dict:
"""Parse one engine run's output into a telemetry dict.
Returns keys: tok_s, hit_pct, profile (dict, seconds), profile_sum,
time_shares (dict, fractions 0..1), verdict, loads_per_tok, cuda (dict),
tf_match (tuple or None), parsed (set of field names found).
Raises RuntimeError only if the core throughput line is missing — everything
else is optional and absent on some run modes (e.g. [PROF] needs PROF=1,
CUDA tier needs gpu_expert_count>0).
"""
out = dict(
tok_s=None, hit_pct=None, profile=None, profile_sum=None,
time_shares=None, verdict=None, loads_per_tok=None,
cuda=None, tf_match=None, stderr=stderr,
)
parsed = set()
blob = stdout + "\n" + stderr # [PROF]/[CUDA] live on stderr; scan both.
m = _first_speed(stdout)
if m:
out["tok_s"] = float(m.group(1)); parsed.add("tok_s")
m = HIT_RE.search(blob)
if m:
out["hit_pct"] = float(m.group(1)); parsed.add("hit_pct")
m = PROFILE_RE.search(stdout)
if m:
service, wait, emm, attn, head, other = (float(x) for x in m.groups())
disk = service + (wait or 0.0)
out["profile"] = dict(zip(PROFILE_KEYS, (disk, emm, attn, head, other)))
out["profile_sum"] = disk + emm + attn + head + other
parsed.add("profile")
# ATTENTION sub-breakdown: projection/RoPE | score-softmax-value | output.
m = ATTN_BREAKDOWN_RE.search(stdout)
if m:
out["attn_breakdown"] = dict(zip(
("proj_rope", "score_sm_value", "out_proj"),
(float(x) for x in m.groups())))
parsed.add("attn_breakdown")
m = SHARES_RE.search(blob)
if m:
io, emm, attn, head, other = (float(x) / 100.0 for x in m.groups())
out["time_shares"] = dict(io=io, matmul=emm, attention=attn, head=head, other=other)
parsed.add("time_shares")
m = VERDICT_RE.search(blob)
if m:
out["verdict"] = m.group(1); parsed.add("verdict")
# [PROF] decode forwards + latency p50/p90/p99/max.
m = LATENCY_RE.search(blob)
if m:
out["latency"] = dict(zip(
("forwards", "p50_ms", "p90_ms", "p99_ms", "max_ms"),
(float(x) for x in m.groups())))
parsed.add("latency")
# [PROF] expert I/O throughput: GB fetched, MB/token, GB/s, service vs felt wait.
m = EXPERT_IO_RE.search(blob)
if m:
out["expert_io"] = dict(zip(
("gb_fetched", "mb_per_tok", "gb_per_s", "loads_per_tok",
"read_service_s", "felt_wait_s"),
(float(x) for x in m.groups())))
parsed.add("expert_io")
m = LOADS_PER_TOK_RE.search(blob)
if m:
out["loads_per_tok"] = float(m.group(1)); parsed.add("loads_per_tok")
# experts loaded/token with per-layer spread + baseline topk (run_text summary).
m = EXPERTS_LOADED_RE.search(stdout)
if m:
out["experts_loaded"] = dict(
per_tok=float(m.group(1)), per_layer=float(m.group(2)),
n_sparse_layers=int(m.group(3)), baseline_topk=int(m.group(4)))
parsed.add("experts_loaded")
# speculation: tokens/forward, forwards, tokens, MTP acceptance%.
m = SPECULATION_RE.search(stdout)
if m:
out["speculation"] = dict(zip(
("tok_per_fw", "forwards", "tokens", "mtp_accept_pct"),
(float(m.group(1)), int(m.group(2)), int(m.group(3)), float(m.group(4)))))
parsed.add("speculation")
# routing quality (CACHE_ROUTE inline suffixes on the summary line).
m = SWAP_RE.search(stdout)
if m:
out["swap"] = dict(pct=float(m.group(1)), swaps=int(m.group(2)), slots=int(m.group(3)))
parsed.add("swap")
m = ROUTE_AGREE_RE.search(stdout)
if m:
out["route_agree"] = dict(agree_pct=float(m.group(1)), kl=float(m.group(2)))
parsed.add("route_agree")
# disk-load split by decode phase (DISK_SPLIT=1).
m = DISK_SPLIT_RE.search(stdout)
if m:
out["disk_split"] = dict(zip(
("draft", "absorb", "verify_main", "mtp_loads", "mtp_gb",
"main_loads", "main_gb", "mtp_bytes_pct"),
(int(m.group(1)), int(m.group(2)), int(m.group(3)), int(m.group(4)),
float(m.group(5)), int(m.group(6)), float(m.group(7)),
float(m.group(8)) if m.group(8) else None)))
parsed.add("disk_split")
# provenance: machine + resolved config.
m = MACHINE_RE.search(blob)
if m:
out["machine"] = dict(cpu=m.group(1).strip(), cores=int(m.group(2)), backend=m.group(3))
parsed.add("machine")
m = CONFIG_RE.search(blob)
if m:
out["config_str"] = m.group(1).strip(); parsed.add("config")
# load banner: load time, resident dense MB, layers, experts, MTP status.
m = LOAD_BANNER_RE.search(stdout)
if m:
out["load"] = dict(zip(
("load_s", "resident_dense_mb", "layers", "experts", "mtp_status", "draft"),
(float(m.group(1)), float(m.group(2)), int(m.group(3)),
int(m.group(4)), m.group(5), int(m.group(6)))))
parsed.add("load")
# LOOKAHEAD routing-recall block (LOOKA=1): list of {predictor, pct, hit, tot}.
la_block = re.search(
r"LOOKAHEAD routing.*?recall.*?:\n((?:^\s+.+?\s+[0-9.]+%\s+\(\d+/\d+\)\s*$\n?)+)",
blob, re.MULTILINE)
if la_block:
out["lookahead"] = []
for row in LOOKAHEAD_RE.finditer(la_block.group(1)):
out["lookahead"].append(dict(
predictor=row.group(1).strip(), pct=float(row.group(2)),
hit=int(row.group(3)), tot=int(row.group(4))))
parsed.add("lookahead")
cuda = dict(enabled=False, expert_count=None, expert_gb=None,
calls_served=None, resident_tensors=None, resident_gb=None,
groups=None, groups_timing=None)
if CUDA_DEVICE_RE.search(stderr):
cuda["enabled"] = True
m = CUDA_TIER_RE.search(stdout)
if m:
cuda["expert_count"] = int(m.group(1))
cuda["expert_gb"] = float(m.group(2))
cuda["calls_served"] = int(m.group(3))
m = CUDA_RESIDENT_RE.search(stderr)
if m:
cuda["resident_tensors"] = int(m.group(1))
cuda["resident_gb"] = float(m.group(2))
m = CUDA_GROUPS_RE.search(stderr)
if m:
cuda["groups"] = dict(zip(
("calls", "experts", "rows", "experts_per_call"),
(int(m.group(1)), int(m.group(2)), int(m.group(3)), float(m.group(4)))))
m = CUDA_GROUPS_TIME_RE.search(stderr)
if m:
cuda["groups_timing"] = dict(zip(
("h2d_ms", "kernel_ms", "d2h_ms"),
(float(m.group(1)), float(m.group(2)), float(m.group(3)))))
out["cuda"] = cuda
m = TF_MATCH_RE.search(stdout)
if m:
out["tf_match"] = (int(m.group(1)), int(m.group(2))); parsed.add("tf_match")
# Capture per-position argmax divergences from the oracle, keyed by position.
mismatches = {int(mm.group(1)): int(mm.group(2))
for mm in TF_MISMATCH_RE.finditer(blob)}
if out["tf_match"] is not None:
out["tf_mismatches"] = mismatches
parsed.add("tf_mismatches")
out["parsed"] = parsed
return out
def run_engine(
env_overlay: dict,
*,
engine: Optional[str] = None,
cap: int = 4,
ebits: int = 4,
dbits: int = 4,
timeout: float = 600.0,
snap: Optional[str] = None,
) -> tuple[dict, subprocess.CompletedProcess]:
"""Run the engine with an env overlay; return (parsed_telemetry, proc).
`engine` defaults to ./glm.exe (colibri's Windows host). `snap` defaults to
the bundled tiny model (glm_tiny) so callers can omit it for fast tests.
The positional argv is `cap ebits dbits`, matching the engine's main().
"""
if engine is None:
engine = str(Path(__file__).resolve().parent.parent / "glm.exe")
env = os.environ.copy()
# Strip CUDA vars by default so a CPU run isn't accidentally GPU-accelerated
# by a leftover env; callers opt in by passing them in env_overlay.
for k in ("COLI_CUDA", "COLI_GPU", "COLI_GPUS", "CUDA_DENSE", "CUDA_EXPERT_GB"):
env.pop(k, None)
env.update(env_overlay)
if snap is not None:
env["SNAP"] = snap
elif "SNAP" not in env:
env["SNAP"] = str(Path(__file__).resolve().parent.parent / "glm_tiny")
proc = subprocess.run(
[engine, str(cap), str(ebits), str(dbits)],
env=env, capture_output=True, text=True, timeout=timeout,
)
telemetry = parse_run(proc.stdout, proc.stderr)
telemetry["returncode"] = proc.returncode
telemetry["env"] = {k: env_overlay[k] for k in env_overlay}
return telemetry, proc
def disk_wait_share(t: dict) -> Optional[float]:
"""Fraction of decode wall-time spent waiting on expert disk reads.
Preferred source: [PROF] time_shares (the engine's own accounting, which
separates felt-wait from read-service). Falls back to PROFILE disk / sum if
[PROF] wasn't emitted (PROF=0 runs). None if neither is available.
"""
if t.get("time_shares"):
return t["time_shares"]["io"]
if t.get("profile") and t.get("profile_sum"):
return t["profile"]["disk"] / t["profile_sum"]
return None
def tf_agreement(cpu: dict, cuda: dict, oracle: list[int]) -> tuple[float, list[int]]:
"""Direct CPU-vs-CUDA argmax agreement on the TF fixture.
Both runs prefilled the SAME oracle sequence; tf_mismatches holds each
backend's actual argmax where it diverged from the oracle. Where a backend
is ABSENT from the mismatch map, its prediction equals the oracle token at
that position. So the reconstructed per-position prediction is:
oracle[i] if i not in mismatches else mismatches[i]
and agreement is the fraction of positions where CPU and CUDA predictions
are identical — independent of how each relates to the oracle.
`oracle` is ref_glm.json's tf_pred (the per-position oracle argmax). Pass
n_positions = len(oracle).
Returns (agreement_fraction, list_of_differing_positions).
"""
cm = cpu.get("tf_mismatches") or {}
gm = cuda.get("tf_mismatches") or {}
diff = []
for i, orc in enumerate(oracle):
cpu_tok = cm.get(i, orc) # matched oracle => oracle token
cuda_tok = gm.get(i, orc)
if cpu_tok != cuda_tok:
diff.append(i)
agree = (len(oracle) - len(diff)) / len(oracle) if oracle else 0.0
return agree, diff
c/tools/eval_glm.py
+57 -9
View File
@@ -22,7 +22,7 @@ USO:
# leve di ricerca: passate al motore via env
TOPP=0.9 python3 tools/eval_glm.py --snap /home/vincenzo/glm52_i4 --data ./bench --tasks mmlu --ram 15
"""
import os, sys, subprocess, argparse, random, json, tempfile, time
import os, sys, subprocess, argparse, random, json, tempfile, time, threading
# mini-set OFFLINE per testare la meccanica (NON misura qualita': domande banali)
SMOKE = [
@@ -114,6 +114,7 @@ def main():
ap.add_argument("--seed", type=int, default=1234)
ap.add_argument("--dry", action="store_true", help="build requests and stop without running the engine")
ap.add_argument("--selftest", action="store_true", help="verify the scoring calculations")
ap.add_argument("--out", default="", help="write incremental results CSV here (one row per request, flushed as it lands)")
a = ap.parse_args()
if a.selftest: # acc/acc_norm con logprob sintetici
@@ -143,15 +144,62 @@ def main():
if a.ram: env["RAM_GB"] = str(a.ram)
cmd = [a.glm, str(a.cap)] + a.bits.split()
print("running:", " ".join(cmd), file=sys.stderr)
# Stream results line-by-line so a crash at request N keeps 1..N-1 and shows
# exactly where it stopped. The engine prints "<lp> <contlen> <greedy>" per
# request to stdout and "[score N req | ...]" progress to stderr; buffering
# both until exit (the old subprocess.run) wastes the whole run on a crash.
out_f = open(a.out, "a") if a.out else None
if out_f:
out_f.write(f"# eval_glm snap={a.snap} tasks={a.tasks} limit={a.limit} seed={a.seed} started={time.strftime('%Y-%m-%dT%H:%M:%S')}\n")
out_f.write("req_idx,task,qi,oi,contlen,contchars,gold,logprob,greedy\n")
out_f.flush()
t0 = time.time()
proc = subprocess.run(cmd, env=env, capture_output=True, text=True)
if proc.returncode != 0:
print("ENGINE ERROR:\n", proc.stderr[-2000:], file=sys.stderr); sys.exit(1)
lines = [l for l in proc.stdout.strip().splitlines() if l and l[0] in "-0123456789"]
if len(lines) != len(reqs):
print(f"WARNING: {len(lines)} outputs for {len(reqs)} requests", file=sys.stderr)
lp = [float(l.split()[0]) for l in lines]
print(f"(engine: {time.time()-t0:.0f}s){proc.stderr.strip().splitlines()[-1] if proc.stderr.strip() else ''}", file=sys.stderr)
proc = subprocess.Popen(cmd, env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE,
text=True, bufsize=1) # line-buffered
lp = [None] * len(reqs)
n_done = 0
# Drain stderr (engine progress lines) to console live on a background thread
# so the [score N req] heartbeat is visible while stdout is consumed below.
def _drain_stderr():
for line in proc.stderr:
print(f" [engine] {line.rstrip()}", file=sys.stderr)
threading.Thread(target=_drain_stderr, daemon=True).start()
for line in proc.stdout:
line = line.strip()
if not line or line[0] not in "-0123456789": continue
parts = line.split()
if n_done >= len(reqs): break
try: logprob = float(parts[0])
except (ValueError, IndexError): continue
lp[n_done] = logprob
greedy = parts[2] if len(parts) > 2 else "?"
t, qi, oi, clen, cchars, gold = meta[n_done]
if out_f:
out_f.write(f"{n_done},{t},{qi},{oi},{clen},{cchars},{gold},{logprob:.6f},{greedy}\n")
out_f.flush()
n_done += 1
if n_done % 5 == 0 or n_done == len(reqs):
elapsed = time.time() - t0
rate = n_done / elapsed if elapsed > 0 else 0
eta = (len(reqs) - n_done) / rate if rate > 0 else 0
print(f"[progress] {n_done}/{len(reqs)} requests scored | {elapsed:.0f}s elapsed | "
f"{rate:.2f} req/s | ETA {eta:.0f}s | last: {t} q{qi} opt{oi} lp={logprob:.3f}",
file=sys.stderr)
proc.wait()
elapsed = time.time() - t0
if out_f:
out_f.write(f"# finished: {n_done}/{len(reqs)} in {elapsed:.0f}s, exit={proc.returncode}\n")
out_f.close()
if proc.returncode != 0 and n_done == 0:
print(f"ENGINE ERROR (exit {proc.returncode})", file=sys.stderr); sys.exit(1)
if n_done != len(reqs):
print(f"WARNING: only {n_done}/{len(reqs)} requests scored (engine exited {proc.returncode}); "
f"scoring partial results.", file=sys.stderr)
# Fill any unscored slots with -inf so argmax never picks them
for i in range(len(lp)):
if lp[i] is None: lp[i] = float("-inf")
print(f"(engine: {elapsed:.0f}s, {n_done}/{len(reqs)} scored, exit {proc.returncode})", file=sys.stderr)
score_accuracy(tasks, meta, perq, lp)
print("\nNOTE: compare acc_norm with GLM-5.2's PUBLISHED model-card score. A close result"
"\n indicates that int4 quantization preserved quality. (Fill REFERENCE in tools/eval_glm.py.)")
c/tools/iq3_pack.py
+170
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@@ -0,0 +1,170 @@
#!/usr/bin/env python3
"""fmt=5 (E8/IQ3 grouped container) index codec — #452 ladder step 2.
The ablation (#453) proved the SCHEME: an IQ3_XXS-style codebook plus rotation
matches our simulated E8 ball (51.5% vs 51.5% on OLMoE). That code quantizes to
lattice points and keeps floats. This module produces the DEPLOYABLE bytes and
reads them back, so the container, the converter and the decode kernels all
agree on one layout.
Layout — one 256-weight super-block, 98 bytes, 3.0625 bpw:
[0 .. 63] uint8 grid index per 4-dim magnitude block (64 blocks)
[64 .. 95] uint32 x8, one per 32-weight sub-block:
bits 0..20 three 7-bit sign words (8 weights each,
bit i set => weight i negative; the 8th sign
is implied by odd parity)
bits 21..27 the fourth 7-bit sign word
bits 28..31 4-bit sub-scale code
[96 .. 97] fp16 super-scale d
value(w) = d * (0.5 + code) * 0.5 * grid[idx][j] * 0.5 * sign
The last 0.5 is the half-unit convention of the published grid (magnitudes are
stored doubled: 4,12,...,62 mean 2.0,6.0,...,31.0).
Odd-parity signs: llama.cpp stores 7 of every 8 signs and derives the 8th so the
product of the eight is +1. The encoder therefore flips the smallest-magnitude
weight of any block whose true signs violate that — the same cost the ablation
priced in, now applied for real.
"""
import json
import os
import numpy as np
QK = 256 # weights per super-block
SUB = 32 # weights per sub-block (one uint32 of signs+scale)
BLOCK_BYTES = QK // 4 + (QK // SUB) * 4 + 2 # 64 + 32 + 2 = 98
_GRID = None
def grid():
"""[256,4] float32 magnitudes in weight units (published table is doubled)."""
global _GRID
if _GRID is None:
path = os.path.join(os.path.dirname(__file__), "iq3xxs_grid.json")
_GRID = np.asarray(json.load(open(path)), dtype=np.float32) * 0.5
return _GRID
def _nearest(mag4):
"""[N,4] magnitudes -> [N] grid indices, argmin ||m-g||^2 without cdist."""
g = grid()
g2 = (g * g).sum(1)
out = np.empty(len(mag4), dtype=np.uint8)
for i in range(0, len(mag4), 1 << 16): # bounded working set
c = mag4[i:i + (1 << 16)]
out[i:i + len(c)] = np.argmin(g2 - 2.0 * (c @ g.T), axis=1).astype(np.uint8)
return out
def encode(x):
"""float32 [..., K] (K % 256 == 0) -> packed uint8 [..., K//256 * 98]."""
x = np.ascontiguousarray(x, dtype=np.float32)
K = x.shape[-1]
if K % QK:
raise ValueError(f"fmt=5 needs K % {QK} == 0, got {K}")
rows = x.reshape(-1, K)
nsb = K // QK
out = np.empty((len(rows), nsb * BLOCK_BYTES), dtype=np.uint8)
for sb in range(nsb):
blk = rows[:, sb * QK:(sb + 1) * QK] # [R,256]
sign = np.where(blk < 0, -1.0, 1.0).astype(np.float32)
mag = np.abs(blk)
# parity fix: flip the smallest magnitude of every 8 whose product is -1
s8 = sign.reshape(len(rows), QK // 8, 8)
m8 = mag.reshape(len(rows), QK // 8, 8)
viol = s8.prod(-1) < 0 # [R,32]
amin = m8.argmin(-1)
r, b = np.nonzero(viol)
s8[r, b, amin[r, b]] *= -1.0
sign = s8.reshape(len(rows), QK)
base = sb * BLOCK_BYTES
# super-scale: RMS anchor, same statistic the ablation searches around
d = np.sqrt((mag * mag).mean(-1, keepdims=True)) / 20.0 + 1e-12
out[:, base + 96:base + 98] = d.astype(np.float16).view(np.uint8)
d = d.astype(np.float16).astype(np.float32) # encode what we store
g = grid()
for ib in range(QK // SUB):
m = mag[:, ib * SUB:(ib + 1) * SUB] # [R,32]
best_err = None
best = None
for code in range(16):
db = d * (0.5 + code) * 0.5
q = (m / np.maximum(db, 1e-20)).reshape(-1, 4)
idx = _nearest(q)
rec = g[idx].reshape(len(rows), SUB) * db
err = ((rec - m) ** 2).sum(-1, keepdims=True)
if best_err is None:
best_err, best = err, (idx.reshape(len(rows), SUB // 4), code)
else:
take = (err < best_err)[:, 0]
if take.any():
keep_idx, keep_code = best
ni = idx.reshape(len(rows), SUB // 4)
keep_idx = np.where(take[:, None], ni, keep_idx)
# per-row code: store alongside, resolved below
keep_code = np.where(take, code, keep_code) if isinstance(
keep_code, np.ndarray) else np.where(
take, code, np.full(len(rows), keep_code))
best = (keep_idx, keep_code)
best_err = np.where(take[:, None], err, best_err)
bidx, bcode = best
if not isinstance(bcode, np.ndarray):
bcode = np.full(len(rows), bcode)
out[:, base + ib * 8:base + (ib + 1) * 8] = bidx.astype(np.uint8)
# signs: four 7-bit words for this sub-block + the 4-bit code
s = sign[:, ib * SUB:(ib + 1) * SUB].reshape(len(rows), 4, 8)
neg = (s < 0).astype(np.uint32)
word = np.zeros(len(rows), dtype=np.uint32)
for l in range(4):
seven = np.zeros(len(rows), dtype=np.uint32)
for j in range(7):
seven |= neg[:, l, j] << j
word |= seven << (7 * l)
word |= (bcode.astype(np.uint32) & 0xF) << 28
off = base + QK // 4 + ib * 4
out[:, off:off + 4] = word.view(np.uint8).reshape(len(rows), 4) if False else \
np.ascontiguousarray(word).view(np.uint8).reshape(len(rows), 4)
return out.reshape(*x.shape[:-1], nsb * BLOCK_BYTES)
def decode(packed, K):
"""packed uint8 [..., K//256*98] -> float32 [..., K]. The kernels' reference."""
packed = np.ascontiguousarray(packed, dtype=np.uint8)
nsb = K // QK
rows = packed.reshape(-1, nsb * BLOCK_BYTES)
out = np.empty((len(rows), K), dtype=np.float32)
g = grid()
for sb in range(nsb):
base = sb * BLOCK_BYTES
d = rows[:, base + 96:base + 98].copy().view(np.float16).astype(np.float32)
for ib in range(QK // SUB):
idx = rows[:, base + ib * 8:base + (ib + 1) * 8] # [R,8]
off = base + QK // 4 + ib * 4
word = np.ascontiguousarray(rows[:, off:off + 4]).view(np.uint32).reshape(-1)
code = (word >> 28) & 0xF
db = d[:, 0] * (0.5 + code) * 0.5 # [R]
mag = g[idx].reshape(len(rows), SUB) # [R,32]
sgn = np.ones((len(rows), 4, 8), dtype=np.float32)
for l in range(4):
seven = (word >> (7 * l)) & 0x7F
par = 0
for j in range(7):
bit = (seven >> j) & 1
sgn[:, l, j] = np.where(bit == 1, -1.0, 1.0)
par ^= bit
sgn[:, l, 7] = np.where(par == 1, -1.0, 1.0) # odd parity closes the block
out[:, sb * QK + ib * SUB:sb * QK + (ib + 1) * SUB] = \
mag * sgn.reshape(len(rows), SUB) * db[:, None]
return out.reshape(*packed.shape[:-1], K)
def bpw():
return BLOCK_BYTES * 8 / QK
c/tools/iq3xxs_grid.json
+1
View File
@@ -0,0 +1 @@
[[4, 4, 4, 4], [20, 4, 4, 4], [36, 4, 4, 4], [12, 12, 4, 4], [28, 12, 4, 4], [62, 12, 4, 4], [4, 20, 4, 4], [20, 20, 4, 4], [12, 28, 4, 4], [20, 36, 4, 4], [28, 62, 4, 4], [44, 62, 4, 4], [12, 4, 12, 4], [28, 4, 12, 4], [4, 12, 12, 4], [20, 12, 12, 4], [12, 20, 12, 4], [44, 20, 12, 4], [4, 28, 12, 4], [20, 28, 12, 4], [12, 36, 12, 4], [36, 44, 12, 4], [4, 62, 12, 4], [4, 4, 20, 4], [20, 4, 20, 4], [36, 4, 20, 4], [12, 12, 20, 4], [4, 20, 20, 4], [20, 20, 20, 4], [12, 28, 20, 4], [28, 28, 20, 4], [62, 28, 20, 4], [12, 44, 20, 4], [62, 44, 20, 4], [44, 62, 20, 4], [12, 4, 28, 4], [62, 4, 28, 4], [4, 12, 28, 4], [20, 12, 28, 4], [44, 20, 28, 4], [4, 62, 28, 4], [28, 12, 36, 4], [62, 28, 36, 4], [36, 36, 36, 4], [62, 44, 36, 4], [28, 62, 36, 4], [44, 62, 36, 4], [12, 4, 44, 4], [62, 4, 44, 4], [20, 28, 44, 4], [20, 44, 44, 4], [44, 28, 52, 4], [36, 52, 52, 4], [4, 12, 62, 4], [36, 12, 62, 4], [52, 12, 62, 4], [28, 36, 62, 4], [12, 52, 62, 4], [12, 4, 4, 12], [28, 4, 4, 12], [4, 12, 4, 12], [20, 12, 4, 12], [12, 20, 4, 12], [28, 20, 4, 12], [4, 28, 4, 12], [20, 28, 4, 12], [36, 28, 4, 12], [62, 36, 4, 12], [4, 44, 4, 12], [4, 4, 12, 12], [20, 4, 12, 12], [12, 12, 12, 12], [4, 20, 12, 12], [20, 20, 12, 12], [12, 4, 20, 12], [28, 4, 20, 12], [4, 12, 20, 12], [20, 12, 20, 12], [12, 20, 20, 12], [4, 28, 20, 12], [20, 62, 20, 12], [4, 4, 28, 12], [20, 4, 28, 12], [4, 20, 28, 12], [12, 28, 28, 12], [52, 36, 28, 12], [52, 52, 28, 12], [12, 4, 36, 12], [44, 4, 36, 12], [4, 44, 36, 12], [4, 20, 44, 12], [36, 20, 44, 12], [52, 36, 44, 12], [12, 62, 44, 12], [44, 4, 52, 12], [20, 20, 62, 12], [4, 36, 62, 12], [4, 4, 4, 20], [20, 4, 4, 20], [12, 12, 4, 20], [28, 12, 4, 20], [4, 20, 4, 20], [20, 20, 4, 20], [52, 20, 4, 20], [12, 28, 4, 20], [20, 36, 4, 20], [12, 4, 12, 20], [28, 4, 12, 20], [44, 4, 12, 20], [4, 12, 12, 20], [20, 12, 12, 20], [12, 20, 12, 20], [4, 28, 12, 20], [28, 52, 12, 20], [62, 52, 12, 20], [4, 62, 12, 20], [4, 4, 20, 20], [20, 4, 20, 20], [12, 12, 20, 20], [62, 12, 20, 20], [4, 20, 20, 20], [20, 20, 20, 20], [62, 28, 20, 20], [4, 36, 20, 20], [44, 44, 20, 20], [12, 4, 28, 20], [4, 12, 28, 20], [36, 12, 28, 20], [4, 62, 28, 20], [36, 62, 28, 20], [44, 28, 36, 20], [28, 44, 36, 20], [28, 4, 44, 20], [62, 20, 44, 20], [12, 36, 44, 20], [36, 62, 44, 20], [12, 4, 62, 20], [28, 4, 62, 20], [52, 12, 62, 20], [44, 36, 62, 20], [12, 4, 4, 28], [4, 12, 4, 28], [20, 12, 4, 28], [12, 20, 4, 28], [28, 20, 4, 28], [4, 44, 4, 28], [44, 52, 4, 28], [20, 62, 4, 28], [4, 4, 12, 28], [20, 4, 12, 28], [4, 20, 12, 28], [12, 28, 12, 28], [36, 36, 12, 28], [52, 36, 12, 28], [12, 4, 20, 28], [28, 4, 20, 28], [4, 12, 20, 28], [44, 20, 20, 28], [20, 44, 20, 28], [20, 62, 20, 28], [12, 12, 28, 28], [28, 28, 28, 28], [4, 28, 36, 28], [62, 36, 36, 28], [20, 62, 36, 28], [4, 4, 44, 28], [52, 4, 44, 28], [20, 20, 44, 28], [44, 44, 44, 28], [36, 12, 52, 28], [52, 28, 52, 28], [28, 52, 52, 28], [28, 28, 62, 28], [4, 52, 62, 28], [36, 4, 4, 36], [62, 12, 4, 36], [44, 28, 4, 36], [62, 28, 4, 36], [28, 44, 4, 36], [62, 44, 4, 36], [36, 62, 12, 36], [4, 20, 20, 36], [62, 28, 20, 36], [4, 36, 20, 36], [4, 52, 20, 36], [52, 52, 20, 36], [62, 4, 28, 36], [44, 36, 28, 36], [36, 4, 36, 36], [12, 44, 36, 36], [36, 52, 36, 36], [44, 20, 44, 36], [28, 36, 44, 36], [4, 62, 44, 36], [44, 4, 62, 36], [4, 12, 62, 36], [20, 12, 62, 36], [4, 28, 62, 36], [20, 12, 4, 44], [12, 36, 4, 44], [4, 62, 4, 44], [4, 4, 12, 44], [52, 4, 12, 44], [52, 20, 12, 44], [44, 44, 12, 44], [36, 12, 20, 44], [20, 28, 20, 44], [20, 62, 20, 44], [20, 4, 28, 44], [28, 44, 28, 44], [4, 12, 36, 44], [28, 20, 36, 44], [62, 20, 36, 44], [20, 62, 36, 44], [20, 4, 44, 44], [12, 28, 44, 44], [4, 44, 52, 44], [36, 20, 62, 44], [20, 36, 62, 44], [36, 20, 4, 52], [36, 36, 4, 52], [52, 36, 4, 52], [36, 52, 4, 52], [12, 20, 12, 52], [12, 52, 12, 52], [62, 12, 20, 52], [36, 52, 20, 52], [4, 28, 28, 52], [52, 28, 28, 52], [36, 36, 36, 52], [44, 4, 44, 52], [20, 44, 44, 52], [28, 28, 52, 52], [28, 4, 62, 52], [12, 20, 62, 52], [28, 4, 4, 62], [44, 4, 4, 62], [62, 4, 4, 62], [4, 12, 4, 62], [20, 28, 4, 62], [20, 44, 4, 62], [52, 20, 12, 62], [4, 36, 12, 62], [20, 12, 20, 62], [44, 36, 20, 62], [20, 44, 20, 62], [4, 4, 28, 62], [44, 12, 28, 62], [28, 28, 28, 62], [4, 52, 28, 62], [12, 20, 36, 62], [12, 36, 36, 62], [4, 4, 44, 62], [20, 4, 44, 62], [36, 20, 44, 62], [4, 28, 52, 62]]
c/tools/quant_ablation.py
+69 -1
View File
@@ -117,11 +117,79 @@ def quantize_param(w, bits, group, rot=False, e8=""):
def _grid_or_e8(x, bits, group, e8):
if e8 == "-iq3":
return _quant_iq3(x.float())
if e8:
return _quant_e8(x.float(), group, bits, ball=(e8 == "-e8"))
return _quant_last_dim(x, bits, group)
# --------------------------------------------------------------------------------------
# IQ3_XXS-style codebook (#452 candidate (a)): llama.cpp's deployed 3.06-bpw scheme.
# 4-dim magnitude blocks quantized to a 256-entry lattice-subset grid (magnitudes on the
# odd ladder 4,12,..,62 in half-units), signs factored out per 8 weights with an odd-parity
# constraint (7 stored + 1 derived: a block whose true signs violate parity gets its
# smallest-magnitude sign flipped — modelled here so the ablation pays the real cost).
# Scales: fp16 super-scale per 256 + 4-bit sub-scale per 32, db = d*(0.5+s)*0.5.
# Grid extracted from ggml-common.h (MIT).
# --------------------------------------------------------------------------------------
_IQ3_GRID = None
def _iq3_grid(device):
global _IQ3_GRID
if _IQ3_GRID is None or _IQ3_GRID.device != device:
import json, os
path = os.path.join(os.path.dirname(__file__), "iq3xxs_grid.json")
_IQ3_GRID = torch.tensor(json.load(open(path)), dtype=torch.float32, device=device)
return _IQ3_GRID # [256,4], half-unit magnitudes (value/2 = weight units)
def _quant_iq3(x):
orig = x.shape
K = orig[-1]
assert K % 256 == 0, "iq3 needs multiples of 256 along the input dim"
xb = x.reshape(-1, 256) # super-blocks
grid = _iq3_grid(x.device) * 0.5 # weight units
out = torch.empty_like(xb)
signs = torch.sign(xb); signs[signs == 0] = 1.0
mags = xb.abs()
for sb in range(8): # 8 sub-blocks of 32
m = mags[:, sb*32:(sb+1)*32] # [N,32]
s = signs[:, sb*32:(sb+1)*32]
# per-8 sign parity: flip the smallest-|w| sign where the product is negative
s8 = s.reshape(-1, 4, 8)
m8 = m.reshape(-1, 4, 8)
viol = (s8.prod(-1) < 0) # odd number of minus signs
idxmin = m8.argmin(-1)
flip = torch.zeros_like(s8)
flip.scatter_(-1, idxmin[..., None], 1.0)
s8 = torch.where(viol[..., None].expand_as(s8) & (flip > 0), -s8, s8)
s = s8.reshape(-1, 32)
# sub-scale search: db candidates from the 4-bit code, super d from block RMS
d = m.pow(2).mean(-1, keepdim=True).sqrt() / 20.0 + 1e-12 # rough anchor
best = None
for code in range(16):
db = d * (0.5 + code) * 0.5
q = m / db # [N,32] target magnitudes
q4 = q.reshape(-1, 4) # 4-dim grid blocks
# chunked argmin ||q-g||^2 = argmin(|g|^2 - 2 q.g): a full cdist on a
# 100M-param tensor materializes tens of GB — this stays at ~256 MB.
g2 = grid.pow(2).sum(-1)
idx = torch.empty(q4.shape[0], dtype=torch.long, device=q4.device)
CH = 1 << 18
for i0 in range(0, q4.shape[0], CH):
cc = q4[i0:i0+CH]
idx[i0:i0+CH] = (g2 - 2.0 * (cc @ grid.T)).argmin(-1)
hit = grid[idx].reshape(-1, 8, 4)
rec = (hit.reshape(-1, 32) * db)
err = (rec - m).pow(2).sum(-1, keepdim=True)
if best is None:
best = (err, rec)
else:
take = err < best[0]
best = (torch.where(take, err, best[0]), torch.where(take, rec, best[1]))
out[:, sb*32:(sb+1)*32] = best[1] * s
return out.reshape(orig)
def _rot_quant(x, bits, group, e8=""):
"""W -> Qn(W@Q) @ Q^T along the last (input) dim — see rotation() above."""
q = rotation(x.shape[-1], x.device)
@@ -206,7 +274,7 @@ def _quant_e8(x, group, bits, ball):
return best_out.reshape(shp)
SCHEME_RE = re.compile(r"^int(2|3|4|8)(?:-g(\d+))?(-e8u?)?(-rot)?(-nohead)?$")
SCHEME_RE = re.compile(r"^int(2|3|4|8)(?:-g(\d+))?(-e8u?|-iq3)?(-rot)?(-nohead)?$")
def parse_scheme(name):
docker/Dockerfile
+15
View File
@@ -0,0 +1,15 @@
FROM debian:stable-slim
# We install the necessary packages to download and compile the code
RUN apt-get update && \
apt-get install -y locales git build-essential python3 python3-pip && \
rm -rf /var/lib/apt/lists/* && \
mkdir /app
# Let's clone the repository
WORKDIR /app
RUN git clone https://github.com/JustVugg/colibri.git .
WORKDIR /app/c
# It's time to compile
RUN ./setup.sh && ./coli build
docker/README.IT.md
+454
View File
@@ -0,0 +1,454 @@
_Read the read me in [English](README.md)._
# Guida a Colibrì - Motore di Inferenza Locale
Una guida semplice per eseguire **Colibrì**, un motore di inferenza locale basato su GLM 5.2, senza conoscenze di programmazione. Se hai già Docker installato, sei a buon punto.
---
## 📋 Sommario
- [Cosa è Colibrì?](#cosa-è-colibrì)
- [Cosa serve](#cosa-serve)
- [Hardware](#hardware)
- [Software](#software)
- [Come iniziare](#come-iniziare)
- [Passo 1: Scarica il modello](#passo-1-scarica-il-modello)
- [Passo 2: Scarica il Dockerfile di Colibrì](#passo-2-scarica-il-dockerfile-di-colibrì)
- [Passo 3: Compila l'immagine Docker](#passo-3-compila-limmagine-docker)
- [Passo 4: Avvia Colibrì](#passo-4-avvia-colibr%C3%AC)
- [Cosa significa quel comando?](#cosa-significa-quel-comando)
- [Usare Colibrì](#usare-colibrì)
- [Entrare in una console Linux dentro il container](#entrare-in-una-console-linux-dentro-il-container)
- [Risoluzione dei problemi](#risoluzione-dei-problemi)
- [Note tecniche](#note-tecniche)
- [Domande frequenti](#domande-frequenti)
- [Supporto e contributi](#supporto-e-contributi)
- [Test sul mio PC](#test-sul-mio-pc)
---
## Cosa è Colibrì?
Colibrì è un'applicazione che ti permette di eseguire un modello di intelligenza artificiale (GLM 5.2) direttamente sul tuo computer, senza connettersi a server esterni. È possbile anche farlo girare in Docker, che isola l'applicazione dal resto del sistema.
> **Nota importante**: Il modello è molto grande. Attendi anche diversi minuti per una risposta a una domanda semplice, specialmente con poca RAM. Alla fine di questo readme vedrai il risultato sul mio PC (senza scheda grafica discreta) e arrivo a 0.01 token al secondo.
---
## Cosa serve
### Hardware
| Memoria RAM | Funziona? | Note |
|:---:|:---:|---|
| < 16 GB | ❌ No | Memoria insufficiente |
| 24 GB | ⚠️ Forse | Possibile, da testare |
| 32 GB | ✅ Sì | Il minimo (ma vedi sezione memoria su Windows) |
| 48+ GB | ✅ Sì | Meglio |
Inoltre: un **disco SSD veloce** è essenziale. Colibrì usa il disco come memoria aggiuntiva. Con una scheda grafica NVidia è ancora meglio.
### Software
- **Docker Desktop** (Windows, Mac, Linux) — [scarica qui](https://www.docker.com/products/docker-desktop/)
- **Python** (solo se vuoi scaricare il modello da casa tua)
- Windows: [python.org](https://www.python.org) oppure Microsoft Store
- Linux: `apt-get install python3 python3-pip`
- Mac: [python.org](https://www.python.org) oppure Homebrew
Non serve nessun ambiente di compilazione. Tutto avviene dentro il container Docker.
---
## Come iniziare
### Passo 1: Scarica il modello
Il modello GLM 5.2 è circa **360 GB**. Scegli uno di questi metodi:
#### Metodo A: Con Python (consigliato)
1. **Installa la libreria per Hugging Face:**
```bash
python -m pip install -U huggingface_hub[cli]
```
Su Linux, usa `python3` al posto di `python`.
2. **Scarica il modello** (apri il terminale nella cartella dove lo vuoi salvare):
```bash
hf_download mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp --local-dir .
```
**Esempio**: se vuoi salvarlo in `C:\LLM\models\glm-5.2` (Windows):
- Apri PowerShell in quella cartella
- Copia e incolla il comando sopra
- Attendi (molto)
#### Metodo B: Senza Python (solo se necessario)
Se sei su Windows e non riesci con Python:
- Scarica manualmente da [Hugging Face](https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp)
- Decomprimi in una cartella (es. `C:\LLM\models\glm-5.2`)
---
### Passo 2: Scarica il Dockerfile di Colibrì
1. Vai a: https://github.com/JustVugg/colibri/blob/main/docker/Dockerfile
2. Clicca il pulsante **Download** (icona ⬇️) in alto a destra
3. Salva il file in una cartella (es. `C:\LLM\Colibrì`)
---
### Passo 3: Compila l'immagine Docker
Apri il terminale (PowerShell su Windows, Terminal su Mac/Linux) **nella cartella dove hai salvato il Dockerfile** e digita:
**Windows:**
```bash
docker build -t colibri-i .
```
**Linux/Mac:**
```bash
sudo docker build -t colibri-i .
```
Attendi che finisca (pochi minuti). Se tutto va bene, vedrai: `Successfully tagged colibri-i:latest`
> **Se vuoi recepire gli aggiornamenti del repository**: Cancella prima l'immagine vecchia con `docker rmi colibri-i` e ricompila.
---
### Passo 4: Avvia Colibrì
Apri il terminale e digita il comando sottostante (sostituisci `C:\LLM\models\glm-5.2` con il percorso reale del tuo PC):
**Windows** (PowerShell):
```bash
$MODEL_PATH="C:\LLM\models\glm-5.2"
docker run --rm -it --name colibri-c `
-v "$MODEL_PATH`:/app/glm-5.2" `
-e COLI_MODEL=/app/glm-5.2 `
colibri-i ./coli chat
```
**Mac/Linux** (Terminal/Bash):
```bash
MODEL_PATH="/path/to/glm-5.2"
docker run --rm -it --name colibri-c \
-v "$MODEL_PATH:/app/glm-5.2" \
-e COLI_MODEL=/app/glm-5.2 \
colibri-i ./coli chat
```
**Esempio per Linux:**
```bash
MODEL_PATH="/home/user/LLM/glm-5.2"
docker run --rm -it --name colibri-c \
-v "$MODEL_PATH:/app/glm-5.2" \
-e COLI_MODEL=/app/glm-5.2 \
colibri-i ./coli chat
```
---
### Cosa significa quel comando?
| Parte | Spiegazione |
|-------|-------------|
| `docker run` | Avvia un container |
| `--rm` | Cancella il container quando chiudi |
| `-it` | Modalità interattiva (puoi scrivere e leggere) |
| `-v "PERCORSO:/app/glm-5.2"` | Collega il tuo modello dentro il container |
| `-e COLI_MODEL=/app/glm-5.2` | Dice a Colibrì dove trovare il modello |
| `colibri-i` | Nome dell'immagine Docker |
| `./coli chat` | Avvia Colibrì in modalità chat |
---
### Usare Colibrì
Una volta avviato, vedrai un prompt come questo:
```
──────────────────────────────────────────────────────────
type and press Enter · Ctrl-C stops the answer · :more continues · :reset clears memory · :q exits
```
**Comandi utili:**
- `Scrivi una domanda + Invio` → Ricevi la risposta
- `Ctrl + C` → Interrompi la risposta
- `:reset` → Cancella la memoria della conversazione
- `:q` → Esci
**Esempio di uso:**
```
› Quanti abitanti ha la Cina?
La Cina è attualmente il paese più popoloso al mondo.
La popolazione è di circa 1,41 miliardi di abitanti.
```
Il modello capisce **italiano, inglese, cinese e altre lingue**, anche se è ottimizzato per inglese e cinese.
---
## Entrare in una console Linux dentro il container
Se vuoi esplorare il container come fosse una macchina Linux normale:
```bash
docker run --rm -it --name colibri-c \
-v "PERCORSO_MODELLO:/app/glm-5.2" \
-e COLI_MODEL=/app/glm-5.2 \
colibri-i /bin/bash
```
Ora sei dentro Linux. Digita `exit` per uscire.
---
## Risoluzione dei problemi
### ❌ "Docker non trovato"
**Causa**: Docker non è installato o il terminale non lo riconosce.
**Soluzione**:
1. Reinstalla [Docker Desktop](https://www.docker.com/products/docker-desktop/)
2. Riavvia il computer
3. Apri un nuovo terminale e riprova
---
### ❌ "Out of memory" (memoria insufficiente) o container che si chiude subito
**Causa**: Il tuo computer non ha abbastanza RAM, oppure su Windows, WSL usa meno memoria di quella disponibile.
**Soluzione per Windows (WSL):**
1. Apri PowerShell e controlla la memoria disponibile a WSL:
```bash
wsl
cat /proc/meminfo | grep MemTotal
exit
```
Dividi il numero per 1.073.741.824 (è 1024³) per averlo in GB.
2. Se WSL usa meno di quello che hai, crea un file di configurazione:
- Apri un editor di testo (Notepad va bene)
- Copia questo:
```ini
[wsl2]
memory=24GB
processors=12
swap=16GB
```
- Salva il file con il nome: `.wslconfig` (con il punto)
- Posizionalo in: `C:\Users\TuoNomeUtente\`
3. Riavvia WSL da PowerShell:
```bash
wsl --shutdown
wsl
```
4. Controlla di nuovo:
```bash
# cat /proc/meminfo | grep MemTotal
# exit
```
**Soluzione per Mac/Linux**: Aumenta la RAM disponibile a Docker dalle impostazioni di Docker Desktop, oppure aggiungi più RAM al computer.
---
### ❌ La risposta è molto lenta
**Cause possibili**:
1. Il disco è lento
2. Hai poca RAM
3. Colibrì sta usando il disco come memoria aggiuntiva (normale)
**Come controllare la velocità del disco:**
**Windows** (PowerShell da amministratore):
```bash
winsat disk -drive C
```
Cambia `C` con la lettera del tuo disco.
**Linux/Mac** (Terminal):
```bash
sudo hdparm -Tt /dev/sda
```
Cambia `/dev/sda` con il tuo disco (vedi con `lsblk` per Linux).
Un **SSD NVMe moderno** arriva a 15 GB/sec. Se il tuo è sotto 2-3 GB/sec, è lento.
---
### ❌ "Permission denied" su Linux
**Causa**: Docker richiede permessi da amministratore.
**Soluzione - Opzione 1** (rapida):
```bash
sudo docker build -t colibri-i .
sudo docker run ... (come sopra, con sudo davanti)
```
**Soluzione - Opzione 2** (permanente):
```bash
sudo usermod -aG docker $USER
# Riavvia il computer
docker run ... (senza sudo)
```
---
### ❌ "Image not found" o errore durante il build
**Causa**: Il Dockerfile è corrotto o non nella cartella giusta.
**Soluzione**:
1. Verifica che il Dockerfile sia nella cartella dove apri il terminale:
```bash
ls Dockerfile # Mac/Linux
dir Dockerfile # Windows
```
2. Riscarica il Dockerfile dal repository GitHub
3. Elimina l'immagine vecchia: `docker rmi colibri-i`
4. Riprova il build
---
### ❌ "hf_download: command not found"
**Causa**: La libreria Hugging Face non è installata correttamente.
**Soluzione**:
```bash
pip install -U huggingface_hub[cli]
# oppure su Linux/Mac:
pip3 install -U huggingface_hub[cli]
```
Poi riprova il comando `hf_download`.
---
### ❌ Il modello non si scarica (timeout o errori di rete)
**Cause**: Connessione lenta o instabile, Hugging Face temporaneamente non disponibile.
**Soluzione**:
1. Attendi e riprova il comando `hf_download`
2. Se continua, scarica manualmente da [qui](https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp)
3. Decomprimi il file ZIP nella cartella desiderata
---
## Note tecniche
### Perché il disco è importante?
Colibrì usa il disco come "RAM aggiuntiva" virtuale (paging). Un disco **veloce** è cruciale per prestazioni decenti.
- **SSD NVMe** (consigliato): 1-15 GB/sec
- **SSD SATA**: 0.5-1 GB/sec
- **Hard disk meccanico**: 0.05-0.1 GB/sec ❌ (troppo lento)
Se il tuo disco è lento, le risposte saranno molto lente anche con molta RAM.
---
### Configurazione di default consigliata per WLS su Windows
Se hai **esattamente 32 GB di RAM** e usi Windows, è molto probabile che WLS di default sia settato per non consumare più di 16 GB di RAM. Bisogna aumentare questo limite [Risoluzione dei problemi](#risoluzione-dei-problemi) . Nel mio caso ho adottato questa configurazione:
```ini
[wsl2]
memory=24GB
processors=12
swap=16GB
```
Ovvero, nel mio caso, ho lasciato 8 GB di RAM e 4 CPU a Windows e dato 24 GB e 12 processori a WSL + Linux.
---
## Domande frequenti
**D: E se ho meno di 32 GB di RAM?**
R: Probabile che non funzioni bene. Puoi provare se hai 24 GB, ma non è garantito.
**D: Posso aumentare la velocità di risposta?**
R: Sì, in parte:
- Usa un SSD NVMe veloce
- Aumenta la RAM
- Riduci la complessità delle domande
- Usa `:reset` per cancellare la memoria e alleggerire il carico
**D: Posso usare Colibrì senza Docker?**
R: Colibrì è nato così, ma questa guida assume Docker. Per compilare da sorgente, vedi il repository GitHub.
**D: Quanta connessione internet mi serve dopo aver scaricato il modello?**
R: Zero. Colibrì funziona completamente offline.
---
## Supporto e contributi
Se trovi errori o hai suggerimenti per migliorare questa guida, aprici una issue o una pull request sul repository GitHub di Colibrì.
Buon divertimento! 🐦
---
## Test sul mio PC
Nel primo caso ho fatto una domanda in italiano, nel secondo in giapponese, e nel terzo ho rifatto la domanda in giapponese ma ho richiesto una risposta in italiano.
```
PS C:\quack\llm\colibri\docker> docker run --rm -it --name colibri-c -v "C:\quack\llm\models\glm-5.2:/app/glm-5.2" -e COLI_MODEL=/app/glm-5.2 colibri-i ./coli chat
▄▀▀▀▄ ▄ colibrì v1.0
▄▄▄▄▀▀▀▀▄▀▀ tiny engine, immense model
▀▀▀▀▀▀▀ GLM-5.2 · 744B MoE · int4 · streaming CPU
▀▀▀▀ chat · glm-5.2 · ram -GB · topp off
▀
──────────────────────────────────────────────────────────
type and press Enter · Ctrl-C stops the answer · :more continues · :reset clears memory · :q exits
╭────────────────────────────────────────────────────────────────────────────────────────────────╮
│ › Quanti abitanti ha la Cina? │
╰────────────────────────────────────────────────────────────────────────────────────────────────╯
◆ colibrì
La Cina è attualmente il paese più popoloso al mondo (sebbene, secondo alcune stime recenti, sia stata ormai superata dall'India).
La popolazione totale della Repubblica Popolare Cinese è di circa 1,41 miliardi di abitanti (dati del 2020-2022 circa).
└─ 76 tok · 0.04 tok/s · hit 3% · RSS 15.9 GB · 2012s
╭────────────────────────────────────────────────────────────────────────────────────────────────╮
│ › 漫画「ワンピース」の主人公の名前を教えてください。名前だけで、それ以上のコメントはありません。 │
╰────────────────────────────────────────────────────────────────────────────────────────────────╯
◆ colibrì
ルフィ
└─ 2 tok · 0.01 tok/s · hit 1% · RSS 16.7 GB · 260s
╭────────────────────────────────────────────────────────────────────────────────────────────────╮
│ › 漫画「ワンピース」の主人公の名前を教えてください。名前だけで、それ以上のコメントはありません。イタリア語で返信 │
╰────────────────────────────────────────────────────────────────────────────────────────────────╯
◆ colibrì
Il nome del protagonista di One Piece è Monkey D. Luffy.
└─ 14 tok · 0.02 tok/s · hit 2% · RSS 17.3 GB · 593s
╭────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ›
```
docker/README.md
+488
View File
@@ -0,0 +1,488 @@
_Leggi il leggimi in [Italiano](README.IT.md)._
# Colibrì Guide - Local Inference Engine
A simple guide to running **Colibrì**, a local inference engine based on GLM 5.2, without needing programming knowledge. If you already have Docker installed, you are well on your way.
---
## 📋 Table of Contents
* [What is Colibrì?](https://www.google.com/search?q=#what-is-colibr%C3%AC)
* [Requirements](https://www.google.com/search?q=#requirements)
* [Hardware](https://www.google.com/search?q=#hardware)
* [Software](https://www.google.com/search?q=#software)
* [How to get started](https://www.google.com/search?q=#how-to-get-started)
* [Step 1: Download the model](https://www.google.com/search?q=#step-1-download-the-model)
* [Step 2: Download the Colibrì Dockerfile](https://www.google.com/search?q=#step-2-download-the-colibr%C3%AC-dockerfile)
* [Step 3: Build the Docker image](https://www.google.com/search?q=#step-3-build-the-docker-image)
* [Step 4: Start Colibrì](https://www.google.com/search?q=#step-4-start-colibr%C3%AC)
* [What does that command mean?](https://www.google.com/search?q=#what-does-that-command-mean)
* [Using Colibrì](https://www.google.com/search?q=#using-colibr%C3%AC)
* [Entering a Linux console inside the container](https://www.google.com/search?q=#entering-a-linux-console-inside-the-container)
* [Troubleshooting](https://www.google.com/search?q=#troubleshooting)
* [Technical notes](https://www.google.com/search?q=#technical-notes)
* [Frequently Asked Questions](https://www.google.com/search?q=#frequently-asked-questions)
* [Support and contributions](https://www.google.com/search?q=#support-and-contributions)
* [Tests on my PC](https://www.google.com/search?q=#tests-on-my-pc)
---
## What is Colibrì?
Colibrì is an application that allows you to run an artificial intelligence model (GLM 5.2) directly on your computer, without connecting to external servers. It is also possible to run it in Docker, which isolates the application from the rest of the system.
> **Important note**: The model is very large. Expect to wait several minutes for an answer to a simple question, especially with low RAM. At the end of this readme, you will see the result on my PC (without a discrete graphics card), and I reach 0.01 tokens per second.
---
## Requirements
### Hardware
| RAM Memory | Works? | Notes |
| --- | --- | --- |
| < 16 GB | ❌ No | Insufficient memory |
| 24 GB | ⚠️ Maybe | Possible, needs testing |
| 32 GB | ✅ Yes | The minimum (but see memory section for Windows) |
| 48+ GB | ✅ Yes | Better |
Additionally: a **fast SSD** is essential. Colibrì uses the disk as additional memory. Having an NVidia graphics card is even better.
### Software
* **Docker Desktop** (Windows, Mac, Linux) — [download here](https://www.google.com/search?q=https://www.docker.com/products/docker-desktop/)
* **Python** (only if you want to download the model yourself)
* Windows: [python.org](https://www.google.com/search?q=https://www.python.org) or Microsoft Store
* Linux: `apt-get install python3 python3-pip`
* Mac: [python.org](https://www.google.com/search?q=https://www.python.org) or Homebrew
No build environment is needed. Everything happens inside the Docker container.
---
## How to get started
### Step 1: Download the model
The GLM 5.2 model is approximately **360 GB**. Choose one of these methods:
#### Method A: Using Python (recommended)
1. **Install the library for Hugging Face:**
```bash
python -m pip install -U huggingface_hub[cli]
```
On Linux, use `python3` instead of `python`.
2. **Download the model** (open the terminal in the folder where you want to save it):
```bash
hf_download mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp --local-dir .
```
**Example**: if you want to save it in `C:\LLM\models\glm-5.2` (Windows):
* Open PowerShell in that folder
* Copy and paste the command above
* Wait (a long time)
#### Method B: Without Python (only if necessary)
If you are on Windows and cannot get it to work with Python:
* Download manually from [Hugging Face](https://www.google.com/search?q=https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp)
* Unzip into a folder (e.g., `C:\LLM\models\glm-5.2`)
---
### Step 2: Download the Colibrì Dockerfile
1. Go to: [https://github.com/JustVugg/colibri/blob/main/docker/Dockerfile](https://www.google.com/search?q=https://github.com/JustVugg/colibri/blob/main/docker/Dockerfile)
2. Click the **Download** button (⬇️ icon) in the top right
3. Save the file in a folder (e.g., `C:\LLM\Colibrì`)
---
### Step 3: Build the Docker image
Open the terminal (PowerShell on Windows, Terminal on Mac/Linux) **in the folder where you saved the Dockerfile** and type:
**Windows:**
```bash
docker build -t colibri-i .
```
**Linux/Mac:**
```bash
sudo docker build -t colibri-i .
```
Wait for it to finish (a few minutes). If everything goes well, you will see: `Successfully tagged colibri-i:latest`
> **If you want to receive repository updates**: First delete the old image with `docker rmi colibri-i` and rebuild.
---
### Step 4: Start Colibrì
Open the terminal and type the command below (replace `C:\LLM\models\glm-5.2` with the actual path on your PC):
**Windows** (PowerShell):
```bash
$MODEL_PATH="C:\LLM\models\glm-5.2"
docker run --rm -it --name colibri-c `
-v "$MODEL_PATH`:/app/glm-5.2" `
-e COLI_MODEL=/app/glm-5.2 `
colibri-i ./coli chat
```
**Mac/Linux** (Terminal/Bash):
```bash
MODEL_PATH="/path/to/glm-5.2"
docker run --rm -it --name colibri-c \
-v "$MODEL_PATH:/app/glm-5.2" \
-e COLI_MODEL=/app/glm-5.2 \
colibri-i ./coli chat
```
**Example for Linux:**
```bash
MODEL_PATH="/home/user/LLM/glm-5.2"
docker run --rm -it --name colibri-c \
-v "$MODEL_PATH:/app/glm-5.2" \
-e COLI_MODEL=/app/glm-5.2 \
colibri-i ./coli chat
```
---
### What does that command mean?
| Part | Explanation |
| --- | --- |
| `docker run` | Starts a container |
| `--rm` | Deletes the container when you close it |
| `-it` | Interactive mode (you can write and read) |
| `-v "PATH:/app/glm-5.2"` | Mounts your model inside the container |
| `-e COLI_MODEL=/app/glm-5.2` | Tells Colibrì where to find the model |
| `colibri-i` | Name of the Docker image |
| `./coli chat` | Starts Colibrì in chat mode |
---
### Using Colibrì
Once started, you will see a prompt like this:
```
──────────────────────────────────────────────────────────
type and press Enter · Ctrl-C stops the answer · :more continues · :reset clears memory · :q exits
```
**Useful commands:**
* `Write a question + Enter` → Receive the answer
* `Ctrl + C` → Stop the answer
* `:reset` → Clear conversation memory
* `:q` → Exit
**Usage example:**
```
› How many inhabitants does China have?
China is currently the most populous country in the world.
The population is approximately 1.41 billion people.
```
The model understands **Italian, English, Chinese, and other languages**, although it is optimized for English and Chinese.
---
## Entering a Linux console inside the container
If you want to explore the container as if it were a normal Linux machine:
```bash
docker run --rm -it --name colibri-c \
-v "MODEL_PATH:/app/glm-5.2" \
-e COLI_MODEL=/app/glm-5.2 \
colibri-i /bin/bash
```
Now you are inside Linux. Type `exit` to leave.
---
## Troubleshooting
### ❌ "Docker not found"
**Cause**: Docker is not installed or the terminal does not recognize it.
**Solution**:
1. Reinstall [Docker Desktop](https://www.google.com/search?q=https://www.docker.com/products/docker-desktop/)
2. Restart your computer
3. Open a new terminal and try again
---
### ❌ "Out of memory" or container closes immediately
**Cause**: Your computer does not have enough RAM, or on Windows, WSL is using less memory than available.
**Solution for Windows (WSL):**
1. Open PowerShell and check the memory available to WSL:
```bash
wsl
cat /proc/meminfo | grep MemTotal
exit
```
Divide the number by 1,073,741,824 (which is 1024³) to get it in GB.
2. If WSL uses less than what you have, create a configuration file:
* Open a text editor (Notepad is fine)
* Copy this:
```ini
[wsl2]
memory=24GB
processors=12
swap=16GB
```
* Save the file with the name: `.wslconfig` (with the dot)
* Place it in: `C:\Users\YourUsername\`
3. Restart WSL from PowerShell:
```bash
wsl --shutdown
wsl
```
4. Check again:
```bash
# cat /proc/meminfo | grep MemTotal
# exit
```
**Solution for Mac/Linux**: Increase the RAM available to Docker from the Docker Desktop settings, or add more RAM to the computer.
---
### ❌ The answer is very slow
**Possible causes**:
1. The disk is slow
2. You have low RAM
3. Colibrì is using the disk as additional memory (normal)
**How to check disk speed:**
**Windows** (PowerShell as administrator):
```bash
winsat disk -drive C
```
Change `C` with your disk letter.
**Linux/Mac** (Terminal):
```bash
sudo hdparm -Tt /dev/sda
```
Change `/dev/sda` with your disk (check with `lsblk` on Linux).
A **modern NVMe SSD** reaches 15 GB/sec. If yours is under 2-3 GB/sec, it is slow.
---
### ❌ "Permission denied" on Linux
**Cause**: Docker requires administrator permissions.
**Solution - Option 1** (quick):
```bash
sudo docker build -t colibri-i .
sudo docker run ... (as above, with sudo in front)
```
**Solution - Option 2** (permanent):
```bash
sudo usermod -aG docker $USER
# Restart the computer
docker run ... (without sudo)
```
---
### ❌ "Image not found" or error during build
**Cause**: The Dockerfile is corrupted or not in the right folder.
**Solution**:
1. Verify that the Dockerfile is in the folder where you open the terminal:
```bash
ls Dockerfile # Mac/Linux
dir Dockerfile # Windows
```
2. Redownload the Dockerfile from the GitHub repository
3. Delete the old image: `docker rmi colibri-i`
4. Retry the build
---
### ❌ "hf_download: command not found"
**Cause**: The Hugging Face library is not installed correctly.
**Solution**:
```bash
pip install -U huggingface_hub[cli]
# or on Linux/Mac:
pip3 install -U huggingface_hub[cli]
```
Then retry the `hf_download` command.
---
### ❌ The model does not download (timeout or network errors)
**Causes**: Slow or unstable connection, Hugging Face temporarily unavailable.
**Solution**:
1. Wait and retry the `hf_download` command
2. If it continues, download manually from [here](https://www.google.com/search?q=https://huggingface.co/mateogrgic/GLM-5.2-colibri-int4-with-int8-mtp)
3. Unzip the ZIP file into the desired folder
---
## Technical notes
### Why is the disk important?
Colibrì uses the disk as "additional virtual RAM" (paging). A **fast** disk is crucial for decent performance.
* **NVMe SSD** (recommended): 1-15 GB/sec
* **SATA SSD**: 0.5-1 GB/sec
* **Rotational hard disk**: 0.05-0.1 GB/sec ❌ (too slow)
If your disk is slow, the answers will be very slow even with a lot of RAM.
---
### Recommended default configuration for WLS on Windows
If you have **exactly 32 GB of RAM** and are using Windows, it is very likely that WLS by default is set to consume no more than 16 GB of RAM. We need to increase this limit [Troubleshooting](https://www.google.com/search?q=#troubleshooting) . In my case I adopted this configuration:
```ini
[wsl2]
memory=24GB
processors=12
swap=16GB
```
That is, in my case, I left 8 GB of RAM and 4 CPUs to Windows and gave 24 GB and 12 processors to WSL + Linux.
---
## Frequently Asked Questions
**Q: What if I have less than 32 GB of RAM?**
A: It is likely that it will not work well. You can try if you have 24 GB, but it is not guaranteed.
**Q: Can I increase the response speed?**
A: Yes, partly:
* Use a fast NVMe SSD
* Increase RAM
* Reduce the complexity of questions
* Use `:reset` to clear memory and lighten the load
**Q: Can I use Colibrì without Docker?**
A: Colibrì was born that way, but this guide assumes Docker. To build from source, see the GitHub repository.
**Q: How much internet connection do I need after downloading the model?**
A: Zero. Colibrì works completely offline.
---
## Support and contributions
If you find errors or have suggestions for improving this guide, open an issue or a pull request on the Colibrì GitHub repository.
Have fun! 🐦
---
## Tests on my PC
In the first case, I asked a question in Italian; in the second, in Japanese; and in the third, I repeated the question in Japanese but requested an answer in Italian.
```
PS C:\quack\llm\colibri\docker> docker run --rm -it --name colibri-c -v "C:\quack\llm\models\glm-5.2:/app/glm-5.2" -e COLI_MODEL=/app/glm-5.2 colibri-i ./coli chat
▄▀▀▀▄ ▄ colibrì v1.0
▄▄▄▄▀▀▀▀▄▀▀ tiny engine, immense model
▀▀▀▀▀▀▀ GLM-5.2 · 744B MoE · int4 · streaming CPU
▀▀▀▀ chat · glm-5.2 · ram -GB · topp off
▀
──────────────────────────────────────────────────────────
type and press Enter · Ctrl-C stops the answer · :more continues · :reset clears memory · :q exits
╭────────────────────────────────────────────────────────────────────────────────────────────────╮
│ › Quanti abitanti ha la Cina? │
╰────────────────────────────────────────────────────────────────────────────────────────────────╯
◆ colibrì
La Cina è attualmente il paese più popoloso al mondo (sebbene, secondo alcune stime recenti, sia stata ormai superata dall'India).
La popolazione totale della Repubblica Popolare Cinese è di circa 1,41 miliardi di abitanti (dati del 2020-2022 circa).
└─ 76 tok · 0.04 tok/s · hit 3% · RSS 15.9 GB · 2012s
╭────────────────────────────────────────────────────────────────────────────────────────────────╮
│ › 漫画「ワンピース」の主人公の名前を教えてください。名前だけで、それ以上のコメントはありません。 │
╰────────────────────────────────────────────────────────────────────────────────────────────────╯
◆ colibrì
ルフィ
└─ 2 tok · 0.01 tok/s · hit 1% · RSS 16.7 GB · 260s
╭────────────────────────────────────────────────────────────────────────────────────────────────╮
│ › 漫画「ワンピース」の主人公の名前を教えてください。名前だけで、それ以上のコメントはありません。イタリア語で返信 │
╰────────────────────────────────────────────────────────────────────────────────────────────────╯
◆ colibrì
Il nome del protagonista di One Piece è Monkey D. Luffy.
└─ 14 tok · 0.02 tok/s · hit 2% · RSS 17.3 GB · 593s
╭────────────────────────────────────────────────────────────────────────────────────────────────╮
│ ›
```
[source: 1]
docs/ENVIRONMENT.md
+9
View File
@@ -114,6 +114,15 @@ Per-drive byte counts are reported in a `MIRROR:` stats line. Combine with `DIRE
| `COLI_CUDA_SHARED_W4A16_MIN_ROWS` | `32` | Min row count to engage the shared-MLP W4A16 kernel. |
| `COLI_METAL_UNTRACKED` | off (Metal only) | `=1` sets `MTLResourceHazardTrackingModeUntracked` on Metal buffers (reduces hazard-tracking overhead). |
> **Windows note.** On Windows, a bare `coli chat` / `coli run` / `coli serve`
> (no `--gpu`/`--vram`/`--auto-tier`) **auto-enables the GPU** when it detects a
> CUDA build (`coli_cuda.dll` next to the engine) and at least one GPU via
> `nvidia-smi`. The expert-tier VRAM budget is then sized automatically from the
> card's free VRAM (same computation as `--auto-tier`). If `nvidia-smi` is not on
> `PATH` the run falls back to CPU with a warning — pass `--vram N` (or add
> `nvidia-smi` to `PATH`) to enable CUDA in that case. `--gpu none` forces
> CPU-only. (Linux/macOS behaviour is unchanged: pass a flag to enable CUDA.)
---
## Advanced / experimental / debug
docs/grammar-draft.md
+24
View File
@@ -83,3 +83,27 @@ this implementation adds: forced spans as a **draft source verified in the targe
own forward** (lossless even under a wrong grammar, composes with MTP/n-gram in one
union batch), deployed where the win is denominated in expert I/O rather than
forward passes.
## Server usage: `response_format` (OpenAI API)
The gateway (`openai_server.py`) turns `response_format` into a **per-request**
grammar carried to the engine over the `SUBMIT` protocol (optional 7th header
field, `gbytes`; 6-field headers from older clients remain valid — the field is
additive and back-compatible in both directions):
```jsonc
{"response_format": {"type": "json_object"}} // generic JSON grammar
{"response_format": {"type": "json_schema",
"json_schema": {"schema": { ... }}}} // compiled by schema_gbnf.h
{"response_format": {"type": "gbnf", "grammar": "root ::= ..."}} // raw GBNF (extension)
```
Semantics are identical to `GRAMMAR=`/`SCHEMA=`: a **draft source, never a
sampling constraint**. A schema outside the supported subset, or malformed GBNF,
costs the speedup — never the request, never the output. Drafting engages for
greedy requests (`temperature: 0`); sampled requests run undrafted. Compile
overhead is negligible: ~8 µs/request for a typical schema, ~18 µs at the
32-level nesting cap (measured, M3 Max). Grammar payloads are capped at 1 MiB.
As with MTP (#100), a drafted greedy run may differ from an undrafted one in
near-tie tokens (the verify forward has a different batch shape); each output
is a valid greedy stream of its own forward shapes.
docs/quickstart.md
+25 -5
View File
@@ -42,11 +42,31 @@ engine needs.
You have two options.
**Option A — download a prebuilt binary (no compiler needed).**
Grab the latest `colibri-<version>-windows-x86_64.zip` from the
[Releases page](https://github.com/JustVugg/colibri/releases), unzip it, and
skip to [step 3](#3-get-the-model). Python 3 (from
[python.org](https://www.python.org/downloads/)) is still needed if you want to
convert a model yourself.
Grab `colibri-<version>-windows-x86_64.zip` from the
[Releases page](https://github.com/JustVugg/colibri/releases) and unzip it.
Inside you'll find:
| File | What it is |
|---|---|
| `colibri-<version>-windows-x86_64.exe` | **the engine** — the C program that actually runs the model |
| `coli` | the command-line launcher (`chat`, `serve`, `convert`, `doctor`, …) |
| `openai_server.py`, `resource_plan.py`, `doctor.py` | Python support for the API server and placement planner |
Two setup steps:
1. **Rename the engine to `glm.exe`** so the launcher can find it (it looks for a
binary named `glm`):
```powershell
Rename-Item colibri-*-windows-x86_64.exe glm.exe
```
2. **Install Python 3** from [python.org](https://www.python.org/downloads/) — the
`coli` launcher and the API gateway are Python scripts (the engine itself is
pure C and needs nothing).
Then continue to [step 3](#3-get-the-model). Prefer to skip the launcher? You can
run the engine directly — `.\glm.exe` reads the model path from the `SNAP`
environment variable (see [docs/windows.md](windows.md)) — but `coli chat` is the
easy path.
**Option B — build from source with MSYS2.**
Install [MSYS2](https://www.msys2.org/), open the **UCRT64** shell, and run:
flake.lock
Generated
+3 -3
View File
@@ -20,11 +20,11 @@
},
"nixpkgs": {
"locked": {
"lastModified": 1784160687,
"narHash": "sha256-iYL/bixrb6FlHFu/gIuBYzq6c6lM5AAXsXNSWXtIgQc=",
"lastModified": 1784280462,
"narHash": "sha256-DtoqIqM7VkR6NxAkcLpMwmi02USwWb3JdmNGLyhthc0=",
"owner": "NixOS",
"repo": "nixpkgs",
"rev": "4382ed2b7a6839d4280a9b386db49cbc5907414d",
"rev": "293d6abedf0478e681a4dfcfcb35b30fc796a32f",
"type": "github"
},
"original": {
flake.nix
+48 -34
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@@ -6,37 +6,49 @@
flake-utils.url = "github:numtide/flake-utils";
};
outputs = { self, nixpkgs, flake-utils }:
flake-utils.lib.eachDefaultSystem (system:
let
pkgs = import nixpkgs { inherit system; };
outputs = {
self,
nixpkgs,
flake-utils,
}:
flake-utils.lib.eachDefaultSystem (
system: let
pkgs = import nixpkgs {inherit system;};
# Python with the packages needed by the offline converter tools
pythonEnv = pkgs.python3.withPackages (ps: with ps; [
torch
safetensors
huggingface-hub
numpy
tokenizers
datasets
]);
pythonEnv = pkgs.python3.withPackages (
ps:
with ps; [
torch
safetensors
huggingface-hub
numpy
tokenizers
datasets
]
);
colibri = pkgs.stdenv.mkDerivation {
pname = "colibri";
version = "1.0";
src = ./.;
# python3 is needed by checkPhase: `make test-c` shells out to
# `python3 tools/run_tests.py` (see c/Makefile, PYTHON ?= python3).
nativeBuildInputs = [ pkgs.makeWrapper pkgs.python3 ];
nativeBuildInputs = with pkgs; [makeWrapper];
buildInputs = [
pkgs.gcc
pkgs.gmp
buildInputs = with pkgs; [
gcc
gmp
];
# python3 is needed by checkPhase: `make test-c` shells out to
# `python3 tools/run_tests.py` (see c/Makefile, PYTHON ?= python3).
nativeCheckInputs = with pkgs; [python3];
# Use x86-64-v3 (AVX2) for a portable binary; override with ARCH=native for local builds
ARCH = "x86-64-v3";
ARCH =
if pkgs.stdenv.hostPlatform.isx86_64
then "x86-64-v3"
else "native";
buildPhase = ''
runHook preBuild
@@ -56,7 +68,8 @@
cp c/glm $out/lib/colibri/glm
cp c/coli $out/lib/colibri/coli
chmod +x $out/lib/colibri/coli
cp c/openai_server.py c/resource_plan.py c/doctor.py $out/lib/colibri/
cp c/openai_server.py c/resource_plan.py c/doctor.py c/version.py \
$out/lib/colibri/
cp -r c/tools/* $out/lib/colibri/tools/
# $out/bin holds the user-facing entry points.
@@ -86,12 +99,11 @@
description = "Run GLM-5.2 (744B MoE) on a consumer machine with ~25 GB RAM";
homepage = "https://github.com/JustVugg/colibri";
license = licenses.asl20;
platforms = platforms.linux;
mainProgram = "glm";
platforms = with platforms; linux ++ darwin;
mainProgram = "coli";
};
};
in
rec {
in {
packages = {
default = colibri;
inherit colibri;
@@ -100,23 +112,25 @@
apps = {
default = {
type = "app";
program = "${colibri}/bin/glm";
program = pkgs.lib.getExe colibri;
};
coli = {
glm = {
type = "app";
program = "${colibri}/bin/coli";
program = "${colibri}/share/colibri/glm";
};
};
devShells.default = pkgs.mkShell {
inputsFrom = [ colibri ];
formatter = pkgs.alejandra;
packages = [
devShells.default = pkgs.mkShell {
inputsFrom = [colibri];
packages = with pkgs; [
pythonEnv
pkgs.gcc
pkgs.gnumake
pkgs.clang-tools # clangd / clang-tidy for IDE support
pkgs.pkg-config
gcc
gnumake
clang-tools # clangd / clang-tidy for IDE support
pkg-config
];
shellHook = ''
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