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
32 lines
1.9 KiB
Bash
Executable File
32 lines
1.9 KiB
Bash
Executable File
#!/bin/bash
|
|
# Output comparison only: greedy text is load-independent, so this can run
|
|
# while the machine is busy.
|
|
set -u
|
|
HERE=/data/zhoutaichang/embedding_infer/analysis/experiments/spark_edge
|
|
V=/data/zhoutaichang/embedding_infer/.venvs/omni-cpu
|
|
G=/data/zhoutaichang/.cache/huggingface/hub/models--XHToken--Spark-X2.5-1.7B-GGUF/snapshots/23e1fcac55e7dd71e4c12a23723cc228ba0e5e85
|
|
BF16=/data/zhoutaichang/.cache/huggingface/hub/models--XHToken--Spark-X2.5-1.7B/snapshots/448e61eb392c00f2c403185c5b56d5e0665bfaab
|
|
cd $HERE
|
|
gen_vllm () {
|
|
LD_PRELOAD="$V/lib/python3.12/site-packages/vllm/libs/libtcmalloc_minimal.so.4:$V/lib/libiomp5.so" \
|
|
CUDA_VISIBLE_DEVICES= VLLM_TARGET_DEVICE=cpu VLLM_CPU_KVCACHE_SPACE=4 VLLM_ENABLE_V1_MULTIPROCESSING=0 \
|
|
OMP_NUM_THREADS=24 VLLM_CPU_OMP_THREADS_BIND=88-111 \
|
|
taskset -c 88-111 timeout 3600 $V/bin/python quality_int4.py gen --engine vllm \
|
|
--model "$1" --out "$2" 2>&1 | tail -2
|
|
}
|
|
gen_vllm "$BF16" quality_vllm_bf16.json
|
|
gen_vllm /data/zhoutaichang/embedding_infer/models/spark2_5-int4-g64 quality_vllm_w4a16.json
|
|
gen_vllm /data/zhoutaichang/embedding_infer/models/spark2_5-gptq-g64 quality_vllm_w4a8.json
|
|
taskset -c 88-111 $V/bin/python quality_int4.py gen --engine llamacpp --threads 24 \
|
|
--gguf $G/Spark-X2.5-1.7B.gguf --out quality_llamacpp_bf16.json 2>&1 | tail -1
|
|
taskset -c 88-111 $V/bin/python quality_int4.py gen --engine llamacpp --threads 24 \
|
|
--gguf $G/Spark-X2.5-1.7B-Q4_K_M.gguf --out quality_llamacpp_q4km.json 2>&1 | tail -1
|
|
echo "### generations done"
|
|
for pair in "quality_vllm_bf16.json quality_vllm_w4a16.json vllm-W4A16-tinygemm" \
|
|
"quality_vllm_bf16.json quality_vllm_w4a8.json vllm-W4A8-gptq" \
|
|
"quality_llamacpp_bf16.json quality_llamacpp_q4km.json llamacpp-Q4_K_M"; do
|
|
set -- $pair
|
|
$V/bin/python quality_int4.py cmp --ref $1 --got $2 --label $3 --out quality_cmp_$3.json 2>&1 | tail -6
|
|
done
|
|
echo "### QUALITY DONE"
|