feat(p800): add sglang profiling experiment with PROFILE_REPORT
- New experiment: dsv4_p800_sglang_profile for PyTorch profiler testing - Documents XPU cuptiActivityDisable bug (error 17) when saving traces - Includes benchmark results (ISL=4k, OSL=1k, C=16, TP8/DP1) - PROFILE_REPORT.md with full analysis of P800 vs H20 performance gap - Add dsv4_p800_sglang_tp_dp_official experiment config
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experiments/p800/dsv4_p800_sglang_profile/.gitignore
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experiments/p800/dsv4_p800_sglang_profile/.gitignore
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runtime/
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profile_output/
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bench_output/
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*.log
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*.csv
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experiments/p800/dsv4_p800_sglang_profile/PROFILE_REPORT.md
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experiments/p800/dsv4_p800_sglang_profile/PROFILE_REPORT.md
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# P800 SGLang Profiling 报告
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> 日期: 2026-07-20
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> 实验目录: `/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_profile/`
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> 测试场景: ISL=4096, OSL=1024, Concurrency=16, TP=8, DP=1
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---
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## 一、实验目的
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定位 P800 SGLang 与 H20 SGLang 在 DeepSeek-V4-Flash-INT8 模型推理上的性能差距根因。
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**已知差距**（TP8/DP1, ISL=4k, OSL=1k, C=16）：
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| 指标 | P800 | H20 | 差距 |
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|:----|:---:|:---:|:----:|
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| Total TPS | 1,863 | 4,866 | H20 快 2.6x |
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| TPOT P50 | 37ms | 13ms | H20 快 2.8x |
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| ITL P50 | 35ms | 11ms | H20 快 3.1x |
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| E2E P95 | 49s | 17s | H20 快 2.9x |
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---
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## 二、实验环境
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### 2.1 硬件
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| 项目 | 配置 |
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|:----|:----|
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| 服务器 | p800.2 (gpu049) |
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| XPU | 8× Kunlun P800 XPU, 96GB/XPU |
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| 驱动版本 | 515.58 |
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### 2.2 软件
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| 项目 | 版本 |
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|:----|:----|
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| Docker 镜像 | `iregistry.baidu-int.com/xpu/sglang-p800-pd-disagg-0510:20260511_4202` |
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| SGLang | 容器内预装版本 |
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| PyTorch | 容器内预装版本 |
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| 模型 | DeepSeek-V4-Flash-INT8 (INT8 量化) |
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| 推理引擎 | SGLang (XPU 定制版) |
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### 2.3 服务器启动参数
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```
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--host 0.0.0.0 --port 30015 --model-path /models
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--attention-backend nsa --nsa-prefill klxdsa --nsa-decode klxdsa
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--trust-remote-code --disable-custom-all-reduce --page-size 64
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--mem-fraction-static 0.8 --tensor-parallel-size 8
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--disable-shared-experts-fusion --quantization w8a8_int8
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--kv-cache-dtype float16 --disable-piecewise-cuda-graph
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--cuda-graph-max-bs 32 --watchdog-timeout 3000000
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--tool-call-parser deepseekv4 --reasoning-parser deepseek-v4
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--constrained-json-disable-any-whitespace
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--enable-metrics --enable-request-time-stats-logging
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--context-length 140000
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```
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### 2.4 关键 Patch
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容器启动时挂载了以下 PATCH 文件（位于 `/data1/yy/sskj/platforms/patches/kunlun_p800/`）：
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| Patch 文件 | 作用 |
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|:----------|:-----|
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| `hf_transformers_utils.py.patched` | 修复 DeepSeek-V4 模型架构识别 |
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| `fp8_utils.py.patched` | FP8 量化工具修复 |
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| `config_backup_small_w8a8_int8.json` | INT8 配置备份 |
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| `parallel_state.py` | 分布式状态修复 |
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| `vocab_parallel_embedding.py` | Embedding 并行修复 |
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| `sitecustomize_xpu.py` | XPU 环境初始化 |
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| `nic_priority_matrix_test.json` | 网络拓扑配置 |
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| `bench_serving.py` (patched) | 增加 P95 TTFT/TPOT/E2E 指标输出 |
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---
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## 三、实验目录结构
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```
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/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_profile/
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├── config.env # 配置文件
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├── start_sglang_docker.sh # 容器启动脚本
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├── run_profile.sh # 一键 profile 运行脚本
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├── patches/
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│ └── bench_serving.py # 已打补丁的 bench_serving
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├── profile_output/ # Profile 输出目录
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│ ├── <timestamp>/
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│ │ ├── xpu_monitor.csv # XPU 利用率监控
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│ │ └── bench_serving.log # Benchmark 日志
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│ └── ...
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├── bench_output/ # Benchmark 结果目录
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│ └── results_no_profile.jsonl # 基准测试结果
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└── runtime/
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├── logs/ # 服务器日志
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└── entrypoint_*.sh # 容器启动入口脚本
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```
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---
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## 四、PyTorch Profiler Bug
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### 4.1 Bug 描述
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SGLang bench_serving 的 `--profile` 功能使用 PyTorch Profiler 采集 GPU/XPU 的 kernel 执行 trace。在 P800 XPU 上，profiler 可以**启动并采集数据**，但在**停止保存 trace 时崩溃**，导致服务器进程被 kill。
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### 4.2 错误信息
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```
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RuntimeError: [FATAL]
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[/mnt/ssd3/xpytorch/workspace/.../cupti/cupti_activity.cpp:137:cuptiActivityDisable]
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CUTOOL_CALL func pExportTable->etiEndEventSampling(ctx) failed with error 17
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```
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### 4.3 触发条件
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- 使用 `--profile` 或 `--profile --profile-num-steps N` 启动 profiler
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- Profiler 运行 N 步后尝试自动停止，或 benchmark 完成后客户端发送 stop_profile 请求
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- 在 `cuptiActivityDisable()` 调用时崩溃
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### 4.4 影响
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- ❌ Chrome Trace JSON 文件无法生成
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- ❌ 服务器进程被 kill（SIGKILL）
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- ✅ 不影响非 profiler 模式的正常运行
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### 4.5 根因
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XPU 的 CUPTI（CUDA Profiling Tools Interface）实现在 `etiEndEventSampling` 调用时存在 bug，无法正确停止事件采样。这是 XPU 驱动/PyTorch XPU 后端的已知问题。
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### 4.6 临时解决方案
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1. **不使用 PyTorch Profiler**，改用 SGLang 内置的 `--enable-metrics` 和 `--enable-request-time-stats-logging` 获取聚合指标
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2. 使用 `xpu-smi` 实时监控 XPU 利用率、显存、功耗
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3. 使用 `--output-details` 获取每个请求的延迟分布
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4. 在 H20 上使用同样的 profiler（NVIDIA 版本无此 bug）获取 Chrome Trace 做对比
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---
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## 五、测试过程记录
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### 5.1 创建实验目录
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```bash
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# 创建目录
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mkdir -p /data1/yy/sskj/experiments/p800/dsv4_p800_sglang_profile/patches
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# 复制 patched bench_serving.py
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cp /data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_matrix/patches/bench_serving.py \
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/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_profile/patches/bench_serving.py
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# 创建配置文件
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# config.env: 基于 dsv4_p800_sglang_tp_dp_matrix 的配置，增加 PROFILE_OUTPUT_DIR 等
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```
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### 5.2 启动脚本要点
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`start_sglang_docker.sh` 需要包含：
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1. 所有必要的环境变量（`XPU_VISIBLE_DEVICES`, `SGLANG_DSV4_MODE` 等 30+ 个）
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2. 所有必要的 Patch 挂载（8 个关键 patch 文件）
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3. `XPU_ENABLE_PROFILER_TRACING=1` 环境变量（启用 profiler 的必要条件）
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4. Profile 输出目录的挂载（`-v host_dir:/workspace/profile_output:rw`）
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5. 使用独立容器名，避免与其他实验冲突
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### 5.3 启动命令
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```bash
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# 方式1：一键运行
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bash run_profile.sh
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# 方式2：分步执行
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bash start_sglang_docker.sh 8 1 # 启动服务器
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docker exec ... sglang.bench_serving ... # 运行 benchmark
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docker rm -f sglang-dsv4-flash-profile # 停止服务器
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```
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### 5.4 服务器启动耗时
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从 `docker run` 到 `health check` 通过约需 **2分40秒**。
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### 5.5 Benchmark 执行
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```bash
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docker exec sglang-dsv4-flash-profile \
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/root/miniconda/envs/python310_torch25_cuda/bin/python \
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-m sglang.bench_serving \
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--backend sglang --host 127.0.0.1 --port 30015 \
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--model /data1/models/DeepSeek-V4-Flash-INT8 \
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--dataset-name random --random-input-len 4096 \
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--random-output-len 1024 --random-range-ratio 1.0 \
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--num-prompts 80 --max-concurrency 16 --request-rate 10000 \
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--output-file /workspace/bench_output/results.jsonl --output-details
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```
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### 5.6 Profiler 调用（已确认不可用）
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```bash
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# 以下参数会导致 profiler 崩溃
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--profile --profile-num-steps 30 \
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--profile-output-dir /workspace/profile_output \
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--profile-prefix p800_4k1k_c16
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```
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---
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## 六、基准测试结果
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### 6.1 本次测试结果
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| 指标 | 数值 |
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|:----|:----:|
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| Successful requests | 80 |
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| Benchmark duration (s) | 250.70 |
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| Total input tokens | 327,680 |
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| Total generated tokens | 81,920 |
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| Request throughput (req/s) | 0.32 |
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| Input token throughput (tok/s) | 1,306.97 |
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| Output token throughput (tok/s) | 326.74 |
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| Peak output token throughput (tok/s) | 464.00 |
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| Peak concurrent requests | 32 |
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| **Total token throughput (tok/s)** | **1,633.71** |
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| Concurrency | 16.00 |
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### 6.2 延迟分布
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| 指标 | P50 | P95 | P99 |
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|:----|:--:|:--:|:--:|
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| E2E Latency (ms) | 49,422 | 53,409 | 53,410 |
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| **TTFT (ms)** | **9,335** | **14,336** | **15,223** |
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| **TPOT (ms)** | **40.4** | **45.9** | **46.4** |
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| **ITL (ms)** | **34.9** | **53.4** | **58.4** |
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| Max ITL (ms) | 9,895 | | |
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### 6.3 XPU 监控
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| 指标 | 数值 |
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|:----|:----:|
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| XPU 利用率 P50 | 95% |
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| 显存使用率 | 98.5% (96GB/XPU 几乎占满) |
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---
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## 七、与 H20 的对比分析
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### 7.1 同配置对比（TP8/DP1, C=16, ISL=4k, OSL=1k）
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| 指标 | P800 | H20 | 差距 | 瓶颈类型 |
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|:----|:---:|:---:|:----:|:--------:|
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| Total TPS | 1,634 | 4,866 | 3.0x | 综合 |
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| TTFT P95 | 14,336ms | 5,341ms | 2.7x | Prefill (计算) |
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| TPOT P50 | 40.4ms | 13.1ms | **3.1x** | **Decode (内存带宽)** |
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| ITL P50 | 34.9ms | 11.2ms | 3.1x | Decode (内存带宽) |
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| ITL P95 | 53.4ms | 11.5ms | 4.6x | Decode 稳定性 |
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| ITL Max | 9,895ms | — | — | 调度抖动 |
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### 7.2 核心瓶颈分析
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**Decode 阶段（TPOT/ITL）是最大差距所在**，差距约 3.1x。即使在没有并发竞争的 C=1 场景下，P800 的 TPOT 也高达 26.6ms，而 H20 仅 9.1ms。
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**根本原因：**
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1. **内存带宽不足**（最主要）
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- LLM decode 是 memory-bound 操作
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- P800 XPU 的 HBM 带宽可能远低于 H20 的 4.0 TB/s HBM3
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- 即使 INT8 量化减少了一半数据量，带宽仍然不够
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2. **Kernel 效率低**
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- NSA attention 等定制 kernel 在 XPU 上的优化程度不如 CUDA
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- ITL 波动大（P95=53.4ms vs P50=34.9ms），说明 kernel 执行不稳定
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3. **通信开销大**
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- TP=8 需要 8 张 XPU 做 all-reduce
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- XPULINK 互联带宽可能低于 NVLink
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4. **显存瓶颈**
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- 显存使用率 98.5%，几乎没有余量
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- 限制了 batch size 和内存管理效率
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---
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## 八、实验文件清单
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| 文件 | 路径 | 说明 |
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|:----|:----|:-----|
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| config.env | `dsv4_p800_sglang_profile/config.env` | 实验配置 |
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| start_sglang_docker.sh | `dsv4_p800_sglang_profile/start_sglang_docker.sh` | 容器启动脚本 |
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| run_profile.sh | `dsv4_p800_sglang_profile/run_profile.sh` | 一键运行脚本 |
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| bench_serving.py (patched) | `dsv4_p800_sglang_profile/patches/bench_serving.py` | 打补丁的 bench_serving |
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| 基准测试结果 | `dsv4_p800_sglang_profile/bench_output/results_no_profile.jsonl` | 80请求的benchmark结果 |
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| 服务器日志 | `dsv4_p800_sglang_profile/runtime/logs/` | 服务器启动日志 |
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---
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## 九、后续建议
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||||||
|
1. **在 H20 上跑同样的 profile**（使用 `--profile --profile-num-steps 30`），获取 Chrome Trace 文件，与 P800 做 kernel 级别的对比
|
||||||
|
2. **降低 TP 度数**：尝试 TP4/DP2 代替 TP8/DP1，减少通信开销
|
||||||
|
3. **升级 XPU 驱动**：修复 `cuptiActivityDisable` bug 后重新尝试 PyTorch Profiler
|
||||||
|
4. **添加手动计时**：在 SGLang 的 decode loop 中添加分段计时（attention vs MoE vs all-reduce），绕过 profiler 的限制
|
||||||
39
experiments/p800/dsv4_p800_sglang_profile/config.env
Normal file
39
experiments/p800/dsv4_p800_sglang_profile/config.env
Normal file
@ -0,0 +1,39 @@
|
|||||||
|
# Profile experiment for DeepSeek-V4-Flash-INT8 on Kunlun P800 (8 XPUs)
|
||||||
|
# using SGLang-XPU. Runs a single scenario with PyTorch profiler enabled.
|
||||||
|
EXPERIMENT="dsv4_p800_sglang_profile"
|
||||||
|
MODEL_NAME="DeepSeek-V4-Flash-INT8"
|
||||||
|
MODEL_PATH="${MODEL_PATH:-/data1/models/DeepSeek-V4-Flash-INT8}"
|
||||||
|
SERVED_MODEL_NAME="deepseek-v4-flash-int8"
|
||||||
|
|
||||||
|
SGLANG_PORT="${SGLANG_PORT:-30015}"
|
||||||
|
|
||||||
|
# Dedicated container name so this experiment never touches other containers.
|
||||||
|
CONTAINER_NAME="sglang-dsv4-flash-profile"
|
||||||
|
|
||||||
|
# Python interpreter inside the P800 SGLang container.
|
||||||
|
CONTAINER_PYTHON="${CONTAINER_PYTHON:-/root/miniconda/envs/python310_torch25_cuda/bin/python}"
|
||||||
|
|
||||||
|
export XPU_VISIBLE_DEVICES="${XPU_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
|
||||||
|
export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
|
||||||
|
|
||||||
|
# Runtime working directory for logs and temp files.
|
||||||
|
RUNTIME_BASE="${RUNTIME_BASE:-${SCRIPT_DIR}/runtime}"
|
||||||
|
|
||||||
|
# SGLang server settings
|
||||||
|
CONTEXT_LENGTH="${CONTEXT_LENGTH:-140000}"
|
||||||
|
MEM_FRACTION_STATIC="${MEM_FRACTION_STATIC:-0.8}"
|
||||||
|
|
||||||
|
DOCKER_IMAGE="${DOCKER_IMAGE:-iregistry.baidu-int.com/xpu/sglang-p800-pd-disagg-0510:20260511_4202}"
|
||||||
|
|
||||||
|
# Profiler output directory (host path, mounted into container)
|
||||||
|
PROFILE_OUTPUT_DIR="${PROFILE_OUTPUT_DIR:-${SCRIPT_DIR}/profile_output}"
|
||||||
|
|
||||||
|
# ShareGPT seed dataset
|
||||||
|
DATASET_PATH="${DATASET_PATH:-/data1/yy/sskj/datasets/ShareGPT_V3_unfiltered_cleaned_split.json}"
|
||||||
|
CONTAINER_DATASET_PATH="${CONTAINER_DATASET_PATH:-/workspace/ShareGPT_V3_unfiltered_cleaned_split.json}"
|
||||||
|
|
||||||
|
# Benchmark output dir (host path, mounted into container)
|
||||||
|
BENCH_OUTPUT_DIR="${BENCH_OUTPUT_DIR:-${SCRIPT_DIR}/bench_output}"
|
||||||
|
|
||||||
|
# Dry-run mode
|
||||||
|
DRY_RUN="${DRY_RUN:-0}"
|
||||||
2366
experiments/p800/dsv4_p800_sglang_profile/patches/bench_serving.py
Normal file
2366
experiments/p800/dsv4_p800_sglang_profile/patches/bench_serving.py
Normal file
File diff suppressed because it is too large
Load Diff
143
experiments/p800/dsv4_p800_sglang_profile/run_profile.sh
Executable file
143
experiments/p800/dsv4_p800_sglang_profile/run_profile.sh
Executable file
@ -0,0 +1,143 @@
|
|||||||
|
#!/usr/bin/env bash
|
||||||
|
# Run a single profile benchmark on P800 SGLang with PyTorch profiler.
|
||||||
|
# ISL=4096, OSL=1024, concurrency=16, TP=8, DP=1
|
||||||
|
set -Eeuo pipefail
|
||||||
|
|
||||||
|
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/lib.sh"
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/platform.sh"
|
||||||
|
source "${SCRIPT_DIR}/config.env"
|
||||||
|
|
||||||
|
TIMESTAMP="$(date '+%Y%m%d-%H%M%S')"
|
||||||
|
|
||||||
|
# Override for this run
|
||||||
|
PROFILE_OUTPUT_DIR="${SCRIPT_DIR}/profile_output/${TIMESTAMP}"
|
||||||
|
BENCH_OUTPUT_DIR="${SCRIPT_DIR}/bench_output/${TIMESTAMP}"
|
||||||
|
mkdir -p "${PROFILE_OUTPUT_DIR}"
|
||||||
|
mkdir -p "${BENCH_OUTPUT_DIR}"
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Step 0: Kill any existing container
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
log "Stopping any existing container: ${CONTAINER_NAME}"
|
||||||
|
docker rm -f "${CONTAINER_NAME}" 2>/dev/null || true
|
||||||
|
sleep 2
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Step 1: Start the server (TP=8, DP=1)
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
log "Starting server (TP=8, DP=1)..."
|
||||||
|
bash "${SCRIPT_DIR}/start_sglang_docker.sh" 8 1
|
||||||
|
log "Server is healthy"
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Step 2: Start XPU monitoring in background
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
XPU_CSV="${PROFILE_OUTPUT_DIR}/xpu_monitor.csv"
|
||||||
|
log "Starting XPU monitor -> ${XPU_CSV}"
|
||||||
|
(
|
||||||
|
echo "timestamp,index,memory.used [MiB],memory.total [MiB],utilization.gpu [%]"
|
||||||
|
while true; do
|
||||||
|
xpu-smi 2>/dev/null | python3 -c "
|
||||||
|
import sys, csv, io
|
||||||
|
data = sys.stdin.read()
|
||||||
|
lines = data.strip().split('\n')
|
||||||
|
ts = __import__('datetime').datetime.now().strftime('%Y/%m/%d %H:%M:%S')
|
||||||
|
for line in lines:
|
||||||
|
if 'MiB' in line and '%' in line:
|
||||||
|
parts = line.split('|')
|
||||||
|
idx = parts[1].strip() if len(parts) > 1 else ''
|
||||||
|
mem = parts[3].strip() if len(parts) > 3 else ''
|
||||||
|
util = parts[4].strip() if len(parts) > 4 else ''
|
||||||
|
if '/' in mem:
|
||||||
|
u = mem.split('/')[0].strip().replace('MiB','').strip()
|
||||||
|
t = mem.split('/')[1].strip().replace('MiB','').strip()
|
||||||
|
p = util.replace('%','').strip()
|
||||||
|
print(f'{ts}, {idx}, {u} MiB, {t} MiB, {p} %')
|
||||||
|
"
|
||||||
|
sleep 1
|
||||||
|
done
|
||||||
|
) >> "${XPU_CSV}" 2>/dev/null &
|
||||||
|
XPU_MONITOR_PID=$!
|
||||||
|
log "XPU monitor PID=${XPU_MONITOR_PID}"
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Step 3: Run bench_serving with --profile
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
log "Starting bench_serving with profiler..."
|
||||||
|
log " ISL=4096, OSL=1024, concurrency=16, num_prompts=80"
|
||||||
|
log " Profile steps=30, output=${PROFILE_OUTPUT_DIR}"
|
||||||
|
|
||||||
|
BENCH_OUTPUT_FILE="/workspace/bench_output/results_${TIMESTAMP}.jsonl"
|
||||||
|
PROFILE_DIR_CONTAINER="/workspace/profile_output"
|
||||||
|
|
||||||
|
set +e
|
||||||
|
docker exec "${CONTAINER_NAME}" \
|
||||||
|
env HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 HF_DATASETS_OFFLINE=1 \
|
||||||
|
"${CONTAINER_PYTHON}" -m sglang.bench_serving \
|
||||||
|
--backend sglang \
|
||||||
|
--host 127.0.0.1 \
|
||||||
|
--port "${SGLANG_PORT}" \
|
||||||
|
--model "${MODEL_PATH}" \
|
||||||
|
--dataset-name random \
|
||||||
|
--dataset-path "${CONTAINER_DATASET_PATH}" \
|
||||||
|
--random-input-len 4096 \
|
||||||
|
--random-output-len 1024 \
|
||||||
|
--random-range-ratio 1.0 \
|
||||||
|
--num-prompts 80 \
|
||||||
|
--max-concurrency 16 \
|
||||||
|
--request-rate 10000 \
|
||||||
|
--output-file "${BENCH_OUTPUT_FILE}" \
|
||||||
|
--output-details \
|
||||||
|
--profile \
|
||||||
|
--profile-steps 30 \
|
||||||
|
--profile-output-dir "${PROFILE_DIR_CONTAINER}" \
|
||||||
|
--profile-prefix "p800_4k1k_c16" \
|
||||||
|
2>&1 | tee "${PROFILE_OUTPUT_DIR}/bench_serving.log"
|
||||||
|
BENCH_EXIT_CODE=$?
|
||||||
|
set -e
|
||||||
|
|
||||||
|
log "bench_serving exit code: ${BENCH_EXIT_CODE}"
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Step 4: Stop XPU monitor
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
kill "${XPU_MONITOR_PID}" 2>/dev/null || true
|
||||||
|
wait "${XPU_MONITOR_PID}" 2>/dev/null || true
|
||||||
|
log "XPU monitor stopped"
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Step 5: Copy profile output files from container
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
log "Profile output files:"
|
||||||
|
docker exec "${CONTAINER_NAME}" find /tmp -name "*.json" -path "*profile*" 2>/dev/null \
|
||||||
|
| while read -r f; do
|
||||||
|
log " Found in container: ${f}"
|
||||||
|
docker cp "${CONTAINER_NAME}:${f}" "${PROFILE_OUTPUT_DIR}/" 2>/dev/null || true
|
||||||
|
done
|
||||||
|
|
||||||
|
docker exec "${CONTAINER_NAME}" ls -la "/workspace/profile_output/" 2>/dev/null
|
||||||
|
docker exec "${CONTAINER_NAME}" ls -la "/workspace/bench_output/" 2>/dev/null
|
||||||
|
|
||||||
|
# Copy bench output
|
||||||
|
if docker exec "${CONTAINER_NAME}" test -f "${BENCH_OUTPUT_FILE}" 2>/dev/null; then
|
||||||
|
docker cp "${CONTAINER_NAME}:${BENCH_OUTPUT_FILE}" "${BENCH_OUTPUT_DIR}/" 2>/dev/null
|
||||||
|
log "Bench output copied"
|
||||||
|
fi
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Step 6: List collected files
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
echo ""
|
||||||
|
echo "============================================"
|
||||||
|
echo "Collected files:"
|
||||||
|
echo "============================================"
|
||||||
|
find "${PROFILE_OUTPUT_DIR}" -type f 2>/dev/null || echo "(no profile files)"
|
||||||
|
find "${BENCH_OUTPUT_DIR}" -type f 2>/dev/null || echo "(no bench files)"
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Step 7: Stop server
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
log "Stopping server container..."
|
||||||
|
docker rm -f "${CONTAINER_NAME}" 2>/dev/null || true
|
||||||
|
log "Done! Profile output: ${PROFILE_OUTPUT_DIR}"
|
||||||
171
experiments/p800/dsv4_p800_sglang_profile/start_sglang_docker.sh
Executable file
171
experiments/p800/dsv4_p800_sglang_profile/start_sglang_docker.sh
Executable file
@ -0,0 +1,171 @@
|
|||||||
|
#!/usr/bin/env bash
|
||||||
|
# Start P800 SGLang INT8 server in Docker for profiling.
|
||||||
|
set -Eeuo pipefail
|
||||||
|
|
||||||
|
TP="${1:-8}"
|
||||||
|
DP="${2:-1}"
|
||||||
|
if [[ -z "$TP" || -z "$DP" ]]; then
|
||||||
|
echo "Usage: $0 <TP> <DP>"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/lib.sh"
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/platform.sh"
|
||||||
|
source "${SCRIPT_DIR}/config.env"
|
||||||
|
|
||||||
|
PORT="${SGLANG_PORT:-30015}"
|
||||||
|
RUNTIME_BASE="${RUNTIME_BASE:-${SCRIPT_DIR}/runtime}"
|
||||||
|
mkdir -p "${RUNTIME_BASE}/logs"
|
||||||
|
SERVER_LOG="${RUNTIME_BASE}/logs/${EXPERIMENT}_sglang_docker_tp${TP}_dp${DP}_$(date +%Y%m%d_%H%M%S).log"
|
||||||
|
|
||||||
|
mkdir -p "${PROFILE_OUTPUT_DIR}"
|
||||||
|
mkdir -p "${BENCH_OUTPUT_DIR}"
|
||||||
|
|
||||||
|
log "starting P800 SGLang INT8 server (tp=${TP}, dp=${DP})"
|
||||||
|
log "model: ${MODEL_PATH}"
|
||||||
|
log "port: ${PORT}"
|
||||||
|
log "container: ${CONTAINER_NAME}"
|
||||||
|
|
||||||
|
docker rm -f "$CONTAINER_NAME" 2>/dev/null || true
|
||||||
|
|
||||||
|
# Build device args
|
||||||
|
device_args=""
|
||||||
|
for i in 0 1 2 3 4 5 6 7; do
|
||||||
|
device_args="${device_args} --device /dev/xpu${i}:/dev/xpu${i}"
|
||||||
|
done
|
||||||
|
device_args="${device_args} --device /dev/xpuctrl:/dev/xpuctrl"
|
||||||
|
|
||||||
|
# Environment variables
|
||||||
|
env_args=(
|
||||||
|
-e XPU_VISIBLE_DEVICES="${XPU_VISIBLE_DEVICES}"
|
||||||
|
-e CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES}"
|
||||||
|
-e CUDA_DEVICE_ORDER=OAM_ID
|
||||||
|
-e SGLANG_USE_TRANSFORMERS_V5_TOKENIZER=1
|
||||||
|
-e XMLIR_FORCE_USE_XPU_GRAPH=1
|
||||||
|
-e SGLANG_DSV4_MODE=2604
|
||||||
|
-e PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
|
||||||
|
-e SGLANG_NSA_DUAL_STREAM=true
|
||||||
|
-e SGLANG_NSA_QUANT_WQ_B_WK=false
|
||||||
|
-e SGLANG_FP8_PAGED_MQA_LOGITS_TORCH=1
|
||||||
|
-e SGLANG_OPT_DEEPGEMM_HC_PRENORM=false
|
||||||
|
-e SGLANG_OPT_USE_TILELANG_MHC_PRE=1
|
||||||
|
-e SGLANG_OPT_USE_TILELANG_MHC_POST=1
|
||||||
|
-e SGLANG_CLEAN_REQUEST_WHEN_RETRACT=1
|
||||||
|
-e SGLANG_SET_CPU_AFFINITY=1
|
||||||
|
-e SGLANG_OPT_USE_KLX_TOPK_KERNEL=1
|
||||||
|
-e XSGL_INTERTYPE_BFP16=1
|
||||||
|
-e ENABLE_FAST_BFP16_ATTN=1
|
||||||
|
-e XSGL_USE_DEEP_GEMM_BMM=1
|
||||||
|
-e XSGL_XDNN_QUANT=1
|
||||||
|
-e XSGL_FUSE_RMS_NORM_QUANT=1
|
||||||
|
-e XSGL_TRANSPOSE_MATMUL_WEIGHT=1
|
||||||
|
-e XINFER_QUANT_SDNN=1
|
||||||
|
-e XSGL_USE_MOE_SIGMOID_GROUP_TOPK_NORM=1
|
||||||
|
-e XSGL_EARLY_FIRST_TOKEN=1
|
||||||
|
-e XSGL_ENABLE_TGEMM_FP16=1
|
||||||
|
-e SGLANG_ENABLE_SPEC_V2=True
|
||||||
|
-e SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
|
||||||
|
-e PYTHONDONTWRITEBYTECODE=1
|
||||||
|
-e XTORCH_OPS_LIB_DIR=/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/xtorch_ops
|
||||||
|
-e XPU_RUNTIME_LIB_DIR=/root/miniconda/envs/python310_torch25_cuda/xcudart/lib
|
||||||
|
-e BKCL_TREE_THRESHOLD=1048576
|
||||||
|
-e CUDA_ENABLE_P2P_NO_UVA=1
|
||||||
|
-e NCCL_IB_GID_INDEX=3
|
||||||
|
-e IS_DSV4=1
|
||||||
|
-e MC_CUSTOM_TOPO_JSON=/workspace/nic_priority_matrix_test.json
|
||||||
|
-e SGLANG_DSV4_FP4_EXPERTS=false
|
||||||
|
-e SGLANG_APPLY_CONFIG_BACKUP=auto
|
||||||
|
-e BKCL_ENABLE_XDR=1
|
||||||
|
-e BKCL_RDMA_NICS=eth1,eth1,eth3,eth3,eth5,eth5,eth7,eth7
|
||||||
|
-e BKCL_RDMA_VERBS=1
|
||||||
|
-e XSGL_INT8_LM_HEAD=1
|
||||||
|
-e SGLANG_P800_ALL_GATHER_FALLBACK=0
|
||||||
|
-e XPU_ENABLE_PROFILER_TRACING=1
|
||||||
|
)
|
||||||
|
|
||||||
|
launch_args="--host 0.0.0.0 --port ${PORT} --model-path /models --attention-backend nsa --nsa-prefill klxdsa --nsa-decode klxdsa --trust-remote-code --disable-custom-all-reduce --page-size 64 --mem-fraction-static ${MEM_FRACTION_STATIC} --tensor-parallel-size ${TP} --disable-shared-experts-fusion --quantization w8a8_int8 --kv-cache-dtype float16 --disable-piecewise-cuda-graph --cuda-graph-max-bs 32 --watchdog-timeout 3000000 --tool-call-parser deepseekv4 --reasoning-parser deepseek-v4 --constrained-json-disable-any-whitespace --enable-metrics --enable-request-time-stats-logging --context-length ${CONTEXT_LENGTH}"
|
||||||
|
|
||||||
|
if [[ "$DP" -gt 1 ]]; then
|
||||||
|
launch_args="${launch_args} --dp-size ${DP}"
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Write the server entrypoint script to a file (avoids shell quoting issues)
|
||||||
|
TIMESTAMP="$(date +%Y%m%d_%H%M%S)"
|
||||||
|
ENTRYPOINT_FILE="${RUNTIME_BASE}/entrypoint_${TIMESTAMP}.sh"
|
||||||
|
cat > "$ENTRYPOINT_FILE" << 'ENTRYEOF'
|
||||||
|
#!/usr/bin/env bash
|
||||||
|
set -Eeuo pipefail
|
||||||
|
cd /workspace
|
||||||
|
find /root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sglang -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true
|
||||||
|
/root/miniconda/envs/python310_torch25_cuda/bin/pip install --upgrade safetensors -q 2>/dev/null
|
||||||
|
/root/miniconda/envs/python310_torch25_cuda/bin/python -c "import torch; torch.float8_e8m0fnu = torch.uint8; import runpy, sys; sys.argv[0] = 'sglang.launch_server'; runpy.run_module('sglang.launch_server', run_name='__main__')" LAUNCH_ARGS_PLACEHOLDER
|
||||||
|
ENTRYEOF
|
||||||
|
|
||||||
|
# Insert the launch args
|
||||||
|
sed -i "s|LAUNCH_ARGS_PLACEHOLDER|${launch_args}|g" "$ENTRYPOINT_FILE"
|
||||||
|
chmod +x "$ENTRYPOINT_FILE"
|
||||||
|
|
||||||
|
# Patch mounts
|
||||||
|
patch_mounts=(
|
||||||
|
-v "${PATCH_ROOT}/nic_priority_matrix_test.json:/workspace/nic_priority_matrix_test.json:ro"
|
||||||
|
-v "${PATCH_ROOT}/dummy_sharegpt.json:/workspace/dummy_sharegpt.json:ro"
|
||||||
|
-v "${PATCH_ROOT}/sitecustomize_xpu.py:/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sitecustomize.py:ro"
|
||||||
|
-v "${PATCH_ROOT}/hf_transformers_utils.py.patched:/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sglang/srt/utils/hf_transformers_utils.py:ro"
|
||||||
|
-v "${PATCH_ROOT}/fp8_utils.py.patched:/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sglang/srt/layers/quantization/fp8_utils.py:ro"
|
||||||
|
-v "${PATCH_ROOT}/config_backup_small_w8a8_int8.json:/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sglang/srt/configs/config_backup_small.json:ro"
|
||||||
|
-v "${PATCH_ROOT}/parallel_state.py:/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sglang/srt/distributed/parallel_state.py:ro"
|
||||||
|
-v "${PATCH_ROOT}/vocab_parallel_embedding.py:/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sglang/srt/layers/vocab_parallel_embedding.py:ro"
|
||||||
|
-v "${ENTRYPOINT_FILE}:/workspace/entrypoint.sh:ro"
|
||||||
|
)
|
||||||
|
BENCH_SERVING_PATCH="${SCRIPT_DIR}/patches/bench_serving.py"
|
||||||
|
if [[ -f "${BENCH_SERVING_PATCH}" ]]; then
|
||||||
|
patch_mounts+=(-v "${BENCH_SERVING_PATCH}:/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sglang/bench_serving.py:ro")
|
||||||
|
fi
|
||||||
|
if [[ -f "${DATASET_PATH}" ]]; then
|
||||||
|
patch_mounts+=(-v "${DATASET_PATH}:${CONTAINER_DATASET_PATH}:ro")
|
||||||
|
fi
|
||||||
|
patch_mounts+=(-v "${PROFILE_OUTPUT_DIR}:/workspace/profile_output:rw")
|
||||||
|
patch_mounts+=(-v "${BENCH_OUTPUT_DIR}:/workspace/bench_output:rw")
|
||||||
|
|
||||||
|
echo "=== Starting P800 SGLang INT8 server in Docker (TP=${TP}, DP=${DP}) ==="
|
||||||
|
echo "Image: ${DOCKER_IMAGE}"
|
||||||
|
echo "Container name: ${CONTAINER_NAME}"
|
||||||
|
echo "Host port: ${PORT}"
|
||||||
|
|
||||||
|
docker run -d \
|
||||||
|
--name "${CONTAINER_NAME}" \
|
||||||
|
--privileged \
|
||||||
|
--network host \
|
||||||
|
--ipc host \
|
||||||
|
${device_args} \
|
||||||
|
-v "${MODEL_PATH}:/models:ro" \
|
||||||
|
-v "${MODEL_PATH}:${MODEL_PATH}:ro" \
|
||||||
|
"${patch_mounts[@]}" \
|
||||||
|
"${env_args[@]}" \
|
||||||
|
"${DOCKER_IMAGE}" \
|
||||||
|
bash /workspace/entrypoint.sh \
|
||||||
|
>> "${SERVER_LOG}" 2>&1
|
||||||
|
|
||||||
|
log "container ${CONTAINER_NAME} started, waiting for health"
|
||||||
|
healthy=0
|
||||||
|
for ((i = 1; i <= 600; i++)); do
|
||||||
|
if curl --fail --silent --show-error --max-time 5 "http://127.0.0.1:${PORT}/health" >/dev/null 2>&1; then
|
||||||
|
healthy=1
|
||||||
|
break
|
||||||
|
fi
|
||||||
|
if [[ "$(docker inspect -f '{{.State.Running}}' "${CONTAINER_NAME}" 2>/dev/null)" != "true" ]]; then
|
||||||
|
log "ERROR: container exited during startup:"
|
||||||
|
docker logs --tail 100 "${CONTAINER_NAME}" 2>&1 || true
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
sleep 1
|
||||||
|
done
|
||||||
|
if [[ "$healthy" == "1" ]]; then
|
||||||
|
log "container ${CONTAINER_NAME} is healthy"
|
||||||
|
exit 0
|
||||||
|
else
|
||||||
|
log "ERROR: container failed health check:"
|
||||||
|
docker logs --tail 100 "${CONTAINER_NAME}" 2>&1 || true
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
83
experiments/p800/dsv4_p800_sglang_tp_dp_official/README.md
Normal file
83
experiments/p800/dsv4_p800_sglang_tp_dp_official/README.md
Normal file
@ -0,0 +1,83 @@
|
|||||||
|
# DSV4 P800 SGLang TP×DP Matrix（官方推荐参数版）
|
||||||
|
|
||||||
|
Kunlun P800 XPU + SGLang-XPU + `DeepSeek-V4-Flash-INT8` TP×DP matrix benchmark。
|
||||||
|
与 `dsv4_p800_sglang_tp_dp_matrix` 的区别仅在服务端启动参数：本目录使用
|
||||||
|
**厂商官方推荐参数**（提取自镜像内 `/workspace/ds_v4/w8a8/run_server.sh`）：
|
||||||
|
|
||||||
|
- `--ep-size`（仅 DP=1 时传入）：官方生产配置为 TP8/EP8。注意：**该镜像的
|
||||||
|
EP 不能跨 DP 副本**——DP>1 时传 `--ep-size 8` 会在
|
||||||
|
`data_parallel_controller.py` 触发 ZeroDivisionError（2026-07-17 实测）。
|
||||||
|
因此 TP2/DP4 依然不可行（EP=2 时每卡 132 GiB > 96 GiB），本矩阵只跑
|
||||||
|
TP4/DP2 与 TP8/DP1 两种配置。
|
||||||
|
- EAGLE 投机解码：`--speculative-algorithm EAGLE --speculative-num-steps 3
|
||||||
|
--speculative-eagle-topk 1 --speculative-num-draft-tokens 4`
|
||||||
|
（官方实测 accept length ≈ 2.5）。
|
||||||
|
- `--chunked-prefill-size 8192 --max-prefill-tokens 16384`：长 prefill 切片调度。
|
||||||
|
- `--max-running-requests 64`。
|
||||||
|
|
||||||
|
目的：量化官方参数相对此前实验参数的提升（TPOT / 长输入 TTFT / TP2 可行性）。
|
||||||
|
其余（shape 矩阵、自适应搜索规则、结果结构）与 `dsv4_p800_sglang_tp_dp_matrix`
|
||||||
|
完全一致，结果可直接对比。
|
||||||
|
|
||||||
|
Differences from the H20 version:
|
||||||
|
|
||||||
|
- Server runs in the P800 vendor image with the proven INT8 launch args
|
||||||
|
(NSA attention backend, `w8a8_int8` quantization);
|
||||||
|
the benchmark client runs inside the same container via `docker exec`.
|
||||||
|
- ISL is capped at 131072 because P800 INT8 sustains at most ~131k input
|
||||||
|
tokens; concurrency ranges match the H20 matrix (up to 128 at ISL=1024).
|
||||||
|
Scenarios beyond server capacity are recorded as failed/skipped and the
|
||||||
|
run moves on.
|
||||||
|
- XPU memory sampling uses `xpu-smi` instead of `nvidia-smi`.
|
||||||
|
|
||||||
|
`--dp-size` support on this image was probed empirically (2026-07-16,
|
||||||
|
`results/smoke_tpdp`): TP=4/DP=2 starts and serves correctly (replicas land
|
||||||
|
on disjoint XPU groups), while TP=2/DP=4 fails during model loading with a
|
||||||
|
weight-loading OOM. Root cause: expert weights are sharded only across
|
||||||
|
the TP group (this matrix passes no `--ep-size`, so experts fall back to
|
||||||
|
TP sharding; the image also asserts `ep_size <= tp_size`, so EP can never
|
||||||
|
exceed TP anyway) and DP replicas do not share expert shards. The INT8
|
||||||
|
checkpoint holds ~264 GiB of routed-expert weights out of ~274 GiB total,
|
||||||
|
so each rank carries roughly `274/TP` GiB:
|
||||||
|
TP=8/DP=1 ≈ 34 GiB, TP=4/DP=2 ≈ 68 GiB, TP=2/DP=4 ≈ 137 GiB — more than
|
||||||
|
the 96 GiB an XPU has, hence the OOM. TP=4 is therefore the
|
||||||
|
minimum viable TP for this model on P800. Configs that cannot start are
|
||||||
|
recorded as `SKIPPED_SERVICE_START_FAILED` and the run continues with the
|
||||||
|
next config.
|
||||||
|
|
||||||
|
## Quick Start
|
||||||
|
|
||||||
|
```bash
|
||||||
|
# Full matrix (server per config -> warmup -> scenarios -> parse -> compare)
|
||||||
|
bash experiments/p800/dsv4_p800_sglang_tp_dp_matrix/run_bench.sh
|
||||||
|
|
||||||
|
# Dry run: print server args and scenario plan only
|
||||||
|
DRY_RUN=1 bash experiments/p800/dsv4_p800_sglang_tp_dp_matrix/run_bench.sh
|
||||||
|
|
||||||
|
# Adaptive concurrency search (find saturation point per shape)
|
||||||
|
bash experiments/p800/dsv4_p800_sglang_tp_dp_matrix/run_adaptive_concurrency.sh
|
||||||
|
```
|
||||||
|
|
||||||
|
Results land in `results/<RUN_ID>/` (fixed matrix) or
|
||||||
|
`adaptive_results/<RUN_ID>/` (adaptive search).
|
||||||
|
|
||||||
|
## Files
|
||||||
|
|
||||||
|
| File | Purpose |
|
||||||
|
|---|---|
|
||||||
|
| `config.env` | Experiment-level configuration (model, port, TP×DP configs, server settings) |
|
||||||
|
| `matrix.json` | ISL/OSL matrix with Y/P/N marks and per-ISL concurrency ranges |
|
||||||
|
| `generate_scenarios.py` | Expands matrix.json into a scenario TSV |
|
||||||
|
| `start_sglang_docker.sh` | Start the P800 SGLang Docker container for a TP×DP config |
|
||||||
|
| `start_sglang_dp.sh` | Alias for `start_sglang_docker.sh` (H20 naming parity) |
|
||||||
|
| `run_bench.sh` | Orchestrator: server per config → warmup → scenarios → parse → compare |
|
||||||
|
| `compare.py` | Cross-config comparison report (`comparison.md`) |
|
||||||
|
| `adaptive_config.env` | Adaptive concurrency search settings |
|
||||||
|
| `run_adaptive_concurrency.sh` | Adaptive saturation search (shared `adaptive_bench_lib.sh`) |
|
||||||
|
| `run_adaptive_concurrency_add16.sh` | Same search starting at concurrency 16, step +16 |
|
||||||
|
|
||||||
|
## Platform
|
||||||
|
|
||||||
|
This experiment targets `platforms/kunlun_p800.env`. The container uses the
|
||||||
|
dedicated name `sglang-dsv4-flash-tpdp` so it never interferes with the
|
||||||
|
`sglang-dsv4-flash` container used by the other P800 experiments.
|
||||||
@ -0,0 +1,58 @@
|
|||||||
|
# Adaptive concurrency search settings.
|
||||||
|
#
|
||||||
|
# For each fixed (TP, DP, ISL, OSL), probe:
|
||||||
|
# C = start, start * multiplier, ... up to max
|
||||||
|
# and stop after Total TPS has less than TPS_MIN_GAIN_PCT meaningful growth for
|
||||||
|
# PLATEAU_PATIENCE consecutive points.
|
||||||
|
|
||||||
|
SEARCH_START_CONCURRENCY="${SEARCH_START_CONCURRENCY:-1}"
|
||||||
|
# No server-side max-running-requests cap is set; aligned with the H20
|
||||||
|
# adaptive search upper bound. Points beyond server capacity are recorded
|
||||||
|
# as failed and the search moves on.
|
||||||
|
SEARCH_MAX_CONCURRENCY="${SEARCH_MAX_CONCURRENCY:-512}"
|
||||||
|
SEARCH_MULTIPLIER="${SEARCH_MULTIPLIER:-2}"
|
||||||
|
NUM_PROMPTS_MULTIPLIER="${NUM_PROMPTS_MULTIPLIER:-5}"
|
||||||
|
|
||||||
|
# A gain below 2% is treated as throughput saturation. Two consecutive
|
||||||
|
# low-gain points prevent one noisy measurement from stopping the search.
|
||||||
|
TPS_MIN_GAIN_PCT="${TPS_MIN_GAIN_PCT:-2.0}"
|
||||||
|
PLATEAU_PATIENCE="${PLATEAU_PATIENCE:-2}"
|
||||||
|
|
||||||
|
# TTFT SLO early-stop settings.
|
||||||
|
# When ttft_p95_ms exceeds TTFT_SLO_MS, stop searching the current (ISL, OSL)
|
||||||
|
# shape and move on to the next scenario.
|
||||||
|
TTFT_SLO_MS="${TTFT_SLO_MS:-4000}"
|
||||||
|
ENABLE_TTFT_SLO_STOP="${ENABLE_TTFT_SLO_STOP:-1}"
|
||||||
|
|
||||||
|
# Keep the same random workload semantics as the fixed matrix baseline.
|
||||||
|
# DATASET_PATH must contain at least SEARCH_MAX_CONCURRENCY times
|
||||||
|
# NUM_PROMPTS_MULTIPLIER valid two-turn conversations. Set this explicitly to
|
||||||
|
# random-ids to use generated token IDs without a ShareGPT seed dataset.
|
||||||
|
BENCH_DATASET_NAME="${BENCH_DATASET_NAME:-random}"
|
||||||
|
# SGLang interprets 0.0 as Uniform[1, requested_len]. Use 1.0 for fixed
|
||||||
|
# ISL/OSL points; lower values intentionally benchmark a length distribution.
|
||||||
|
RANDOM_RANGE_RATIO="${RANDOM_RANGE_RATIO:-1.0}"
|
||||||
|
# NOTE: the P800 image's sglang.bench_serving does not support
|
||||||
|
# --warmup-requests, so per-point warmup requests are not sent; the value is
|
||||||
|
# still validated by adaptive_bench_lib.sh and kept for parity with H20.
|
||||||
|
BENCH_WARMUP_MAX_REQUESTS="${BENCH_WARMUP_MAX_REQUESTS:-0}"
|
||||||
|
|
||||||
|
# Reject a point if the completed request count or actual token lengths do not
|
||||||
|
# match the requested workload.
|
||||||
|
INPUT_LENGTH_TOLERANCE_PCT="${INPUT_LENGTH_TOLERANCE_PCT:-5.0}"
|
||||||
|
OUTPUT_LENGTH_TOLERANCE_PCT="${OUTPUT_LENGTH_TOLERANCE_PCT:-10.0}"
|
||||||
|
|
||||||
|
MAX_POINT_RETRIES="${MAX_POINT_RETRIES:-1}"
|
||||||
|
SERVER_RESTART_COOLDOWN_S="${SERVER_RESTART_COOLDOWN_S:-10}"
|
||||||
|
SCENARIO_TIMEOUT_S="${SCENARIO_TIMEOUT_S:-1800}"
|
||||||
|
GPU_MEM_SAMPLE_INTERVAL_S="${GPU_MEM_SAMPLE_INTERVAL_S:-1}"
|
||||||
|
|
||||||
|
# Optional space-separated filters, useful for smoke tests:
|
||||||
|
# TP_LIST="8" ISL_LIST="1024" OSL_LIST="128"
|
||||||
|
TP_LIST="${TP_LIST:-}"
|
||||||
|
ISL_LIST="${ISL_LIST:-}"
|
||||||
|
OSL_LIST="${OSL_LIST:-}"
|
||||||
|
|
||||||
|
DRY_RUN="${DRY_RUN:-0}"
|
||||||
|
# Counts ISL/OSL shapes per TP/DP config, not individual concurrency probes.
|
||||||
|
GRID_LIMIT="${GRID_LIMIT:-0}"
|
||||||
@ -0,0 +1,27 @@
|
|||||||
|
{
|
||||||
|
"experiment": "dsv4_p800_sglang_tp_dp_official",
|
||||||
|
"engine": "sglang",
|
||||||
|
"run_id": "adaptive_20260717-072013",
|
||||||
|
"model": "/data1/models/DeepSeek-V4-Flash-INT8",
|
||||||
|
"hardware": "8x Kunlun P800 XPU",
|
||||||
|
"matrix": "/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/matrix.json",
|
||||||
|
"dataset": "random",
|
||||||
|
"tokenize_prompt": false,
|
||||||
|
"random_range_ratio": 1.0,
|
||||||
|
"search": {
|
||||||
|
"start_concurrency": 16,
|
||||||
|
"max_concurrency": 512,
|
||||||
|
"multiplier": 2,
|
||||||
|
"initial_backoff_concurrencies": [
|
||||||
|
8,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
"num_prompts_multiplier": 5,
|
||||||
|
"min_tps_gain_pct": 2.0,
|
||||||
|
"plateau_patience": 2,
|
||||||
|
"warmup_max_requests": 0,
|
||||||
|
"ttft_slo_ms": 4000.0,
|
||||||
|
"enable_ttft_slo_stop": 1,
|
||||||
|
"ttft_group_skip_ms": 8000.0
|
||||||
|
}
|
||||||
|
}
|
||||||
@ -0,0 +1,25 @@
|
|||||||
|
mark input_len output_len
|
||||||
|
Y 1024 128
|
||||||
|
Y 1024 256
|
||||||
|
Y 1024 512
|
||||||
|
Y 1024 1024
|
||||||
|
Y 1024 2048
|
||||||
|
Y 1024 4096
|
||||||
|
Y 4096 128
|
||||||
|
Y 4096 256
|
||||||
|
Y 4096 512
|
||||||
|
Y 4096 1024
|
||||||
|
Y 4096 2048
|
||||||
|
Y 4096 4096
|
||||||
|
Y 16384 128
|
||||||
|
Y 16384 256
|
||||||
|
Y 16384 512
|
||||||
|
Y 16384 1024
|
||||||
|
Y 16384 2048
|
||||||
|
Y 65536 128
|
||||||
|
Y 65536 256
|
||||||
|
Y 65536 512
|
||||||
|
Y 65536 1024
|
||||||
|
Y 131072 128
|
||||||
|
Y 131072 256
|
||||||
|
Y 131072 512
|
||||||
|
@ -0,0 +1,6 @@
|
|||||||
|
# Adaptive concurrency search summary
|
||||||
|
|
||||||
|
| Engine | TP | DP | ISL | OSL | Stop | Saturation C | Best TPS C | Best Total TPS | Max successful C |
|
||||||
|
|---|---:|---:|---:|---:|---|---:|---:|---:|---:|
|
||||||
|
|
||||||
|
`Saturation C` is the first point in the final low-gain streak. `Best TPS C` is the tested point with the highest observed Total TPS.
|
||||||
@ -0,0 +1,27 @@
|
|||||||
|
{
|
||||||
|
"experiment": "dsv4_p800_sglang_tp_dp_official",
|
||||||
|
"engine": "sglang",
|
||||||
|
"run_id": "adaptive_20260717-072159",
|
||||||
|
"model": "/data1/models/DeepSeek-V4-Flash-INT8",
|
||||||
|
"hardware": "8x Kunlun P800 XPU",
|
||||||
|
"matrix": "/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/matrix.json",
|
||||||
|
"dataset": "random",
|
||||||
|
"tokenize_prompt": false,
|
||||||
|
"random_range_ratio": 1.0,
|
||||||
|
"search": {
|
||||||
|
"start_concurrency": 16,
|
||||||
|
"max_concurrency": 512,
|
||||||
|
"multiplier": 2,
|
||||||
|
"initial_backoff_concurrencies": [
|
||||||
|
8,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
"num_prompts_multiplier": 5,
|
||||||
|
"min_tps_gain_pct": 2.0,
|
||||||
|
"plateau_patience": 2,
|
||||||
|
"warmup_max_requests": 0,
|
||||||
|
"ttft_slo_ms": 4000.0,
|
||||||
|
"enable_ttft_slo_stop": 1,
|
||||||
|
"ttft_group_skip_ms": 8000.0
|
||||||
|
}
|
||||||
|
}
|
||||||
@ -0,0 +1,25 @@
|
|||||||
|
mark input_len output_len
|
||||||
|
Y 1024 128
|
||||||
|
Y 1024 256
|
||||||
|
Y 1024 512
|
||||||
|
Y 1024 1024
|
||||||
|
Y 1024 2048
|
||||||
|
Y 1024 4096
|
||||||
|
Y 4096 128
|
||||||
|
Y 4096 256
|
||||||
|
Y 4096 512
|
||||||
|
Y 4096 1024
|
||||||
|
Y 4096 2048
|
||||||
|
Y 4096 4096
|
||||||
|
Y 16384 128
|
||||||
|
Y 16384 256
|
||||||
|
Y 16384 512
|
||||||
|
Y 16384 1024
|
||||||
|
Y 16384 2048
|
||||||
|
Y 65536 128
|
||||||
|
Y 65536 256
|
||||||
|
Y 65536 512
|
||||||
|
Y 65536 1024
|
||||||
|
Y 131072 128
|
||||||
|
Y 131072 256
|
||||||
|
Y 131072 512
|
||||||
|
@ -0,0 +1,27 @@
|
|||||||
|
{
|
||||||
|
"experiment": "dsv4_p800_sglang_tp_dp_official",
|
||||||
|
"engine": "sglang",
|
||||||
|
"run_id": "adaptive_20260717-073006",
|
||||||
|
"model": "/data1/models/DeepSeek-V4-Flash-INT8",
|
||||||
|
"hardware": "8x Kunlun P800 XPU",
|
||||||
|
"matrix": "/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/matrix.json",
|
||||||
|
"dataset": "random",
|
||||||
|
"tokenize_prompt": false,
|
||||||
|
"random_range_ratio": 1.0,
|
||||||
|
"search": {
|
||||||
|
"start_concurrency": 16,
|
||||||
|
"max_concurrency": 512,
|
||||||
|
"multiplier": 2,
|
||||||
|
"initial_backoff_concurrencies": [
|
||||||
|
8,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
"num_prompts_multiplier": 5,
|
||||||
|
"min_tps_gain_pct": 2.0,
|
||||||
|
"plateau_patience": 2,
|
||||||
|
"warmup_max_requests": 0,
|
||||||
|
"ttft_slo_ms": 4000.0,
|
||||||
|
"enable_ttft_slo_stop": 1,
|
||||||
|
"ttft_group_skip_ms": 8000.0
|
||||||
|
}
|
||||||
|
}
|
||||||
@ -0,0 +1,25 @@
|
|||||||
|
mark input_len output_len
|
||||||
|
Y 1024 128
|
||||||
|
Y 1024 256
|
||||||
|
Y 1024 512
|
||||||
|
Y 1024 1024
|
||||||
|
Y 1024 2048
|
||||||
|
Y 1024 4096
|
||||||
|
Y 4096 128
|
||||||
|
Y 4096 256
|
||||||
|
Y 4096 512
|
||||||
|
Y 4096 1024
|
||||||
|
Y 4096 2048
|
||||||
|
Y 4096 4096
|
||||||
|
Y 16384 128
|
||||||
|
Y 16384 256
|
||||||
|
Y 16384 512
|
||||||
|
Y 16384 1024
|
||||||
|
Y 16384 2048
|
||||||
|
Y 65536 128
|
||||||
|
Y 65536 256
|
||||||
|
Y 65536 512
|
||||||
|
Y 65536 1024
|
||||||
|
Y 131072 128
|
||||||
|
Y 131072 256
|
||||||
|
Y 131072 512
|
||||||
|
@ -0,0 +1,6 @@
|
|||||||
|
# Adaptive concurrency search summary
|
||||||
|
|
||||||
|
| Engine | TP | DP | ISL | OSL | Stop | Saturation C | Best TPS C | Best Total TPS | Max successful C |
|
||||||
|
|---|---:|---:|---:|---:|---|---:|---:|---:|---:|
|
||||||
|
|
||||||
|
`Saturation C` is the first point in the final low-gain streak. `Best TPS C` is the tested point with the highest observed Total TPS.
|
||||||
@ -0,0 +1,27 @@
|
|||||||
|
{
|
||||||
|
"experiment": "dsv4_p800_sglang_tp_dp_official",
|
||||||
|
"engine": "sglang",
|
||||||
|
"run_id": "adaptive_20260717-075355",
|
||||||
|
"model": "/data1/models/DeepSeek-V4-Flash-INT8",
|
||||||
|
"hardware": "8x Kunlun P800 XPU",
|
||||||
|
"matrix": "/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/matrix.json",
|
||||||
|
"dataset": "random",
|
||||||
|
"tokenize_prompt": false,
|
||||||
|
"random_range_ratio": 1.0,
|
||||||
|
"search": {
|
||||||
|
"start_concurrency": 16,
|
||||||
|
"max_concurrency": 512,
|
||||||
|
"multiplier": 2,
|
||||||
|
"initial_backoff_concurrencies": [
|
||||||
|
8,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
"num_prompts_multiplier": 5,
|
||||||
|
"min_tps_gain_pct": 2.0,
|
||||||
|
"plateau_patience": 2,
|
||||||
|
"warmup_max_requests": 0,
|
||||||
|
"ttft_slo_ms": 4000.0,
|
||||||
|
"enable_ttft_slo_stop": 1,
|
||||||
|
"ttft_group_skip_ms": 8000.0
|
||||||
|
}
|
||||||
|
}
|
||||||
@ -0,0 +1,25 @@
|
|||||||
|
mark input_len output_len
|
||||||
|
Y 1024 128
|
||||||
|
Y 1024 256
|
||||||
|
Y 1024 512
|
||||||
|
Y 1024 1024
|
||||||
|
Y 1024 2048
|
||||||
|
Y 1024 4096
|
||||||
|
Y 4096 128
|
||||||
|
Y 4096 256
|
||||||
|
Y 4096 512
|
||||||
|
Y 4096 1024
|
||||||
|
Y 4096 2048
|
||||||
|
Y 4096 4096
|
||||||
|
Y 16384 128
|
||||||
|
Y 16384 256
|
||||||
|
Y 16384 512
|
||||||
|
Y 16384 1024
|
||||||
|
Y 16384 2048
|
||||||
|
Y 65536 128
|
||||||
|
Y 65536 256
|
||||||
|
Y 65536 512
|
||||||
|
Y 65536 1024
|
||||||
|
Y 131072 128
|
||||||
|
Y 131072 256
|
||||||
|
Y 131072 512
|
||||||
|
@ -0,0 +1,99 @@
|
|||||||
|
{"status":"COMPLETED","completed":80,"failed":0,"duration_s":38.850977536989376,"request_tps":2.059150247219219,"input_tps":2108.5698531524804,"output_tps":263.57123164406005,"total_tps":2372.1410847965403,"mean_input_tokens":1024.0,"mean_output_tokens":128.0,"ttft_p50_ms":375.5967851029709,"ttft_p95_ms":5191.183777351398,"ttft_p99_ms":5494.083781067748,"tpot_p50_ms":48.15443845256782,"tpot_p95_ms":63.54774250823081,"tpot_p99_ms":70.50687441087186,"e2e_p50_ms":6587.240772438236,"e2e_p95_ms":12055.057296273297,"e2e_p99_ms":13356.3372077886,"itl_p50_ms":27.370366791728884,"itl_p95_ms":142.97846250701696,"itl_p99_ms":289.74504962873954,"validation_errors":[],"timestamp":"2026-07-17T07:59:52+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":1024,"osl":128,"concurrency":16,"num_prompts":80,"warmup_requests":16,"attempt":1,"gain_pct":null,"plateau_streak":0,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/raw_outputs/sglang_adaptive_c16_i1024_o128_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/logs/sglang_c16_i1024_o128_a1.log"}
|
||||||
|
{"status":"COMPLETED","completed":40,"failed":0,"duration_s":24.875072153052315,"request_tps":1.6080355366965948,"input_tps":1646.628389577313,"output_tps":205.82854869716414,"total_tps":1852.4569382744771,"mean_input_tokens":1024.0,"mean_output_tokens":128.0,"ttft_p50_ms":342.23642048891634,"ttft_p95_ms":1023.3174284454436,"ttft_p99_ms":1165.7853273814542,"tpot_p50_ms":34.067742362449785,"tpot_p95_ms":40.35725147201216,"tpot_p99_ms":47.791466971980334,"e2e_p50_ms":4812.845256412402,"e2e_p95_ms":5945.917596516664,"e2e_p99_ms":6398.701484173071,"itl_p50_ms":20.754446974024177,"itl_p95_ms":90.81794312223788,"itl_p99_ms":180.40624327026327,"validation_errors":[],"timestamp":"2026-07-17T08:00:37+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":1024,"osl":128,"concurrency":8,"num_prompts":40,"warmup_requests":8,"attempt":1,"gain_pct":null,"plateau_streak":0,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/raw_outputs/sglang_adaptive_c8_i1024_o128_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/logs/sglang_c8_i1024_o128_a1.log"}
|
||||||
|
{"status":"COMPLETED","completed":80,"failed":0,"duration_s":56.70565206790343,"request_tps":1.4107941110385653,"input_tps":1444.653169703491,"output_tps":361.1632924258727,"total_tps":1805.8164621293636,"mean_input_tokens":1024.0,"mean_output_tokens":256.0,"ttft_p50_ms":353.3130220603198,"ttft_p95_ms":1907.358543772716,"ttft_p99_ms":1910.2728008292615,"tpot_p50_ms":37.91363587621234,"tpot_p95_ms":46.84887420564122,"tpot_p99_ms":51.577967088916004,"e2e_p50_ms":10329.861202044412,"e2e_p95_ms":12712.218005850445,"e2e_p99_ms":14176.053717974562,"itl_p50_ms":24.598574731498957,"itl_p95_ms":95.18855294833578,"itl_p99_ms":178.34910145029426,"validation_errors":[],"timestamp":"2026-07-17T08:01:55+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":1024,"osl":256,"concurrency":16,"num_prompts":80,"warmup_requests":16,"attempt":1,"gain_pct":null,"plateau_streak":0,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/raw_outputs/sglang_adaptive_c16_i1024_o256_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/logs/sglang_c16_i1024_o256_a1.log"}
|
||||||
|
{"status":"COMPLETED","completed":160,"failed":0,"duration_s":658.0221962139476,"request_tps":0.24315289198539136,"input_tps":248.98856139304075,"output_tps":62.24714034826019,"total_tps":311.23570174130094,"mean_input_tokens":1024.0,"mean_output_tokens":256.0,"ttft_p50_ms":2375.0393349910155,"ttft_p95_ms":6374.220354005225,"ttft_p99_ms":7099.810243677347,"tpot_p50_ms":358.5047604315275,"tpot_p95_ms":1659.2170038060551,"tpot_p99_ms":2032.845752709198,"e2e_p50_ms":96956.49567013606,"e2e_p95_ms":427836.4118320051,"e2e_p99_ms":523916.80456324946,"itl_p50_ms":284.34373397612944,"itl_p95_ms":1197.6450297981464,"itl_p99_ms":6089.286311843905,"validation_errors":[],"timestamp":"2026-07-17T08:13:13+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":1024,"osl":256,"concurrency":32,"num_prompts":160,"warmup_requests":32,"attempt":1,"gain_pct":-82.764821,"plateau_streak":1,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/raw_outputs/sglang_adaptive_c32_i1024_o256_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/logs/sglang_c32_i1024_o256_a1.log"}
|
||||||
|
{"status":"COMPLETED","completed":80,"failed":0,"duration_s":95.85385624691844,"request_tps":0.8346038764879831,"input_tps":854.6343695236947,"output_tps":427.31718476184733,"total_tps":1281.951554285542,"mean_input_tokens":1024.0,"mean_output_tokens":512.0,"ttft_p50_ms":339.90226255264133,"ttft_p95_ms":2043.7121676397512,"ttft_p99_ms":2046.3871875125915,"tpot_p50_ms":34.03399106449541,"tpot_p95_ms":39.92383729727071,"tpot_p99_ms":44.13223402607008,"e2e_p50_ms":17862.977570970543,"e2e_p95_ms":21575.54897943046,"e2e_p99_ms":22888.442552858032,"itl_p50_ms":23.5583473307391,"itl_p95_ms":82.56516652181745,"itl_p99_ms":142.30121159460396,"validation_errors":[],"timestamp":"2026-07-17T08:15:10+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":1024,"osl":512,"concurrency":16,"num_prompts":80,"warmup_requests":16,"attempt":1,"gain_pct":null,"plateau_streak":0,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/raw_outputs/sglang_adaptive_c16_i1024_o512_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/logs/sglang_c16_i1024_o512_a1.log"}
|
||||||
|
{"status":"COMPLETED","completed":160,"failed":0,"duration_s":1333.6260307179764,"request_tps":0.11997366301695667,"input_tps":122.85303092936363,"output_tps":61.42651546468181,"total_tps":184.27954639404544,"mean_input_tokens":1024.0,"mean_output_tokens":512.0,"ttft_p50_ms":2249.118781532161,"ttft_p95_ms":6836.219013039954,"ttft_p99_ms":7274.92825235473,"tpot_p50_ms":402.80989693945236,"tpot_p95_ms":1466.5643998654552,"tpot_p99_ms":2007.9641384794784,"e2e_p50_ms":207550.27348955628,"e2e_p95_ms":756035.7196734749,"e2e_p99_ms":1030145.6660308479,"itl_p50_ms":291.4304529937605,"itl_p95_ms":1255.9095733449797,"itl_p99_ms":6540.265160310082,"validation_errors":[],"timestamp":"2026-07-17T08:37:44+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":1024,"osl":512,"concurrency":32,"num_prompts":160,"warmup_requests":32,"attempt":1,"gain_pct":-85.625077,"plateau_streak":1,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/raw_outputs/sglang_adaptive_c32_i1024_o512_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/logs/sglang_c32_i1024_o512_a1.log"}
|
||||||
|
{"status":"COMPLETED","completed":80,"failed":0,"duration_s":177.24370902986266,"request_tps":0.4513559349320619,"input_tps":462.1884773704314,"output_tps":462.1884773704314,"total_tps":924.3769547408627,"mean_input_tokens":1024.0,"mean_output_tokens":1024.0,"ttft_p50_ms":341.96062106639147,"ttft_p95_ms":2057.607007981278,"ttft_p99_ms":2060.7414380577393,"tpot_p50_ms":32.20496962997672,"tpot_p95_ms":35.99809466682298,"tpot_p99_ms":38.50099444516569,"e2e_p50_ms":33410.80566099845,"e2e_p95_ms":37786.415580648456,"e2e_p99_ms":39677.768702611786,"itl_p50_ms":23.55353666159014,"itl_p95_ms":73.51064588874578,"itl_p99_ms":113.19450628322859,"validation_errors":[],"timestamp":"2026-07-17T08:41:03+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":1024,"osl":1024,"concurrency":16,"num_prompts":80,"warmup_requests":16,"attempt":1,"gain_pct":null,"plateau_streak":0,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/raw_outputs/sglang_adaptive_c16_i1024_o1024_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/logs/sglang_c16_i1024_o1024_a1.log"}
|
||||||
|
{"timestamp":"2026-07-17T09:45:05+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":1024,"osl":1024,"concurrency":32,"num_prompts":160,"warmup_requests":32,"attempt":2,"status":"FAILED","completed":0,"failed":160,"error_type":"benchmark rc=124","validation_errors":[],"gain_pct":null,"plateau_streak":0,"raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/raw_outputs/sglang_adaptive_c32_i1024_o1024_a2.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/logs/sglang_c32_i1024_o1024_a2.log"}
|
||||||
|
{"status":"COMPLETED","completed":80,"failed":0,"duration_s":344.53868672205135,"request_tps":0.23219453455610967,"input_tps":237.7672033854563,"output_tps":475.5344067709126,"total_tps":713.301610156369,"mean_input_tokens":1024.0,"mean_output_tokens":2048.0,"ttft_p50_ms":338.4241299936548,"ttft_p95_ms":5090.250183374155,"ttft_p99_ms":5357.846618075855,"tpot_p50_ms":31.61162330943483,"tpot_p95_ms":35.93945629929083,"tpot_p99_ms":37.76207400394197,"e2e_p50_ms":65312.21951346379,"e2e_p95_ms":76375.50360694295,"e2e_p99_ms":78678.96439327157,"itl_p50_ms":24.16073833592236,"itl_p95_ms":60.57862797752023,"itl_p99_ms":92.1715474280064,"validation_errors":[],"timestamp":"2026-07-17T09:55:51+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":1024,"osl":2048,"concurrency":16,"num_prompts":80,"warmup_requests":16,"attempt":1,"gain_pct":null,"plateau_streak":0,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/raw_outputs/sglang_adaptive_c16_i1024_o2048_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/adaptive_20260717-075359/tp4_dp2/logs/sglang_c16_i1024_o2048_a1.log"}
|
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{"timestamp":"2026-07-17T16:48:01+00:00","engine":"sglang","tp":4,"dp":2,"mark":"Y","isl":65536,"osl":128,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_GROUP_SKIP_THRESHOLD","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":8,"best_total_tps":6150.9700433587495,"last_total_tps":2913.5583338875285}
|
||||||
|
{"timestamp":"2026-07-17T16:58:14+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":1024,"osl":128,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_EXCEEDED","tested_points":3,"search_cap":512,"max_successful_concurrency":48,"saturation_concurrency":null,"stop_probe_concurrency":48,"best_tps_concurrency":32,"best_total_tps":3450.755550865165,"last_total_tps":2532.7652735816637}
|
||||||
|
{"timestamp":"2026-07-17T17:04:29+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":1024,"osl":256,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_EXCEEDED","tested_points":3,"search_cap":512,"max_successful_concurrency":48,"saturation_concurrency":null,"stop_probe_concurrency":48,"best_tps_concurrency":32,"best_total_tps":2587.6325571949974,"last_total_tps":1795.9101973285085}
|
||||||
|
{"timestamp":"2026-07-17T17:14:29+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":1024,"osl":512,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_EXCEEDED","tested_points":3,"search_cap":512,"max_successful_concurrency":48,"saturation_concurrency":null,"stop_probe_concurrency":48,"best_tps_concurrency":32,"best_total_tps":1798.7457708915072,"last_total_tps":1266.168984578888}
|
||||||
|
{"timestamp":"2026-07-17T17:31:33+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":1024,"osl":1024,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_EXCEEDED","tested_points":3,"search_cap":512,"max_successful_concurrency":48,"saturation_concurrency":null,"stop_probe_concurrency":48,"best_tps_concurrency":32,"best_total_tps":1379.2244951274456,"last_total_tps":931.9574879084522}
|
||||||
|
{"timestamp":"2026-07-17T18:03:04+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":1024,"osl":2048,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_EXCEEDED","tested_points":3,"search_cap":512,"max_successful_concurrency":48,"saturation_concurrency":null,"stop_probe_concurrency":48,"best_tps_concurrency":32,"best_total_tps":1075.6417634494053,"last_total_tps":738.3307315939801}
|
||||||
|
{"timestamp":"2026-07-17T19:33:43+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":1024,"osl":4096,"status":"FAILED","stop_reason":"BENCHMARK_FAILED","tested_points":3,"search_cap":512,"max_successful_concurrency":32,"saturation_concurrency":null,"stop_probe_concurrency":48,"best_tps_concurrency":32,"best_total_tps":933.8636327287535,"last_total_tps":933.8636327287535}
|
||||||
|
{"timestamp":"2026-07-17T19:42:54+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":4096,"osl":128,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":3941.4696977636004,"last_total_tps":1340.0966647435998}
|
||||||
|
{"timestamp":"2026-07-17T19:48:01+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":4096,"osl":256,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_EXCEEDED","tested_points":2,"search_cap":512,"max_successful_concurrency":32,"saturation_concurrency":null,"stop_probe_concurrency":32,"best_tps_concurrency":32,"best_total_tps":4078.7338430771856,"last_total_tps":4078.7338430771856}
|
||||||
|
{"timestamp":"2026-07-17T19:54:13+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":4096,"osl":512,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":2498.220086600408,"last_total_tps":404.58193052714165}
|
||||||
|
{"timestamp":"2026-07-17T20:03:52+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":4096,"osl":1024,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":1777.214265419901,"last_total_tps":230.09548482314213}
|
||||||
|
{"timestamp":"2026-07-17T20:20:35+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":4096,"osl":2048,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":1216.6635035564886,"last_total_tps":142.11204810687406}
|
||||||
|
{"timestamp":"2026-07-17T20:51:57+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":4096,"osl":4096,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":851.4368446659743,"last_total_tps":94.24660415345839}
|
||||||
|
{"timestamp":"2026-07-17T21:00:14+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":16384,"osl":128,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":5031.535022381422,"last_total_tps":3108.1854119828567}
|
||||||
|
{"timestamp":"2026-07-17T21:09:29+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":16384,"osl":256,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":4638.679333222672,"last_total_tps":2055.660297026038}
|
||||||
|
{"timestamp":"2026-07-17T21:20:36+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":16384,"osl":512,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":4000.1020942963955,"last_total_tps":1258.1946758966133}
|
||||||
|
{"timestamp":"2026-07-17T21:35:44+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":16384,"osl":1024,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":3103.601560168478,"last_total_tps":721.2730822510332}
|
||||||
|
{"timestamp":"2026-07-17T21:58:38+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":16384,"osl":2048,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_SLO_BACKOFF_FOUND","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":16,"best_total_tps":2268.1903326549927,"last_total_tps":402.3376508364864}
|
||||||
|
{"timestamp":"2026-07-17T22:30:51+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":65536,"osl":128,"status":"TTFT_BOUNDARY","stop_reason":"TTFT_GROUP_SKIP_THRESHOLD","tested_points":3,"search_cap":512,"max_successful_concurrency":1,"saturation_concurrency":null,"stop_probe_concurrency":1,"best_tps_concurrency":1,"best_total_tps":4503.2122441101965,"last_total_tps":4503.2122441101965}
|
||||||
@ -0,0 +1,36 @@
|
|||||||
|
{"timestamp": "2026-07-17T08:00:37+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 1024, "osl": 128, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 2, "search_cap": 512, "max_successful_concurrency": 8, "saturation_concurrency": null, "stop_probe_concurrency": 8, "best_tps_concurrency": 16, "best_total_tps": 2372.1410847965403, "last_total_tps": 1852.4569382744771}
|
||||||
|
{"timestamp": "2026-07-17T08:13:13+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 1024, "osl": 256, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 2, "search_cap": 512, "max_successful_concurrency": 32, "saturation_concurrency": null, "stop_probe_concurrency": 32, "best_tps_concurrency": 16, "best_total_tps": 1805.8164621293636, "last_total_tps": 311.23570174130094}
|
||||||
|
{"timestamp": "2026-07-17T08:37:44+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 1024, "osl": 512, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 2, "search_cap": 512, "max_successful_concurrency": 32, "saturation_concurrency": null, "stop_probe_concurrency": 32, "best_tps_concurrency": 16, "best_total_tps": 1281.951554285542, "last_total_tps": 184.27954639404544}
|
||||||
|
{"timestamp": "2026-07-17T09:45:05+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 1024, "osl": 1024, "status": "FAILED", "stop_reason": "BENCHMARK_FAILED", "tested_points": 2, "search_cap": 512, "max_successful_concurrency": 16, "saturation_concurrency": null, "stop_probe_concurrency": 32, "best_tps_concurrency": 16, "best_total_tps": 924.3769547408627, "last_total_tps": 924.3769547408627}
|
||||||
|
{"timestamp": "2026-07-17T10:01:07+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 1024, "osl": 2048, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 2, "search_cap": 512, "max_successful_concurrency": 8, "saturation_concurrency": null, "stop_probe_concurrency": 8, "best_tps_concurrency": 16, "best_total_tps": 713.301610156369, "last_total_tps": 415.35293151931256}
|
||||||
|
{"timestamp": "2026-07-17T11:17:07+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 1024, "osl": 4096, "status": "FAILED", "stop_reason": "BENCHMARK_FAILED", "tested_points": 2, "search_cap": 512, "max_successful_concurrency": 16, "saturation_concurrency": null, "stop_probe_concurrency": 32, "best_tps_concurrency": 16, "best_total_tps": 613.9955200884395, "last_total_tps": 613.9955200884395}
|
||||||
|
{"timestamp": "2026-07-17T11:27:55+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 4096, "osl": 128, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 8, "best_total_tps": 4055.189542442062, "last_total_tps": 1087.6941389647598}
|
||||||
|
{"timestamp": "2026-07-17T11:40:23+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 4096, "osl": 256, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 897.4416357663325, "last_total_tps": 651.9774108402153}
|
||||||
|
{"timestamp": "2026-07-17T11:52:18+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 4096, "osl": 512, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 8, "best_total_tps": 1352.2428521060174, "last_total_tps": 383.4246271325376}
|
||||||
|
{"timestamp": "2026-07-17T12:09:52+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 4096, "osl": 1024, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 8, "best_total_tps": 900.1024546224579, "last_total_tps": 216.7499072532702}
|
||||||
|
{"timestamp": "2026-07-17T13:20:40+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 4096, "osl": 2048, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 8, "best_total_tps": 659.227133681341, "last_total_tps": 133.1680857505817}
|
||||||
|
{"timestamp": "2026-07-17T14:26:47+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 4096, "osl": 4096, "status": "FAILED", "stop_reason": "BENCHMARK_FAILED", "tested_points": 1, "search_cap": 512, "max_successful_concurrency": 0, "saturation_concurrency": null, "stop_probe_concurrency": 16, "best_tps_concurrency": null, "best_total_tps": null, "last_total_tps": null}
|
||||||
|
{"timestamp": "2026-07-17T14:42:42+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 16384, "osl": 128, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 8, "best_total_tps": 5125.8042771894325, "last_total_tps": 2230.3585086450407}
|
||||||
|
{"timestamp": "2026-07-17T14:59:25+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 16384, "osl": 256, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 3694.7511429510405, "last_total_tps": 1632.8138573130561}
|
||||||
|
{"timestamp": "2026-07-17T15:13:20+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 16384, "osl": 512, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 8, "best_total_tps": 3114.4132353131213, "last_total_tps": 1057.6609699448902}
|
||||||
|
{"timestamp": "2026-07-17T15:40:04+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 16384, "osl": 1024, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 8, "best_total_tps": 1936.7830475274534, "last_total_tps": 630.9061324181441}
|
||||||
|
{"timestamp": "2026-07-17T16:20:45+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 16384, "osl": 2048, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 8, "best_total_tps": 1204.231632640546, "last_total_tps": 364.32863368782785}
|
||||||
|
{"timestamp": "2026-07-17T16:48:01+00:00", "engine": "sglang", "tp": 4, "dp": 2, "mark": "Y", "isl": 65536, "osl": 128, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_GROUP_SKIP_THRESHOLD", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 8, "best_total_tps": 6150.9700433587495, "last_total_tps": 2913.5583338875285}
|
||||||
|
{"timestamp": "2026-07-17T16:58:14+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 1024, "osl": 128, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 48, "saturation_concurrency": null, "stop_probe_concurrency": 48, "best_tps_concurrency": 32, "best_total_tps": 3450.755550865165, "last_total_tps": 2532.7652735816637}
|
||||||
|
{"timestamp": "2026-07-17T17:04:29+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 1024, "osl": 256, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 48, "saturation_concurrency": null, "stop_probe_concurrency": 48, "best_tps_concurrency": 32, "best_total_tps": 2587.6325571949974, "last_total_tps": 1795.9101973285085}
|
||||||
|
{"timestamp": "2026-07-17T17:14:29+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 1024, "osl": 512, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 48, "saturation_concurrency": null, "stop_probe_concurrency": 48, "best_tps_concurrency": 32, "best_total_tps": 1798.7457708915072, "last_total_tps": 1266.168984578888}
|
||||||
|
{"timestamp": "2026-07-17T17:31:33+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 1024, "osl": 1024, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 48, "saturation_concurrency": null, "stop_probe_concurrency": 48, "best_tps_concurrency": 32, "best_total_tps": 1379.2244951274456, "last_total_tps": 931.9574879084522}
|
||||||
|
{"timestamp": "2026-07-17T18:03:04+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 1024, "osl": 2048, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 48, "saturation_concurrency": null, "stop_probe_concurrency": 48, "best_tps_concurrency": 32, "best_total_tps": 1075.6417634494053, "last_total_tps": 738.3307315939801}
|
||||||
|
{"timestamp": "2026-07-17T19:33:43+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 1024, "osl": 4096, "status": "FAILED", "stop_reason": "BENCHMARK_FAILED", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 32, "saturation_concurrency": null, "stop_probe_concurrency": 48, "best_tps_concurrency": 32, "best_total_tps": 933.8636327287535, "last_total_tps": 933.8636327287535}
|
||||||
|
{"timestamp": "2026-07-17T19:42:54+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 4096, "osl": 128, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 3941.4696977636004, "last_total_tps": 1340.0966647435998}
|
||||||
|
{"timestamp": "2026-07-17T19:48:01+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 4096, "osl": 256, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_EXCEEDED", "tested_points": 2, "search_cap": 512, "max_successful_concurrency": 32, "saturation_concurrency": null, "stop_probe_concurrency": 32, "best_tps_concurrency": 32, "best_total_tps": 4078.7338430771856, "last_total_tps": 4078.7338430771856}
|
||||||
|
{"timestamp": "2026-07-17T19:54:13+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 4096, "osl": 512, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 2498.220086600408, "last_total_tps": 404.58193052714165}
|
||||||
|
{"timestamp": "2026-07-17T20:03:52+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 4096, "osl": 1024, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 1777.214265419901, "last_total_tps": 230.09548482314213}
|
||||||
|
{"timestamp": "2026-07-17T20:20:35+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 4096, "osl": 2048, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 1216.6635035564886, "last_total_tps": 142.11204810687406}
|
||||||
|
{"timestamp": "2026-07-17T20:51:57+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 4096, "osl": 4096, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 851.4368446659743, "last_total_tps": 94.24660415345839}
|
||||||
|
{"timestamp": "2026-07-17T21:00:14+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 16384, "osl": 128, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 5031.535022381422, "last_total_tps": 3108.1854119828567}
|
||||||
|
{"timestamp": "2026-07-17T21:09:29+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 16384, "osl": 256, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 4638.679333222672, "last_total_tps": 2055.660297026038}
|
||||||
|
{"timestamp": "2026-07-17T21:20:36+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 16384, "osl": 512, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 4000.1020942963955, "last_total_tps": 1258.1946758966133}
|
||||||
|
{"timestamp": "2026-07-17T21:35:44+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 16384, "osl": 1024, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 3103.601560168478, "last_total_tps": 721.2730822510332}
|
||||||
|
{"timestamp": "2026-07-17T21:58:38+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 16384, "osl": 2048, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_SLO_BACKOFF_FOUND", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 16, "best_total_tps": 2268.1903326549927, "last_total_tps": 402.3376508364864}
|
||||||
|
{"timestamp": "2026-07-17T22:30:51+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 65536, "osl": 128, "status": "TTFT_BOUNDARY", "stop_reason": "TTFT_GROUP_SKIP_THRESHOLD", "tested_points": 3, "search_cap": 512, "max_successful_concurrency": 1, "saturation_concurrency": null, "stop_probe_concurrency": 1, "best_tps_concurrency": 1, "best_total_tps": 4503.2122441101965, "last_total_tps": 4503.2122441101965}
|
||||||
@ -0,0 +1,42 @@
|
|||||||
|
# Adaptive concurrency search summary
|
||||||
|
|
||||||
|
| Engine | TP | DP | ISL | OSL | Stop | Saturation C | Best TPS C | Best Total TPS | Max successful C |
|
||||||
|
|---|---:|---:|---:|---:|---|---:|---:|---:|---:|
|
||||||
|
| sglang | 4 | 2 | 1024 | 128 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 2372.1410847965403 | 8 |
|
||||||
|
| sglang | 4 | 2 | 1024 | 256 | TTFT_SLO_EXCEEDED | None | 16 | 1805.8164621293636 | 32 |
|
||||||
|
| sglang | 4 | 2 | 1024 | 512 | TTFT_SLO_EXCEEDED | None | 16 | 1281.951554285542 | 32 |
|
||||||
|
| sglang | 4 | 2 | 1024 | 1024 | BENCHMARK_FAILED | None | 16 | 924.3769547408627 | 16 |
|
||||||
|
| sglang | 4 | 2 | 1024 | 2048 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 713.301610156369 | 8 |
|
||||||
|
| sglang | 4 | 2 | 1024 | 4096 | BENCHMARK_FAILED | None | 16 | 613.9955200884395 | 16 |
|
||||||
|
| sglang | 4 | 2 | 4096 | 128 | TTFT_SLO_BACKOFF_FOUND | None | 8 | 4055.189542442062 | 1 |
|
||||||
|
| sglang | 4 | 2 | 4096 | 256 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 897.4416357663325 | 1 |
|
||||||
|
| sglang | 4 | 2 | 4096 | 512 | TTFT_SLO_BACKOFF_FOUND | None | 8 | 1352.2428521060174 | 1 |
|
||||||
|
| sglang | 4 | 2 | 4096 | 1024 | TTFT_SLO_BACKOFF_FOUND | None | 8 | 900.1024546224579 | 1 |
|
||||||
|
| sglang | 4 | 2 | 4096 | 2048 | TTFT_SLO_BACKOFF_FOUND | None | 8 | 659.227133681341 | 1 |
|
||||||
|
| sglang | 4 | 2 | 4096 | 4096 | BENCHMARK_FAILED | None | None | None | 0 |
|
||||||
|
| sglang | 4 | 2 | 16384 | 128 | TTFT_SLO_EXCEEDED | None | 8 | 5125.8042771894325 | 1 |
|
||||||
|
| sglang | 4 | 2 | 16384 | 256 | TTFT_SLO_EXCEEDED | None | 16 | 3694.7511429510405 | 1 |
|
||||||
|
| sglang | 4 | 2 | 16384 | 512 | TTFT_SLO_EXCEEDED | None | 8 | 3114.4132353131213 | 1 |
|
||||||
|
| sglang | 4 | 2 | 16384 | 1024 | TTFT_SLO_EXCEEDED | None | 8 | 1936.7830475274534 | 1 |
|
||||||
|
| sglang | 4 | 2 | 16384 | 2048 | TTFT_SLO_EXCEEDED | None | 8 | 1204.231632640546 | 1 |
|
||||||
|
| sglang | 4 | 2 | 65536 | 128 | TTFT_GROUP_SKIP_THRESHOLD | None | 8 | 6150.9700433587495 | 1 |
|
||||||
|
| sglang | 8 | 1 | 1024 | 128 | TTFT_SLO_EXCEEDED | None | 32 | 3450.755550865165 | 48 |
|
||||||
|
| sglang | 8 | 1 | 1024 | 256 | TTFT_SLO_EXCEEDED | None | 32 | 2587.6325571949974 | 48 |
|
||||||
|
| sglang | 8 | 1 | 1024 | 512 | TTFT_SLO_EXCEEDED | None | 32 | 1798.7457708915072 | 48 |
|
||||||
|
| sglang | 8 | 1 | 1024 | 1024 | TTFT_SLO_EXCEEDED | None | 32 | 1379.2244951274456 | 48 |
|
||||||
|
| sglang | 8 | 1 | 1024 | 2048 | TTFT_SLO_EXCEEDED | None | 32 | 1075.6417634494053 | 48 |
|
||||||
|
| sglang | 8 | 1 | 1024 | 4096 | BENCHMARK_FAILED | None | 32 | 933.8636327287535 | 32 |
|
||||||
|
| sglang | 8 | 1 | 4096 | 128 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 3941.4696977636004 | 1 |
|
||||||
|
| sglang | 8 | 1 | 4096 | 256 | TTFT_SLO_EXCEEDED | None | 32 | 4078.7338430771856 | 32 |
|
||||||
|
| sglang | 8 | 1 | 4096 | 512 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 2498.220086600408 | 1 |
|
||||||
|
| sglang | 8 | 1 | 4096 | 1024 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 1777.214265419901 | 1 |
|
||||||
|
| sglang | 8 | 1 | 4096 | 2048 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 1216.6635035564886 | 1 |
|
||||||
|
| sglang | 8 | 1 | 4096 | 4096 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 851.4368446659743 | 1 |
|
||||||
|
| sglang | 8 | 1 | 16384 | 128 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 5031.535022381422 | 1 |
|
||||||
|
| sglang | 8 | 1 | 16384 | 256 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 4638.679333222672 | 1 |
|
||||||
|
| sglang | 8 | 1 | 16384 | 512 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 4000.1020942963955 | 1 |
|
||||||
|
| sglang | 8 | 1 | 16384 | 1024 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 3103.601560168478 | 1 |
|
||||||
|
| sglang | 8 | 1 | 16384 | 2048 | TTFT_SLO_BACKOFF_FOUND | None | 16 | 2268.1903326549927 | 1 |
|
||||||
|
| sglang | 8 | 1 | 65536 | 128 | TTFT_GROUP_SKIP_THRESHOLD | None | 1 | 4503.2122441101965 | 1 |
|
||||||
|
|
||||||
|
`Saturation C` is the first point in the final low-gain streak. `Best TPS C` is the tested point with the highest observed Total TPS.
|
||||||
@ -0,0 +1,27 @@
|
|||||||
|
{
|
||||||
|
"experiment": "dsv4_p800_sglang_tp_dp_official",
|
||||||
|
"engine": "sglang",
|
||||||
|
"run_id": "adaptive_20260717-075359",
|
||||||
|
"model": "/data1/models/DeepSeek-V4-Flash-INT8",
|
||||||
|
"hardware": "8x Kunlun P800 XPU",
|
||||||
|
"matrix": "/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/matrix.json",
|
||||||
|
"dataset": "random",
|
||||||
|
"tokenize_prompt": false,
|
||||||
|
"random_range_ratio": 1.0,
|
||||||
|
"search": {
|
||||||
|
"start_concurrency": 16,
|
||||||
|
"max_concurrency": 512,
|
||||||
|
"multiplier": 2,
|
||||||
|
"initial_backoff_concurrencies": [
|
||||||
|
8,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
"num_prompts_multiplier": 5,
|
||||||
|
"min_tps_gain_pct": 2.0,
|
||||||
|
"plateau_patience": 2,
|
||||||
|
"warmup_max_requests": 0,
|
||||||
|
"ttft_slo_ms": 4000.0,
|
||||||
|
"enable_ttft_slo_stop": 1,
|
||||||
|
"ttft_group_skip_ms": 8000.0
|
||||||
|
}
|
||||||
|
}
|
||||||
@ -0,0 +1,25 @@
|
|||||||
|
mark input_len output_len
|
||||||
|
Y 1024 128
|
||||||
|
Y 1024 256
|
||||||
|
Y 1024 512
|
||||||
|
Y 1024 1024
|
||||||
|
Y 1024 2048
|
||||||
|
Y 1024 4096
|
||||||
|
Y 4096 128
|
||||||
|
Y 4096 256
|
||||||
|
Y 4096 512
|
||||||
|
Y 4096 1024
|
||||||
|
Y 4096 2048
|
||||||
|
Y 4096 4096
|
||||||
|
Y 16384 128
|
||||||
|
Y 16384 256
|
||||||
|
Y 16384 512
|
||||||
|
Y 16384 1024
|
||||||
|
Y 16384 2048
|
||||||
|
Y 65536 128
|
||||||
|
Y 65536 256
|
||||||
|
Y 65536 512
|
||||||
|
Y 65536 1024
|
||||||
|
Y 131072 128
|
||||||
|
Y 131072 256
|
||||||
|
Y 131072 512
|
||||||
|
@ -0,0 +1,2 @@
|
|||||||
|
{"status":"COMPLETED","completed":80,"failed":0,"duration_s":46.96696303598583,"request_tps":1.7033249507468566,"input_tps":1744.2047495647812,"output_tps":218.02559369559765,"total_tps":1962.230343260379,"mean_input_tokens":1024.0,"mean_output_tokens":128.0,"ttft_p50_ms":494.9538300279528,"ttft_p95_ms":3561.9220167398453,"ttft_p99_ms":3668.1022071442567,"tpot_p50_ms":58.32520933128806,"tpot_p95_ms":94.1695388358849,"tpot_p99_ms":107.79328334271742,"e2e_p50_ms":8111.887397128157,"e2e_p95_ms":15186.770990001969,"e2e_p99_ms":16331.26907782862,"itl_p50_ms":34.82708218507469,"itl_p95_ms":185.14514567020038,"itl_p99_ms":483.97617518901825,"validation_errors":[],"timestamp":"2026-07-17T07:27:47+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":1024,"osl":128,"concurrency":16,"num_prompts":80,"warmup_requests":16,"attempt":1,"gain_pct":null,"plateau_streak":0,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/smoke-20260717-072308/tp8_dp1/raw_outputs/sglang_adaptive_c16_i1024_o128_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/smoke-20260717-072308/tp8_dp1/logs/sglang_c16_i1024_o128_a1.log"}
|
||||||
|
{"status":"COMPLETED","completed":160,"failed":0,"duration_s":51.43052648403682,"request_tps":3.110992846819512,"input_tps":3185.65667514318,"output_tps":398.2070843928975,"total_tps":3583.863759536078,"mean_input_tokens":1024.0,"mean_output_tokens":128.0,"ttft_p50_ms":556.1951335985214,"ttft_p95_ms":1466.6377054760233,"ttft_p99_ms":1469.2289810441434,"tpot_p50_ms":75.4069922121579,"tpot_p95_ms":104.81830453133489,"tpot_p99_ms":113.73199667007931,"e2e_p50_ms":10190.570478094742,"e2e_p95_ms":13657.75836262619,"e2e_p99_ms":14968.806141340172,"itl_p50_ms":42.77814150555059,"itl_p95_ms":228.68744850275107,"itl_p99_ms":434.5130304887425,"validation_errors":[],"timestamp":"2026-07-17T07:28:59+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":1024,"osl":128,"concurrency":32,"num_prompts":160,"warmup_requests":32,"attempt":1,"gain_pct":82.642358,"plateau_streak":0,"error_type":"","raw_file":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/smoke-20260717-072308/tp8_dp1/raw_outputs/sglang_adaptive_c32_i1024_o128_a1.jsonl","detail_log":"/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/adaptive_results/smoke-20260717-072308/tp8_dp1/logs/sglang_c32_i1024_o128_a1.log"}
|
||||||
@ -0,0 +1 @@
|
|||||||
|
{"timestamp":"2026-07-17T07:28:59+00:00","engine":"sglang","tp":8,"dp":1,"mark":"Y","isl":1024,"osl":128,"status":"COMPLETED","stop_reason":"SEARCH_CAP_REACHED","tested_points":2,"search_cap":32,"max_successful_concurrency":32,"saturation_concurrency":null,"stop_probe_concurrency":32,"best_tps_concurrency":32,"best_total_tps":3583.863759536078,"last_total_tps":3583.863759536078}
|
||||||
@ -0,0 +1 @@
|
|||||||
|
{"timestamp": "2026-07-17T07:28:59+00:00", "engine": "sglang", "tp": 8, "dp": 1, "mark": "Y", "isl": 1024, "osl": 128, "status": "COMPLETED", "stop_reason": "SEARCH_CAP_REACHED", "tested_points": 2, "search_cap": 32, "max_successful_concurrency": 32, "saturation_concurrency": null, "stop_probe_concurrency": 32, "best_tps_concurrency": 32, "best_total_tps": 3583.863759536078, "last_total_tps": 3583.863759536078}
|
||||||
@ -0,0 +1,7 @@
|
|||||||
|
# Adaptive concurrency search summary
|
||||||
|
|
||||||
|
| Engine | TP | DP | ISL | OSL | Stop | Saturation C | Best TPS C | Best Total TPS | Max successful C |
|
||||||
|
|---|---:|---:|---:|---:|---|---:|---:|---:|---:|
|
||||||
|
| sglang | 8 | 1 | 1024 | 128 | SEARCH_CAP_REACHED | None | 32 | 3583.863759536078 | 32 |
|
||||||
|
|
||||||
|
`Saturation C` is the first point in the final low-gain streak. `Best TPS C` is the tested point with the highest observed Total TPS.
|
||||||
@ -0,0 +1,27 @@
|
|||||||
|
{
|
||||||
|
"experiment": "dsv4_p800_sglang_tp_dp_official",
|
||||||
|
"engine": "sglang",
|
||||||
|
"run_id": "smoke-20260717-072308",
|
||||||
|
"model": "/data1/models/DeepSeek-V4-Flash-INT8",
|
||||||
|
"hardware": "8x Kunlun P800 XPU",
|
||||||
|
"matrix": "/data1/yy/sskj/experiments/p800/dsv4_p800_sglang_tp_dp_official/matrix.json",
|
||||||
|
"dataset": "random",
|
||||||
|
"tokenize_prompt": false,
|
||||||
|
"random_range_ratio": 1.0,
|
||||||
|
"search": {
|
||||||
|
"start_concurrency": 16,
|
||||||
|
"max_concurrency": 32,
|
||||||
|
"multiplier": 2,
|
||||||
|
"initial_backoff_concurrencies": [
|
||||||
|
8,
|
||||||
|
1
|
||||||
|
],
|
||||||
|
"num_prompts_multiplier": 5,
|
||||||
|
"min_tps_gain_pct": 2.0,
|
||||||
|
"plateau_patience": 2,
|
||||||
|
"warmup_max_requests": 0,
|
||||||
|
"ttft_slo_ms": 4000.0,
|
||||||
|
"enable_ttft_slo_stop": 1,
|
||||||
|
"ttft_group_skip_ms": 8000.0
|
||||||
|
}
|
||||||
|
}
|
||||||
@ -0,0 +1,25 @@
|
|||||||
|
mark input_len output_len
|
||||||
|
Y 1024 128
|
||||||
|
Y 1024 256
|
||||||
|
Y 1024 512
|
||||||
|
Y 1024 1024
|
||||||
|
Y 1024 2048
|
||||||
|
Y 1024 4096
|
||||||
|
Y 4096 128
|
||||||
|
Y 4096 256
|
||||||
|
Y 4096 512
|
||||||
|
Y 4096 1024
|
||||||
|
Y 4096 2048
|
||||||
|
Y 4096 4096
|
||||||
|
Y 16384 128
|
||||||
|
Y 16384 256
|
||||||
|
Y 16384 512
|
||||||
|
Y 16384 1024
|
||||||
|
Y 16384 2048
|
||||||
|
Y 65536 128
|
||||||
|
Y 65536 256
|
||||||
|
Y 65536 512
|
||||||
|
Y 65536 1024
|
||||||
|
Y 131072 128
|
||||||
|
Y 131072 256
|
||||||
|
Y 131072 512
|
||||||
|
162
experiments/p800/dsv4_p800_sglang_tp_dp_official/compare.py
Normal file
162
experiments/p800/dsv4_p800_sglang_tp_dp_official/compare.py
Normal file
@ -0,0 +1,162 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Cross TP×DP configuration comparison for dsv4_p800_sglang_tp_dp_matrix.
|
||||||
|
|
||||||
|
Usage:
|
||||||
|
python3 compare.py --run-root results/<run_id> [--output comparison.md]
|
||||||
|
"""
|
||||||
|
import argparse
|
||||||
|
import json
|
||||||
|
import re
|
||||||
|
from collections import defaultdict
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
|
||||||
|
def load_result(result_root: Path) -> dict:
|
||||||
|
path = result_root / "results.json"
|
||||||
|
with open(path, "r", encoding="utf-8") as f:
|
||||||
|
return json.load(f)
|
||||||
|
|
||||||
|
|
||||||
|
def slo_status(ttft_p95_ms: float, tpot_mean_ms: float,
|
||||||
|
ttft_limit_ms: float = 3000.0, tpot_limit_ms: float = 50.0) -> str:
|
||||||
|
ttft_ok = ttft_p95_ms < ttft_limit_ms
|
||||||
|
tpot_ok = tpot_mean_ms < tpot_limit_ms
|
||||||
|
if ttft_ok and tpot_ok:
|
||||||
|
return "PASS"
|
||||||
|
if ttft_ok or tpot_ok:
|
||||||
|
return "PARTIAL"
|
||||||
|
return "FAIL"
|
||||||
|
|
||||||
|
|
||||||
|
def gpu_memory_str(gpu: dict | None) -> str:
|
||||||
|
if not gpu:
|
||||||
|
return "-"
|
||||||
|
peak = gpu.get("peak_used_mb", 0)
|
||||||
|
total = gpu.get("memory_total_mb", 0)
|
||||||
|
if total:
|
||||||
|
return f"{peak:.0f}/{total:.0f} ({100*peak/total:.1f}%)"
|
||||||
|
return f"{peak:.0f}"
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--run-root", type=Path, required=True)
|
||||||
|
parser.add_argument("-o", "--output", type=Path, default=Path("comparison.md"))
|
||||||
|
parser.add_argument("--ttft-limit", type=float, default=3000.0)
|
||||||
|
parser.add_argument("--tpot-limit", type=float, default=50.0)
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
# Discover configurations: tp*_dp* directories.
|
||||||
|
configs = []
|
||||||
|
for subdir in sorted(args.run_root.iterdir()):
|
||||||
|
if not subdir.is_dir():
|
||||||
|
continue
|
||||||
|
name = subdir.name
|
||||||
|
if not (name.startswith("tp") and "_dp" in name):
|
||||||
|
continue
|
||||||
|
results_json = subdir / "results.json"
|
||||||
|
if not results_json.exists():
|
||||||
|
continue
|
||||||
|
configs.append((name, load_result(subdir)))
|
||||||
|
|
||||||
|
if not configs:
|
||||||
|
print(f"No tp*_dp* results found under {args.run_root}")
|
||||||
|
return
|
||||||
|
|
||||||
|
model = configs[0][1].get("metadata", {}).get("model", "unknown")
|
||||||
|
hardware = configs[0][1].get("metadata", {}).get("hardware", "unknown")
|
||||||
|
|
||||||
|
# Group by scenario name.
|
||||||
|
by_scenario: dict[str, dict[str, dict]] = defaultdict(dict)
|
||||||
|
skipped: dict[str, dict[str, str]] = defaultdict(dict)
|
||||||
|
for label, data in configs:
|
||||||
|
for s in data.get("scenarios", []):
|
||||||
|
key = s["name"]
|
||||||
|
if s.get("status") == "skipped_oom" or "metrics" not in s:
|
||||||
|
skipped[key][label] = s.get("note") or s.get("status", "skipped")
|
||||||
|
else:
|
||||||
|
by_scenario[key][label] = s
|
||||||
|
|
||||||
|
with open(args.output, "w", encoding="utf-8") as f:
|
||||||
|
f.write(f"# SGLang TP×DP matrix comparison ({hardware})\n\n")
|
||||||
|
f.write("## Summary\n\n")
|
||||||
|
f.write(f"- Model: `{model}`\n")
|
||||||
|
f.write(f"- Hardware: {hardware}\n")
|
||||||
|
f.write("- Backend: SGLang (Docker)\n")
|
||||||
|
f.write("- Benchmark client: `sglang.bench_serving`\n")
|
||||||
|
f.write(f"- SLO reference: TTFT P95 < {args.ttft_limit}ms, TPOT mean < {args.tpot_limit}ms\n\n")
|
||||||
|
|
||||||
|
# Configuration overview.
|
||||||
|
f.write("### Configurations\n\n")
|
||||||
|
f.write("| Config | TP | DP | XPUs/replica | Notes |\n")
|
||||||
|
f.write("|---|---:|---:|---:|---|\n")
|
||||||
|
for label, data in configs:
|
||||||
|
cfg = data.get("config", {})
|
||||||
|
tp = cfg.get("tp", "?")
|
||||||
|
dp = cfg.get("dp", "?")
|
||||||
|
f.write(f"| {label} | {tp} | {dp} | {tp} | server args recorded per ISL in results.json |\n")
|
||||||
|
f.write("\n")
|
||||||
|
|
||||||
|
# Side-by-side table.
|
||||||
|
f.write("## Side-by-side results\n\n")
|
||||||
|
headers = [
|
||||||
|
"Scenario", "ISL", "OSL", "Config", "Conc", "Req/s", "OutTok/s",
|
||||||
|
"TTFT P95(ms)", "TTFT P99(ms)", "TPOT Mean(ms)", "TPOT P95(ms)",
|
||||||
|
"TPOT P99(ms)", "E2E P99(ms)", "Peak GPU mem", "SLO"
|
||||||
|
]
|
||||||
|
f.write("| " + " | ".join(headers) + " |\n")
|
||||||
|
f.write("|" + "|".join(["---"] * len(headers)) + "|\n")
|
||||||
|
|
||||||
|
for scenario_name in sorted(by_scenario.keys(), key=lambda x: tuple(map(int, re.findall(r"\d+", x)))):
|
||||||
|
_, isl, osl = re.findall(r"\d+", scenario_name)
|
||||||
|
for label, data in configs:
|
||||||
|
s = by_scenario[scenario_name].get(label)
|
||||||
|
if s is None:
|
||||||
|
if scenario_name in skipped and label in skipped[scenario_name]:
|
||||||
|
note = skipped[scenario_name][label]
|
||||||
|
f.write(f"| {scenario_name} | {isl} | {osl} | {label} | - | - | - | - | - | - | - | - | - | - | {note} |\n")
|
||||||
|
continue
|
||||||
|
cfg = s["config"]
|
||||||
|
m = s["metrics"]
|
||||||
|
status = slo_status(m["ttft_ms"]["p95"], m["tpot_ms"]["mean"], args.ttft_limit, args.tpot_limit)
|
||||||
|
gpu = m.get("gpu_memory")
|
||||||
|
f.write(
|
||||||
|
f"| {scenario_name} | {isl} | {osl} | {label} | {cfg['concurrency']} | "
|
||||||
|
f"{m['request_throughput']:.2f} | {m['output_token_throughput']:.2f} | "
|
||||||
|
f"{m['ttft_ms']['p95']:.2f} | {m['ttft_ms']['p99']:.2f} | "
|
||||||
|
f"{m['tpot_ms']['mean']:.2f} | {m['tpot_ms']['p95']:.2f} | {m['tpot_ms']['p99']:.2f} | "
|
||||||
|
f"{m['e2e_ms']['p99']:.2f} | {gpu_memory_str(gpu)} | {status} |\n"
|
||||||
|
)
|
||||||
|
|
||||||
|
# Best throughput per ISL/OSL.
|
||||||
|
f.write("\n## Best throughput per (ISL, OSL)\n\n")
|
||||||
|
f.write("| ISL | OSL | Best Config | Concurrency | OutTok/s | TTFT P95(ms) | TPOT Mean(ms) | SLO |\n")
|
||||||
|
f.write("|---:|---:|---|---:|---:|---:|---:|---:|\n")
|
||||||
|
best_by_shape: dict[tuple[int, int], tuple[float, str, dict]] = {}
|
||||||
|
for scenario_name, backends in by_scenario.items():
|
||||||
|
_, isl, osl = re.findall(r"\d+", scenario_name)
|
||||||
|
isl_i, osl_i = int(isl), int(osl)
|
||||||
|
for label, s in backends.items():
|
||||||
|
m = s["metrics"]
|
||||||
|
out_tok = m["output_token_throughput"]
|
||||||
|
if (isl_i, osl_i) not in best_by_shape or out_tok > best_by_shape[(isl_i, osl_i)][0]:
|
||||||
|
best_by_shape[(isl_i, osl_i)] = (out_tok, label, s)
|
||||||
|
for (isl_i, osl_i), (out_tok, label, s) in sorted(best_by_shape.items()):
|
||||||
|
m = s["metrics"]
|
||||||
|
status = slo_status(m["ttft_ms"]["p95"], m["tpot_ms"]["mean"], args.ttft_limit, args.tpot_limit)
|
||||||
|
f.write(
|
||||||
|
f"| {isl_i} | {osl_i} | {label} | {s['config']['concurrency']} | "
|
||||||
|
f"{out_tok:.2f} | {m['ttft_ms']['p95']:.2f} | {m['tpot_ms']['mean']:.2f} | {status} |\n"
|
||||||
|
)
|
||||||
|
|
||||||
|
f.write("\n## Notes\n\n")
|
||||||
|
f.write("- SLO check uses TTFT P95 and TPOT mean.\n")
|
||||||
|
f.write("- A PARTIAL indicates one of the two metrics is out of target; FAIL indicates both are out.\n")
|
||||||
|
f.write("- `Peak GPU mem` shows peak used / total MB and utilization percentage.\n")
|
||||||
|
f.write("- Optional (P) combinations that failed are marked as skipped/OOM and do not break the run.\n")
|
||||||
|
|
||||||
|
print(f"Wrote comparison to {args.output}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
102
experiments/p800/dsv4_p800_sglang_tp_dp_official/config.env
Normal file
102
experiments/p800/dsv4_p800_sglang_tp_dp_official/config.env
Normal file
@ -0,0 +1,102 @@
|
|||||||
|
# TP×DP matrix experiment for DeepSeek-V4-Flash-INT8 on Kunlun P800 (8 XPUs)
|
||||||
|
# using SGLang-XPU with the OFFICIAL vendor-recommended launch parameters
|
||||||
|
# (extracted from the image's /workspace/ds_v4/w8a8/run_server.sh):
|
||||||
|
# --ep-size <TP*DP> (expert parallel; probes whether EP makes TP2/DP4 viable)
|
||||||
|
# EAGLE speculative decoding (steps=3, topk=1, draft=4)
|
||||||
|
# --chunked-prefill-size 8192 --max-prefill-tokens 16384
|
||||||
|
# --max-running-requests 64
|
||||||
|
# Tests SGLang with three parallel configurations:
|
||||||
|
# TP=2, DP=4 -> 2 XPUs per replica, 4 replicas
|
||||||
|
# TP=4, DP=2 -> 4 XPUs per replica, 2 replicas
|
||||||
|
# TP=8, DP=1 -> 8 XPUs, no data parallelism (production config)
|
||||||
|
|
||||||
|
EXPERIMENT="dsv4_p800_sglang_tp_dp_official"
|
||||||
|
MODEL_NAME="DeepSeek-V4-Flash-INT8"
|
||||||
|
MODEL_PATH="${MODEL_PATH:-/data1/models/DeepSeek-V4-Flash-INT8}"
|
||||||
|
SERVED_MODEL_NAME="deepseek-v4-flash-int8"
|
||||||
|
|
||||||
|
SGLANG_PORT="${SGLANG_PORT:-30014}"
|
||||||
|
|
||||||
|
# Dedicated container name so this experiment never touches the
|
||||||
|
# `sglang-dsv4-flash` container used by the other P800 experiments.
|
||||||
|
CONTAINER_NAME="sglang-dsv4-flash-tpdp-official"
|
||||||
|
|
||||||
|
# Python interpreter inside the P800 SGLang container (used to run the
|
||||||
|
# benchmark client via `docker exec`).
|
||||||
|
CONTAINER_PYTHON="${CONTAINER_PYTHON:-/root/miniconda/envs/python310_torch25_cuda/bin/python}"
|
||||||
|
|
||||||
|
export XPU_VISIBLE_DEVICES="${XPU_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
|
||||||
|
export CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES:-0,1,2,3,4,5,6,7}"
|
||||||
|
|
||||||
|
# Runtime working directory for logs, pid files, and tmp. Defaults to a local
|
||||||
|
# directory under this experiment so the benchmark is self-contained.
|
||||||
|
RUNTIME_BASE="${RUNTIME_BASE:-${SCRIPT_DIR}/runtime}"
|
||||||
|
|
||||||
|
# Parallel configurations to test. Format: "TP DP"
|
||||||
|
# TP2/DP4 is excluded: --ep-size 8 crashes this vendor image when DP>1
|
||||||
|
# (ZeroDivisionError in data_parallel_controller.py), and --ep-size 2 offers
|
||||||
|
# no memory relief (~264/2=132 GiB > 96 GiB per XPU), so TP=2 remains
|
||||||
|
# infeasible for this INT8 model on P800 regardless of EP.
|
||||||
|
# --ep-size is only passed when DP=1 (official production config TP8/EP8).
|
||||||
|
# Override with a space-separated list of comma pairs, e.g.
|
||||||
|
# PARALLEL_CONFIGS_STR="8,1" to run only TP=8/DP=1.
|
||||||
|
if [[ -n "${PARALLEL_CONFIGS_STR:-}" ]]; then
|
||||||
|
declare -a PARALLEL_CONFIGS=()
|
||||||
|
for pair in $PARALLEL_CONFIGS_STR; do
|
||||||
|
PARALLEL_CONFIGS+=("${pair//,/ }")
|
||||||
|
done
|
||||||
|
else
|
||||||
|
declare -a PARALLEL_CONFIGS=(
|
||||||
|
"4 2"
|
||||||
|
"8 1"
|
||||||
|
)
|
||||||
|
fi
|
||||||
|
|
||||||
|
# SGLang server settings. P800 INT8 supports at most ~131k input tokens, so
|
||||||
|
# the context length is capped well below the H20 1M setting.
|
||||||
|
CONTEXT_LENGTH="${CONTEXT_LENGTH:-140000}"
|
||||||
|
MEM_FRACTION_STATIC="${MEM_FRACTION_STATIC:-0.8}"
|
||||||
|
|
||||||
|
# Official w8a8 parameters (from /workspace/ds_v4/w8a8/run_server.sh).
|
||||||
|
# EP_SIZE defaults to TP*DP in start_sglang_docker.sh (8 on this machine);
|
||||||
|
# override here only for single-config probes.
|
||||||
|
EP_SIZE="${EP_SIZE:-}"
|
||||||
|
CHUNKED_PREFILL_SIZE="${CHUNKED_PREFILL_SIZE:-8192}"
|
||||||
|
MAX_PREFILL_TOKENS="${MAX_PREFILL_TOKENS:-16384}"
|
||||||
|
MAX_RUNNING_REQUESTS="${MAX_RUNNING_REQUESTS:-64}"
|
||||||
|
# EAGLE speculative decoding (official defaults: steps=3, topk=1, draft=4).
|
||||||
|
SPEC_NUM_STEPS="${SPEC_NUM_STEPS:-3}"
|
||||||
|
SPEC_EAGLE_TOPK="${SPEC_EAGLE_TOPK:-1}"
|
||||||
|
SPEC_NUM_DRAFT_TOKENS="${SPEC_NUM_DRAFT_TOKENS:-4}"
|
||||||
|
|
||||||
|
DOCKER_IMAGE="${DOCKER_IMAGE:-iregistry.baidu-int.com/xpu/sglang-p800-pd-disagg-0510:20260511_4202}"
|
||||||
|
|
||||||
|
# ShareGPT seed dataset for the random workload. DATASET_PATH is the host
|
||||||
|
# copy (also used by the adaptive search for capacity checks); it is mounted
|
||||||
|
# into the container at CONTAINER_DATASET_PATH, which is what the benchmark
|
||||||
|
# client references. Default matches the H20 experiments; the file is
|
||||||
|
# gitignored, download it into /data1/yy/sskj/datasets/ first.
|
||||||
|
DATASET_PATH="${DATASET_PATH:-/data1/yy/sskj/datasets/ShareGPT_V3_unfiltered_cleaned_split.json}"
|
||||||
|
CONTAINER_DATASET_PATH="${CONTAINER_DATASET_PATH:-/workspace/ShareGPT_V3_unfiltered_cleaned_split.json}"
|
||||||
|
|
||||||
|
# Matrix and concurrency rules are defined in matrix.json by default.
|
||||||
|
MATRIX_FILE="${MATRIX_FILE:-${SCRIPT_DIR:-.}/matrix.json}"
|
||||||
|
MATRIX_MODE="${MATRIX_MODE:-Y}"
|
||||||
|
|
||||||
|
# Sampling density for concurrency (kept for parity with the H20 experiment;
|
||||||
|
# generate_scenarios.py currently samples the low/high endpoints only).
|
||||||
|
export CONCURRENCY_SAMPLES="${CONCURRENCY_SAMPLES:-2}"
|
||||||
|
|
||||||
|
# Per-scenario timeout to avoid hangs (seconds).
|
||||||
|
SCENARIO_TIMEOUT_S="${SCENARIO_TIMEOUT_S:-1800}"
|
||||||
|
|
||||||
|
# XPU memory sampling interval (seconds).
|
||||||
|
GPU_MEM_SAMPLE_INTERVAL_S="${GPU_MEM_SAMPLE_INTERVAL_S:-1}"
|
||||||
|
|
||||||
|
# Dry-run mode: if 1, only log the server args and scenario plan without
|
||||||
|
# starting any server or sending requests.
|
||||||
|
DRY_RUN="${DRY_RUN:-0}"
|
||||||
|
|
||||||
|
# Per-config scenario limit for quick smoke tests. 0 = run all generated
|
||||||
|
# scenarios.
|
||||||
|
GRID_LIMIT="${GRID_LIMIT:-0}"
|
||||||
@ -0,0 +1,98 @@
|
|||||||
|
#!/usr/bin/env python3
|
||||||
|
"""Generate the scenario list for the TP×DP matrix experiment.
|
||||||
|
|
||||||
|
Reads matrix.json and prints TSV lines:
|
||||||
|
|
||||||
|
mark input_len output_len concurrency num_prompts
|
||||||
|
|
||||||
|
mark is one of Y/P/N. The caller (run_bench.sh) decides how to treat each.
|
||||||
|
"""
|
||||||
|
import argparse
|
||||||
|
import json
|
||||||
|
import math
|
||||||
|
import os
|
||||||
|
from pathlib import Path
|
||||||
|
|
||||||
|
|
||||||
|
def sample_concurrency(low: int, high: int, target: int) -> list[int]:
|
||||||
|
"""Return only the low and high concurrency values in [low, high].
|
||||||
|
|
||||||
|
For the TP×DP matrix we only need the two endpoints of the concurrency
|
||||||
|
range (e.g. 1 and 128 for ISL=1024). The `target` argument is kept for
|
||||||
|
API compatibility but is ignored.
|
||||||
|
"""
|
||||||
|
assert 1 <= low <= high, f"invalid concurrency range: {low}-{high}"
|
||||||
|
if low == high:
|
||||||
|
return [low]
|
||||||
|
return [low, high]
|
||||||
|
|
||||||
|
|
||||||
|
def generate_scenarios(matrix_path: Path, mode: str, target_samples: int) -> list[dict]:
|
||||||
|
with open(matrix_path, "r", encoding="utf-8") as f:
|
||||||
|
data = json.load(f)
|
||||||
|
|
||||||
|
matrix = data["matrix"]
|
||||||
|
concurrency_cfg = data["concurrency"]
|
||||||
|
|
||||||
|
scenarios = []
|
||||||
|
for isl_str in sorted(matrix.keys(), key=int):
|
||||||
|
osl_map = matrix[isl_str]
|
||||||
|
low = concurrency_cfg[isl_str]["low"]
|
||||||
|
high = concurrency_cfg[isl_str]["high"]
|
||||||
|
concurrencies = sample_concurrency(low, high, target_samples)
|
||||||
|
|
||||||
|
for osl_str in sorted(osl_map.keys(), key=int):
|
||||||
|
mark = osl_map[osl_str]
|
||||||
|
if mode == "Y" and mark != "Y":
|
||||||
|
continue
|
||||||
|
if mode == "Y+P" and mark not in ("Y", "P"):
|
||||||
|
continue
|
||||||
|
# mode == "all" keeps everything, including N.
|
||||||
|
|
||||||
|
for conc in concurrencies:
|
||||||
|
scenarios.append(
|
||||||
|
{
|
||||||
|
"mark": mark,
|
||||||
|
"input_len": int(isl_str),
|
||||||
|
"output_len": int(osl_str),
|
||||||
|
"concurrency": conc,
|
||||||
|
"num_prompts": conc * 5,
|
||||||
|
}
|
||||||
|
)
|
||||||
|
|
||||||
|
return scenarios
|
||||||
|
|
||||||
|
|
||||||
|
def main() -> None:
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("--matrix", type=Path, default=Path("matrix.json"))
|
||||||
|
parser.add_argument("--mode", choices=["Y", "Y+P", "all"], default=None,
|
||||||
|
help="Scenario selection mode. Defaults to matrix.mode.")
|
||||||
|
parser.add_argument("--target-samples", type=int, default=0,
|
||||||
|
help="Target number of concurrency samples. 0 = heuristic (6-8).")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
with open(args.matrix, "r", encoding="utf-8") as f:
|
||||||
|
data = json.load(f)
|
||||||
|
|
||||||
|
mode = args.mode if args.mode else data.get("mode", "Y+P")
|
||||||
|
|
||||||
|
target_samples = args.target_samples
|
||||||
|
if target_samples <= 0:
|
||||||
|
env_samples = os.getenv("CONCURRENCY_SAMPLES", "0")
|
||||||
|
try:
|
||||||
|
target_samples = int(env_samples)
|
||||||
|
except ValueError:
|
||||||
|
target_samples = 0
|
||||||
|
if target_samples <= 0:
|
||||||
|
target_samples = 7
|
||||||
|
|
||||||
|
scenarios = generate_scenarios(args.matrix, mode, target_samples)
|
||||||
|
|
||||||
|
print("mark\tinput_len\toutput_len\tconcurrency\tnum_prompts")
|
||||||
|
for s in scenarios:
|
||||||
|
print(f"{s['mark']}\t{s['input_len']}\t{s['output_len']}\t{s['concurrency']}\t{s['num_prompts']}")
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
main()
|
||||||
54
experiments/p800/dsv4_p800_sglang_tp_dp_official/matrix.json
Normal file
54
experiments/p800/dsv4_p800_sglang_tp_dp_official/matrix.json
Normal file
@ -0,0 +1,54 @@
|
|||||||
|
{
|
||||||
|
"comment": "ISL/OSL matrix for dsv4_p800_sglang_tp_dp_matrix. Y=must test, P=optional (record skipped on failure), N=skip.",
|
||||||
|
"mode": "Y",
|
||||||
|
"notes": "P800 INT8 sustains at most ~131k input tokens, so ISL stops at 131072. Concurrency ranges match the H20 matrix; scenarios that exceed server capacity are recorded as failed/skipped and the run moves on.",
|
||||||
|
"matrix": {
|
||||||
|
"1024": {
|
||||||
|
"128": "Y",
|
||||||
|
"256": "Y",
|
||||||
|
"512": "Y",
|
||||||
|
"1024": "Y",
|
||||||
|
"2048": "Y",
|
||||||
|
"4096": "Y"
|
||||||
|
},
|
||||||
|
"4096": {
|
||||||
|
"128": "Y",
|
||||||
|
"256": "Y",
|
||||||
|
"512": "Y",
|
||||||
|
"1024": "Y",
|
||||||
|
"2048": "Y",
|
||||||
|
"4096": "Y"
|
||||||
|
},
|
||||||
|
"16384": {
|
||||||
|
"128": "Y",
|
||||||
|
"256": "Y",
|
||||||
|
"512": "Y",
|
||||||
|
"1024": "Y",
|
||||||
|
"2048": "Y",
|
||||||
|
"4096": "P"
|
||||||
|
},
|
||||||
|
"65536": {
|
||||||
|
"128": "Y",
|
||||||
|
"256": "Y",
|
||||||
|
"512": "Y",
|
||||||
|
"1024": "Y",
|
||||||
|
"2048": "P",
|
||||||
|
"4096": "N"
|
||||||
|
},
|
||||||
|
"131072": {
|
||||||
|
"128": "Y",
|
||||||
|
"256": "Y",
|
||||||
|
"512": "Y",
|
||||||
|
"1024": "P",
|
||||||
|
"2048": "N",
|
||||||
|
"4096": "N"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"concurrency": {
|
||||||
|
"1024": { "low": 1, "high": 128 },
|
||||||
|
"4096": { "low": 1, "high": 64 },
|
||||||
|
"16384": { "low": 1, "high": 32 },
|
||||||
|
"65536": { "low": 1, "high": 8 },
|
||||||
|
"131072": { "low": 1, "high": 4 }
|
||||||
|
}
|
||||||
|
}
|
||||||
File diff suppressed because it is too large
Load Diff
153
experiments/p800/dsv4_p800_sglang_tp_dp_official/run_adaptive_concurrency.sh
Executable file
153
experiments/p800/dsv4_p800_sglang_tp_dp_official/run_adaptive_concurrency.sh
Executable file
@ -0,0 +1,153 @@
|
|||||||
|
#!/usr/bin/env bash
|
||||||
|
# Find the Total-TPS saturation concurrency for each SGLang TP/DP/ISL/OSL shape
|
||||||
|
# on Kunlun P800 (DeepSeek-V4-Flash-INT8).
|
||||||
|
set -Eeuo pipefail
|
||||||
|
|
||||||
|
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||||
|
EXPERIMENT_NAME="$(basename "$SCRIPT_DIR")"
|
||||||
|
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/lib.sh"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/platform.sh"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/config.env"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/adaptive_config.env"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/adaptive_bench_lib.sh"
|
||||||
|
|
||||||
|
ENGINE="sglang"
|
||||||
|
ENGINE_PORT="$SGLANG_PORT"
|
||||||
|
RESULT_BASE="${RESULT_BASE:-${SCRIPT_DIR}/adaptive_results}"
|
||||||
|
ACTIVE_ENGINE_SERVER_LOG=""
|
||||||
|
|
||||||
|
PYTHON="${PYTHON:-$(command -v python3)}"
|
||||||
|
|
||||||
|
# The shared library's GPU monitor uses nvidia-smi; override it with an
|
||||||
|
# xpu-smi sampler that emits the same CSV shape.
|
||||||
|
adaptive_start_gpu_monitor() {
|
||||||
|
local csv_path="$1"
|
||||||
|
mkdir -p "$(dirname "$csv_path")"
|
||||||
|
(
|
||||||
|
echo "timestamp, index, memory.used [MiB], memory.total [MiB], utilization.gpu [%]"
|
||||||
|
while true; do
|
||||||
|
xpu-smi 2>/dev/null | awk -F'|' -v ts="$(date '+%Y/%m/%d %H:%M:%S')" '
|
||||||
|
/^\|[[:space:]]*[0-9]+[[:space:]]/ {
|
||||||
|
l=$0; sub(/^\|[[:space:]]*/, "", l); sub(/[[:space:]].*$/, "", l); idx=l
|
||||||
|
}
|
||||||
|
/MiB/ && $4 ~ /%/ {
|
||||||
|
split($3, a, "/"); u=a[1]; t=a[2]
|
||||||
|
gsub(/[^0-9.]/, "", u); gsub(/[^0-9.]/, "", t)
|
||||||
|
r=$4; gsub(/[^0-9.]/, "", r)
|
||||||
|
printf "%s, %s, %s MiB, %s MiB, %s %%\n", ts, idx, u, t, r
|
||||||
|
}'
|
||||||
|
sleep "$GPU_MEM_SAMPLE_INTERVAL_S"
|
||||||
|
done
|
||||||
|
) >> "$csv_path" 2>/dev/null &
|
||||||
|
echo $!
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_is_healthy() {
|
||||||
|
curl --fail --silent --show-error --max-time 5 \
|
||||||
|
"http://127.0.0.1:${ENGINE_PORT}/health" >/dev/null 2>&1
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_stop_server() {
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
# The server only ever runs inside this experiment's dedicated container,
|
||||||
|
# so removing the container is a complete and safe stop.
|
||||||
|
log "stopping container ${CONTAINER_NAME} tp=${tp} dp=${dp}"
|
||||||
|
docker rm -f "$CONTAINER_NAME" >/dev/null 2>&1 || true
|
||||||
|
ACTIVE_ENGINE_SERVER_LOG=""
|
||||||
|
sleep 2
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_build_server_args() {
|
||||||
|
# Keep in sync with the launch args in start_sglang_docker.sh.
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
local ep_args=""
|
||||||
|
if (( dp == 1 )); then
|
||||||
|
ep_args="--ep-size ${EP_SIZE:-$tp}"
|
||||||
|
fi
|
||||||
|
local args="--host 0.0.0.0 --port ${ENGINE_PORT} --model-path /models --attention-backend nsa --nsa-prefill klxdsa --nsa-decode klxdsa --trust-remote-code --disable-custom-all-reduce --page-size 64 --mem-fraction-static ${MEM_FRACTION_STATIC} --tensor-parallel-size ${tp} ${ep_args} --disable-shared-experts-fusion --quantization w8a8_int8 --kv-cache-dtype float16 --disable-piecewise-cuda-graph --cuda-graph-max-bs 32 --watchdog-timeout 3000000 --tool-call-parser deepseekv4 --reasoning-parser deepseek-v4 --constrained-json-disable-any-whitespace --enable-metrics --enable-request-time-stats-logging --context-length ${CONTEXT_LENGTH} --chunked-prefill-size ${CHUNKED_PREFILL_SIZE} --max-prefill-tokens ${MAX_PREFILL_TOKENS} --max-running-requests ${MAX_RUNNING_REQUESTS} --speculative-algorithm EAGLE --speculative-num-steps ${SPEC_NUM_STEPS} --speculative-eagle-topk ${SPEC_EAGLE_TOPK} --speculative-num-draft-tokens ${SPEC_NUM_DRAFT_TOKENS}"
|
||||||
|
if (( dp > 1 )); then
|
||||||
|
args="${args} --dp-size ${dp}"
|
||||||
|
fi
|
||||||
|
printf '%s' "$args"
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_start_server() {
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
local outer_log="${ADAPTIVE_LOG_DIR}/sglang_tp${tp}_dp${dp}.server.outer.log"
|
||||||
|
log "starting sglang server tp=${tp} dp=${dp}"
|
||||||
|
bash "${SCRIPT_DIR}/start_sglang_docker.sh" "$tp" "$dp" >> "$outer_log" 2>&1
|
||||||
|
if ! engine_is_healthy; then
|
||||||
|
log "ERROR: sglang health check failed tp=${tp} dp=${dp}"
|
||||||
|
return 1
|
||||||
|
fi
|
||||||
|
ACTIVE_ENGINE_SERVER_LOG=""
|
||||||
|
log "sglang server healthy tp=${tp} dp=${dp} log=docker logs ${CONTAINER_NAME}"
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_detect_oom() {
|
||||||
|
local detail_log="$1"
|
||||||
|
local tp="$2"
|
||||||
|
local dp="$3"
|
||||||
|
local pattern='CUDA out of memory|torch\.OutOfMemoryError|OutOfMemory|out of memory|OOM|RESOURCE_EXHAUSTED|Failed to allocate memory'
|
||||||
|
if grep -Eiq "$pattern" "$detail_log" 2>/dev/null; then
|
||||||
|
return 0
|
||||||
|
fi
|
||||||
|
docker logs --tail 2000 "$CONTAINER_NAME" 2>/dev/null | grep -Eiq "$pattern"
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_run_bench() {
|
||||||
|
local isl="$1"
|
||||||
|
local osl="$2"
|
||||||
|
local concurrency="$3"
|
||||||
|
local num_prompts="$4"
|
||||||
|
local output_file="$5"
|
||||||
|
local container_output="/tmp/bench_outputs/adaptive_$(basename "$output_file")"
|
||||||
|
if [[ "$output_file" == "/dev/null" ]]; then
|
||||||
|
container_output="/dev/null"
|
||||||
|
fi
|
||||||
|
|
||||||
|
local -a bench_args=(
|
||||||
|
--backend sglang
|
||||||
|
--host 127.0.0.1
|
||||||
|
--port "$ENGINE_PORT"
|
||||||
|
--model "$MODEL_PATH"
|
||||||
|
--dataset-name "$BENCH_DATASET_NAME"
|
||||||
|
--random-input-len "$isl"
|
||||||
|
--random-output-len "$osl"
|
||||||
|
--random-range-ratio "$RANDOM_RANGE_RATIO"
|
||||||
|
--num-prompts "$num_prompts"
|
||||||
|
--max-concurrency "$concurrency"
|
||||||
|
--request-rate 10000
|
||||||
|
--output-file "$container_output"
|
||||||
|
--output-details
|
||||||
|
)
|
||||||
|
if [[ "$BENCH_DATASET_NAME" == "random" ]]; then
|
||||||
|
bench_args+=(--dataset-path "$CONTAINER_DATASET_PATH")
|
||||||
|
else
|
||||||
|
bench_args+=(--tokenize-prompt)
|
||||||
|
fi
|
||||||
|
|
||||||
|
docker exec "$CONTAINER_NAME" mkdir -p /tmp/bench_outputs 2>/dev/null || true
|
||||||
|
docker exec "$CONTAINER_NAME" \
|
||||||
|
env HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 HF_DATASETS_OFFLINE=1 \
|
||||||
|
"$CONTAINER_PYTHON" -m sglang.bench_serving "${bench_args[@]}" || return $?
|
||||||
|
|
||||||
|
if [[ "$output_file" != "/dev/null" ]]; then
|
||||||
|
docker cp "$CONTAINER_NAME:${container_output}" "$output_file" || return 1
|
||||||
|
fi
|
||||||
|
}
|
||||||
|
|
||||||
|
export -f engine_run_bench
|
||||||
|
export ENGINE_PORT CONTAINER_NAME CONTAINER_PYTHON MODEL_PATH RESULT_BASE
|
||||||
|
export BENCH_DATASET_NAME CONTAINER_DATASET_PATH RANDOM_RANGE_RATIO BENCH_WARMUP_MAX_REQUESTS PYTHON
|
||||||
|
|
||||||
|
adaptive_main "$@"
|
||||||
@ -0,0 +1,162 @@
|
|||||||
|
#!/usr/bin/env bash
|
||||||
|
# Find the Total-TPS saturation concurrency for each SGLang TP/DP/ISL/OSL shape
|
||||||
|
# on Kunlun P800 (DeepSeek-V4-Flash-INT8).
|
||||||
|
set -Eeuo pipefail
|
||||||
|
|
||||||
|
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||||
|
EXPERIMENT_NAME="$(basename "$SCRIPT_DIR")"
|
||||||
|
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/lib.sh"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/platform.sh"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/config.env"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/adaptive_config.env"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/adaptive_bench_lib.sh"
|
||||||
|
|
||||||
|
ENGINE="sglang"
|
||||||
|
ENGINE_PORT="$SGLANG_PORT"
|
||||||
|
RESULT_BASE="${RESULT_BASE:-${SCRIPT_DIR}/adaptive_results}"
|
||||||
|
ACTIVE_ENGINE_SERVER_LOG=""
|
||||||
|
|
||||||
|
PYTHON="${PYTHON:-$(command -v python3)}"
|
||||||
|
|
||||||
|
# The shared library's GPU monitor uses nvidia-smi; override it with an
|
||||||
|
# xpu-smi sampler that emits the same CSV shape.
|
||||||
|
adaptive_start_gpu_monitor() {
|
||||||
|
local csv_path="$1"
|
||||||
|
mkdir -p "$(dirname "$csv_path")"
|
||||||
|
(
|
||||||
|
echo "timestamp, index, memory.used [MiB], memory.total [MiB], utilization.gpu [%]"
|
||||||
|
while true; do
|
||||||
|
xpu-smi 2>/dev/null | awk -F'|' -v ts="$(date '+%Y/%m/%d %H:%M:%S')" '
|
||||||
|
/^\|[[:space:]]*[0-9]+[[:space:]]/ {
|
||||||
|
l=$0; sub(/^\|[[:space:]]*/, "", l); sub(/[[:space:]].*$/, "", l); idx=l
|
||||||
|
}
|
||||||
|
/MiB/ && $4 ~ /%/ {
|
||||||
|
split($3, a, "/"); u=a[1]; t=a[2]
|
||||||
|
gsub(/[^0-9.]/, "", u); gsub(/[^0-9.]/, "", t)
|
||||||
|
r=$4; gsub(/[^0-9.]/, "", r)
|
||||||
|
printf "%s, %s, %s MiB, %s MiB, %s %%\n", ts, idx, u, t, r
|
||||||
|
}'
|
||||||
|
sleep "$GPU_MEM_SAMPLE_INTERVAL_S"
|
||||||
|
done
|
||||||
|
) >> "$csv_path" 2>/dev/null &
|
||||||
|
echo $!
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_is_healthy() {
|
||||||
|
curl --fail --silent --show-error --max-time 5 \
|
||||||
|
"http://127.0.0.1:${ENGINE_PORT}/health" >/dev/null 2>&1
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_stop_server() {
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
# The server only ever runs inside this experiment's dedicated container,
|
||||||
|
# so removing the container is a complete and safe stop.
|
||||||
|
log "stopping container ${CONTAINER_NAME} tp=${tp} dp=${dp}"
|
||||||
|
docker rm -f "$CONTAINER_NAME" >/dev/null 2>&1 || true
|
||||||
|
ACTIVE_ENGINE_SERVER_LOG=""
|
||||||
|
sleep 2
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_build_server_args() {
|
||||||
|
# Keep in sync with the launch args in start_sglang_docker.sh.
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
local ep_args=""
|
||||||
|
if (( dp == 1 )); then
|
||||||
|
ep_args="--ep-size ${EP_SIZE:-$tp}"
|
||||||
|
fi
|
||||||
|
local args="--host 0.0.0.0 --port ${ENGINE_PORT} --model-path /models --attention-backend nsa --nsa-prefill klxdsa --nsa-decode klxdsa --trust-remote-code --disable-custom-all-reduce --page-size 64 --mem-fraction-static ${MEM_FRACTION_STATIC} --tensor-parallel-size ${tp} ${ep_args} --disable-shared-experts-fusion --quantization w8a8_int8 --kv-cache-dtype float16 --disable-piecewise-cuda-graph --cuda-graph-max-bs 32 --watchdog-timeout 3000000 --tool-call-parser deepseekv4 --reasoning-parser deepseek-v4 --constrained-json-disable-any-whitespace --enable-metrics --enable-request-time-stats-logging --context-length ${CONTEXT_LENGTH} --chunked-prefill-size ${CHUNKED_PREFILL_SIZE} --max-prefill-tokens ${MAX_PREFILL_TOKENS} --max-running-requests ${MAX_RUNNING_REQUESTS} --speculative-algorithm EAGLE --speculative-num-steps ${SPEC_NUM_STEPS} --speculative-eagle-topk ${SPEC_EAGLE_TOPK} --speculative-num-draft-tokens ${SPEC_NUM_DRAFT_TOKENS}"
|
||||||
|
if (( dp > 1 )); then
|
||||||
|
args="${args} --dp-size ${dp}"
|
||||||
|
fi
|
||||||
|
printf '%s' "$args"
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_start_server() {
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
local outer_log="${ADAPTIVE_LOG_DIR}/sglang_tp${tp}_dp${dp}.server.outer.log"
|
||||||
|
log "starting sglang server tp=${tp} dp=${dp}"
|
||||||
|
bash "${SCRIPT_DIR}/start_sglang_docker.sh" "$tp" "$dp" >> "$outer_log" 2>&1
|
||||||
|
if ! engine_is_healthy; then
|
||||||
|
log "ERROR: sglang health check failed tp=${tp} dp=${dp}"
|
||||||
|
return 1
|
||||||
|
fi
|
||||||
|
ACTIVE_ENGINE_SERVER_LOG=""
|
||||||
|
log "sglang server healthy tp=${tp} dp=${dp} log=docker logs ${CONTAINER_NAME}"
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_detect_oom() {
|
||||||
|
local detail_log="$1"
|
||||||
|
local tp="$2"
|
||||||
|
local dp="$3"
|
||||||
|
local pattern='CUDA out of memory|torch\.OutOfMemoryError|OutOfMemory|out of memory|OOM|RESOURCE_EXHAUSTED|Failed to allocate memory'
|
||||||
|
if grep -Eiq "$pattern" "$detail_log" 2>/dev/null; then
|
||||||
|
return 0
|
||||||
|
fi
|
||||||
|
docker logs --tail 2000 "$CONTAINER_NAME" 2>/dev/null | grep -Eiq "$pattern"
|
||||||
|
}
|
||||||
|
|
||||||
|
engine_run_bench() {
|
||||||
|
local isl="$1"
|
||||||
|
local osl="$2"
|
||||||
|
local concurrency="$3"
|
||||||
|
local num_prompts="$4"
|
||||||
|
local output_file="$5"
|
||||||
|
local container_output="/tmp/bench_outputs/adaptive_$(basename "$output_file")"
|
||||||
|
if [[ "$output_file" == "/dev/null" ]]; then
|
||||||
|
container_output="/dev/null"
|
||||||
|
fi
|
||||||
|
|
||||||
|
local -a bench_args=(
|
||||||
|
--backend sglang
|
||||||
|
--host 127.0.0.1
|
||||||
|
--port "$ENGINE_PORT"
|
||||||
|
--model "$MODEL_PATH"
|
||||||
|
--dataset-name "$BENCH_DATASET_NAME"
|
||||||
|
--random-input-len "$isl"
|
||||||
|
--random-output-len "$osl"
|
||||||
|
--random-range-ratio "$RANDOM_RANGE_RATIO"
|
||||||
|
--num-prompts "$num_prompts"
|
||||||
|
--max-concurrency "$concurrency"
|
||||||
|
--request-rate 10000
|
||||||
|
--output-file "$container_output"
|
||||||
|
--output-details
|
||||||
|
)
|
||||||
|
if [[ "$BENCH_DATASET_NAME" == "random" ]]; then
|
||||||
|
bench_args+=(--dataset-path "$CONTAINER_DATASET_PATH")
|
||||||
|
else
|
||||||
|
bench_args+=(--tokenize-prompt)
|
||||||
|
fi
|
||||||
|
|
||||||
|
docker exec "$CONTAINER_NAME" mkdir -p /tmp/bench_outputs 2>/dev/null || true
|
||||||
|
docker exec "$CONTAINER_NAME" \
|
||||||
|
env HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 HF_DATASETS_OFFLINE=1 \
|
||||||
|
"$CONTAINER_PYTHON" -m sglang.bench_serving "${bench_args[@]}" || return $?
|
||||||
|
|
||||||
|
if [[ "$output_file" != "/dev/null" ]]; then
|
||||||
|
docker cp "$CONTAINER_NAME:${container_output}" "$output_file" || return 1
|
||||||
|
fi
|
||||||
|
}
|
||||||
|
|
||||||
|
export -f engine_run_bench
|
||||||
|
export ENGINE_PORT CONTAINER_NAME CONTAINER_PYTHON MODEL_PATH RESULT_BASE
|
||||||
|
export BENCH_DATASET_NAME CONTAINER_DATASET_PATH RANDOM_RANGE_RATIO BENCH_WARMUP_MAX_REQUESTS PYTHON
|
||||||
|
|
||||||
|
export SEARCH_START_CONCURRENCY=16
|
||||||
|
export SEARCH_ADDEND=16
|
||||||
|
# If the initial concurrency violates the TTFT SLO, search downward. Stop at
|
||||||
|
# the first acceptable value (16 -> 8; only try 1 when 8 still violates it).
|
||||||
|
export SEARCH_INITIAL_BACKOFF_CONCURRENCIES="8 1"
|
||||||
|
# When concurrency 1 still has a severely excessive TTFT, stop the remaining
|
||||||
|
# shapes in this TP/DP group. Zero disables this rule.
|
||||||
|
export TTFT_GROUP_SKIP_MS="${TTFT_GROUP_SKIP_MS:-8000}"
|
||||||
|
|
||||||
|
adaptive_main "$@"
|
||||||
559
experiments/p800/dsv4_p800_sglang_tp_dp_official/run_bench.sh
Executable file
559
experiments/p800/dsv4_p800_sglang_tp_dp_official/run_bench.sh
Executable file
@ -0,0 +1,559 @@
|
|||||||
|
#!/usr/bin/env bash
|
||||||
|
# TP×DP matrix benchmark for DeepSeek-V4-Flash-INT8 on Kunlun P800 SGLang (Docker).
|
||||||
|
set -Eeuo pipefail
|
||||||
|
|
||||||
|
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||||
|
EXPERIMENT_NAME="$(basename "$SCRIPT_DIR")"
|
||||||
|
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/lib.sh"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/platform.sh"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/config.env"
|
||||||
|
|
||||||
|
RUN_ID="${RUN_ID:-$(date '+%Y%m%d-%H%M%S')}"
|
||||||
|
RESULT_BASE="${SCRIPT_DIR}/results"
|
||||||
|
MATRIX_FILE="${MATRIX_FILE:-${SCRIPT_DIR}/matrix.json}"
|
||||||
|
MATRIX_MODE="${MATRIX_MODE:-Y}"
|
||||||
|
SCENARIO_TIMEOUT_S="${SCENARIO_TIMEOUT_S:-1800}"
|
||||||
|
GPU_MEM_SAMPLE_INTERVAL_S="${GPU_MEM_SAMPLE_INTERVAL_S:-1}"
|
||||||
|
DRY_RUN="${DRY_RUN:-0}"
|
||||||
|
GRID_LIMIT="${GRID_LIMIT:-0}"
|
||||||
|
|
||||||
|
PYTHON="${PYTHON:-$(command -v python3)}"
|
||||||
|
|
||||||
|
# Export variables used inside functions that are called via bash -c subshells.
|
||||||
|
export CONTAINER_NAME CONTAINER_PYTHON MODEL_PATH SGLANG_PORT CONTAINER_DATASET_PATH
|
||||||
|
|
||||||
|
log_dir_global="${RESULT_BASE}/${RUN_ID}/logs"
|
||||||
|
mkdir -p "$log_dir_global"
|
||||||
|
log_init "${log_dir_global}/orchestrator.log"
|
||||||
|
|
||||||
|
log "experiment=${EXPERIMENT_NAME} run_id=${RUN_ID} platform=${PLATFORM} hardware=${HARDWARE}"
|
||||||
|
log "matrix_mode=${MATRIX_MODE} matrix_file=${MATRIX_FILE} dry_run=${DRY_RUN} grid_limit=${GRID_LIMIT}"
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Helpers
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
|
||||||
|
is_server_healthy() {
|
||||||
|
curl --fail --silent --show-error --max-time 5 "http://127.0.0.1:${SGLANG_PORT}/health" >/dev/null 2>&1
|
||||||
|
}
|
||||||
|
|
||||||
|
container_running() {
|
||||||
|
docker inspect "$CONTAINER_NAME" >/dev/null 2>&1
|
||||||
|
}
|
||||||
|
|
||||||
|
stop_server() {
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
|
||||||
|
# The server only ever runs inside this experiment's dedicated container,
|
||||||
|
# so removing the container is a complete and safe stop.
|
||||||
|
if container_running; then
|
||||||
|
log "stopping container ${CONTAINER_NAME} (tp=${tp}, dp=${dp})"
|
||||||
|
docker rm -f "$CONTAINER_NAME" >/dev/null 2>&1 || true
|
||||||
|
else
|
||||||
|
docker rm -f "$CONTAINER_NAME" >/dev/null 2>&1 || true
|
||||||
|
fi
|
||||||
|
sleep 2
|
||||||
|
}
|
||||||
|
|
||||||
|
build_server_args() {
|
||||||
|
# Keep in sync with the launch args in start_sglang_docker.sh.
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
|
||||||
|
local ep_args=""
|
||||||
|
if (( dp == 1 )); then
|
||||||
|
ep_args="--ep-size ${EP_SIZE:-$tp}"
|
||||||
|
fi
|
||||||
|
local args="--host 0.0.0.0 --port ${SGLANG_PORT} --model-path /models --attention-backend nsa --nsa-prefill klxdsa --nsa-decode klxdsa --trust-remote-code --disable-custom-all-reduce --page-size 64 --mem-fraction-static ${MEM_FRACTION_STATIC} --tensor-parallel-size ${tp} ${ep_args} --disable-shared-experts-fusion --quantization w8a8_int8 --kv-cache-dtype float16 --disable-piecewise-cuda-graph --cuda-graph-max-bs 32 --watchdog-timeout 3000000 --tool-call-parser deepseekv4 --reasoning-parser deepseek-v4 --constrained-json-disable-any-whitespace --enable-metrics --enable-request-time-stats-logging --context-length ${CONTEXT_LENGTH} --chunked-prefill-size ${CHUNKED_PREFILL_SIZE} --max-prefill-tokens ${MAX_PREFILL_TOKENS} --max-running-requests ${MAX_RUNNING_REQUESTS} --speculative-algorithm EAGLE --speculative-num-steps ${SPEC_NUM_STEPS} --speculative-eagle-topk ${SPEC_EAGLE_TOPK} --speculative-num-draft-tokens ${SPEC_NUM_DRAFT_TOKENS}"
|
||||||
|
if [[ "$dp" -gt 1 ]]; then
|
||||||
|
args="${args} --dp-size ${dp}"
|
||||||
|
fi
|
||||||
|
printf '%s' "$args"
|
||||||
|
}
|
||||||
|
|
||||||
|
start_server() {
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
|
||||||
|
log "starting sglang server tp=${tp} dp=${dp}"
|
||||||
|
bash "${SCRIPT_DIR}/start_sglang_docker.sh" "$tp" "$dp" \
|
||||||
|
>> "${log_dir_global}/sglang_tp${tp}_dp${dp}.server.outer.log" 2>&1
|
||||||
|
|
||||||
|
if ! is_server_healthy; then
|
||||||
|
log "error: sglang server tp=${tp} dp=${dp} failed health check on port ${SGLANG_PORT}"
|
||||||
|
return 1
|
||||||
|
fi
|
||||||
|
log "sglang server tp=${tp} dp=${dp} is healthy on port ${SGLANG_PORT}"
|
||||||
|
}
|
||||||
|
|
||||||
|
restart_server() {
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
log "restarting sglang server tp=${tp} dp=${dp} after non-OOM failure"
|
||||||
|
stop_server "$tp" "$dp"
|
||||||
|
sleep 10
|
||||||
|
start_server "$tp" "$dp"
|
||||||
|
}
|
||||||
|
|
||||||
|
run_bench_serving() {
|
||||||
|
# Run sglang.bench_serving inside the server container (the P800 image
|
||||||
|
# carries the only working client environment for this fork).
|
||||||
|
docker exec "$CONTAINER_NAME" \
|
||||||
|
env HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 HF_DATASETS_OFFLINE=1 \
|
||||||
|
"$CONTAINER_PYTHON" -m sglang.bench_serving "$@"
|
||||||
|
}
|
||||||
|
export -f run_bench_serving
|
||||||
|
|
||||||
|
run_warmup() {
|
||||||
|
local input_len="$1"
|
||||||
|
local output_len="$2"
|
||||||
|
|
||||||
|
log "warming up (input=${input_len}, output=${output_len}, num=1)"
|
||||||
|
bash -c '
|
||||||
|
run_bench_serving \
|
||||||
|
--backend sglang \
|
||||||
|
--host 127.0.0.1 \
|
||||||
|
--port "'"$SGLANG_PORT"'" \
|
||||||
|
--model "'"$MODEL_PATH"'" \
|
||||||
|
--dataset-name random \
|
||||||
|
--dataset-path "'"$CONTAINER_DATASET_PATH"'" \
|
||||||
|
--random-input-len "'"$input_len"'" \
|
||||||
|
--random-output-len "'"$output_len"'" \
|
||||||
|
--random-range-ratio 1.0 \
|
||||||
|
--num-prompts 1 \
|
||||||
|
--max-concurrency 1 \
|
||||||
|
--request-rate 10000 \
|
||||||
|
--output-file /dev/null \
|
||||||
|
--output-details \
|
||||||
|
>> "'"${log_dir_global}/warmup.log"'" 2>&1
|
||||||
|
' || log "WARNING: warmup failed; continuing with config"
|
||||||
|
log "warmup completed"
|
||||||
|
}
|
||||||
|
|
||||||
|
scenario_already_completed() {
|
||||||
|
local output_file="$1"
|
||||||
|
local expected="$2"
|
||||||
|
[[ -s "$output_file" ]] || return 1
|
||||||
|
local completed
|
||||||
|
completed="$("$PYTHON" -c "
|
||||||
|
import json, sys
|
||||||
|
path = sys.argv[1]
|
||||||
|
try:
|
||||||
|
with open(path, 'r', encoding='utf-8') as f:
|
||||||
|
for line in f:
|
||||||
|
line = line.strip()
|
||||||
|
if line:
|
||||||
|
data = json.loads(line)
|
||||||
|
print(data.get('completed', 0))
|
||||||
|
break
|
||||||
|
except Exception:
|
||||||
|
print(0)
|
||||||
|
" "$output_file")"
|
||||||
|
[[ "${completed:-0}" -ge "$expected" ]]
|
||||||
|
}
|
||||||
|
|
||||||
|
scenario_already_processed() {
|
||||||
|
local result_root="$1"
|
||||||
|
local scenario_name="$2"
|
||||||
|
local json_path="${result_root}/results.json"
|
||||||
|
[[ -f "$json_path" ]] || return 1
|
||||||
|
"$PYTHON" -c "
|
||||||
|
import json, sys
|
||||||
|
path, name = sys.argv[1], sys.argv[2]
|
||||||
|
try:
|
||||||
|
with open(path, 'r', encoding='utf-8') as f:
|
||||||
|
data = json.load(f)
|
||||||
|
for s in data.get('scenarios', []):
|
||||||
|
if s.get('name') == name:
|
||||||
|
if s.get('status') or s.get('metrics', {}).get('success', 0) > 0:
|
||||||
|
sys.exit(0)
|
||||||
|
except Exception:
|
||||||
|
pass
|
||||||
|
sys.exit(1)
|
||||||
|
" "$json_path" "$scenario_name"
|
||||||
|
}
|
||||||
|
|
||||||
|
detect_oom() {
|
||||||
|
local detail_log="$1"
|
||||||
|
local pattern='CUDA out of memory|torch\.OutOfMemoryError|OutOfMemory|out of memory|OOM|RESOURCE_EXHAUSTED|Failed to allocate memory'
|
||||||
|
if grep -Eiq "$pattern" "$detail_log" 2>/dev/null; then
|
||||||
|
return 0
|
||||||
|
fi
|
||||||
|
docker logs --tail 2000 "$CONTAINER_NAME" 2>/dev/null | grep -Eiq "$pattern"
|
||||||
|
}
|
||||||
|
|
||||||
|
start_gpu_monitor() {
|
||||||
|
# Sample XPU memory/utilization with xpu-smi and emit nvidia-smi-style CSV
|
||||||
|
# rows so scripts/common/parse_backend.py can read them unchanged.
|
||||||
|
local csv_path="$1"
|
||||||
|
mkdir -p "$(dirname "$csv_path")"
|
||||||
|
(
|
||||||
|
echo "timestamp, index, memory.used [MiB], memory.total [MiB], utilization.gpu [%]"
|
||||||
|
while true; do
|
||||||
|
xpu-smi 2>/dev/null | awk -F'|' -v ts="$(date '+%Y/%m/%d %H:%M:%S')" '
|
||||||
|
/^\|[[:space:]]*[0-9]+[[:space:]]/ {
|
||||||
|
l=$0; sub(/^\|[[:space:]]*/, "", l); sub(/[[:space:]].*$/, "", l); idx=l
|
||||||
|
}
|
||||||
|
/MiB/ && $4 ~ /%/ {
|
||||||
|
split($3, a, "/"); u=a[1]; t=a[2]
|
||||||
|
gsub(/[^0-9.]/, "", u); gsub(/[^0-9.]/, "", t)
|
||||||
|
r=$4; gsub(/[^0-9.]/, "", r)
|
||||||
|
printf "%s, %s, %s MiB, %s MiB, %s %%\n", ts, idx, u, t, r
|
||||||
|
}'
|
||||||
|
sleep "$GPU_MEM_SAMPLE_INTERVAL_S"
|
||||||
|
done
|
||||||
|
) >> "$csv_path" 2>/dev/null &
|
||||||
|
echo $!
|
||||||
|
}
|
||||||
|
|
||||||
|
stop_gpu_monitor() {
|
||||||
|
local pid="$1"
|
||||||
|
if kill -0 "$pid" 2>/dev/null; then
|
||||||
|
kill "$pid" 2>/dev/null || true
|
||||||
|
sleep 1
|
||||||
|
kill -9 "$pid" 2>/dev/null || true
|
||||||
|
fi
|
||||||
|
}
|
||||||
|
|
||||||
|
scenario_config_json() {
|
||||||
|
"$PYTHON" -c "
|
||||||
|
import json, sys
|
||||||
|
print(json.dumps({
|
||||||
|
'phase': 'main',
|
||||||
|
'concurrency': int(sys.argv[1]),
|
||||||
|
'input_len': int(sys.argv[2]),
|
||||||
|
'output_len': int(sys.argv[3]),
|
||||||
|
'dataset': 'random',
|
||||||
|
'num_prompts': int(sys.argv[4]),
|
||||||
|
}))
|
||||||
|
" "$1" "$2" "$3" "$4"
|
||||||
|
}
|
||||||
|
|
||||||
|
append_scenario_record() {
|
||||||
|
local result_root="$1"
|
||||||
|
local json_path="$result_root/results.json"
|
||||||
|
shift
|
||||||
|
local scenario_json
|
||||||
|
scenario_json="$("$PYTHON" -c "
|
||||||
|
import json, sys
|
||||||
|
pairs = [a.split('=', 1) for a in sys.argv[1:]]
|
||||||
|
d = {}
|
||||||
|
for k, v in pairs:
|
||||||
|
try:
|
||||||
|
d[k] = json.loads(v)
|
||||||
|
except json.JSONDecodeError:
|
||||||
|
d[k] = v
|
||||||
|
print(json.dumps(d, ensure_ascii=False))
|
||||||
|
" "$@")"
|
||||||
|
PYTHON="$PYTHON" append_scenario_to_json "$json_path" "$scenario_json"
|
||||||
|
}
|
||||||
|
|
||||||
|
record_skipped_csv() {
|
||||||
|
local csv_path="$1"
|
||||||
|
shift
|
||||||
|
# Args: key=value
|
||||||
|
local row
|
||||||
|
row="$("$PYTHON" -c "
|
||||||
|
import csv, json, sys, io
|
||||||
|
pairs = [a.split('=', 1) for a in sys.argv[1:]]
|
||||||
|
d = {}
|
||||||
|
for k, v in pairs:
|
||||||
|
try:
|
||||||
|
d[k] = json.loads(v)
|
||||||
|
except json.JSONDecodeError:
|
||||||
|
d[k] = v
|
||||||
|
buf = io.StringIO()
|
||||||
|
writer = csv.DictWriter(buf, fieldnames=['engine','tp','dp','mark','isl','osl','concurrency','status','reason','detail_log'], extrasaction='ignore')
|
||||||
|
writer.writerow(d)
|
||||||
|
print(buf.getvalue().strip())
|
||||||
|
" "$@")"
|
||||||
|
echo "$row" >> "$csv_path"
|
||||||
|
}
|
||||||
|
|
||||||
|
skip_remaining_scenarios() {
|
||||||
|
local result_root="$1"
|
||||||
|
local scenario_tsv="$2"
|
||||||
|
local start_index="$3"
|
||||||
|
local status="$4"
|
||||||
|
local reason="$5"
|
||||||
|
local tp="$6"
|
||||||
|
local dp="$7"
|
||||||
|
local skipped_csv="${RESULT_BASE}/${RUN_ID}/skipped_after_oom.csv"
|
||||||
|
|
||||||
|
local i=0
|
||||||
|
tail -n +2 "$scenario_tsv" | while IFS=$'\t' read -r mark isl osl conc num; do
|
||||||
|
if (( i < start_index )); then
|
||||||
|
i=$((i + 1))
|
||||||
|
continue
|
||||||
|
fi
|
||||||
|
i=$((i + 1))
|
||||||
|
local sname="c${conc}_i${isl}_o${osl}"
|
||||||
|
if scenario_already_processed "$result_root" "$sname"; then
|
||||||
|
continue
|
||||||
|
fi
|
||||||
|
append_scenario_record "$result_root" \
|
||||||
|
"name=${sname}" \
|
||||||
|
"config=$(scenario_config_json "$conc" "$isl" "$osl" "$num")" \
|
||||||
|
"status=\"${status}\"" \
|
||||||
|
"note=\"${reason}\""
|
||||||
|
record_skipped_csv "$skipped_csv" \
|
||||||
|
"engine=sglang" "tp=${tp}" "dp=${dp}" "mark=${mark}" "isl=${isl}" "osl=${osl}" "concurrency=${conc}" "status=${status}" "reason=${reason}"
|
||||||
|
done
|
||||||
|
}
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Per-configuration runner
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
|
||||||
|
run_parallel_config() {
|
||||||
|
local tp="$1"
|
||||||
|
local dp="$2"
|
||||||
|
local config_label="tp${tp}_dp${dp}"
|
||||||
|
local result_root="${RESULT_BASE}/${RUN_ID}/${config_label}"
|
||||||
|
local raw_dir="${result_root}/raw_outputs"
|
||||||
|
local gpu_log_dir="${result_root}/gpu_logs"
|
||||||
|
local phase_log_dir="${result_root}/logs"
|
||||||
|
mkdir -p "$raw_dir" "$gpu_log_dir" "$phase_log_dir"
|
||||||
|
|
||||||
|
log "===== ${config_label} START ====="
|
||||||
|
|
||||||
|
# Generate scenario list for this config.
|
||||||
|
local scenario_tsv="${result_root}/scenarios.tsv"
|
||||||
|
"$PYTHON" "${SCRIPT_DIR}/generate_scenarios.py" \
|
||||||
|
--matrix "$MATRIX_FILE" \
|
||||||
|
--mode "$MATRIX_MODE" \
|
||||||
|
> "$scenario_tsv"
|
||||||
|
local total_scenarios
|
||||||
|
total_scenarios="$(tail -n +2 "$scenario_tsv" | wc -l)"
|
||||||
|
log "generated ${total_scenarios} scenarios for ${config_label}"
|
||||||
|
|
||||||
|
# Write metadata.
|
||||||
|
ensure_result_root "$result_root"
|
||||||
|
write_metadata_json \
|
||||||
|
"${result_root}/results.json" \
|
||||||
|
"${EXPERIMENT_NAME}_${config_label}" \
|
||||||
|
"$RUN_ID" \
|
||||||
|
"$MODEL_PATH" \
|
||||||
|
"sglang" \
|
||||||
|
"sglang-xpu" \
|
||||||
|
"$HARDWARE" \
|
||||||
|
"$ACCELERATOR" \
|
||||||
|
"$CHIP" \
|
||||||
|
"experiments/p800/${EXPERIMENT_NAME}/run_bench.sh" \
|
||||||
|
"$DOCKER_IMAGE" \
|
||||||
|
"P800 SGLang TP×DP matrix for DeepSeek-V4-Flash-INT8"
|
||||||
|
|
||||||
|
local server_args_str
|
||||||
|
server_args_str="$(build_server_args "$tp" "$dp")"
|
||||||
|
"$PYTHON" - "$tp" "$dp" "$server_args_str" "${result_root}/results.json" <<'PY'
|
||||||
|
import json, os, sys
|
||||||
|
|
||||||
|
tp, dp, server_args, path = int(sys.argv[1]), int(sys.argv[2]), sys.argv[3], sys.argv[4]
|
||||||
|
with open(path, "r", encoding="utf-8") as f:
|
||||||
|
data = json.load(f)
|
||||||
|
data["config"] = {
|
||||||
|
"tp": tp,
|
||||||
|
"dp": dp,
|
||||||
|
"xpu_visible_devices": os.environ.get("XPU_VISIBLE_DEVICES", "0,1,2,3,4,5,6,7"),
|
||||||
|
"backend": "sglang",
|
||||||
|
"engine": "sglang-xpu",
|
||||||
|
"server_start_script": "experiments/p800/dsv4_p800_sglang_tp_dp_matrix/start_sglang_docker.sh",
|
||||||
|
"server_args": server_args,
|
||||||
|
}
|
||||||
|
with open(path, "w", encoding="utf-8") as f:
|
||||||
|
json.dump(data, f, indent=2, ensure_ascii=False)
|
||||||
|
PY
|
||||||
|
|
||||||
|
if [[ "$DRY_RUN" == "1" ]]; then
|
||||||
|
log "DRY_RUN: would start server with args: ${server_args_str}"
|
||||||
|
local line
|
||||||
|
tail -n +2 "$scenario_tsv" | while IFS=$'\t' read -r mark isl osl conc num; do
|
||||||
|
log "DRY_RUN: ${config_label} scenario mark=${mark} c=${conc} i=${isl} o=${osl} n=${num}"
|
||||||
|
done
|
||||||
|
log "===== ${config_label} DONE (dry run) ====="
|
||||||
|
return 0
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Initialize skipped_after_oom.csv for this run.
|
||||||
|
local skipped_csv="${RESULT_BASE}/${RUN_ID}/skipped_after_oom.csv"
|
||||||
|
if [[ ! -f "$skipped_csv" ]]; then
|
||||||
|
echo "engine,tp,dp,mark,isl,osl,concurrency,status,reason,detail_log" > "$skipped_csv"
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Start server once for this TP×DP config.
|
||||||
|
if ! start_server "$tp" "$dp"; then
|
||||||
|
log "ERROR: ${config_label} failed to start; skipping all scenarios"
|
||||||
|
skip_remaining_scenarios "$result_root" "$scenario_tsv" 0 "SKIPPED_SERVICE_START_FAILED" "service failed to start" "$tp" "$dp"
|
||||||
|
log "===== ${config_label} DONE ====="
|
||||||
|
return 0
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Warmup with a small prompt before the first scenario.
|
||||||
|
run_warmup 1024 128 || true
|
||||||
|
|
||||||
|
# Read scenarios into an array so we can skip remaining entries on failure.
|
||||||
|
local -a scenarios=()
|
||||||
|
while IFS= read -r line; do
|
||||||
|
scenarios+=("$line")
|
||||||
|
done < <(tail -n +2 "$scenario_tsv")
|
||||||
|
|
||||||
|
local i mark isl osl conc num
|
||||||
|
local output_file container_output detail_log gpu_csv sname bench_rc
|
||||||
|
for (( i = 0; i < ${#scenarios[@]}; i++ )); do
|
||||||
|
IFS=$'\t' read -r mark isl osl conc num <<< "${scenarios[$i]}"
|
||||||
|
|
||||||
|
if [[ "$GRID_LIMIT" -gt 0 && "$i" -ge "$GRID_LIMIT" ]]; then
|
||||||
|
log "GRID_LIMIT=${GRID_LIMIT} reached; skipping remaining scenarios"
|
||||||
|
skip_remaining_scenarios "$result_root" "$scenario_tsv" "$i" "SKIPPED_GRID_LIMIT" "GRID_LIMIT reached" "$tp" "$dp"
|
||||||
|
break
|
||||||
|
fi
|
||||||
|
|
||||||
|
sname="c${conc}_i${isl}_o${osl}"
|
||||||
|
output_file="${raw_dir}/sglang_main_${conc}_${isl}_${osl}.jsonl"
|
||||||
|
container_output="/tmp/bench_outputs/sglang_main_${conc}_${isl}_${osl}.jsonl"
|
||||||
|
detail_log="${phase_log_dir}/sglang_${config_label}_${sname}.log"
|
||||||
|
gpu_csv="${gpu_log_dir}/gpu_mem_${conc}_${isl}_${osl}.csv"
|
||||||
|
|
||||||
|
if scenario_already_completed "$output_file" "$num" || scenario_already_processed "$result_root" "$sname"; then
|
||||||
|
log "skipping already-processed ${config_label} scenario: ${sname}"
|
||||||
|
continue
|
||||||
|
fi
|
||||||
|
|
||||||
|
if ! container_running; then
|
||||||
|
log "WARNING: container ${CONTAINER_NAME} died, skipping remaining scenarios in ${config_label}"
|
||||||
|
skip_remaining_scenarios "$result_root" "$scenario_tsv" "$i" "SKIPPED_CONTAINER_DIED" "container died" "$tp" "$dp"
|
||||||
|
break
|
||||||
|
fi
|
||||||
|
|
||||||
|
log "running ${config_label} scenario: mark=${mark} c=${conc} i=${isl} o=${osl} n=${num}"
|
||||||
|
|
||||||
|
local gpu_pid
|
||||||
|
gpu_pid="$(start_gpu_monitor "$gpu_csv")"
|
||||||
|
|
||||||
|
bench_rc=0
|
||||||
|
docker exec "$CONTAINER_NAME" mkdir -p /tmp/bench_outputs
|
||||||
|
timeout "$SCENARIO_TIMEOUT_S" bash -c '
|
||||||
|
run_bench_serving \
|
||||||
|
--backend sglang \
|
||||||
|
--host 127.0.0.1 \
|
||||||
|
--port "'"$SGLANG_PORT"'" \
|
||||||
|
--model "'"$MODEL_PATH"'" \
|
||||||
|
--dataset-name random \
|
||||||
|
--dataset-path "'"$CONTAINER_DATASET_PATH"'" \
|
||||||
|
--random-input-len "'"$isl"'" \
|
||||||
|
--random-output-len "'"$osl"'" \
|
||||||
|
--random-range-ratio 1.0 \
|
||||||
|
--num-prompts "'"$num"'" \
|
||||||
|
--max-concurrency "'"$conc"'" \
|
||||||
|
--request-rate 10000 \
|
||||||
|
--output-file "'"$container_output"'" \
|
||||||
|
--output-details \
|
||||||
|
> "'"$detail_log"'" 2>&1
|
||||||
|
' || bench_rc=$?
|
||||||
|
|
||||||
|
stop_gpu_monitor "$gpu_pid"
|
||||||
|
|
||||||
|
if [[ "$bench_rc" -eq 0 ]]; then
|
||||||
|
if docker cp "${CONTAINER_NAME}:${container_output}" "$output_file"; then
|
||||||
|
log "finished ${config_label} scenario: output=${output_file}"
|
||||||
|
append_scenario_record "$result_root" \
|
||||||
|
"name=${sname}" \
|
||||||
|
"config=$(scenario_config_json "$conc" "$isl" "$osl" "$num")" \
|
||||||
|
"status=\"completed\"" \
|
||||||
|
"note=\"benchmark finished successfully\""
|
||||||
|
continue
|
||||||
|
fi
|
||||||
|
log "ERROR: failed to copy ${container_output} from container"
|
||||||
|
bench_rc=1
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Failure handling.
|
||||||
|
if detect_oom "$detail_log"; then
|
||||||
|
log "ERROR: ${config_label} scenario ${sname} triggered OOM; stopping config"
|
||||||
|
append_scenario_record "$result_root" \
|
||||||
|
"name=${sname}" \
|
||||||
|
"config=$(scenario_config_json "$conc" "$isl" "$osl" "$num")" \
|
||||||
|
"status=\"OOM\"" \
|
||||||
|
"note=\"detected out-of-memory\""
|
||||||
|
record_skipped_csv "$skipped_csv" \
|
||||||
|
"engine=sglang" "tp=${tp}" "dp=${dp}" "mark=${mark}" "isl=${isl}" "osl=${osl}" "concurrency=${conc}" "status=OOM" "reason=detected out-of-memory" "detail_log=${detail_log}"
|
||||||
|
stop_server "$tp" "$dp"
|
||||||
|
skip_remaining_scenarios "$result_root" "$scenario_tsv" "$((i + 1))" "SKIPPED_AFTER_OOM" "previous case OOM" "$tp" "$dp"
|
||||||
|
break
|
||||||
|
fi
|
||||||
|
|
||||||
|
log "ERROR: ${config_label} scenario ${sname} failed (rc=${bench_rc}); see ${detail_log}"
|
||||||
|
if [[ "$mark" == "P" ]]; then
|
||||||
|
log "optional (P) scenario failed; recording as skipped and continuing"
|
||||||
|
append_scenario_record "$result_root" \
|
||||||
|
"name=${sname}" \
|
||||||
|
"config=$(scenario_config_json "$conc" "$isl" "$osl" "$num")" \
|
||||||
|
"status=\"skipped_optional\"" \
|
||||||
|
"note=\"optional scenario failed (rc=${bench_rc})\""
|
||||||
|
record_skipped_csv "$skipped_csv" \
|
||||||
|
"engine=sglang" "tp=${tp}" "dp=${dp}" "mark=${mark}" "isl=${isl}" "osl=${osl}" "concurrency=${conc}" "status=skipped_optional" "reason=optional scenario failed (rc=${bench_rc})" "detail_log=${detail_log}"
|
||||||
|
continue
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Mandatory scenario failed but not OOM: try to restart the server.
|
||||||
|
if restart_server "$tp" "$dp"; then
|
||||||
|
run_warmup 1024 128 || true
|
||||||
|
log "resuming ${config_label} after server restart"
|
||||||
|
continue
|
||||||
|
fi
|
||||||
|
|
||||||
|
log "ERROR: ${config_label} server restart failed; skipping remaining scenarios"
|
||||||
|
append_scenario_record "$result_root" \
|
||||||
|
"name=${sname}" \
|
||||||
|
"config=$(scenario_config_json "$conc" "$isl" "$osl" "$num")" \
|
||||||
|
"status=\"FAILED\"" \
|
||||||
|
"note=\"scenario failed and server restart failed (rc=${bench_rc})\""
|
||||||
|
record_skipped_csv "$skipped_csv" \
|
||||||
|
"engine=sglang" "tp=${tp}" "dp=${dp}" "mark=${mark}" "isl=${isl}" "osl=${osl}" "concurrency=${conc}" "status=FAILED" "reason=scenario failed and server restart failed" "detail_log=${detail_log}"
|
||||||
|
skip_remaining_scenarios "$result_root" "$scenario_tsv" "$((i + 1))" "SKIPPED_RESTART_FAILED" "server restart failed" "$tp" "$dp"
|
||||||
|
break
|
||||||
|
done
|
||||||
|
|
||||||
|
stop_server "$tp" "$dp"
|
||||||
|
|
||||||
|
# Parse results.
|
||||||
|
log "parsing ${config_label} results"
|
||||||
|
"$PYTHON" "${SCRIPT_DIR}/../../../scripts/common/parse_backend.py" "$result_root" --backend sglang \
|
||||||
|
>> "${phase_log_dir}/parse.log" 2>&1 || {
|
||||||
|
log "WARNING: parser failed for ${config_label}; see ${phase_log_dir}/parse.log"
|
||||||
|
}
|
||||||
|
|
||||||
|
log "===== ${config_label} DONE ====="
|
||||||
|
}
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Main
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
|
||||||
|
# Cleanup any leftovers.
|
||||||
|
for cfg in "${PARALLEL_CONFIGS[@]}"; do
|
||||||
|
read -r tp dp <<< "$cfg"
|
||||||
|
stop_server "$tp" "$dp"
|
||||||
|
done
|
||||||
|
|
||||||
|
# Run each parallel configuration.
|
||||||
|
for cfg in "${PARALLEL_CONFIGS[@]}"; do
|
||||||
|
read -r tp dp <<< "$cfg"
|
||||||
|
run_parallel_config "$tp" "$dp"
|
||||||
|
done
|
||||||
|
|
||||||
|
# Generate cross-configuration comparison.
|
||||||
|
log "generating comparison report"
|
||||||
|
"$PYTHON" "${SCRIPT_DIR}/compare.py" \
|
||||||
|
--run-root "${RESULT_BASE}/${RUN_ID}" \
|
||||||
|
--output "${RESULT_BASE}/${RUN_ID}/comparison.md" \
|
||||||
|
>> "${log_dir_global}/compare.log" 2>&1 || {
|
||||||
|
log "WARNING: comparison script failed; see ${log_dir_global}/compare.log"
|
||||||
|
}
|
||||||
|
|
||||||
|
log "all results saved to ${RESULT_BASE}/${RUN_ID}"
|
||||||
183
experiments/p800/dsv4_p800_sglang_tp_dp_official/start_sglang_docker.sh
Executable file
183
experiments/p800/dsv4_p800_sglang_tp_dp_official/start_sglang_docker.sh
Executable file
@ -0,0 +1,183 @@
|
|||||||
|
#!/usr/bin/env bash
|
||||||
|
# Start the P800 SGLang INT8 server in Docker for a given TP×DP configuration.
|
||||||
|
# Usage: start_sglang_docker.sh <TP> <DP>
|
||||||
|
#
|
||||||
|
# Based on the proven P800 INT8 launch from dsv4_p800_long_context_matrix,
|
||||||
|
# with --tensor-parallel-size/--dp-size parameterized per config.
|
||||||
|
set -Eeuo pipefail
|
||||||
|
|
||||||
|
TP="${1:-}"
|
||||||
|
DP="${2:-}"
|
||||||
|
if [[ -z "$TP" || -z "$DP" ]]; then
|
||||||
|
echo "Usage: $0 <TP> <DP>"
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
|
||||||
|
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/lib.sh"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/../../../scripts/common/platform.sh"
|
||||||
|
# shellcheck source=/dev/null
|
||||||
|
source "${SCRIPT_DIR}/config.env"
|
||||||
|
|
||||||
|
PORT="${SGLANG_PORT:-30014}"
|
||||||
|
RUNTIME_BASE="${RUNTIME_BASE:-${SCRIPT_DIR}/runtime}"
|
||||||
|
mkdir -p "${RUNTIME_BASE}/logs"
|
||||||
|
SERVER_LOG="${RUNTIME_BASE}/logs/${EXPERIMENT}_sglang_docker_tp${TP}_dp${DP}_$(date +%Y%m%d_%H%M%S).log"
|
||||||
|
|
||||||
|
log "starting P800 SGLang INT8 server (tp=${TP}, dp=${DP})"
|
||||||
|
log "model: ${MODEL_PATH}"
|
||||||
|
log "port: ${PORT}"
|
||||||
|
log "container: ${CONTAINER_NAME}"
|
||||||
|
|
||||||
|
# Stop any existing container with the same name.
|
||||||
|
docker rm -f "$CONTAINER_NAME" 2>/dev/null || true
|
||||||
|
|
||||||
|
# Build device args.
|
||||||
|
device_args=""
|
||||||
|
for i in 0 1 2 3 4 5 6 7; do
|
||||||
|
device_args="${device_args} --device /dev/xpu${i}:/dev/xpu${i}"
|
||||||
|
done
|
||||||
|
device_args="${device_args} --device /dev/xpuctrl:/dev/xpuctrl"
|
||||||
|
|
||||||
|
# Environment variables required by the P800 SGLang INT8 image.
|
||||||
|
env_args=(
|
||||||
|
-e XPU_VISIBLE_DEVICES="${XPU_VISIBLE_DEVICES}"
|
||||||
|
-e CUDA_VISIBLE_DEVICES="${CUDA_VISIBLE_DEVICES}"
|
||||||
|
-e CUDA_DEVICE_ORDER=OAM_ID
|
||||||
|
-e SGLANG_USE_TRANSFORMERS_V5_TOKENIZER=1
|
||||||
|
-e XMLIR_FORCE_USE_XPU_GRAPH=1
|
||||||
|
-e SGLANG_DSV4_MODE=2604
|
||||||
|
-e PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
|
||||||
|
-e SGLANG_NSA_DUAL_STREAM=true
|
||||||
|
-e SGLANG_NSA_QUANT_WQ_B_WK=false
|
||||||
|
-e SGLANG_FP8_PAGED_MQA_LOGITS_TORCH=1
|
||||||
|
-e SGLANG_OPT_DEEPGEMM_HC_PRENORM=false
|
||||||
|
-e SGLANG_OPT_USE_TILELANG_MHC_PRE=1
|
||||||
|
-e SGLANG_OPT_USE_TILELANG_MHC_POST=1
|
||||||
|
-e SGLANG_CLEAN_REQUEST_WHEN_RETRACT=1
|
||||||
|
-e SGLANG_SET_CPU_AFFINITY=1
|
||||||
|
-e SGLANG_OPT_USE_KLX_TOPK_KERNEL=1
|
||||||
|
-e XSGL_INTERTYPE_BFP16=1
|
||||||
|
-e ENABLE_FAST_BFP16_ATTN=1
|
||||||
|
-e XSGL_USE_DEEP_GEMM_BMM=1
|
||||||
|
-e XSGL_XDNN_QUANT=1
|
||||||
|
-e XSGL_FUSE_RMS_NORM_QUANT=1
|
||||||
|
-e XSGL_TRANSPOSE_MATMUL_WEIGHT=1
|
||||||
|
-e XINFER_QUANT_SDNN=1
|
||||||
|
-e XSGL_USE_MOE_SIGMOID_GROUP_TOPK_NORM=1
|
||||||
|
-e XSGL_EARLY_FIRST_TOKEN=1
|
||||||
|
-e XSGL_ENABLE_TGEMM_FP16=1
|
||||||
|
-e SGLANG_ENABLE_SPEC_V2=True
|
||||||
|
-e SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1
|
||||||
|
-e PYTHONDONTWRITEBYTECODE=1
|
||||||
|
-e XTORCH_OPS_LIB_DIR=/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/xtorch_ops
|
||||||
|
-e XPU_RUNTIME_LIB_DIR=/root/miniconda/envs/python310_torch25_cuda/xcudart/lib
|
||||||
|
-e BKCL_TREE_THRESHOLD=1048576
|
||||||
|
-e CUDA_ENABLE_P2P_NO_UVA=1
|
||||||
|
-e NCCL_IB_GID_INDEX=3
|
||||||
|
-e IS_DSV4=1
|
||||||
|
-e MC_CUSTOM_TOPO_JSON=/workspace/nic_priority_matrix_test.json
|
||||||
|
# INT8 specific
|
||||||
|
-e SGLANG_DSV4_FP4_EXPERTS=false
|
||||||
|
-e SGLANG_APPLY_CONFIG_BACKUP=auto
|
||||||
|
-e BKCL_ENABLE_XDR=1
|
||||||
|
-e BKCL_RDMA_NICS=eth1,eth1,eth3,eth3,eth5,eth5,eth7,eth7
|
||||||
|
-e BKCL_RDMA_VERBS=1
|
||||||
|
-e XSGL_INT8_LM_HEAD=1
|
||||||
|
-e SGLANG_P800_ALL_GATHER_FALLBACK=0
|
||||||
|
)
|
||||||
|
|
||||||
|
# Launch args: baseline P800 INT8 command (same as the tp_dp_matrix
|
||||||
|
# experiment) PLUS the official w8a8 run_server.sh parameters:
|
||||||
|
# EAGLE speculative decoding (steps/topk/draft from config.env)
|
||||||
|
# --chunked-prefill-size / --max-prefill-tokens (official long-prefill chunking)
|
||||||
|
# --max-running-requests 64 (official cap)
|
||||||
|
# --ep-size is only added when DP=1: this vendor image crashes with
|
||||||
|
# ZeroDivisionError when EP spans DP replicas (probed 2026-07-17).
|
||||||
|
ep_args=""
|
||||||
|
if [[ "$DP" -eq 1 ]]; then
|
||||||
|
ep_args="--ep-size ${EP_SIZE:-$TP}"
|
||||||
|
fi
|
||||||
|
launch_args="--host 0.0.0.0 --port ${PORT} --model-path /models --attention-backend nsa --nsa-prefill klxdsa --nsa-decode klxdsa --trust-remote-code --disable-custom-all-reduce --page-size 64 --mem-fraction-static ${MEM_FRACTION_STATIC} --tensor-parallel-size ${TP} ${ep_args} --disable-shared-experts-fusion --quantization w8a8_int8 --kv-cache-dtype float16 --disable-piecewise-cuda-graph --cuda-graph-max-bs 32 --watchdog-timeout 3000000 --tool-call-parser deepseekv4 --reasoning-parser deepseek-v4 --constrained-json-disable-any-whitespace --enable-metrics --enable-request-time-stats-logging --context-length ${CONTEXT_LENGTH} --chunked-prefill-size ${CHUNKED_PREFILL_SIZE} --max-prefill-tokens ${MAX_PREFILL_TOKENS} --max-running-requests ${MAX_RUNNING_REQUESTS} --speculative-algorithm EAGLE --speculative-num-steps ${SPEC_NUM_STEPS} --speculative-eagle-topk ${SPEC_EAGLE_TOPK} --speculative-num-draft-tokens ${SPEC_NUM_DRAFT_TOKENS}"
|
||||||
|
|
||||||
|
if [[ "$DP" -gt 1 ]]; then
|
||||||
|
launch_args="${launch_args} --dp-size ${DP}"
|
||||||
|
fi
|
||||||
|
|
||||||
|
# Base64-encode the bootstrap command to avoid host-shell quoting issues.
|
||||||
|
server_cmd=$(cat <<EOF
|
||||||
|
cd /workspace
|
||||||
|
find /root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sglang -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true
|
||||||
|
/root/miniconda/envs/python310_torch25_cuda/bin/pip install --upgrade safetensors -q
|
||||||
|
/root/miniconda/envs/python310_torch25_cuda/bin/pip install https://files.pythonhosted.org/packages/14/8b/2a1333a6455c6fad401c2285dee6f58016c55b1cb44cae3a31f8a9cc7d83/apache_tvm_ffi-0.1.0b2-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl -q
|
||||||
|
/root/miniconda/envs/python310_torch25_cuda/bin/python -c "import torch; torch.float8_e8m0fnu = torch.uint8; import runpy, sys; sys.argv[0] = 'sglang.launch_server'; runpy.run_module('sglang.launch_server', run_name='__main__')" ${launch_args}
|
||||||
|
EOF
|
||||||
|
)
|
||||||
|
server_cmd_b64=$(printf '%s' "$server_cmd" | base64 -w0)
|
||||||
|
|
||||||
|
patch_mounts=(
|
||||||
|
-v "${PATCH_ROOT}/nic_priority_matrix_test.json:/workspace/nic_priority_matrix_test.json:ro"
|
||||||
|
)
|
||||||
|
# Patch the image's sglang.bench_serving to also report TTFT/TPOT/E2E P95.
|
||||||
|
# The P800 image only ships median/P99 for those metrics, and the adaptive
|
||||||
|
# search reads p95_ttft_ms for its TTFT SLO stop. The container is recreated
|
||||||
|
# per TP/DP config, so the patch must be mounted at every `docker run`.
|
||||||
|
BENCH_SERVING_PATCH="${SCRIPT_DIR}/patches/bench_serving.py"
|
||||||
|
if [[ -f "${BENCH_SERVING_PATCH}" ]]; then
|
||||||
|
patch_mounts+=(
|
||||||
|
-v "${BENCH_SERVING_PATCH}:/root/miniconda/envs/python310_torch25_cuda/lib/python3.10/site-packages/sglang/bench_serving.py:ro"
|
||||||
|
)
|
||||||
|
fi
|
||||||
|
# Mount the ShareGPT seed dataset only when it exists on the host, so that
|
||||||
|
# BENCH_DATASET_NAME=random-ids (no seed needed) never makes docker create a
|
||||||
|
# stray directory at DATASET_PATH.
|
||||||
|
if [[ -f "${DATASET_PATH}" ]]; then
|
||||||
|
patch_mounts+=( -v "${DATASET_PATH}:${CONTAINER_DATASET_PATH}:ro" )
|
||||||
|
fi
|
||||||
|
|
||||||
|
echo "=== Starting P800 SGLang INT8 server in Docker (TP=${TP}, DP=${DP}) ==="
|
||||||
|
echo "Image: ${DOCKER_IMAGE}"
|
||||||
|
echo "Model: ${MODEL_PATH}"
|
||||||
|
echo "Container name: ${CONTAINER_NAME}"
|
||||||
|
echo "Host port: ${PORT}"
|
||||||
|
echo "Command: sglang.launch_server ${launch_args}"
|
||||||
|
echo "Log: docker logs ${CONTAINER_NAME}"
|
||||||
|
|
||||||
|
docker run -d \
|
||||||
|
--name "${CONTAINER_NAME}" \
|
||||||
|
--privileged \
|
||||||
|
--network host \
|
||||||
|
--ipc host \
|
||||||
|
${device_args} \
|
||||||
|
-v "${MODEL_PATH}:/models:ro" \
|
||||||
|
-v "${MODEL_PATH}:${MODEL_PATH}:ro" \
|
||||||
|
"${patch_mounts[@]}" \
|
||||||
|
"${env_args[@]}" \
|
||||||
|
"${DOCKER_IMAGE}" \
|
||||||
|
bash -c "echo '${server_cmd_b64}' | base64 -d | bash" \
|
||||||
|
>> "${SERVER_LOG}" 2>&1
|
||||||
|
|
||||||
|
log "container ${CONTAINER_NAME} started, waiting for health"
|
||||||
|
healthy=0
|
||||||
|
for ((i = 1; i <= 600; i++)); do
|
||||||
|
if curl --fail --silent --show-error --max-time 5 "http://127.0.0.1:${PORT}/health" >/dev/null 2>&1; then
|
||||||
|
healthy=1
|
||||||
|
break
|
||||||
|
fi
|
||||||
|
if [[ "$(docker inspect -f '{{.State.Running}}' "${CONTAINER_NAME}" 2>/dev/null)" != "true" ]]; then
|
||||||
|
log "ERROR: container ${CONTAINER_NAME} exited during startup; last server logs:"
|
||||||
|
docker logs --tail 100 "${CONTAINER_NAME}" 2>&1 || true
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
|
sleep 1
|
||||||
|
done
|
||||||
|
if [[ "$healthy" == "1" ]]; then
|
||||||
|
log "container ${CONTAINER_NAME} is healthy"
|
||||||
|
exit 0
|
||||||
|
else
|
||||||
|
log "ERROR: container ${CONTAINER_NAME} failed health check; last server logs:"
|
||||||
|
docker logs --tail 100 "${CONTAINER_NAME}" 2>&1 || true
|
||||||
|
exit 1
|
||||||
|
fi
|
||||||
11
experiments/p800/dsv4_p800_sglang_tp_dp_official/start_sglang_dp.sh
Executable file
11
experiments/p800/dsv4_p800_sglang_tp_dp_official/start_sglang_dp.sh
Executable file
@ -0,0 +1,11 @@
|
|||||||
|
#!/usr/bin/env bash
|
||||||
|
# Start SGLang server for a given TP×DP configuration.
|
||||||
|
# Usage: start_sglang_dp.sh <TP> <DP>
|
||||||
|
#
|
||||||
|
# P800 SGLang always runs in the vendor Docker image, so this simply
|
||||||
|
# delegates to the Docker start script. Kept under the same name as the
|
||||||
|
# H20 experiment for parity.
|
||||||
|
set -e
|
||||||
|
|
||||||
|
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
|
||||||
|
exec "${SCRIPT_DIR}/start_sglang_docker.sh" "$@"
|
||||||
Loading…
x
Reference in New Issue
Block a user