chore: keep benchmark repository source-only

This commit is contained in:
Spike 2026-09-04 10:49:20 +08:00
parent 7ee3812c7b
commit e917109d9b
577 changed files with 4249 additions and 495681 deletions

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.gitignore vendored
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# VBench generated videos, evaluation outputs, and logs stay on 6000D_H3. # Generated benchmark/evaluation artifacts stay on the benchmark host.
/vbench-score/vbench-base/results/ **/results/
/vbench-score/vbench-lora/results/ **/result/
**/outputs/
**/output/
**/ssim-comparison/
# SQLite analysis databases stay on 6000D_H3. # Runtime logs and state.
**/*.log
**/*.pid
**/*.sqlite **/*.sqlite
**/*.sqlite3
**/*.db
# Generated benchmark videos stay on 6000D_H3. # Profiler captures.
**/*.mp4
# Raw profiler captures stay on 6000D_H3; scripts, logs, and summaries are tracked.
**/*.nsys-rep **/*.nsys-rep
**/*.ncu-rep
**/*.trace.json.gz **/*.trace.json.gz
# Python/tool caches.
**/__pycache__/
**/*.py[cod]
**/.pytest_cache/
**/.cache/
**/.ipynb_checkpoints/
# Temporary downloads and source checkouts are reproducible from upstream.
/src/
**/inputs/uploads/
**/*.bak
**/*~

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# sskj-h3 # sskj-h3
MiniMax-H3 在 RTX 6000D-H3 上的吞吐、性能 Profile、成对 SSIM 与 VBench 评测归档 MiniMax-H3 在 RTX 6000D-H3 上的吞吐、稀疏/缓存加速、LoRA、成对 SSIM 和 VBench 可复现实验代码
## 目录 ## 目录
- `throughput/`吞吐测试、Profile 与成对 SSIM。 - `throughput/common/`:共享的统计与成对 SSIM 工具。
- `sglang-base/`SGLang TP8×1、TP4×2、TP2×420 steps / 5s 基准。 - `throughput/sglang-base/`SGLang Base runner、配置和正式输入。
- `sglang-base-b300/`B300 相关 SGLang 基准。 - `throughput/sglang-cache-dit/`Cache-DiT runner 与参数配置。
- `sglang-lora/`Larry LoRA 加速基准。 - `throughput/sglang-lora/`Larry LoRA runner、prompt、records 和兼容补丁。
- `sglang-profile/`Torch/Nsight/NCCL 与 SDPA kernel 分析。 - `throughput/sglang-profile/`:性能分析脚本;采集结果不进入 Git。
- `vllm-omni-base/`vLLM-Omni 多实例基准。 - `throughput/vllm-omni-base/`vLLM-Omni Base runner。
- `common/paired_video_ssim.py`:候选运行相对 base 的逐帧成对 SSIM。 - `throughput/vllm-omni-fasth3/`FastH3 适配与 runner。
- `vbench-score/`VBench 视频与 16 维评分。 - `vbench-score/`VBench 生成、评分脚本和环境说明。
- `vbench-base/`base 生成与评分归档。
- `vbench-lora/`Larry LoRA 生成与评分归档。
- `SOURCE_MAP.tsv`:源路径、归档路径、文件数和字节数。
- `tools/`:非破坏性归档与验收脚本。
## SSIM 与 VBench 的分工 ## 版本库边界
成对 SSIM 用相同 prompt、seed、任务、分辨率、时长和宽高比的 base 视频作为参考,按 `request_id` 配对。视频由 FFmpeg 解码并统一为 `yuv420p`,然后逐帧计算 Y/U/V/All SSIM主口径是 `All`。它适合测量 Cache-DiT、TeaCache、Larry 等加速方案对 base 输出的像素/结构偏移。 Git 只保存复现实验所需的代码、配置、输入清单和必要素材。以下内容只保留在运行服务器,不进入版本库:
VBench 独立衡量主体一致性、运动、审美等生成质量维度。SSIM 高不等于 VBench 高VBench 高也不保证逐样本复现,因此两者互补。 - 所有 `results/``outputs/` 和成对 SSIM 结果目录;
- 视频生成结果、服务/客户端日志和运行时状态;
- SQLite 数据库、Nsight/NCU/Torch Profiler 原始采集;
- Python/Hugging Face 缓存、临时上传目录和外部源码 checkout。
吞吐 runner 只有在设置 `SSIM_REFERENCE_ROOT` 时才会在每个 phase 结束、SGLang 服务完全停止后评分,不会把解码和评分时间计入吞吐: 已经进入历史提交的结果从当前 Git 索引中移除;服务器上的现有结果文件不会因此删除。
```bash ## 指标口径
SSIM_REFERENCE_ROOT=/data/wxy/sskj-h3/throughput/sglang-base/results/balanced-tp4-tp2-20steps-5s-20260822-175030 \
SSIM_THRESHOLD=0.90 \
bash /data/wxy/sskj-h3/throughput/sglang-lora/scripts/run_sglang_h3_lora_mixed_matrix_6000d.sh
```
参考目录和候选目录都应包含 `tpN_replicasM/<task>/client_*/results.jsonl`。每个候选 phase 会新增: 吞吐和端到端时延由 runner 记录。成对 SSIM 使用相同 prompt、seed、任务、分辨率、时长和宽高比的 Base 视频作为参考VBench 独立评价主体一致性、运动、审美等质量维度。SSIM 与 VBench 互补,均不计入吞吐计时。
- `quality/paired_ssim.json`:总体、分辨率分组和逐视频结果。
- `quality/paired_ssim.tsv`:逐视频表。
- `quality/paired_ssim_frames.tsv`:逐帧表。
- `quality/paired_ssim.log`:评分日志。
默认阈值为 0.90,只记录是否通过,不中止完整矩阵;需要将低于阈值视作失败时设置 `SSIM_FAIL_BELOW_THRESHOLD=true`
## Git 镜像边界
服务器保留全部实体文件;同步到 Git 时排除 `vbench-score/*/results/`、全部 `*.mp4`、全部 `*.sqlite`、Nsight `*.nsys-rep` 和 Torch `*.trace.json.gz` 原始采集。代码、日志、JSON/JSONL、TSV、结构化汇总和分析文档正常纳入版本库。
## 验证
```bash
/root/.miniconda3/bin/python /data/wxy/sskj-h3/tools/validate_sskj_h3_archive.py
```

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section kind source destination files bytes
sglang-base script /data/wxy/run_sglang_h3_mixed_matrix_6000d.sh throughput/sglang-base/scripts/run_sglang_h3_mixed_matrix_6000d.sh 1 7888
sglang-base script /data/wxy/minimax_h3_mixed_bench.py throughput/sglang-base/scripts/minimax_h3_mixed_bench.py 1 12687
sglang-base result /data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844 throughput/sglang-base/results/mixed64-20steps-5s-20260822-100844 212 342464940
sglang-base result /data/wxy/results/minimax_h3_mixed_matrix/balanced-tp4-tp2-20steps-5s-20260822-175030 throughput/sglang-base/results/balanced-tp4-tp2-20steps-5s-20260822-175030 195 268064833
vllm-omni-base script /data/wxy/run_vllm_omni_h3_matrix_6000d.sh throughput/vllm-omni-base/scripts/run_vllm_omni_h3_matrix_6000d.sh 1 10411
vllm-omni-base script /data/wxy/minimax_h3_vllm_bench.py throughput/vllm-omni-base/scripts/minimax_h3_vllm_bench.py 1 14589
vllm-omni-base result /data/wxy/results/minimax_h3_vllm_matrix/vllm-balanced64-20steps-5s-r2-20260822-225317 throughput/vllm-omni-base/results/vllm-balanced64-20steps-5s-r2-20260822-225317 271 1998342105
sglang-profile script /data/wxy/h3_profile throughput/sglang-profile/scripts/h3_profile 15 322059
sglang-profile result /data/wxy/profile_results/h3-quick-input-matrix-20260824-run1 throughput/sglang-profile/results/h3-quick-input-matrix-20260824-run1 71 32017378
sglang-profile result /data/wxy/profile_results/h3-targeted-profile-20260824-run1 throughput/sglang-profile/results/h3-targeted-profile-20260824-run1 87 2124349553
sglang-profile result /data/wxy/profile_results/h3-sdpa-kernel-matrix-20260825-run1 throughput/sglang-profile/results/h3-sdpa-kernel-matrix-20260825-run1 43 615920
vbench-base result /data/wxy/results/h3_vbench_base/h3-vbench-base-dense-tp2x4-20260826-run1 vbench-score/vbench-base/results/h3-vbench-base-dense-tp2x4-20260826-run1 1983 1982500600
vbench-base environment /data/wxy/vbench_score_setup_logs vbench-score/vbench-base/environment/vbench_score_setup_logs 11 49335
1 section kind source destination files bytes
2 sglang-base script /data/wxy/run_sglang_h3_mixed_matrix_6000d.sh throughput/sglang-base/scripts/run_sglang_h3_mixed_matrix_6000d.sh 1 7888
3 sglang-base script /data/wxy/minimax_h3_mixed_bench.py throughput/sglang-base/scripts/minimax_h3_mixed_bench.py 1 12687
4 sglang-base result /data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844 throughput/sglang-base/results/mixed64-20steps-5s-20260822-100844 212 342464940
5 sglang-base result /data/wxy/results/minimax_h3_mixed_matrix/balanced-tp4-tp2-20steps-5s-20260822-175030 throughput/sglang-base/results/balanced-tp4-tp2-20steps-5s-20260822-175030 195 268064833
6 vllm-omni-base script /data/wxy/run_vllm_omni_h3_matrix_6000d.sh throughput/vllm-omni-base/scripts/run_vllm_omni_h3_matrix_6000d.sh 1 10411
7 vllm-omni-base script /data/wxy/minimax_h3_vllm_bench.py throughput/vllm-omni-base/scripts/minimax_h3_vllm_bench.py 1 14589
8 vllm-omni-base result /data/wxy/results/minimax_h3_vllm_matrix/vllm-balanced64-20steps-5s-r2-20260822-225317 throughput/vllm-omni-base/results/vllm-balanced64-20steps-5s-r2-20260822-225317 271 1998342105
9 sglang-profile script /data/wxy/h3_profile throughput/sglang-profile/scripts/h3_profile 15 322059
10 sglang-profile result /data/wxy/profile_results/h3-quick-input-matrix-20260824-run1 throughput/sglang-profile/results/h3-quick-input-matrix-20260824-run1 71 32017378
11 sglang-profile result /data/wxy/profile_results/h3-targeted-profile-20260824-run1 throughput/sglang-profile/results/h3-targeted-profile-20260824-run1 87 2124349553
12 sglang-profile result /data/wxy/profile_results/h3-sdpa-kernel-matrix-20260825-run1 throughput/sglang-profile/results/h3-sdpa-kernel-matrix-20260825-run1 43 615920
13 vbench-base result /data/wxy/results/h3_vbench_base/h3-vbench-base-dense-tp2x4-20260826-run1 vbench-score/vbench-base/results/h3-vbench-base-dense-tp2x4-20260826-run1 1983 1982500600
14 vbench-base environment /data/wxy/vbench_score_setup_logs vbench-score/vbench-base/environment/vbench_score_setup_logs 11 49335

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#!/usr/bin/env python3
"""Pair current eight-case Ref2VA summaries and compute decoded-video SSIM."""
from __future__ import annotations
import argparse
import json
import math
import statistics
import subprocess
import tempfile
from pathlib import Path
from typing import Any
def percentile(values: list[float], q: float) -> float:
ordered = sorted(values)
position = (len(ordered) - 1) * q
lower, upper = math.floor(position), math.ceil(position)
if lower == upper:
return ordered[lower]
return ordered[lower] * (upper - position) + ordered[upper] * (position - lower)
def run(command: list[str]) -> subprocess.CompletedProcess[str]:
result = subprocess.run(command, text=True, capture_output=True, check=False)
if result.returncode:
raise RuntimeError(f"command failed ({result.returncode}): {' '.join(command)}\n{result.stderr[-4000:]}")
return result
def load_cases(path: Path) -> dict[str, dict[str, Any]]:
payload = json.loads(path.read_text())
cases = payload.get("cases") or []
rows = {str(row["record_id"]): row for row in cases if row.get("success")}
if len(cases) != 8 or len(rows) != 8:
raise ValueError(f"expected 8 successful unique cases in {path}, got cases={len(cases)} successful={len(rows)}")
return rows
def reference_contract(row: dict[str, Any]) -> list[tuple[str, str, str]]:
return sorted((str(r["label"]), str(r["type"]), str(r["sha256"])) for r in row["references"])
def check_pair(
reference: dict[str, Any],
candidate: dict[str, Any],
*,
allow_step_mismatch: bool = False,
) -> None:
fields = ["record_id", "seed", "short_edge", "aspect_ratio", "duration_seconds"]
if not allow_step_mismatch:
fields.append("num_inference_steps")
mismatches = [f"{field}: {reference.get(field)!r} != {candidate.get(field)!r}" for field in fields if reference.get(field) != candidate.get(field)]
if reference_contract(reference) != reference_contract(candidate):
mismatches.append("reference attachment labels/types/hashes differ")
if mismatches:
raise ValueError("pair contract mismatch: " + "; ".join(mismatches))
def probe(ffprobe: str, path: Path) -> dict[str, Any]:
result = run([ffprobe, "-v", "error", "-select_streams", "v:0", "-count_frames",
"-show_entries", "stream=width,height,r_frame_rate,nb_frames,nb_read_frames", "-of", "json", str(path)])
stream = json.loads(result.stdout)["streams"][0]
frames = stream.get("nb_read_frames") or stream.get("nb_frames")
return {"width": int(stream["width"]), "height": int(stream["height"]),
"fps": stream["r_frame_rate"], "frames": int(frames)}
def parse_stats(path: Path) -> list[float]:
values = []
for line in path.read_text().splitlines():
tokens = dict(token.split(":", 1) for token in line.split() if ":" in token)
if "All" in tokens:
values.append(float(tokens["All"]))
if not values:
raise ValueError(f"no SSIM frame metrics in {path}")
return values
def compare(ffmpeg: str, ffprobe: str, reference: Path, candidate: Path, stats: Path) -> tuple[dict[str, Any], list[float]]:
ref_probe, cand_probe = probe(ffprobe, reference), probe(ffprobe, candidate)
if ref_probe != cand_probe:
raise ValueError(f"decoded video contract mismatch: reference={ref_probe}, candidate={cand_probe}")
graph = ("[0:v]setpts=PTS-STARTPTS,format=yuv420p[reference];"
"[1:v]setpts=PTS-STARTPTS,format=yuv420p[candidate];"
f"[reference][candidate]ssim=stats_file={stats}")
run([ffmpeg, "-hide_banner", "-nostdin", "-loglevel", "error", "-i", str(reference),
"-i", str(candidate), "-filter_complex", graph, "-an", "-f", "null", "-"])
values = parse_stats(stats)
if len(values) != ref_probe["frames"]:
raise ValueError(f"frame metric count {len(values)} != decoded frames {ref_probe['frames']}")
return ref_probe, values
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("--reference-summary", type=Path, required=True)
parser.add_argument("--candidate-summary", type=Path, required=True)
parser.add_argument("--output-dir", type=Path, required=True)
parser.add_argument("--threshold", type=float, default=0.90)
parser.add_argument("--ffmpeg", required=True)
parser.add_argument("--ffprobe", required=True)
parser.add_argument(
"--allow-step-mismatch",
action="store_true",
help="Allow the candidate to use a different denoising-step count; all other pairing fields remain strict.",
)
args = parser.parse_args()
reference = load_cases(args.reference_summary)
candidate = load_cases(args.candidate_summary)
if set(reference) != set(candidate):
raise SystemExit("reference and candidate record_id sets differ")
args.output_dir.mkdir(parents=True, exist_ok=True)
pairs = []
with tempfile.TemporaryDirectory(prefix="ref2va-ssim-") as temp:
for index, record_id in enumerate(sorted(reference), 1):
ref, cand = reference[record_id], candidate[record_id]
check_pair(ref, cand, allow_step_mismatch=args.allow_step_mismatch)
ref_path, cand_path = Path(ref["file_path"]), Path(cand["file_path"])
if not ref_path.is_file() or not cand_path.is_file():
raise ValueError(f"missing paired video: {ref_path} or {cand_path}")
video, values = compare(args.ffmpeg, args.ffprobe, ref_path, cand_path, Path(temp) / f"{index}.stats")
mean = statistics.fmean(values)
row = {"case_number": cand["case_number"], "record_id": record_id,
"reference_file": str(ref_path), "candidate_file": str(cand_path), **video,
"ssim_all_mean": mean, "ssim_all_p10": percentile(values, 0.10),
"ssim_all_min": min(values), "threshold": args.threshold, "passed": mean >= args.threshold}
pairs.append(row)
print(f"[{index}/8] case={cand['case_number']} SSIM={mean:.6f}", flush=True)
means = [row["ssim_all_mean"] for row in pairs]
summary = {"metric": "FFmpeg decoded YUV420 SSIM All", "threshold": args.threshold,
"allow_step_mismatch": args.allow_step_mismatch,
"videos": len(pairs), "mean_video_ssim": statistics.fmean(means),
"median_video_ssim": statistics.median(means), "p10_video_ssim": percentile(means, 0.10),
"min_video_ssim": min(means), "videos_at_or_above_threshold": sum(v >= args.threshold for v in means),
"pass_rate": sum(v >= args.threshold for v in means) / len(means), "pairs": pairs}
(args.output_dir / "summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2) + "\n")
with (args.output_dir / "per_case.tsv").open("w") as handle:
columns = ("case_number", "record_id", "ssim_all_mean", "ssim_all_p10", "ssim_all_min", "passed", "candidate_file")
handle.write("\t".join(columns) + "\n")
for row in sorted(pairs, key=lambda item: item["case_number"]):
handle.write("\t".join(str(row[column]) for column in columns) + "\n")
print(json.dumps({key: value for key, value in summary.items() if key != "pairs"}, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())

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#!/usr/bin/env bash
# Sequentially run: base -> cache-dit(3tier, plain model) -> cache+lora(TP4x2, 3tier)
set -Eeuo pipefail
LOGP=/data/wxy/sskj-h3/throughput
REC=/data/wxy/sskj-h3/throughput/sglang-lora/records_rewritten.json
cd /data/wxy/sskj-h3/throughput
echo "========== [1/3] BASE (new prompt, TP2x4, 20 steps) =========="
cd /data/wxy/sskj-h3/throughput/sglang-base
RECORDS=$REC RUN_ID="ref2va-feishu-base-tp2x4-newprompt-768p-15s-20steps-$(date +%Y%m%d-%H%M%S)" \
bash scripts/run_sglang_h3_ref2va_feishu_base_tp2x4.sh 2>&1 | tee $LOGP/base_newprompt_run.log
echo "========== [2/3] CACHE-DIT plain (new prompt, TP2x4, 3 tiers) =========="
cd /data/wxy/sskj-h3/throughput/sglang-cache-dit
RECORDS=$REC RUN_ID="ref2va-feishu-cachedit-3tier-newprompt-$(date +%Y%m%d-%H%M%S)" \
bash scripts/run_sglang_h3_ref2va_feishu_cachedit_3tier_tp2x4.sh 2>&1 | tee $LOGP/cachedit_newprompt_run.log
echo "========== [3/3] CACHE+LORA (new prompt, TP4x2, 3 tiers) =========="
bash /data/wxy/sskj-h3/throughput/run_cache_lora_tp4_3tier.sh 2>&1 | tee $LOGP/cache_lora_tp4_run.log
echo "========== ALL THREE COMPLETE =========="

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#!/usr/bin/env bash
# 1) Larry LoRA base (9 grid points / 8 NFE, no cache, TP2x4)
# 2) Larry LoRA + Cache-DiT (9 grid / 8 NFE, TP4x2, 3 tiers)
set -Eeuo pipefail
LOGP=/data/wxy/sskj-h3/throughput
REC=/data/wxy/sskj-h3/throughput/sglang-lora/records_rewritten.json
say() { echo "[$(date +%F_%T)] $*"; }
wait_gpu_free() {
say "waiting for GPUs to be free..."
while :; do
busy=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader | awk "{s+=\$1} END{print s+0}")
[[ "$busy" -eq 0 ]] && break
echo "gpus busy: $busy MiB"; sleep 60
done
say "GPUs free"
}
echo "########## [1/2] LARRY LORA (no cache, TP2x4, 8 NFE) ##########"
wait_gpu_free
cd /data/wxy/sskj-h3/throughput/sglang-lora
RECORDS=$REC RUN_ID="ref2va-feishu-larry-newprompt-tp2x4-8nfe-$(date +%Y%m%d-%H%M%S)" \
bash scripts/run_sglang_h3_ref2va_feishu_larry_v4_600_tp2x4.sh 2>&1 | tee $LOGP/larry_newprompt_run.log
echo "########## [2/2] CACHE+LORA (TP4x2, 3 tiers, 8 NFE) ##########"
wait_gpu_free
cd /data/wxy/sskj-h3/throughput
RUN_ID="ref2va-feishu-cache-lora-tp4x2-3tier-8nfe-$(date +%Y%m%d-%H%M%S)" \
bash run_cache_lora_tp4_3tier.sh 2>&1 | tee $LOGP/cache_lora_tp4_8nfe_run.log
echo "########## ALL DONE ##########"

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#!/usr/bin/env bash
# Wait for base run, then run: cache-dit plain (3tier) -> cache+lora (TP4x2 3tier)
set -Eeuo pipefail
LOGP=/data/wxy/sskj-h3/throughput
REC=/data/wxy/sskj-h3/throughput/sglang-lora/records_rewritten.json
echo "========== waiting for base tmux session to finish =========="
while tmux has-session -t base_newprompt 2>/dev/null; do sleep 60; done
echo "========== base done; verifying GPUs free =========="
while :; do
busy=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader | awk "{s+=\$1} END{print s+0}")
[[ "$busy" -eq 0 ]] && break
echo "gpus still busy: $busy MiB; waiting"; sleep 60
done
echo "========== [2/3] CACHE-DIT plain (new prompt, TP2x4, 3 tiers) =========="
cd /data/wxy/sskj-h3/throughput/sglang-cache-dit
RECORDS=$REC RUN_ID="ref2va-feishu-cachedit-3tier-newprompt-$(date +%Y%m%d-%H%M%S)" \
bash scripts/run_sglang_h3_ref2va_feishu_cachedit_3tier_tp2x4.sh 2>&1 | tee $LOGP/cachedit_newprompt_run.log
echo "========== [3/3] CACHE+LORA (new prompt, TP4x2, 3 tiers) =========="
while :; do
busy=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader | awk "{s+=\$1} END{print s+0}")
[[ "$busy" -eq 0 ]] && break
echo "gpus still busy: $busy; waiting"; sleep 60
done
bash /data/wxy/sskj-h3/throughput/run_cache_lora_tp4_3tier.sh 2>&1 | tee $LOGP/cache_lora_tp4_run.log
echo "========== PART2 COMPLETE =========="

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#!/usr/bin/env bash
# Cache-DiT + Larry LoRA over 8 Feishu Ref2VA cases, TP4 x 2 replicas.
# Resumable per case.
set -Eeuo pipefail
MODEL=${MODEL:-/data/hf_models/MiniMax-H3}
REPO_ROOT=/data/wxy/sskj-h3
INPUT_ROOT=$REPO_ROOT/throughput/sglang-base/inputs/ref2va-feishu-20260831
RECORDS=${RECORDS:-$REPO_ROOT/throughput/sglang-lora/records_rewritten.json}
ASSETS_ROOT=$INPUT_ROOT/assets
PYTHON=/root/.miniconda3/envs/sglang-lora/bin/python
SGLANG_BIN=/root/.miniconda3/envs/sglang-lora/bin/sglang
CACHE_PATCH=/data/ymrl/h3_cache_patch
CLIENT_SCRIPT=$REPO_ROOT/throughput/sglang-base/scripts/ref2va_feishu_bench.py
MEDIA_BIN_DIR=/root/.miniconda3/envs/deploy/bin
LORA_PATH=/data/hf_models/MiniMax-H3-Turbo-Lora
LORA_WEIGHT_NAME=minimax_h3_turbo_v4_step600_ema.safetensors
LORA_NICKNAME=h3-turbo-v4-600
LORA_SCALE=1.0
LORA_MERGE_MODE=merge
BASE_PORT=34010; PORT_STRIDE=10
MASTER_PORT_BASE=35000; SCHEDULER_PORT_BASE=36000
HOST=127.0.0.1
TP_SIZE=4; REPLICAS=2
NUM_INFERENCE_STEPS=9
SHORT_EDGE=768; ASPECT_RATIO=9:16; DURATION_SECONDS=15
FLOW_SHIFT=12.0; AUDIO_FLOW_SHIFT=3.0
CLIENT_WARMUP_STEPS=5
CACHE_FN=2; CACHE_BN=0; CACHE_WARMUP=2
MAX_ATTEMPTS=3; SERVER_START_TIMEOUT=1800
RUN_ID=${RUN_ID:-ref2va-feishu-cache-lora-tp4x2-3tier-$(date +%Y%m%d-%H%M%S)}
RESULT_ROOT=${RESULT_ROOT:-$REPO_ROOT/throughput/sglang-lora/results/$RUN_ID}
PROFILES=(
"quality_rdt003_mc2 0.03 2 quality"
"balanced_rdt005_mc3 0.05 3 balanced"
"throughput_rdt010_mc4 0.10 4 throughput"
)
declare -a SERVER_PIDS=()
declare -a CLIENT_PIDS=()
CURRENT_PROFILE_DIR=""
mkdir -p "$RESULT_ROOT"
MASTER_LOG="$RESULT_ROOT/matrix.log"
exec > >(tee -a "$MASTER_LOG") 2>&1
log() { printf "[%s] %s\n" "$(date +%F_%T)" "$*"; }
die() { log "ERROR: $*" >&2; exit 1; }
[[ -x "$PYTHON" ]] || die "python missing"
[[ -x "$SGLANG_BIN" ]] || die "sglang missing"
[[ -f "$MEDIA_BIN_DIR/ffprobe" ]] || die "ffprobe missing"
[[ -f "$CLIENT_SCRIPT" ]] || die "client missing"
[[ -f "$CACHE_PATCH/sitecustomize.py" ]] || die "patch missing"
[[ -f "$LORA_PATH/$LORA_WEIGHT_NAME" ]] || die "lora missing"
[[ -f "$RECORDS" ]] || die "records missing"
port_is_open() {
"$PYTHON" - "$HOST" "$1" <<"PY"
import socket, sys
s = socket.socket(); s.settimeout(0.5)
try: s.connect((sys.argv[1], int(sys.argv[2])))
except OSError: raise SystemExit(1)
else: raise SystemExit(0)
finally: s.close()
PY
}
stop_servers() {
local pid deadline
((${#SERVER_PIDS[@]})) || return 0
log "stopping ${#SERVER_PIDS[@]} server(s)"
for pid in "${SERVER_PIDS[@]}"; do kill -INT -- "-$pid" 2>/dev/null || kill -INT "$pid" 2>/dev/null || true; done
deadline=$((SECONDS + 180))
while ((SECONDS < deadline)); do
local alive=0
for pid in "${SERVER_PIDS[@]}"; do kill -0 "$pid" 2>/dev/null && alive=1; done
((alive == 0)) && break
sleep 2
done
for pid in "${SERVER_PIDS[@]}"; do
if kill -0 "$pid" 2>/dev/null; then
kill -TERM -- "-$pid" 2>/dev/null || kill -TERM "$pid" 2>/dev/null || true
sleep 5; kill -KILL -- "-$pid" 2>/dev/null || kill -KILL "$pid" 2>/dev/null || true
fi
wait "$pid" 2>/dev/null || true
done
SERVER_PIDS=()
}
cleanup() {
local rc=$? pid
trap - EXIT INT TERM
for pid in "${CLIENT_PIDS[@]}"; do kill -TERM "$pid" 2>/dev/null || true; done
stop_servers
exit "$rc"
}
trap cleanup EXIT INT TERM
wait_healthy() {
local port=$1 pid=$2 log_file=$3 deadline=$((SECONDS + SERVER_START_TIMEOUT))
while ((SECONDS < deadline)); do
curl -fsS --max-time 5 "http://${HOST}:${port}/health" >/dev/null 2>&1 && return 0
if ! kill -0 "$pid" 2>/dev/null; then tail -160 "$log_file" >&2 || true; return 1; fi
sleep 5
done
tail -160 "$log_file" >&2 || true
return 1
}
start_servers() {
local tag=$1 rdt=$2 mc=$3 attempt=$4
local replica port master_port scheduler_port first_gpu gpu_csv offset gpu server_dir server_log candidate
SERVER_PIDS=()
for ((replica=0; replica<REPLICAS; replica++)); do
port=$((BASE_PORT + replica * PORT_STRIDE))
master_port=$((MASTER_PORT_BASE + replica * PORT_STRIDE))
scheduler_port=$((SCHEDULER_PORT_BASE + replica * PORT_STRIDE))
for candidate in "$port" "$((port+1))" "$master_port" "$scheduler_port"; do
port_is_open "$candidate" && die "port busy: $candidate"
done
first_gpu=$((replica * TP_SIZE)); gpu_csv=""
for ((offset=0; offset<TP_SIZE; offset++)); do
gpu=$((first_gpu + offset)); [[ -z "$gpu_csv" ]] && gpu_csv="$gpu" || gpu_csv+=",$gpu"
done
server_dir="$CURRENT_PROFILE_DIR/server_attempt_${attempt}_${replica}_port${port}"
mkdir -p "$server_dir/outputs"
server_log="$server_dir/server.log"
printf "%s\n" "$gpu_csv" >"$server_dir/cuda_visible_devices.txt"
log "starting cache+lora TP$TP_SIZE profile=$tag attempt=$attempt replica=$replica GPUs=$gpu_csv port=$port RDT=$rdt MC=$mc"
CUDA_VISIBLE_DEVICES="$gpu_csv" \
PATH="$MEDIA_BIN_DIR:$PATH" \
PYTHONPATH="$CACHE_PATCH${PYTHONPATH:+:$PYTHONPATH}" \
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
PYTHONUNBUFFERED=1 TOKENIZERS_PARALLELISM=false SGLANG_USE_RUNAI_MODEL_STREAMER=false \
SGLANG_CACHE_DIT_ENABLED=true SGLANG_CACHE_DIT_FN="$CACHE_FN" SGLANG_CACHE_DIT_BN="$CACHE_BN" \
SGLANG_CACHE_DIT_RDT="$rdt" SGLANG_CACHE_DIT_MC="$mc" SGLANG_CACHE_DIT_WARMUP="$CACHE_WARMUP" \
SGLANG_CACHE_DIT_TAYLORSEER=false SGLANG_CACHE_DIT_SCM_PRESET=none SGLANG_CACHE_DIT_SCM_POLICY=dynamic \
lora_path="" setsid "$PYTHON" "$CACHE_PATCH/../scripts-nonexistent" 2>/dev/null; true
CUDA_VISIBLE_DEVICES="$gpu_csv" \
PATH="$MEDIA_BIN_DIR:$PATH" \
PYTHONPATH="$CACHE_PATCH${PYTHONPATH:+:$PYTHONPATH}" \
PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True \
PYTHONUNBUFFERED=1 TOKENIZERS_PARALLELISM=false SGLANG_USE_RUNAI_MODEL_STREAMER=false \
SGLANG_CACHE_DIT_ENABLED=true SGLANG_CACHE_DIT_FN="$CACHE_FN" SGLANG_CACHE_DIT_BN="$CACHE_BN" \
SGLANG_CACHE_DIT_RDT="$rdt" SGLANG_CACHE_DIT_MC="$mc" SGLANG_CACHE_DIT_WARMUP="$CACHE_WARMUP" \
SGLANG_CACHE_DIT_TAYLORSEER=false SGLANG_CACHE_DIT_SCM_PRESET=none SGLANG_CACHE_DIT_SCM_POLICY=dynamic \
setsid "$SGLANG_BIN" serve --model-path "$MODEL" --model-variant Ref2VA --backend sglang \
--performance-mode speed --num-gpus "$TP_SIZE" --tp-size "$TP_SIZE" --ulysses-degree 1 \
--use-fsdp-inference false --enable-torch-compile false --batching-max-size 1 --batching-delay-ms 0 \
--lora-path "$LORA_PATH" --lora-weight-name "$LORA_WEIGHT_NAME" --lora-nickname "$LORA_NICKNAME" \
--lora-scale "$LORA_SCALE" --lora-merge-mode "$LORA_MERGE_MODE" \
--host 0.0.0.0 --port "$port" --master-port "$master_port" --scheduler-port "$scheduler_port" \
--output-path "$server_dir/outputs" >"$server_log" 2>&1 &
SERVER_PIDS+=("$!")
done
for ((replica=0; replica<REPLICAS; replica++)); do
port=$((BASE_PORT + replica * PORT_STRIDE))
server_log="$CURRENT_PROFILE_DIR/server_attempt_${attempt}_${replica}_port${port}/server.log"
wait_healthy "$port" "${SERVER_PIDS[$replica]}" "$server_log" || return 1
log "profile=$tag attempt=$attempt replica=$replica healthy port=$port"
done
}
case_successful() {
local case_number=$1 batch replica path
batch=$((case_number <= 4 ? 1 : 2))
replica=$(((case_number - 1) % REPLICAS))
path="$CURRENT_PROFILE_DIR/batch_$(printf "%02d" "$batch")/client_${replica}_case$(printf "%02d" "$case_number")_port$((BASE_PORT + replica * PORT_STRIDE))/result.json"
"$PYTHON" - "$path" <<"PY"
import json, pathlib, sys
p = pathlib.Path(sys.argv[1])
try:
row = json.loads(p.read_text())
video = pathlib.Path(str(row.get("file_path") or ""))
ok = row.get("success") is True and video.is_file() and video.stat().st_size > 0
except Exception:
ok = False
raise SystemExit(0 if ok else 1)
PY
}
run_batch_pending() {
local tag=$1 batch=$2 first_case=$3 warmup_steps=$4
local replica port case_number client_dir pid launched=0 failed=0
CLIENT_PIDS=()
for ((replica=0; replica<REPLICAS; replica++)); do
case_number=$((first_case + replica))
if case_successful "$case_number"; then
log "profile=$tag batch=$batch case=$case_number already done; skip"
continue
fi
port=$((BASE_PORT + replica * PORT_STRIDE))
client_dir="$CURRENT_PROFILE_DIR/batch_$(printf "%02d" "$batch")/client_${replica}_case$(printf "%02d" "$case_number")_port${port}"
mkdir -p "$client_dir"
"$PYTHON" "$CLIENT_SCRIPT" run --host "$HOST" --port "$port" --replica-index "$replica" \
--case-number "$case_number" --records "$RECORDS" --assets-root "$ASSETS_ROOT" \
--model "$MODEL" --num-inference-steps "$NUM_INFERENCE_STEPS" --warmup-steps "$warmup_steps" \
--short-edge "$SHORT_EDGE" --aspect-ratio "$ASPECT_RATIO" --duration-seconds "$DURATION_SECONDS" \
--flow-shift "$FLOW_SHIFT" --audio-flow-shift "$AUDIO_FLOW_SHIFT" \
--output "$client_dir/result.json" >"$client_dir/client.log" 2>&1 &
pid=$!
CLIENT_PIDS+=("$pid")
launched=$((launched + 1))
log "profile=$tag batch=$batch started case=$case_number replica=$replica port=$port"
done
((launched > 0)) || return 0
for pid in "${CLIENT_PIDS[@]}"; do wait "$pid" || failed=1; done
CLIENT_PIDS=()
((failed == 0)) || { log "profile=$tag batch=$batch had failed requests"; return 1; }
}
completed_count() {
local case_number count=0
for case_number in {1..8}; do case_successful "$case_number" && count=$((count + 1)); done
printf "%s\n" "$count"
}
run_profile() {
local tag=$1 rdt=$2 mc=$3 objective=$4 attempt done_count pass_failed
CURRENT_PROFILE_DIR="$RESULT_ROOT/$tag"
mkdir -p "$CURRENT_PROFILE_DIR"
[[ -f "$CURRENT_PROFILE_DIR/PROFILE_COMPLETED" ]] && { log "profile=$tag already done"; return 0; }
{
printf "profile=%s\nobjective=%s\n" "$tag" "$objective"
printf "rdt=%s\nmc=%s\nfn=%s\nbn=%s\ncache_warmup=%s\nlora_merge_mode=%s\ntp_size=%s\nreplicas=%s\n" \
"$rdt" "$mc" "$CACHE_FN" "$CACHE_BN" "$CACHE_WARMUP" "$LORA_MERGE_MODE" "$TP_SIZE" "$REPLICAS"
printf "num_inference_steps=%s\nshort_edge=%s\naspect_ratio=%s\nduration_seconds=%s\n" \
"$NUM_INFERENCE_STEPS" "$SHORT_EDGE" "$ASPECT_RATIO" "$DURATION_SECONDS"
} >"$CURRENT_PROFILE_DIR/run_config.env"
done_count=$(completed_count)
log "===== profile=$tag RDT=$rdt MC=$mc existing_success=$done_count/8 ====="
for ((attempt=1; attempt<=MAX_ATTEMPTS && done_count<8; attempt++)); do
start_servers "$tag" "$rdt" "$mc" "$attempt" || { stop_servers; sleep 10; continue; }
pass_failed=0
launched=0
# REPLICAS covers subset of the 8 cases per server pass; loop 4 chunks.
for ((slice=0; slice<8; slice+=REPLICAS)); do
for ((r=0; r<REPLICAS; r++)); do
case_number=$((slice + r + 1))
if case_successful "$case_number"; then
log "profile=$tag case=$case_number already done; skip"
continue
fi
batch=$(( case_number <= 4 ? 1 : 2 ))
port=$((BASE_PORT + r * PORT_STRIDE))
client_dir="$CURRENT_PROFILE_DIR/batch_$(printf "%02d" "$batch")/client_${r}_case$(printf "%02d" "$case_number")_port${port}"
mkdir -p "$client_dir"
"$PYTHON" "$CLIENT_SCRIPT" run --host "$HOST" --port "$port" --replica-index "$r" \
--case-number "$case_number" --records "$RECORDS" --assets-root "$ASSETS_ROOT" \
--model "$MODEL" --num-inference-steps "$NUM_INFERENCE_STEPS" --warmup-steps 0 \
--short-edge "$SHORT_EDGE" --aspect-ratio "$ASPECT_RATIO" --duration-seconds "$DURATION_SECONDS" \
--flow-shift "$FLOW_SHIFT" --audio-flow-shift "$AUDIO_FLOW_SHIFT" \
--output "$client_dir/result.json" >"$client_dir/client.log" 2>&1 &
pid=$!
CLIENT_PIDS+=("$pid")
launched=$((launched + 1))
log "profile=$tag started case=$case_number replica=$r port=$port"
done
for pid in "${CLIENT_PIDS[@]}"; do wait "$pid" || pass_failed=1; done
CLIENT_PIDS=()
done
stop_servers
done_count=$(completed_count)
log "profile=$tag attempt=$attempt success=$done_count/8 pass_failed=$pass_failed"
done
((done_count == 8)) || die "profile=$tag incomplete: $done_count/8"
"${CLIENT_SCRIPT%%bench.py}custom_summary" 2>/dev/null || true
printf "%s\n" "$(date -Is)" >"$CURRENT_PROFILE_DIR/PROFILE_COMPLETED"
log "profile=$tag complete 8/8"
}
"$PYTHON" "$CLIENT_SCRIPT" summarize --help >/dev/null 2>&1 || true
for profile in "${PROFILES[@]}"; do
read -r tag rdt mc objective <<< "$profile"
run_profile "$tag" "$rdt" "$mc" "$objective"
done
trap - EXIT INT TERM
log "CACHE+LORA TP4x2 ALL PROFILES COMPLETE: $RESULT_ROOT"

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{
"sparsity": 0.75,
"n_k": 4,
"n_q": 4,
"skip_first_steps": 10,
"skip_first_layers": 0,
"min_seq_len": 4096
}

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@ -1,43 +0,0 @@
[2026-08-22 17:50:30] ===== TP=4 replicas=2 total_requests=64 =====
[2026-08-22 17:50:30] starting variant=FL2VA replica=0 GPUs=0,1,2,3 port=30010
[2026-08-22 17:50:30] starting variant=FL2VA replica=1 GPUs=4,5,6,7 port=30020
[2026-08-22 17:51:51] variant=FL2VA replica=0 healthy port=30010
[2026-08-22 17:51:51] variant=FL2VA replica=1 healthy port=30020
[2026-08-22 17:51:51] started task=fl2va client=0 port=30010 requests=16
[2026-08-22 17:51:51] started task=fl2va client=1 port=30020 requests=16
[2026-08-22 18:47:07] gracefully stopping 2 server(s)
[2026-08-22 18:47:23] starting variant=Ref2VA replica=0 GPUs=0,1,2,3 port=30010
[2026-08-22 18:47:23] starting variant=Ref2VA replica=1 GPUs=4,5,6,7 port=30020
[2026-08-22 18:48:39] variant=Ref2VA replica=0 healthy port=30010
[2026-08-22 18:48:39] variant=Ref2VA replica=1 healthy port=30020
[2026-08-22 18:48:39] started task=ref2va client=0 port=30010 requests=16
[2026-08-22 18:48:39] started task=ref2va client=1 port=30020 requests=16
[2026-08-22 20:23:14] gracefully stopping 2 server(s)
[2026-08-22 20:23:29] ===== TP=2 replicas=4 total_requests=64 =====
[2026-08-22 20:23:29] starting variant=FL2VA replica=0 GPUs=0,1 port=30010
[2026-08-22 20:23:29] starting variant=FL2VA replica=1 GPUs=2,3 port=30020
[2026-08-22 20:23:29] starting variant=FL2VA replica=2 GPUs=4,5 port=30030
[2026-08-22 20:23:29] starting variant=FL2VA replica=3 GPUs=6,7 port=30040
[2026-08-22 20:24:40] variant=FL2VA replica=0 healthy port=30010
[2026-08-22 20:24:40] variant=FL2VA replica=1 healthy port=30020
[2026-08-22 20:24:40] variant=FL2VA replica=2 healthy port=30030
[2026-08-22 20:24:40] variant=FL2VA replica=3 healthy port=30040
[2026-08-22 20:24:40] started task=fl2va client=0 port=30010 requests=8
[2026-08-22 20:24:40] started task=fl2va client=1 port=30020 requests=8
[2026-08-22 20:24:40] started task=fl2va client=2 port=30030 requests=8
[2026-08-22 20:24:40] started task=fl2va client=3 port=30040 requests=8
[2026-08-22 21:14:50] gracefully stopping 4 server(s)
[2026-08-22 21:15:04] starting variant=Ref2VA replica=0 GPUs=0,1 port=30010
[2026-08-22 21:15:04] starting variant=Ref2VA replica=1 GPUs=2,3 port=30020
[2026-08-22 21:15:04] starting variant=Ref2VA replica=2 GPUs=4,5 port=30030
[2026-08-22 21:15:05] starting variant=Ref2VA replica=3 GPUs=6,7 port=30040
[2026-08-22 21:16:15] variant=Ref2VA replica=0 healthy port=30010
[2026-08-22 21:16:20] variant=Ref2VA replica=1 healthy port=30020
[2026-08-22 21:16:20] variant=Ref2VA replica=2 healthy port=30030
[2026-08-22 21:16:20] variant=Ref2VA replica=3 healthy port=30040
[2026-08-22 21:16:20] started task=ref2va client=0 port=30010 requests=8
[2026-08-22 21:16:20] started task=ref2va client=1 port=30020 requests=8
[2026-08-22 21:16:20] started task=ref2va client=2 port=30030 requests=8
[2026-08-22 21:16:20] started task=ref2va client=3 port=30040 requests=8
[2026-08-22 22:43:27] gracefully stopping 4 server(s)
[2026-08-22 22:43:41] mixed matrix complete: /data/wxy/results/minimax_h3_mixed_matrix/balanced-tp4-tp2-20steps-5s-20260822-175030/summary.tsv

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@ -1,5 +0,0 @@
tp replicas task expected recorded completed failed machine_qps latency_mean_s latency_p95_s machine_wall_s
4 2 fl2va 32 32 32 0 0.00972319 205.598608 475.018328 3291.102
4 2 ref2va 32 32 32 0 0.00569328 351.169889 672.717822 5620.662
2 4 fl2va 32 32 32 0 0.01076938 370.914053 875.619170 2971.387
2 4 ref2va 32 32 32 0 0.00623391 641.243720 1251.955540 5133.219
1 tp replicas task expected recorded completed failed machine_qps latency_mean_s latency_p95_s machine_wall_s
2 4 2 fl2va 32 32 32 0 0.00972319 205.598608 475.018328 3291.102
3 4 2 ref2va 32 32 32 0 0.00569328 351.169889 672.717822 5620.662
4 2 4 fl2va 32 32 32 0 0.01076938 370.914053 875.619170 2971.387
5 2 4 ref2va 32 32 32 0 0.00623391 641.243720 1251.955540 5133.219

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@ -1,10 +0,0 @@
task=fl2va replica=0/4 requests=8 port=30010
warmup 1/1: success=True latency=41.17s error=None
request 1/8 id=fl2va-r480-p00 success=True latency=79.46s error=None
request 2/8 id=fl2va-r720-p00 success=True latency=247.15s error=None
request 3/8 id=fl2va-r768-p00 success=True latency=282.02s error=None
request 4/8 id=fl2va-r1080-p00 success=True latency=876.53s error=None
request 5/8 id=fl2va-r480-p04 success=True latency=79.47s error=None
request 6/8 id=fl2va-r720-p04 success=True latency=247.13s error=None
request 7/8 id=fl2va-r768-p04 success=True latency=281.19s error=None
request 8/8 id=fl2va-r1080-p04 success=True latency=875.53s error=None

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@ -1,8 +0,0 @@
{"request_id": "fl2va-r480-p00", "task": "fl2va", "short_edge": 480, "prompt_index": 0, "prompt": "a person swimming in ocean", "seed": 1101, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787401521.531578, "success": true, "error": null, "video_id": "bc3d0b65-facf-46b8-8214-1385d7e99448", "inference_time_s": 77.74135193694383, "peak_memory_mb": 67634.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/balanced-tp4-tp2-20steps-5s-20260822-175030/tp2_replicas4/fl2va/server_0_port30010/outputs/bc3d0b65-facf-46b8-8214-1385d7e99448.mp4", "latency_s": 79.46315487590618, "finished_at_epoch": 1787401600.994735}
{"request_id": "fl2va-r720-p00", "task": "fl2va", "short_edge": 720, "prompt_index": 0, "prompt": "a person swimming in ocean", "seed": 1101, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787401600.9949694, "success": true, "error": null, "video_id": "7aa257e7-821a-4bbb-b68a-dac98546efdb", "inference_time_s": 243.865134291118, "peak_memory_mb": 71460.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/balanced-tp4-tp2-20steps-5s-20260822-175030/tp2_replicas4/fl2va/server_0_port30010/outputs/7aa257e7-821a-4bbb-b68a-dac98546efdb.mp4", "latency_s": 247.14566481718794, "finished_at_epoch": 1787401848.1406357}
{"request_id": "fl2va-r768-p00", "task": "fl2va", "short_edge": 768, "prompt_index": 0, "prompt": "a person swimming in ocean", "seed": 1101, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787401848.1408603, "success": true, "error": null, "video_id": "51202198-5034-4b10-ac1b-bba14c640a17", "inference_time_s": 278.39865747792646, "peak_memory_mb": 72084.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/balanced-tp4-tp2-20steps-5s-20260822-175030/tp2_replicas4/fl2va/server_0_port30010/outputs/51202198-5034-4b10-ac1b-bba14c640a17.mp4", "latency_s": 282.0194108937867, "finished_at_epoch": 1787402130.1602726}
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"latency_mean_s": 302.2650241892261,
"latency_p95_s": 302.28776739907914
},
"1080": {
"requests": 8,
"completed": 8,
"failed": 0,
"latency_mean_s": 672.7605903649237,
"latency_p95_s": 673.3390199276153
}
}
}

View File

@ -1,39 +0,0 @@
[2026-08-22 10:08:44] ===== TP=8 replicas=1 total_requests=64 =====
[2026-08-22 10:08:44] starting variant=FL2VA replica=0 GPUs=0,1,2,3,4,5,6,7 port=30010
[2026-08-22 10:10:09] variant=FL2VA replica=0 healthy port=30010
[2026-08-22 10:10:09] started task=fl2va client=0 port=30010 requests=32
[2026-08-22 11:20:13] gracefully stopping 1 server(s)
[2026-08-22 11:20:27] starting variant=Ref2VA replica=0 GPUs=0,1,2,3,4,5,6,7 port=30010
[2026-08-22 11:21:47] variant=Ref2VA replica=0 healthy port=30010
[2026-08-22 11:21:47] started task=ref2va client=0 port=30010 requests=32
[2026-08-22 13:18:54] gracefully stopping 1 server(s)
[2026-08-22 13:19:08] ===== TP=4 replicas=2 total_requests=64 =====
[2026-08-22 13:19:08] starting variant=FL2VA replica=0 GPUs=0,1,2,3 port=30010
[2026-08-22 13:19:08] starting variant=FL2VA replica=1 GPUs=4,5,6,7 port=30020
[2026-08-22 13:20:24] variant=FL2VA replica=0 healthy port=30010
[2026-08-22 13:20:29] variant=FL2VA replica=1 healthy port=30020
[2026-08-22 13:20:29] started task=fl2va client=0 port=30010 requests=16
[2026-08-22 13:20:29] started task=fl2va client=1 port=30020 requests=16
[2026-08-22 14:43:10] gracefully stopping 2 server(s)
[2026-08-22 14:43:24] starting variant=Ref2VA replica=0 GPUs=0,1,2,3 port=30010
[2026-08-22 14:43:24] starting variant=Ref2VA replica=1 GPUs=4,5,6,7 port=30020
[2026-08-22 14:44:40] variant=Ref2VA replica=0 healthy port=30010
[2026-08-22 14:44:40] variant=Ref2VA replica=1 healthy port=30020
[2026-08-22 14:44:40] started task=ref2va client=0 port=30010 requests=16
[2026-08-22 14:44:40] started task=ref2va client=1 port=30020 requests=16
[2026-08-22 16:52:51] gracefully stopping 2 server(s)
[2026-08-22 16:53:06] ===== TP=2 replicas=4 total_requests=64 =====
[2026-08-22 16:53:06] starting variant=FL2VA replica=0 GPUs=0,1 port=30010
[2026-08-22 16:53:06] starting variant=FL2VA replica=1 GPUs=2,3 port=30020
[2026-08-22 16:53:06] starting variant=FL2VA replica=2 GPUs=4,5 port=30030
[2026-08-22 16:53:06] starting variant=FL2VA replica=3 GPUs=6,7 port=30040
[2026-08-22 16:54:47] variant=FL2VA replica=0 healthy port=30010
[2026-08-22 16:54:52] variant=FL2VA replica=1 healthy port=30020
[2026-08-22 16:54:52] variant=FL2VA replica=2 healthy port=30030
[2026-08-22 16:54:52] variant=FL2VA replica=3 healthy port=30040
[2026-08-22 16:54:52] started task=fl2va client=0 port=30010 requests=8
[2026-08-22 16:54:52] started task=fl2va client=1 port=30020 requests=8
[2026-08-22 16:54:52] started task=fl2va client=2 port=30030 requests=8
[2026-08-22 16:54:52] started task=fl2va client=3 port=30040 requests=8
[2026-08-22 17:46:47] gracefully stopping 4 server(s)
[2026-08-22 17:48:47] server pid=1730750 did not exit after SIGINT; terminating process group

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@ -1,5 +0,0 @@
tp replicas task expected recorded completed failed machine_qps latency_mean_s latency_p95_s machine_wall_s
8 1 fl2va 32 32 32 0 0.00764554 130.794822 290.225216 4185.444
8 1 ref2va 32 32 32 0 0.00457956 218.361235 402.082677 6987.570
4 2 fl2va 32 32 32 0 0.00648192 205.302947 474.939789 4936.807
4 2 ref2va 32 32 32 0 0.00419241 351.122060 672.720443 7632.846
1 tp replicas task expected recorded completed failed machine_qps latency_mean_s latency_p95_s machine_wall_s
2 8 1 fl2va 32 32 32 0 0.00764554 130.794822 290.225216 4185.444
3 8 1 ref2va 32 32 32 0 0.00457956 218.361235 402.082677 6987.570
4 4 2 fl2va 32 32 32 0 0.00648192 205.302947 474.939789 4936.807
5 4 2 ref2va 32 32 32 0 0.00419241 351.122060 672.720443 7632.846

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@ -1,10 +0,0 @@
task=fl2va replica=0/4 requests=8 port=30010
warmup 1/1: success=True latency=40.54s error=None
request 1/8 id=fl2va-r480-p00 success=True latency=79.43s error=None
request 2/8 id=fl2va-r480-p01 success=True latency=79.43s error=None
request 3/8 id=fl2va-r480-p02 success=True latency=79.40s error=None
request 4/8 id=fl2va-r480-p03 success=True latency=79.43s error=None
request 5/8 id=fl2va-r480-p04 success=True latency=79.44s error=None
request 6/8 id=fl2va-r480-p05 success=True latency=80.43s error=None
request 7/8 id=fl2va-r480-p06 success=True latency=79.43s error=None
request 8/8 id=fl2va-r480-p07 success=True latency=80.44s error=None

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@ -1,8 +0,0 @@
{"request_id": "fl2va-r480-p00", "task": "fl2va", "short_edge": 480, "prompt_index": 0, "prompt": "a person swimming in ocean", "seed": 1101, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787388933.0928838, "success": true, "error": null, "video_id": "db5f80e7-cd2d-4dac-a960-00946f26d6c3", "inference_time_s": 77.56454470497556, "peak_memory_mb": 67634.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_0_port30010/outputs/db5f80e7-cd2d-4dac-a960-00946f26d6c3.mp4", "latency_s": 79.4321119680535, "finished_at_epoch": 1787389012.5249982}
{"request_id": "fl2va-r480-p01", "task": "fl2va", "short_edge": 480, "prompt_index": 1, "prompt": "a person giving a presentation to a room full of colleagues", "seed": 1102, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389012.5252483, "success": true, "error": null, "video_id": "6700f3e7-ca8a-45db-8bc7-6f56761b1212", "inference_time_s": 77.6838906579651, "peak_memory_mb": 67620.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_0_port30010/outputs/6700f3e7-ca8a-45db-8bc7-6f56761b1212.mp4", "latency_s": 79.42871477105655, "finished_at_epoch": 1787389091.9539642}
{"request_id": "fl2va-r480-p02", "task": "fl2va", "short_edge": 480, "prompt_index": 2, "prompt": "a person washing the dishes", "seed": 1103, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389091.9540782, "success": true, "error": null, "video_id": "bb798ca9-238f-422b-9d43-7d6e09466f99", "inference_time_s": 77.68046283395961, "peak_memory_mb": 67620.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_0_port30010/outputs/bb798ca9-238f-422b-9d43-7d6e09466f99.mp4", "latency_s": 79.39657493703999, "finished_at_epoch": 1787389171.3506544}
{"request_id": "fl2va-r480-p03", "task": "fl2va", "short_edge": 480, "prompt_index": 3, "prompt": "a person eating a burger", "seed": 1104, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389171.3508759, "success": true, "error": null, "video_id": "a3d8cae5-464d-4ee5-b7b1-fb84e33a1d8b", "inference_time_s": 77.56235035299323, "peak_memory_mb": 67620.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_0_port30010/outputs/a3d8cae5-464d-4ee5-b7b1-fb84e33a1d8b.mp4", "latency_s": 79.43277477496304, "finished_at_epoch": 1787389250.7836528}
{"request_id": "fl2va-r480-p04", "task": "fl2va", "short_edge": 480, "prompt_index": 4, "prompt": "a person walking in the snowstorm", "seed": 1105, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389250.7838778, "success": true, "error": null, "video_id": "4ed04a47-cc2d-4c2b-b499-faf98c060027", "inference_time_s": 77.63598313112743, "peak_memory_mb": 67620.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_0_port30010/outputs/4ed04a47-cc2d-4c2b-b499-faf98c060027.mp4", "latency_s": 79.43960106209852, "finished_at_epoch": 1787389330.2234812}
{"request_id": "fl2va-r480-p05", "task": "fl2va", "short_edge": 480, "prompt_index": 5, "prompt": "a person drinking coffee in a cafe", "seed": 1106, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389330.2237022, "success": true, "error": null, "video_id": "3251e3fa-ea2c-498c-9041-8936262c1e1e", "inference_time_s": 77.63496194197796, "peak_memory_mb": 67620.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_0_port30010/outputs/3251e3fa-ea2c-498c-9041-8936262c1e1e.mp4", "latency_s": 80.4291545751039, "finished_at_epoch": 1787389410.652859}
{"request_id": "fl2va-r480-p06", "task": "fl2va", "short_edge": 480, "prompt_index": 6, "prompt": "a person playing guitar", "seed": 1107, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389410.6531034, "success": true, "error": null, "video_id": "add6266c-a093-4299-b1b4-27c1d12a6b7f", "inference_time_s": 77.56414216081612, "peak_memory_mb": 67620.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_0_port30010/outputs/add6266c-a093-4299-b1b4-27c1d12a6b7f.mp4", "latency_s": 79.43009263789281, "finished_at_epoch": 1787389490.083198}
{"request_id": "fl2va-r480-p07", "task": "fl2va", "short_edge": 480, "prompt_index": 7, "prompt": "a bicycle leaning against a tree", "seed": 1108, "replica_index": 0, "port": 30010, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389490.0834153, "success": true, "error": null, "video_id": "0e1c7945-17ec-4df2-97e8-8307000bdc9c", "inference_time_s": 77.57319070305675, "peak_memory_mb": 67620.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_0_port30010/outputs/0e1c7945-17ec-4df2-97e8-8307000bdc9c.mp4", "latency_s": 80.43677772697993, "finished_at_epoch": 1787389570.5201948}

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@ -1,10 +0,0 @@
task=fl2va replica=1/4 requests=8 port=30020
warmup 1/1: success=True latency=80.66s error=None
request 1/8 id=fl2va-r720-p00 success=True latency=247.12s error=None
request 2/8 id=fl2va-r720-p01 success=True latency=246.05s error=None
request 3/8 id=fl2va-r720-p02 success=True latency=246.07s error=None
request 4/8 id=fl2va-r720-p03 success=True latency=247.09s error=None
request 5/8 id=fl2va-r720-p04 success=True latency=247.11s error=None
request 6/8 id=fl2va-r720-p05 success=True latency=247.10s error=None
request 7/8 id=fl2va-r720-p06 success=True latency=246.07s error=None
request 8/8 id=fl2va-r720-p07 success=True latency=247.10s error=None

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@ -1,8 +0,0 @@
{"request_id": "fl2va-r720-p00", "task": "fl2va", "short_edge": 720, "prompt_index": 0, "prompt": "a person swimming in ocean", "seed": 1101, "replica_index": 1, "port": 30020, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787388973.2191465, "success": true, "error": null, "video_id": "ca3efd7a-b2f1-4b20-884c-e04dbf687be9", "inference_time_s": 243.89434165507555, "peak_memory_mb": 71474.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_1_port30020/outputs/ca3efd7a-b2f1-4b20-884c-e04dbf687be9.mp4", "latency_s": 247.12140905903652, "finished_at_epoch": 1787389220.3405576}
{"request_id": "fl2va-r720-p01", "task": "fl2va", "short_edge": 720, "prompt_index": 1, "prompt": "a person giving a presentation to a room full of colleagues", "seed": 1102, "replica_index": 1, "port": 30020, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389220.340822, "success": true, "error": null, "video_id": "5166d3cf-e952-4c9c-bcff-1269e96fe96a", "inference_time_s": 243.90199903794564, "peak_memory_mb": 71460.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_1_port30020/outputs/5166d3cf-e952-4c9c-bcff-1269e96fe96a.mp4", "latency_s": 246.0544263520278, "finished_at_epoch": 1787389466.3952498}
{"request_id": "fl2va-r720-p02", "task": "fl2va", "short_edge": 720, "prompt_index": 2, "prompt": "a person washing the dishes", "seed": 1103, "replica_index": 1, "port": 30020, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389466.395492, "success": true, "error": null, "video_id": "b0d27385-4ef2-4c89-b761-658cfe6d1795", "inference_time_s": 243.8752841730602, "peak_memory_mb": 71460.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_1_port30020/outputs/b0d27385-4ef2-4c89-b761-658cfe6d1795.mp4", "latency_s": 246.0662707220763, "finished_at_epoch": 1787389712.4617646}
{"request_id": "fl2va-r720-p03", "task": "fl2va", "short_edge": 720, "prompt_index": 3, "prompt": "a person eating a burger", "seed": 1104, "replica_index": 1, "port": 30020, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389712.4620128, "success": true, "error": null, "video_id": "0607da74-5b8b-44b7-8bcc-1eadf916a140", "inference_time_s": 243.88277996098623, "peak_memory_mb": 71460.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_1_port30020/outputs/0607da74-5b8b-44b7-8bcc-1eadf916a140.mp4", "latency_s": 247.08703045896254, "finished_at_epoch": 1787389959.5490458}
{"request_id": "fl2va-r720-p04", "task": "fl2va", "short_edge": 720, "prompt_index": 4, "prompt": "a person walking in the snowstorm", "seed": 1105, "replica_index": 1, "port": 30020, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787389959.5498533, "success": true, "error": null, "video_id": "10e114fd-f56a-413c-b978-1a79cc246701", "inference_time_s": 243.91541406488977, "peak_memory_mb": 71460.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_1_port30020/outputs/10e114fd-f56a-413c-b978-1a79cc246701.mp4", "latency_s": 247.10956849483773, "finished_at_epoch": 1787390206.6594236}
{"request_id": "fl2va-r720-p05", "task": "fl2va", "short_edge": 720, "prompt_index": 5, "prompt": "a person drinking coffee in a cafe", "seed": 1106, "replica_index": 1, "port": 30020, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787390206.65972, "success": true, "error": null, "video_id": "54526e23-0928-4120-bd2a-8b4a02280c57", "inference_time_s": 243.93079768191092, "peak_memory_mb": 71460.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_1_port30020/outputs/54526e23-0928-4120-bd2a-8b4a02280c57.mp4", "latency_s": 247.09676117403433, "finished_at_epoch": 1787390453.7564828}
{"request_id": "fl2va-r720-p06", "task": "fl2va", "short_edge": 720, "prompt_index": 6, "prompt": "a person playing guitar", "seed": 1107, "replica_index": 1, "port": 30020, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787390453.7567298, "success": true, "error": null, "video_id": "aa4287cf-5eb3-4d58-a59e-d1c978bfc0d2", "inference_time_s": 243.91315856878646, "peak_memory_mb": 71460.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_1_port30020/outputs/aa4287cf-5eb3-4d58-a59e-d1c978bfc0d2.mp4", "latency_s": 246.0730729731731, "finished_at_epoch": 1787390699.8298047}
{"request_id": "fl2va-r720-p07", "task": "fl2va", "short_edge": 720, "prompt_index": 7, "prompt": "a bicycle leaning against a tree", "seed": 1108, "replica_index": 1, "port": 30020, "num_inference_steps": 20, "duration_seconds": 5.0, "aspect_ratio": "16:9", "started_at_epoch": 1787390699.8300402, "success": true, "error": null, "video_id": "6103b51a-69ee-4a88-b3ff-56ec54d8d8d2", "inference_time_s": 243.9139737098012, "peak_memory_mb": 71460.0, "file_path": "/data/wxy/results/minimax_h3_mixed_matrix/mixed64-20steps-5s-20260822-100844/tp2_replicas4/fl2va/server_1_port30020/outputs/6103b51a-69ee-4a88-b3ff-56ec54d8d8d2.mp4", "latency_s": 247.10418270900846, "finished_at_epoch": 1787390946.9342246}

View File

@ -1,10 +0,0 @@
task=fl2va replica=2/4 requests=8 port=30030
warmup 1/1: success=True latency=87.16s error=None
request 1/8 id=fl2va-r768-p00 success=True latency=281.25s error=None
request 2/8 id=fl2va-r768-p01 success=True latency=282.20s error=None
request 3/8 id=fl2va-r768-p02 success=True latency=281.23s error=None
request 4/8 id=fl2va-r768-p03 success=True latency=281.24s error=None
request 5/8 id=fl2va-r768-p04 success=True latency=281.24s error=None
request 6/8 id=fl2va-r768-p05 success=True latency=281.31s error=None
request 7/8 id=fl2va-r768-p06 success=True latency=281.21s error=None
request 8/8 id=fl2va-r768-p07 success=True latency=282.25s error=None

View File

@ -1,8 +0,0 @@
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