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
60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
"""Same Spark layer, three phone runtimes, one device.
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Isolates runtime choice from export choice: if QNN, TFLite and ONNX Runtime
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land close together, the export layout is what sets on-device speed, not the
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engine. Run on the sliding layer, which is 21 of Spark's 28 layers.
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"""
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import json
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import qai_hub as hub
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HERE = "/data/zhoutaichang/embedding_infer/analysis/experiments/spark_edge"
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DEV = "Samsung Galaxy S25"
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PATH = f"{HERE}/onnx/spark_sliding_ctx1024.onnx"
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# Each runtime spells precision differently: only QNN takes
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# --quantize_full_type, TFLite and ONNX reject it outright, and a QNN context
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# binary is NPU-only so the GPU leg has to go through TFLite.
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RUNTIMES = [
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("qnn_npu", "--target_runtime qnn_context_binary --quantize_full_type float16", "npu"),
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("tflite_npu", "--target_runtime tflite", "npu"),
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("onnx_npu", "--target_runtime onnx", "npu"),
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("tflite_gpu", "--target_runtime tflite", "gpu"),
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("tflite_cpu", "--target_runtime tflite", "cpu"),
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]
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def main():
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mdl = hub.upload_model(PATH, name="spark25_sliding_runtimes")
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pend = {}
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for name, opt, unit in RUNTIMES:
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try:
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cj = hub.submit_compile_job(model=mdl, device=hub.Device(DEV),
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name=f"spark sliding {name}", options=opt)
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pend[name] = (cj, unit)
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print("compile", name, cj.job_id, flush=True)
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except Exception as e:
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print("submit failed", name, str(e)[:160], flush=True)
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prof, res = {}, {}
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for name, (cj, unit) in pend.items():
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st = cj.wait()
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print("compile", name, st.code, (st.message or "")[:140], flush=True)
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if st.success:
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prof[name] = hub.submit_profile_job(
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model=cj.get_target_model(), device=hub.Device(DEV),
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name=f"spark sliding {name}", options=f"--compute_unit {unit}")
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for name, pj in prof.items():
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st = pj.wait()
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if st.success:
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p = pj.download_profile()
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res[name] = p["execution_summary"]["estimated_inference_time"] / 1e3
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print(f" {name}: {res[name]:.3f} ms/layer", flush=True)
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else:
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res[name] = None
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print(f" {name} FAILED: {(st.message or '')[:180]}", flush=True)
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json.dump(res, open(f"{HERE}/aihub_spark_runtimes.json", "w"), indent=1)
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print("done", flush=True)
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main()
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