diff --git a/BI_V100_BENCHMARK_RUNBOOK.md b/BI_V100_BENCHMARK_RUNBOOK.md new file mode 100644 index 00000000..80524e35 --- /dev/null +++ b/BI_V100_BENCHMARK_RUNBOOK.md @@ -0,0 +1,233 @@ +# BI-V100 Benchmark Runbook + +在 Phanthy Cloud 实机上执行。目标：拿到实测数据，替换所有 `ns*0.5, l2w*0.6` 猜测值。 + +## 环境确认 + +```bash +# 已确认：CUDA 10.2, 4×BI-V100, corex 运行时 +# Python: /usr/local/corex/lib64/python3/dist-packages 里有 torch + vllm + +# 先确认 torch 可用 +python3 -c "import torch; print(torch.cuda.device_count(), torch.cuda.get_device_name(0))" + +# 确认 SMEM 到底是 48KB 还是 32KB（hardware.cuh 和 _custom_ops.py 有矛盾） +python3 -c " +import torch +props = torch.cuda.get_device_properties(0) +print(f'sharedMemPerBlock: {props.total_memory}') # 总显存 +# PyTorch 不直接暴露 SMEM，用 CUDA runtime 查 +" + +# 用这个方法精确测 SMEM +python3 -c " +import torch, torch.utils.cpp_extension +# 如果 cpp_extension 可用，编译一个查 SMEM 的 kernel +# 否则用下面的方法推断 +import ctypes +try: + cuda = ctypes.CDLL('libcuda.so') + # cudaDeviceGetAttribute + val = ctypes.c_int(0) + # attribute 48 = CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK + cuda.cuDeviceGetAttribute(ctypes.byref(val), 48, 0) + print(f'SMEM per block: {val.value} bytes ({val.value/1024:.0f} KB)') +except: + print('libcuda not accessible, try ixsmi or corex API') +" +``` + +## Phase 0: 硬件探测（5 分钟） + +这是最关键的一步——确认 SMEM 到底是多少。 + +```bash +cd ~/project_6 + +# 探测脚本 +python3 << 'PROBE' +import torch +import time + +device = torch.device('cuda:0') +print(f"Device: {torch.cuda.get_device_name(0)}") +print(f"Total memory: {torch.cuda.get_device_properties(0).total_memory / 1024**3:.1f} GB") +print(f"SM count: {torch.cuda.get_device_properties(0).multi_processor_count}") + +# SMEM 探测：分配越来越大的 shared memory 直到失败 +# 用一个简单的 kernel 测试实际可用 SMEM +print("\n--- SMEM probe via allocation ---") +for smem_kb in [32, 48, 64, 96]: + smem_bytes = smem_kb * 1024 + try: + # torch.zeros 不直接测 SMEM，用 tensor 大小间接推断 + # 真正的 SMEM 测试需要自定义 kernel + pass + except: + pass + +# 更直接的方法：torch.cuda.get_device_properties +props = torch.cuda.get_device_properties(0) +print(f"\ntorch.cuda properties:") +for attr in dir(props): + if not attr.startswith('_'): + try: + val = getattr(props, attr) + if isinstance(val, (int, float, str)): + print(f" {attr}: {val}") + except: + pass + +# 测带宽 +print("\n--- Memory bandwidth probe ---") +sizes = [2**20, 2**24, 2**28] # 1MB, 16MB, 256MB +for n in sizes: + x = torch.randn(n, device=device) + y = torch.empty_like(x) + + torch.cuda.synchronize() + warmup = 5 + repeats = 20 + for _ in range(warmup): + y.copy_(x) + torch.cuda.synchronize() + + start = time.perf_counter() + for _ in range(repeats): + y.copy_(x) + torch.cuda.synchronize() + elapsed = time.perf_counter() - start + + bytes_moved = n * 4 * 2 * repeats # read + write, float32 + bw = bytes_moved / elapsed / 1e9 + print(f" {n*4/1024/1024:>6.0f} MB: {bw:.0f} GB/s") + +print("\nDone. Use SM count and BW to validate hardware.cuh values.") +PROBE +``` + +## Phase 1: Quick benchmark（~40 分钟总计） + +按竞赛权重优先级跑：reduce (83% weight) → topk → scan → transform + +```bash +cd ~/project_6 + +# 确保用 GPU 0（最空闲的） +export CUDA_VISIBLE_DEVICES=2 + +# --- reduce: 最高优先级，~2 分钟 --- +python3 muh/bench_bi100.py --algo reduce --dtype float32 --quick -o results/ +python3 muh/bench_bi100.py --algo reduce --dtype float16 --quick -o results/ +python3 muh/bench_bi100.py --algo reduce --dtype bfloat16 --quick -o results/ + +# --- topk: 采样热路径，<1 分钟 --- +python3 muh/bench_bi100.py --algo topk --dtype float32 --quick -o results/ +python3 muh/bench_bi100.py --algo topk --dtype float16 --quick -o results/ + +# --- scan: prefix scan，~29 分钟 --- +# scan 的搜索空间最大，先跑 quick +python3 muh/bench_bi100.py --algo scan --dtype float32 --quick -o results/ + +# --- transform: 元素级操作，~8 分钟 --- +python3 muh/bench_bi100.py --algo transform --dtype float16 --quick -o results/ +python3 muh/bench_bi100.py --algo transform --dtype bfloat16 --quick -o results/ + +echo "=== Quick benchmark complete ===" +ls -la results/ +``` + +## Phase 2: 如果 SMEM 是 32KB（检查 Phase 0 结果后决定） + +```bash +# 如果 Phase 0 确认 SMEM=32KB，重跑所有 benchmark +python3 muh/bench_bi100.py --algo reduce --dtype float32 --quick --smem-limit 32768 -o results_32k/ +python3 muh/bench_bi100.py --algo topk --dtype float32 --quick --smem-limit 32768 -o results_32k/ +python3 muh/bench_bi100.py --algo scan --dtype float32 --quick --smem-limit 32768 -o results_32k/ +``` + +## Phase 3: 端到端验证 + +benchmark top-5 候选值在真实 vllm 推理中的效果。 + +```bash +cd ~/project_6 + +# 启动 vllm 服务（用竞赛配置） +python3 -m vllm.entrypoints.openai.api_server \ + --model /model \ + --served-model-name llm \ + --max-model-len 100000 \ + --gpu-memory-utilization 0.9 \ + --trust-remote-code \ + -tp 4 \ + --max-num-seqs 8 \ + --disable-log-requests \ + --disable-frontend-multiprocessing \ + --max-num-batched-tokens 8192 \ + --enable-chunked-prefill \ + --max-seq-len-to-capture 32768 \ + --num-scheduler-steps 8 \ + --preemption-mode recompute \ + --enable-prefix-caching & + +# 等服务启动 +sleep 120 + +# 测 output TPS（83% 权重） +python3 << 'E2E' +import requests, time, json + +url = "http://localhost:80/v1/chat/completions" +headers = {"Content-Type": "application/json"} + +# 短输入长输出 = 测 decode（Output TPS） +payload = { + "model": "llm", + "messages": [{"role": "user", "content": "请详细解释量子计算的基本原理，包括量子比特、量子门、量子纠缠和量子退相干。请尽可能详细。"}], + "max_tokens": 2048, + "temperature": 0.7, + "stream": False +} + +# Warmup +for _ in range(3): + r = requests.post(url, headers=headers, json=payload, timeout=300) + +# Timed +times = [] +tokens = [] +for i in range(5): + start = time.perf_counter() + r = requests.post(url, headers=headers, json=payload, timeout=300) + elapsed = time.perf_counter() - start + + data = r.json() + output_tokens = data["usage"]["completion_tokens"] + tps = output_tokens / elapsed + times.append(elapsed) + tokens.append(output_tokens) + print(f" Run {i+1}: {output_tokens} tokens in {elapsed:.2f}s = {tps:.1f} tok/s") + +avg_tps = sum(t/e for t,e in zip(tokens, times)) / len(times) +print(f"\nAvg Output TPS: {avg_tps:.1f}") +print(f"Weighted score contribution: {avg_tps * 16.796:.0f} (83% of total)") +E2E + +# 停止 vllm +kill %1 +``` + +## 结果回填 + +拿到 results/ 里的 JSON 后，回到 Claude 对话： + +``` +把 results/reduce_float32.json 的内容贴给我， +我会用实测 top-5 替换 tuning_reduce.cuh 的 bi100_* 值。 +``` + +每个 JSON 里的 best point 直接映射到 C++ header 的 bi100_* struct： +- `ipt` → `items_per_thread` / `items` +- `tpb` → `threads_per_block` / `threads` +- `ipv` → `vec_size` / `items_per_vec_load`