a4e38b9e33
Move all experiments under hardware-specific folders: - experiments/h200/ : H200 GPU experiments (15 dirs) - experiments/h20/ : H20 GPU experiments (2 dirs) - experiments/p800/ : Kunlun P800 experiments (3 dirs) - experiments/pro6000/ : RTX 6000D experiments (2 dirs) This improves discoverability and keeps hardware-specific configs isolated from each other.
75 lines
3.1 KiB
Python
Executable File
75 lines
3.1 KiB
Python
Executable File
#!/usr/bin/env python3
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"""Generate a side-by-side comparison of vLLM+DSpark and vLLM default.
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Usage:
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python3 compare.py --dspark <dspark_result_root> --default <default_result_root> \
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[--output comparison.md]
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"""
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import argparse
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import json
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from collections import defaultdict
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from pathlib import Path
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def load_result(result_root: Path) -> dict:
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path = result_root / "results.json"
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with open(path, "r", encoding="utf-8") as f:
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return json.load(f)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--dspark", type=Path, required=True)
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parser.add_argument("--default", type=Path, required=True)
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parser.add_argument("-o", "--output", type=Path, default=Path("comparison.md"))
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args = parser.parse_args()
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dspark_data = load_result(args.dspark)
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default_data = load_result(args.default)
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by_scenario = defaultdict(dict)
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for data in (dspark_data, default_data):
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backend = data["metadata"]["engine"]
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for s in data.get("scenarios", []):
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key = s["name"]
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by_scenario[key][backend] = s
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with open(args.output, "w", encoding="utf-8") as f:
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f.write("# vLLM+DSpark vs vLLM default on DeepSeek-V4-Flash (H200, TP=8)\n\n")
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f.write("## Summary\n\n")
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f.write("- DSpark model: `/data/models/DeepSeek-V4-Flash-DSpark`\n")
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f.write("- Default model: `/data/models/DeepSeek-V4-Flash`\n")
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f.write("- Hardware: 8x NVIDIA H200 143GB\n")
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f.write("- Tensor Parallelism: 8\n")
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f.write("- Benchmark client: `sglang.bench_serving --backend vllm`\n")
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f.write("- DSpark flags: `--spec-method dspark --spec-model <model> --spec-tokens 5`\n\n")
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f.write("## Side-by-side results (TTFT focused)\n\n")
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f.write("| Scenario | Backend | Conc | Input | Output | Req/s | OutTok/s | Mean TTFT(ms) | P95 TTFT(ms) | P99 TTFT(ms) | Mean TPOT(ms) | P99 TPOT(ms) | Mean E2E(ms) |\n")
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f.write("|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|\n")
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for scenario_name in sorted(by_scenario.keys()):
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for backend in ("vllm-dspark", "vllm-default"):
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s = by_scenario[scenario_name].get(backend)
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if s is None:
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continue
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cfg = s["config"]
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m = s["metrics"]
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f.write(
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f"| {scenario_name} | {backend} | {cfg['concurrency']} | {cfg['input_len']} | {cfg['output_len']} | "
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f"{m['request_throughput']:.2f} | {m['output_token_throughput']:.2f} | "
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f"{m['ttft_ms']['mean']:.2f} | {m['ttft_ms']['p95']:.2f} | {m['ttft_ms']['p99']:.2f} | "
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f"{m['tpot_ms']['mean']:.2f} | {m['tpot_ms']['p99']:.2f} | "
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f"{m['e2e_ms']['mean']:.2f} |\n"
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)
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f.write("\n## Notes\n\n")
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f.write("- TTFT mean/P95/P99 are the main focus for verifying whether DSpark increases time-to-first-token.\n")
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f.write("- Mean TPOT and E2E are included to check whether speculative decoding pays back after first token.\n")
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print(f"Wrote comparison to {args.output}")
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if __name__ == "__main__":
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main()
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