Gahow Wang
c7afdc5074
Added --fire-and-forget flag to cache_aware_proxy.py for async prefill dispatch. Results on 6P+2D config: Await: TTFT=1.48s TPOT=0.066s E2E=5.95s 94% success FnF: TTFT=5.32s TPOT=0.037s E2E=11.9s 85% success Fire-and-forget improves TPOT by 44% (pipeline overlap) but degrades TTFT by 260% (decode internally waits for KV, less efficiently than proxy-level await) and increases errors from KV race conditions. Full 4-way ablation summary in analyze_ablations.py. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
107 lines
4.3 KiB
Python
107 lines
4.3 KiB
Python
"""4-way ablation analysis: Combined vs 4P4D vs 6P2D vs 6P2D-FnF."""
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import csv, json, statistics, os
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def gpu_stats(path, groups):
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rows = list(csv.DictReader(open(path)))
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by_gpu = {}
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for r in rows:
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g = int(r["gpu"])
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by_gpu.setdefault(g, []).append(float(r["util_pct"]))
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result = {}
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for gname, indices in groups.items():
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vals = []
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for i in indices:
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vals.extend(by_gpu.get(i, []))
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if vals:
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s = sorted(vals)
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p = lambda q: s[min(int(q*len(s)), len(s)-1)]
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nz = sum(1 for v in vals if v > 0)
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result[gname] = {"mean": statistics.fmean(vals), "p50": p(.5), "p90": p(.9),
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"max": max(vals), "active": nz*100//len(vals)}
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return result
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def lat_stats(path):
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rows = [json.loads(l) for l in open(path)]
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ok = [r for r in rows if not r.get("error")]
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ttfts = sorted([r["ttft_s"] for r in ok if r.get("ttft_s")])
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tpots = sorted([r["tpot_s"] for r in ok if r.get("tpot_s") and r["tpot_s"]>0])
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lats = sorted([r["latency_s"] for r in ok])
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p = lambda v,q: v[min(int(q*len(v)),len(v)-1)] if v else 0
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return {"ok": len(ok), "n": len(rows),
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"t50": p(ttfts,.5), "t90": p(ttfts,.9),
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"p50": p(tpots,.5), "p90": p(tpots,.9),
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"e50": p(lats,.5), "e90": p(lats,.9)}
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configs = [
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("gpu_ab_combined", "Combined 8colo", {"All": list(range(8))}),
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("gpu_ab_pdsep", "4P+4D await", {"P": [0,1,2,3], "D": [4,5,6,7], "All": list(range(8))}),
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("gpu_ab_6p2d", "6P+2D await", {"P": list(range(6)), "D": [6,7], "All": list(range(8))}),
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("gpu_ab_6p2d_fnf", "6P+2D fire-forget", {"P": list(range(6)), "D": [6,7], "All": list(range(8))}),
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]
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sep = "=" * 90
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print(sep)
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print(" ABLATION RESULTS: GPU Utilization + Latency")
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print(" All use cache-aware + token-level LB scheduler")
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print(sep)
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# GPU
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print("\n GPU UTILIZATION (All GPUs aggregate):")
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fmt = " %-20s %7s %7s %7s %7s %7s"
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print(fmt % ("Config", "Mean%", "P50%", "P90%", "Max%", "Active"))
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print(" " + "-" * 55)
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for dirname, label, groups in configs:
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gpath = "outputs/%s/gpu_util.csv" % dirname
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if not os.path.exists(gpath): continue
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gs = gpu_stats(gpath, groups)
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if "All" in gs:
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s = gs["All"]
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print(fmt % (label, "%.1f" % s["mean"], "%.0f" % s["p50"],
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"%.0f" % s["p90"], "%.0f" % s["max"], "%d%%" % s["active"]))
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# P vs D breakdown for PD-Sep configs
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print("\n GPU UTILIZATION (P vs D breakdown):")
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for dirname, label, groups in configs:
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if dirname == "gpu_ab_combined": continue
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gpath = "outputs/%s/gpu_util.csv" % dirname
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if not os.path.exists(gpath): continue
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gs = gpu_stats(gpath, groups)
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parts = []
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if "P" in gs: parts.append("P:%.1f%%(%d%%act)" % (gs["P"]["mean"], gs["P"]["active"]))
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if "D" in gs: parts.append("D:%.1f%%(%d%%act)" % (gs["D"]["mean"], gs["D"]["active"]))
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print(" %-20s %s" % (label, " ".join(parts)))
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# Latency
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print("\n LATENCY:")
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fmt2 = " %-20s %7s %8s %8s %8s %8s %8s"
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print(fmt2 % ("Config", "OK/N", "TTFT50", "TTFT90", "TPOT50", "TPOT90", "E2E50"))
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print(" " + "-" * 68)
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for dirname, label, _ in configs:
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mpath = "outputs/%s/metrics.jsonl" % dirname
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if not os.path.exists(mpath): continue
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s = lat_stats(mpath)
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print(fmt2 % (label, "%d/%d" % (s["ok"], s["n"]),
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"%.3f" % s["t50"], "%.3f" % s["t90"],
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"%.3f" % s["p50"], "%.3f" % s["p90"], "%.3f" % s["e50"]))
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# Ablation conclusions
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print("\n" + sep)
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print(" ABLATION CONCLUSIONS")
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print(sep)
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print("""
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Ablation 1 — P/D ratio (6P+2D vs 4P+4D):
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TTFT: 1.99s -> 1.48s (-26%) More prefill GPUs = less queue
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TPOT: 0.075 -> 0.077 (~same) Decode still memory-bound
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Decode GPU util: 7.8% -> 19.0% (+143%) Less waste
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Verdict: HELPS — fewer decode GPUs is better for this workload
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Ablation 2 — Fire-and-forget vs Await-prefill (on 6P+2D):
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TTFT: 1.48s -> 5.32s (+260%) WORSE — decode waits for KV internally
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TPOT: 0.066 -> 0.037 (-44%) BETTER — pipeline overlap helps decode
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Error: 6% -> 15% MORE errors from KV race conditions
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Verdict: HURTS overall — TTFT degradation outweighs TPOT gain
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Overall: Combined 8colo remains best for single-machine agentic workload.
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PD-Sep optimizations (ratio tuning, scheduling) narrow the gap but don't close it.
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""")
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