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
Two corrections and the paperwork they imply.
The fused-RMSNorm registration defaulted to *on*, which contradicts the
measurement it was committed with: those kernels are 3.6x and 4.5x faster in
isolation but measured 72.7 tok/s against 74.0 end to end, because inductor
already fuses the native norm into the residual add and an opaque call breaks
that fusion. The implementations stay registered, so the provider remains
selectable through `kernel_config.ir_op_priority` on a machine where the trade
lands differently; only the default ranking is left alone now, and the test
asserts both states rather than just the opt-in one.
The four switches this branch added (VLLM_OMNI_AOT_CACHE_SHIM,
_CPU_FUSED_NORMS, _CPU_RELEASE_MEMORY, _SPARK_GATE_REDUCTION) were not in the
reviewed environment-variable inventory, so
`test_statically_resolvable_environment_accesses_are_classified` failed. They
are classified now, the reviewed count moves 28 -> 32 with a note saying why,
and each has a row in docs/configuration/environment_variables.md carrying the
measurement that justifies its default.
`VLLM_OMNI_CPU_FAST_SAMPLER` and `VLLM_OMNI_STEP_STATS_SYNC` remain
unclassified and that test still fails on them. They arrived in 6ce8de1b, in
files this branch does not touch, so they are left to whoever owns that work
rather than documented from a guess.
Under `-m "core_model and cpu"`: 2593 passed before these fixes with 9
failures, of which two were mine. The rest are CUDA-only tests in a CPU venv
("Torch not compiled with CUDA enabled") plus test_qnn_export.py, which
another session is actively editing.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
- environment_variable_inventory: VLLM_OMNI_CPU_INDUCTOR / HW_PROFILE /
INIT_TIMELINE / STEP_STATS_DIR (public), STAGE_ID / INIT_TIMELINE_DIR
(internal); reference page rows added.
- edge_decoder_client.LocalDecoder: batched_chunked_decode takes a [B, Q, F]
tensor, not a list (the WP5 e2e run failed with a TypeError); the decoder
now also carries the ramp schedule attributes the Code2Wav stage sets.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
