90fc378d2b
- Add dsv4_h20_vllm_tp_dp_matrix experiment (vLLM backend) - Add dsv4_h20_sglang_tp_dp_matrix experiment (SGLang backend) - Add nvidia_h20 platform config with H20-specific paths - Fix platform.sh auto-detection to distinguish H20 from H200 - Fix vLLM Docker startup: remove --entrypoint override for CDI compat - Fix vLLM 0.25.1 CLI args: remove redundant 'serve' from SERVER_ARGS - Download ShareGPT dataset to local datasets dir - Rename DSL to OSL across both experiments
99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
#!/usr/bin/env python3
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"""Generate the scenario list for the TP×DP matrix experiment.
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Reads matrix.json and prints TSV lines:
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mark input_len output_len concurrency num_prompts
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mark is one of Y/P/N. The caller (run_bench.sh) decides how to treat each.
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"""
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import argparse
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import json
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import math
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import os
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from pathlib import Path
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def sample_concurrency(low: int, high: int, target: int) -> list[int]:
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"""Return only the low and high concurrency values in [low, high].
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For the TP×DP matrix we only need the two endpoints of the concurrency
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range (e.g. 1 and 128 for ISL=1024). The `target` argument is kept for
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API compatibility but is ignored.
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"""
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assert 1 <= low <= high, f"invalid concurrency range: {low}-{high}"
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if low == high:
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return [low]
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return [low, high]
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def generate_scenarios(matrix_path: Path, mode: str, target_samples: int) -> list[dict]:
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with open(matrix_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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matrix = data["matrix"]
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concurrency_cfg = data["concurrency"]
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scenarios = []
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for isl_str in sorted(matrix.keys(), key=int):
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osl_map = matrix[isl_str]
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low = concurrency_cfg[isl_str]["low"]
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high = concurrency_cfg[isl_str]["high"]
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concurrencies = sample_concurrency(low, high, target_samples)
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for osl_str in sorted(osl_map.keys(), key=int):
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mark = osl_map[osl_str]
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if mode == "Y" and mark != "Y":
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continue
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if mode == "Y+P" and mark not in ("Y", "P"):
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continue
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# mode == "all" keeps everything, including N.
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for conc in concurrencies:
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scenarios.append(
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{
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"mark": mark,
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"input_len": int(isl_str),
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"output_len": int(osl_str),
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"concurrency": conc,
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"num_prompts": conc * 5,
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}
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)
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return scenarios
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def main() -> None:
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parser = argparse.ArgumentParser()
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parser.add_argument("--matrix", type=Path, default=Path("matrix.json"))
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parser.add_argument("--mode", choices=["Y", "Y+P", "all"], default=None,
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help="Scenario selection mode. Defaults to matrix.mode.")
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parser.add_argument("--target-samples", type=int, default=0,
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help="Target number of concurrency samples. 0 = heuristic (6-8).")
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args = parser.parse_args()
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with open(args.matrix, "r", encoding="utf-8") as f:
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data = json.load(f)
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mode = args.mode if args.mode else data.get("mode", "Y+P")
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target_samples = args.target_samples
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if target_samples <= 0:
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env_samples = os.getenv("CONCURRENCY_SAMPLES", "0")
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try:
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target_samples = int(env_samples)
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except ValueError:
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target_samples = 0
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if target_samples <= 0:
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target_samples = 7
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scenarios = generate_scenarios(args.matrix, mode, target_samples)
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print("mark\tinput_len\toutput_len\tconcurrency\tnum_prompts")
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for s in scenarios:
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print(f"{s['mark']}\t{s['input_len']}\t{s['output_len']}\t{s['concurrency']}\t{s['num_prompts']}")
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if __name__ == "__main__":
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main()
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