Commit Graph

9 Commits

Author SHA1 Message Date
woolcoxm 171aa69fcb fix(resource_plan): take last two lscpu fields, not [1]/[2] (#332 review)
JustVugg caught a regression by running the PR: the code asked
`lscpu -p=CPU,Core,Socket` and read fields[1]/fields[2], but the comment's
claim was inverted -- `lscpu -p=<list>` emits EXACTLY the requested columns
(no CPU prefix), while bare `lscpu -p` prepends CPU.

On machines where the requested list is short or lscpu collapses to two
columns, every line was skipped by the `< 3` guard, cores stayed empty, and
the code fell through to os.cpu_count() -- the LOGICAL count. Result: 6
physical cores reported as 12 (SMT over-subscription), the opposite of the
fix.

Correct per review:
  - ask lscpu for exactly 'core,socket'
  - take fields[-2], fields[-1] (correct whether or not CPU is prepended)
  - keep the warning scaffolding + the _resolve_physical_cores clamp
    (those are the actual #325 fix; only the indexing was wrong)

Test now exercises BOTH the 2-column (-p=core,socket) and 3-column
(bare -p, CPU prefix) layouts, asserting 12 physical cores each. The
2-column case is the one that regressed: old parser returns 24, new
returns 12.
2026-07-20 10:56:54 -04:00
woolcoxm bd3efb1c97 resource_plan: stop setting OMP_PROC_BIND/OMP_PLACES from --auto-tier (#325)
The core-count fix was necessary but not sufficient: @liangstein confirmed
physical_cpu_count() now returns 64 on his box, yet --auto-tier STILL pinned
decode to one core. A complete env diff between the plain (working) and
--auto-tier (broken) paths showed exactly three keys the plan adds:

  OMP_NUM_THREADS = 64   (correct, verified)
  OMP_PROC_BIND   = spread
  OMP_PLACES      = cores

Since OMP_NUM_THREADS was already correct, the culprit is the affinity pair.
The mechanism: environment_for_plan() sets OMP_PROC_BIND=spread + OMP_PLACES=cores
in the launcher's env. The engine's hot-thread tuning (glm.c main, the
COLI_OMP_TUNED self-exec) then tries setenv("OMP_PROC_BIND","close", overwrite=0)
-- but overwrite=0 cannot replace an already-set var, so the plan's "spread"
wins. On the reporter's libgomp + multi-socket topology, spread + places=cores
collapsed the team to a single CPU even with 64 threads configured.

Fix: don't set affinity from the plan at all. The engine deliberately chose
"close" for cache locality (the tiny back-to-back per-expert matmuls want
adjacent cores), and the plain path already leaves affinity to the engine.
Removing the plan's spread/places makes --auto-tier match the working plain
path; a user wanting a specific policy can still set OMP_PROC_BIND/OMP_PLACES
in their own environment (environment_for_plan only setdefaults OMP_NUM_THREADS).

Verified via env diff: after the fix, --auto-tier adds ONLY OMP_NUM_THREADS
beyond the plain env -- the engine's own close/bind tuning now wins on both
paths identically.

Note: this could not be reproduced on Windows (MinGW libgomp prints "Affinity
not supported on this configuration" and ignores the vars entirely); it is
Linux-libgomp-specific, matching the reporter's Rocky 9 box.

Tests: rewrite test_applies_plan_without_overriding_explicit_settings to assert
the plan sets NO affinity vars on any platform (the old test encoded the buggy
platform-dependent spread/cores contract). Add
test_plan_does_not_set_omp_affinity_vars as a focused regression. 78/78 pass.
2026-07-20 10:56:54 -04:00
woolcoxm f27a89ea82 resource_plan: fix --auto-tier pinning decode to one core (#325)
physical_cpu_count() silently returned 1 in two situations, and that value
flowed through build_plan -> OMP_NUM_THREADS to pin every matmul region to a
single thread under --auto-tier (reported on Rocky Linux 9, 512 GB RAM).

Two root causes:

1. The lscpu parse counted the wrong thing. `lscpu -p=core,socket` prepends the
   CPU column, so the output is actually CPU,Core,Socket; the old set
   comprehension collected (CPU,core,socket) tuples that were unique per logical
   CPU. Now parse CPU,Core,Socket and dedupe on (core, socket) to get true
   physical cores (the SMT-doubling the surrounding comments warn against was
   the actual behavior).

2. Any probe failure fell through to `os.cpu_count() or 1`. On a cgroup'd or
   otherwise constrained box os.cpu_count() can be 1 (or None), silently
   capping the run. Skip offline core/socket fields ("-" instead of raising
   ValueError) so a single offline row no longer discards the whole parse, and
   replace the silent `or 1` fallback with os.cpu_count() (logical) plus an
   explicit warning. Only return 1 when nothing at all is detected, and warn.

Also harden the win32 branch: declare argtypes/restype on
GetLogicalProcessorInformationEx (an undeclared 64-bit WinAPI returns c_int and
takes c_int pointers, so the probe could silently fail), and warn on its
fallbacks. Replace the silent max(1, int()) clamp in build_plan with
_resolve_physical_cores() that clamps to 1 with a warning instead of masking.

Tests: the existing OMP_NUM_THREADS test passed physical_cpus=24 explicitly, so
it never exercised the real probe -- that is why this regressed. Add regression
coverage for the lscpu physical-core dedup, offline fields, lscpu-missing
fallback, zero-cores degenerate case, and an end-to-end build_plan +
environment_for_plan check that OMP_NUM_THREADS reflects physical (not logical)
cores.
2026-07-20 10:53:26 -04:00
ZacharyZcR 8a9a0fca4d plan: auto-tune heuristics — bottleneck classification + parameter decisions
Extend resource_plan to classify the hardware into bottleneck regimes
(disk / memory / mixed / compute) and derive tuning knobs automatically:

  MTP:   off when compute-bound (42% loss at full residency, #389)
         or disk-bound with <90% hit (union growth adds reads)
  PIPE:  COLI_CUDA_PIPE=1 single-GPU, =2 multi-GPU, PIPE=1 CPU disk
  NUMA:  selective interleave for GPU hosts, blanket hint for CPU-only
  PIN:   PIN_GB=all when fully resident + no GPU
  OMP:   COLI_NO_OMP_TUNE=1 for Metal (spin steals GPU power)

`coli plan` now shows an auto-tune section with each knob and its
reason. `environment_for_plan()` applies them via setdefault so
explicit user settings always win.

plan version stays at 2 (additive fields: bottleneck_class,
projected_hit_rate, tune). 7 new tests covering all regimes.
2026-07-19 21:20:00 +08:00
ZacharyZcR 9c17dc6d29 plan NUMA interleave on multi-socket Linux 2026-07-18 18:38:40 +08:00
KingIcyCreamProjects 1a243cfc3e coli: measured Windows launcher defaults — OMP tuning parity + DIRECT/PIPE/PILOT_REAL
On Windows the engine self-exec OMP tuning never runs (Linux/FreeBSD-only)
and posix_fadvise readahead is a compat.h no-op, so a stock Windows run
leaves large measured wins on the table. The launcher now setdefaults, on
win32 only, each independently overridable by setting the variable:

- OMP_WAIT_POLICY=active, GOMP_SPINCOUNT=200000, OMP_DYNAMIC=FALSE,
  OMP_NUM_THREADS=<physical cores> (parity with the glm.c self-exec block;
  COLI_NO_OMP_TUNE disables exactly this block, presence-based like the
  engine). OMP_PROC_BIND/OMP_PLACES deliberately omitted and also removed
  from environment_for_plan on win32: MinGW libgomp has no affinity support
  ("Affinity not supported on this configuration").
- DIRECT=1: unbuffered expert reads. Measured on a 9950X3D + Samsung 9100
  PRO Gen5 + Win11: iobench 10.68 GB/s O_DIRECT vs 9.03 buffered (warm);
  end-to-end REPLAY 0.48 -> 1.02 tok/s. Matches #162 (1.47x same class).
- PIPE=1: load/matmul overlap, byte-identical output; +8% on top of DIRECT
  (PIPE_WORKERS untouched at 8 - 4/8/16 swept flat on Gen5).
- PILOT_REAL=1: real cross-layer prefetch, the only working prefetch on
  Windows; +11% and expert hit rate +19 points.

Full ladder methodology and numbers: 96-token greedy REPLAY, one lever per
step, medians of 3-4 runs (see the fork tuning doc referenced in the PR).
tests/test_env_defaults.py covers the defaults, explicit-override-wins,
the kill-switch scope, and the non-win32 no-op.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-07-15 12:06:48 -05:00
woolcoxm 2319b942d2 Windows native port: serve-mode pipe fix + RAM detection + POSIX guards, AVX-VNNI kernel, gated CUDA DLL (#131, fixes #123)
Rebased onto current dev, split into 3 logical parts (all validated):
1. CPU portability (serve-mode _O_BINARY pipe fix — stock main hangs on MinGW without it; RAM detection cap 0->9/layer; POSIX guards for select/mmap/madvise; warmup script).
2. AVX-VNNI 128-bit int8/int4 dot kernel (Alder Lake+/Meteor Lake+), bit-identical to AVX2 (author-verified on Meteor Lake; compiles out to AVX2 elsewhere) + _mm256_extracti128_si256 typo fix that blocked -march=native.
3. CUDA DLL via LoadLibrary, gated behind CUDA_DLL=1 (host never links cudart; silent CPU fallback if absent; author-verified on RTX 5070 Ti).

Validated here: make check 59/59, oracle 32/32 TF, Windows cross-compile clean + glm.exe loads+runs via WSL interop. Fixes the #123 Windows build failure.
2026-07-13 20:54:30 +02:00
ZacharyZcR cbd599024e Unify continuous batching + heterogeneous runtime: decode batching, physical-core planning, disjoint VRAM/RAM placement, topp-policy warning (CPU-validated, CUDA on 6x5090) (#68)
* Fuse CUDA expert MLP execution

* Group CUDA expert transfers by device

* Instrument grouped CUDA expert execution

* Bound grouped CUDA decode scratch

* Execute expert groups across GPUs in parallel

* Release host backing for multi-GPU experts

* Define quality-preserving memory policies

* Overlap cold expert loading with resident compute

* Adapt expert placement with session LFRU

* Fuse q4 expert gate and up dispatch

* Plan CPU work on physical cores

* Batch grouped expert CUDA kernels

* Separate VRAM and RAM expert placement

* Add ragged multi-sequence decode forward

* feat(runtime): add continuous decode scheduler

* Route concurrent API requests through batch scheduler

* Harden multiplex request lifecycle and framing

* Cancel disconnected multiplex requests

* Bind API port before starting the engine

* fix automatic KV slot allocation

* add native int4 Tensor Core grouped GEMM

* add Tensor Core throughput benchmark

* optimize packed int4 low-row kernels

* add asynchronous CUDA staging streams

* document validated six-GPU dense acceleration

* tune six-GPU expert hot set

* raise validated expert hot-set target

* add CUDA MLA absorption core

* fuse grouped expert gate and up projections

* Warn for explicit lossy routing flags
2026-07-13 14:30:36 +02:00
ZacharyZcR 3cbc52ab00 coli plan: read-only Disk/RAM/VRAM placement planner (mirrors engine budget math, versioned --json, --auto-tier) (#27)
* Add tiered resource planner

* Apply resource plans to runner configuration

---------

Co-authored-by: JustVugg <JustVugg@users.noreply.github.com>
2026-07-10 12:51:23 +02:00