One package that makes the POSIX assumptions measured in W0-W8 hold in
every process vLLM / vLLM-Omni starts, and reports what it did:
- provides fcntl (flock/lockf on msvcrt.locking; LOCK_SH served as
exclusive) and uvloop (asyncio.run on a selector loop) when absent;
- installs WindowsSelectorEventLoopPolicy and a signal.signal fallback
for loop.add_signal_handler on the Windows loop classes;
- replaces whole vLLM's get_open_zmq_ipc_path / find_loaded_library when
the wheel lacks the branch patch and the upstream body hash is known;
- verifies the wheel-only sites (sentinel poll, int64 seed) and reports
wheel_required at the exact site instead of failing downstream;
- shm_dir() replaces the /dev/shm literals; os.geteuid provided.
Activated from vllm_omni/patch.py and as vllm.general_plugins /
vllm_omni.general_plugins entry points (idempotent; no-op off Windows).
prints the audit; the M0 placement
report embeds it. 16 Linux tests through injected primitives.
Design: analysis/design_windows_compat_plugin_20260921.md
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
uvloop ships no Windows wheels, so 'import uvloop' at module scope in
entrypoints/cli/serve.py killed vllm-omni serve on Windows at import
(measured in the serving e2e after the fcntl fix). _run_forever() uses
uvloop where present and asyncio.run on WindowsSelectorEventLoopPolicy
otherwise, which zmq.asyncio needs. The remaining serve-import blocker
on the Windows venv was a missing dependency, torchaudio (vLLM's own
requirement), installed into the venvs rather than patched.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
test_e2e_serve_text.py no longer imports tests.helpers.runtime (it pulls
the media/diffusion test helpers and therefore diffusers, absent from the
Windows venv); a small Popen launcher with a psutil process-tree kill
replaces OmniServer for this one test.
vllm-omni serve died at import on Windows: entrypoints/cli imports
benchmarks.patch -> benchmarks/omniinteract.py, which did 'import fcntl'
at module scope. Its output lock is rebuilt on the fork's
_filelock_compat primitive (fcntl on POSIX, msvcrt on Windows).
engine/stage_phase_lock.py (parallel_stage_init's SH/EX device locks)
gets the same treatment: fcntl optional, modes spelled locally, and on
Windows both modes map to the exclusive byte lock -- more serialising,
never less safe. Measured: the serving e2e's server exited with
ModuleNotFoundError: No module named 'fcntl' on both Windows venvs.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
pytest-asyncio builds its loops from the current policy; on win32 that is
the Proactor loop, which zmq.asyncio cannot use, so the engine/serving e2e
hung at the first async socket (30 min, no output, GPU idle) while the
accept CLI -- which installs WindowsSelectorEventLoopPolicy itself --
passed on the same venv. The conftest now installs the same policy and
provides it as pytest-asyncio's event_loop_policy fixture.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Drives `vllm-omni serve <model> --omni` as a subprocess through the
online-serving launcher and checks /health, /v1/models, non-streaming
and streaming chat completions, and that a client disconnect mid-stream
leaves the server answering. Sets VLLM_USE_FLASHINFER_SAMPLER=0 like
LocalTextEngine does: the flashinfer sampler JITs with nvcc at runtime,
which a venv without a CUDA toolkit does not have (measured: the WSL
reference venv died in profile_run with 'Could not find nvcc').
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
The 0.29.0 loader de-duplicates tied parameters itself, so the explicit
lm_head. skip for tie_word_embeddings is no longer needed (and its keyword
no longer exists: engine init failed with TypeError on the first 0.29.0
parity run).
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
vllm_omni/edge/local/acceptance_chat_prompts.json was invisible to the WIP
snapshot because .gitignore ignores *.json; without it the acceptance CLI
and the parity runner cannot start on a fresh export.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
The pair for vLLM v0.29.0: rc1's docs pin vllm==0.29.0 and a static scan of
rc1's vllm.* imports against the v0.29.0 tree finds nothing missing, while
our 0.28-based tree needs seven modules v0.29.0 no longer has
(vllm.inputs.preprocess, entrypoints.launcher, openai.cli_args,
openai.engine.protocol, serve.utils.server_utils, layers.batch_invariant,
models.hunyuan_v1).
Ten files conflicted. Upstream's structure is the base everywhere; the
fork's features are re-attached to it:
- deploy_profile / apply_deploy_profile run before rc1's resolve_omni_config
pops deploy_config (async_omni_engine, cli/serve); the deploy-profile
selector and the resolve_model_config_path chain it needs stay in
entrypoints/utils.py; stage_overrides takes rc1's dict type.
- config_factory keeps the lazy diffusion output-type import; the diffusion
registry import in rc1's new config/resolver.py is made lazy for the same
WP6 reason (TTS path must not load diffusion).
- requirements: our core/media split kept; rc1's transformers>=5.13 floor
and kernels==0.16.1 applied in core.txt / media.txt.
- TTS: rc1's split_granularity and bounded seed plus our output_mode=
codec_tokens, _SessionInbox cancel handling and codec DELTA accumulation;
our private _conditioning_cache_salt is superseded by rc1's public
tts_adapters/base.py::conditioning_cache_salt.
- stage_runtime: rc1's single ExitStack G2 path (lock released before READY)
replaces our two-path block, with the four worker_phase names the init
profiler consumes still emitted; _cpu_placed / host admission kept.
Not yet run against vLLM 0.29.0: the tests listed in the W6 record.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Verbatim copy of the edge/omni-edge working tree (tracked diffs + untracked
edge/local, tests/edge/local, docs/edge, pipeline files). Taken so that
vLLM-Omni v0.29.0rc1 can be merged on a branch without touching the
user's working tree. logs_edge/ and benchmark .npy outputs excluded.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Renamed from vllm-embedding: nothing here is about embeddings, and the name had
come from the parent working directory. edge-infer says what it does. The
GitHub repository is renamed to match; nothing had been pushed yet.
The audit found the packaging was incomplete in two ways.
Scripts: 56 of them produced results that are published in this repo but were
not included, so the numbers could be read and not checked. Added under
benchmarks/edge_harness/experiments/ (one-off drivers: the AI Hub on-device
runs, the 4-bit GEMM microbenchmark, fidelity and parity checks, checkpoint
build and round-trip, activation capture, and the load generator used to prove
the contention guard actually refuses) and benchmarks/edge_harness/runs/ (the
24 shell drivers behind published grids). Both directories carry a README
saying plainly that they are a record of what was run rather than a supported
interface -- most hardcode absolute paths from the machine they ran on. The
supported entry points remain the tools one level up.
One broken relative link fixed (comparison.md is under docs/engines/, not
docs/analysis/). All other links and every script reference in the docs now
resolve inside the repo.
Secret scan: the token in ~/.qai_hub/client.ini is absent from the worktree and
from all 61 commits. Worth noting that an earlier version of my own check
reported a false positive, because `grep -rl ... | head` takes its exit status
from head, which always succeeds -- the same class of mistake as the measurement
errors this project keeps recording. The AI Hub scripts authenticate by reading
that file at runtime and contain no credential.
README reframed for the new name and the wider scope: the repository is the
engine changes, the harness, the raw results, the baselines and the analyses,
not just a patch set.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01L1ax5wYLA6XbqEL1i8L9gZ
The previous commit said "vLLM itself is not modified" and described the
changes as going "through vLLM's own extension points". Challenged on it, and
the challenge was right: those are two different claims and only one of them
holds as written.
What is true: no file in the vLLM tree or in site-packages/vllm is edited, the
checkout is clean upstream and the wheel is stock 0.28.0+cpu, so there is no
patch series to apply and no vLLM fork to maintain.
What was wrong:
1. vllm_omni/patch.py does nine _patch_* functions performing ten direct
attribute replacements -- CpuPlatform.check_and_update_config,
CPUWorker.load_model, torch.compiler.load_compiled_function. That is
monkey-patching, not an extension point, and calling it one made the
arrangement sound more robust than it is. Only the registries are genuine
hooks: register_quantization_config, ir.ops.*.register_impl, platform
plugins, direct_register_custom_op.
2. "The changes survive a wheel upgrade rather than being lost on it" is true of
the registrations and overstated for the patches. Each patch is keyed to a
specific method on a specific vLLM class; an upstream rename or behaviour
change can break it or silently turn it into a no-op wrapper. The failure
mode is a missing optimisation, not a crash, which is worse -- so the doc now
says to re-run the harness after upgrading.
The section is rewritten to separate the two mechanisms, list what each patch
works around, and note that two of the three exist for upstream bugs and should
disappear when those are fixed.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01L1ax5wYLA6XbqEL1i8L9gZ
The packaging commit shipped the code and a summary of the numbers. It left out
everything the numbers are measured *against*, which is most of what makes them
checkable.
Added:
baselines/ llama.cpp runs (thread sweeps, NUMA sweeps, the
interleaved A/B passes behind the headline decode
figure) and the quality references: each build
against its own bf16 over 12 greedy prompts.
benchmarks/.../results/ 102 artifacts backing the published figures, each
with a provenance block naming the model,
quantization, thread set and build SHAs.
docs/engines/ the engine analyses -- llama.cpp, ExecuTorch, MNN,
ncnn, vLLM, vLLM-Omni -- and the cross-engine and
device-simulation comparisons.
docs/analysis/ the full study documents the summaries distil.
docs/edge/comparisons.md where this fork sits against llama.cpp and why:
decode is llama.cpp's, prefill is ours by ~2x, and
since neither engine exceeds 39% of the measured
222 GB/s ceiling the decode gap is the execution
model rather than the kernels.
docs/edge/environment.md pins and machine, and the correction below.
On "the modified vllm": there isn't one, and the doc says so plainly because it
is the obvious assumption. The vLLM checkout is clean upstream (0 commits
ahead, 0 uncommitted) and the installed wheel is stock 0.28.0+cpu. This fork
changes vLLM's CPU *behaviour* through vLLM's own extension points at import
time -- three CPU patches in vllm_omni/patch.py, the quantization-config
registry, the IR op registry, platform hooks -- specifically so the changes
survive a wheel upgrade rather than being lost on it. One of those patches is
why "KV sizing is not a memory lever" looked true for months: CpuPlatform
overwrote kv_cache_memory_bytes unconditionally, so the setting had no effect.
There is no patch series to apply; pin the version and let patch.py do the rest.
.gitignore gains explicit exceptions: upstream's blanket `*.json` and `results/`
rules were silently eating the measurement artifacts, which in this fork are the
substance rather than build output. Binary captures stay ignored.
No vLLM or llama.cpp source is redistributed -- only output produced with them.
NOTICE records both.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01L1ax5wYLA6XbqEL1i8L9gZ
Turns a branch of accumulated edge/CPU work into something someone else can
read, reproduce and continue.
README.md states what the fork is for (multimodal models on edge CPUs rather
than servers), the headline memory and speed results with their costs, and why
the numbers are worth reading -- five retracted conclusions, all measurement
errors rather than coding errors. Upstream's README is preserved at
docs/README.vllm-omni.md and NOTICE records the Apache-2.0 derivation and the
files this fork changes.
docs/edge/:
methodology.md the measurement discipline, which is the most transferable
thing here: what each of the five retractions was, and the
rule it left behind. Includes the two traps specific to this
stack -- vLLM's AOT compile cache keying on config so two
arms sharing an artifact load the wrong graph, and allocated
not being resident until mincore(2) says so.
results.md every measured figure with its spread and baseline, and an
explicit note that the four memory levers overlap and must
not be added.
cpu-4bit.md the two 4-bit paths, the packed layout as measured rather
than assumed, why the path choice is a config field, and the
GPTQ output-head trap that invalidated a whole round of W4A8
numbers.
harness.md how to run a comparison, and the two arguments that matter
more than they look (--context-len, --prompt-len), each of
which has produced a wrong conclusion by being left at its
default.
ROADMAP.md orders by whether the verification method is correct rather than by
expected size, lists what is done with results, what is next with expected
gains and how each would be verified, and -- at equal length -- what was
considered and rejected, including the CPU megakernel with the measurements
that rule it out for now.
benchmarks/edge_harness/ brings the tooling into the repo so the numbers are
reproducible: the grid runner, the single-run benchmarks, the memory ledger,
the fidelity metric, the Android agent suite, the quantizers, and the tests
that pin the harness's judgement. 82 tests pass from the new location.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01L1ax5wYLA6XbqEL1i8L9gZ
512 was a first guess. Repacking the 131072-row tied embedding at 24 threads:
rows chunks time int32 transient
whole 1 208 ms 1024 MiB
512 256 116 ms 4 MiB
2048 64 79 ms 16 MiB
8192 16 89 ms 64 MiB
32768 4 129 ms 256 MiB
2048 is both the fastest and nearly the leanest. The sweep also settles a
suspicion: chunking is faster than packing the whole matrix, because the
working set stays in cache, so this costs nothing at load. The one pass that
appeared to show a 30 s init regression was the pass the harness had flagged
as contended.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01L1ax5wYLA6XbqEL1i8L9gZ
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