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
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
"""Microbenchmark of candidate CPU 4-bit weight kernels at Spark-X2.5 shapes.
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Decode is one token, so every linear is a GEMV and the only thing that matters
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is how fast the weight bytes can be streamed. llama.cpp's Q4_K_M moves ~4.5
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bits per weight; anything we pick has to get close to that at a comparable
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fraction of peak bandwidth.
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"""
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import argparse, json, time
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import torch
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SHAPES = [
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("qkv", 3072, 2048),
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("o", 2048, 2048),
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("gate_up", 13312, 2048),
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("down", 2048, 6656),
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("lm_head", 131072, 2048),
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]
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def timeit(fn, iters, warmup=5):
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for _ in range(warmup):
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fn()
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best = float("inf")
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for _ in range(iters):
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t = time.perf_counter()
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fn()
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best = min(best, time.perf_counter() - t)
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return best
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--m", type=int, default=1)
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ap.add_argument("--iters", type=int, default=30)
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ap.add_argument("--group", type=int, default=128)
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ap.add_argument("--out", default="int4_kernels.json")
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a = ap.parse_args()
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torch.set_num_threads(torch.get_num_threads())
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print("threads", torch.get_num_threads())
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rows = []
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for name, N, K in SHAPES:
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x16 = torch.randn(a.m, K, dtype=torch.bfloat16)
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x32 = x16.float()
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w16 = torch.randn(N, K, dtype=torch.bfloat16)
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rec = {"name": name, "N": N, "K": K, "M": a.m}
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# bf16 reference (oneDNN through aten)
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t = timeit(lambda: torch.nn.functional.linear(x16, w16), a.iters)
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rec["bf16_ms"] = t * 1e3
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rec["bf16_GBps"] = N * K * 2 / t / 1e9
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# int8 weight-only (aten _weight_int8pack_mm)
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try:
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w8 = torch.randint(-127, 127, (N, K), dtype=torch.int8)
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s8 = torch.rand(N, dtype=torch.bfloat16) * 0.01
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t = timeit(lambda: torch.ops.aten._weight_int8pack_mm(x16, w8, s8), a.iters)
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rec["int8_ms"] = t * 1e3
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rec["int8_GBps"] = N * K / t / 1e9
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except Exception as e:
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rec["int8_err"] = f"{type(e).__name__}: {e}"
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# int4 group-wise (aten _weight_int4pack_mm_for_cpu)
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for dt, xx in (("bf16", x16), ("fp32", x32)):
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try:
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wint = torch.randint(0, 15, (N, K), dtype=torch.int32)
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packed = torch.ops.aten._convert_weight_to_int4pack_for_cpu(wint, 1)
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sz = torch.rand(K // a.group, N, 2, dtype=xx.dtype)
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f = lambda: torch.ops.aten._weight_int4pack_mm_for_cpu(xx, packed, a.group, sz)
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out = f()
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t = timeit(f, a.iters)
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rec[f"int4_{dt}_ms"] = t * 1e3
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rec[f"int4_{dt}_GBps"] = N * K * 0.5 / t / 1e9
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rec[f"int4_{dt}_out"] = str(tuple(out.shape)) + " " + str(out.dtype)
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except Exception as e:
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rec[f"int4_{dt}_err"] = f"{type(e).__name__}: {e}"
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rows.append(rec)
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print(json.dumps(rec))
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with open(a.out, "w") as f:
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json.dump(rows, f, indent=2)
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if __name__ == "__main__":
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main()
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