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
94 lines
3.5 KiB
Python
94 lines
3.5 KiB
Python
"""Profile Spark-X2.5 decoder layers on real Snapdragon hardware.
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One sliding layer and one full-attention layer are profiled separately. The
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sliding layer's cache is a fixed 512 entries at any context; the full layer's
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grows, so it is profiled at 1024 and 4096 to show the slope. A whole decode
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step is 21 sliding + 7 full layers plus the head.
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"""
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import json, sys
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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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HID, HEADS, KV, D = 2048, 8, 2, 256
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SW = 512
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GRAPHS = [
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("sliding", 1024, SW),
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("full", 1024, 1024),
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("full", 4096, 4096),
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]
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QUANTS = [
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("fp16", "--target_runtime qnn_context_binary --quantize_full_type float16"),
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("w8a16", "--target_runtime qnn_context_binary --quantize_full_type w8a16 --quantize_io"),
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("w4a16", "--target_runtime qnn_context_binary --quantize_full_type w4a16 --quantize_io"),
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]
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DEVICES = ["Samsung Galaxy S25"]
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def spec_for(ctx, cache_len):
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s = {
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"x": ((1, 1, HID), "float32"),
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"cos_sw": ((1, 1, 1, D), "float32"),
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"sin_sw": ((1, 1, 1, D), "float32"),
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"cos_full": ((1, 1, 1, D // 4), "float32"),
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"sin_full": ((1, 1, 1, D // 4), "float32"),
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"mask_sw": ((1, 1, 1, SW), "float32"),
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"mask_full": ((1, 1, 1, ctx + 1), "float32"),
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"k_cache_0": ((1, KV, cache_len, D), "float32"),
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"v_cache_0": ((1, KV, cache_len, D), "float32"),
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}
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return s
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def main():
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results, pending = {}, {}
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for kind, ctx, cache_len in GRAPHS:
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path = f"{HERE}/onnx/spark_{kind}_ctx{ctx}.onnx"
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mdl = hub.upload_model(path, name=f"spark25_{kind}_ctx{ctx}")
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print("uploaded", kind, ctx, flush=True)
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for dev in DEVICES:
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for q, opt in QUANTS:
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key = (kind, ctx, dev, q)
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try:
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# No input_specs: every shape is static, and the ONNX
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# export prunes the inputs a given layer type never
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# reads (a full layer has no sliding mask), so a
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# hand-written spec would not match the graph.
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cj = hub.submit_compile_job(
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model=mdl, device=hub.Device(dev),
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name=f"spark {kind} ctx{ctx} {q}", options=opt)
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pending[key] = cj
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print("compile submitted", key, cj.job_id, flush=True)
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except Exception as e:
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print("submit failed", key, str(e)[:160], flush=True)
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prof = {}
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for key, cj in pending.items():
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st = cj.wait()
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print("compile", key, st.code, (st.message or "")[:160], flush=True)
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if st.success:
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prof[key] = hub.submit_profile_job(
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model=cj.get_target_model(), device=hub.Device(key[2]),
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name=f"spark {key[0]} ctx{key[1]} {key[3]}",
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options="--compute_unit npu")
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for key, pj in prof.items():
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st = pj.wait()
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kind, ctx, dev, q = key
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tag = f"{kind}|ctx{ctx}|{dev}|{q}"
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if st.success:
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p = pj.download_profile()
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ms = p["execution_summary"]["estimated_inference_time"] / 1e3
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results[tag] = ms
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json.dump(p, open(f"{HERE}/profile_spark_{kind}_{ctx}_{q}.json", "w"))
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print(f" {tag}: {ms:.3f} ms/layer", flush=True)
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else:
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results[tag] = None
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print(f" {tag} FAILED: {(st.message or '')[:200]}", flush=True)
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json.dump(results, open(f"{HERE}/aihub_spark_layers.json", "w"), indent=1)
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print("done", flush=True)
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if __name__ == "__main__":
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sys.exit(main())
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