tzhouamandClaude Opus 5
3834de3105
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
113 lines
5.8 KiB
Markdown
113 lines
5.8 KiB
Markdown
# edge-infer
|
||
|
||
Running multimodal models on **edge CPUs** — a mini-PC or a gateway box with
|
||
tens of gigabytes of RAM and no GPU — instead of on servers.
|
||
|
||
Built as a fork of [vLLM-Omni](https://github.com/vllm-project/vllm-omni),
|
||
because it is the only engine with a multi-stage multimodal pipeline,
|
||
continuous batching and an OpenAI-compatible server; the alternatives were
|
||
evaluated and the reasoning is in [docs/engines/](docs/engines/).
|
||
|
||
The work turned out to be less about kernels than about three things nobody
|
||
profiles: how much memory is resident while the model *loads*, how much is
|
||
resident that nothing ever reads, and whether the number you just measured came
|
||
from the code path you thought it did. The largest single win here — 843 MB —
|
||
was a setting that had been marked "no effect, do not retry" because the code
|
||
path under test discarded it.
|
||
|
||
This repository holds the engine changes, the measurement harness, the raw
|
||
results, the baselines they are compared against, and the written analyses.
|
||
Upstream's own README is preserved at [docs/README.vllm-omni.md](docs/README.vllm-omni.md).
|
||
|
||
## What's here
|
||
|
||
| area | where |
|
||
|---|---|
|
||
| 4-bit CPU quantization | `vllm_omni/model_executor/layers/quantization/cpu_int4.py` — W4A16 on PyTorch's tinygemm kernel, prefill reading the kernel's own weight layout instead of a duplicate |
|
||
| Memory | RoPE tables sized to the servable context; weights repacked in blocks; a KV budget that is actually honoured on CPU |
|
||
| CPU platform + vLLM patches | `vllm_omni/platforms/cpu/`, `vllm_omni/patch.py` — vLLM is monkey-patched at import; its *source* is unmodified and no vLLM is redistributed. See [docs/edge/environment.md](docs/edge/environment.md) |
|
||
| Device routing | `vllm_omni/edge/weight_path.py` picks the weight format per device class, from measurements rather than assumption |
|
||
| Measurement harness | `benchmarks/edge_harness/` — the grid runner, benchmarks, memory ledger, fidelity metric and agent suite every number here came from |
|
||
| Raw results | `benchmarks/edge_harness/results/` — 100+ artifacts backing the published figures, each with provenance |
|
||
| Baselines | `baselines/` — llama.cpp runs and the comparison scripts |
|
||
| Engine analyses | `docs/engines/` — llama.cpp, ExecuTorch, MNN, ncnn, vLLM, vLLM-Omni |
|
||
| Study documents | `docs/analysis/` — the full reports these summaries distil |
|
||
|
||
## Headline results
|
||
|
||
Spark-X2.5-1.7B, Xeon Platinum 8480C, 24 pinned node-local cores, W4A16 g128.
|
||
Every figure is a median of at least three interleaved passes against a control
|
||
arm, on a shared host where end-to-end throughput swings ~30% run to run.
|
||
|
||
**Memory** — each measured as its own A/B with the alternative reachable by a flag:
|
||
|
||
| lever | saving | cost |
|
||
|---|---|---|
|
||
| KV budget 1 GiB → 192 MiB | **843 MB** | fewer cached tokens |
|
||
| RoPE table capped to `max_model_len` | **618 MB** | none |
|
||
| One packed weight layout instead of two | **615 MB** | ~1.6× prefill |
|
||
| Blocked weight repack | **489 MB** of load-time peak | none |
|
||
|
||
These overlap and **must not be added** — see [docs/edge/results.md](docs/edge/results.md).
|
||
|
||
**Speed**, same machine:
|
||
|
||
| path | decode | prefill (pp512) |
|
||
|---|---|---|
|
||
| W4A8 (AMX `int4_scaled_mm_cpu`) | 74.4 tok/s | — |
|
||
| W4A16 (tinygemm) | 71.6 tok/s | 2190 tok/s |
|
||
| `cpu_gemm_wna16` fallback | 32.3 tok/s | — |
|
||
| llama.cpp Q4_K_M (reference) | 90.0 tok/s | 1121 tok/s |
|
||
|
||
Decode is llama.cpp's; prefill is ours by ~2×. Neither engine is
|
||
bandwidth-bound — both sit at 33–39% of the measured 222 GB/s ceiling — so the
|
||
decode gap is the execution model, not the kernels. See
|
||
[docs/edge/comparisons.md](docs/edge/comparisons.md).
|
||
|
||
## Why the numbers here are worth reading
|
||
|
||
This project has retracted five conclusions, every one of them a measurement
|
||
error rather than a coding error. The harness exists because of them:
|
||
|
||
- a "3.1 GB saving" that was `VmRSS` counting the mmap'd checkpoint;
|
||
- W4A8 timings taken on a checkpoint whose output head was silently left in bf16;
|
||
- a memory benchmark reading `/proc/self/status` while the model ran in a *worker process*;
|
||
- "KV sizing saves only 188 MB" — measured through a code path that discarded the setting, so both arms ran the same cache;
|
||
- a 1.9× prefill cost quoted from the one pass of a grid whose other passes had crashed.
|
||
|
||
[docs/edge/methodology.md](docs/edge/methodology.md) is the resulting discipline,
|
||
and it is the most transferable thing in this repository.
|
||
|
||
## Quick start
|
||
|
||
```bash
|
||
# CPU build of vLLM, then this fork
|
||
pip install vllm==0.28.0+cpu
|
||
VLLM_TARGET_DEVICE=cpu pip install -e .
|
||
|
||
# a right-sized edge deployment
|
||
VLLM_CPU_KVCACHE_SPACE=1 python -c "
|
||
from vllm import LLM, SamplingParams
|
||
llm = LLM(model='<your-cpu_int4-checkpoint>', quantization='cpu_int4',
|
||
dtype='bfloat16', max_model_len=2048, max_num_seqs=1,
|
||
kv_cache_memory_bytes=192 << 20) # the single largest memory lever
|
||
print(llm.generate(['Hello'], SamplingParams(max_tokens=32))[0].outputs[0].text)
|
||
"
|
||
```
|
||
|
||
## Documentation
|
||
|
||
| | |
|
||
|---|---|
|
||
| [docs/edge/results.md](docs/edge/results.md) | every measured figure, with spread and baseline |
|
||
| [docs/edge/methodology.md](docs/edge/methodology.md) | how to measure on a shared machine without fooling yourself — the five retractions and the rules they left |
|
||
| [docs/edge/cpu-4bit.md](docs/edge/cpu-4bit.md) | the two 4-bit paths, the packed layout as measured, building a checkpoint |
|
||
| [docs/edge/comparisons.md](docs/edge/comparisons.md) | against llama.cpp and the other engines considered |
|
||
| [docs/edge/environment.md](docs/edge/environment.md) | pins, machine, and why there is no vLLM patch series |
|
||
| [docs/edge/harness.md](docs/edge/harness.md) | running a comparison |
|
||
| [ROADMAP.md](ROADMAP.md) | what is next, with expected gains and how each would be verified — and what was rejected |
|
||
|
||
## Licence
|
||
|
||
Apache-2.0, inherited from vLLM-Omni. See [LICENSE](LICENSE) and [NOTICE](NOTICE).
|