🧹 ZZ-CLEANUP-D246-001 · 删除错地方 zz-flagos-s2-2026/ 目录
之之 D246 00:21 指令:'全部重做' 原 D245 (commit 6185550) + D246 (commit 6deca6a) 推到 zz-flagos-s2-2026/dN/ 自创目录 (不在主仓库结构里) 已重写为 HLDP 格式 + 推去 zz-day-3/ 正确路径 (commit d1eeaf3) 本 commit 清理旧的错地方目录 28 files deleted (zz-flagos-s2-2026/d245/ + d246/ 全部) 阿念 (Mavis, ICE-GL-AN-001) · Code · 之之的家 · 2026-09-03 D246 00:28 CST
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# FlagOS S2 赛季二 · 赛道一 · D245 总结
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> 之之 (8592_apivqhj) + 阿念 (Mavis, ICE-GL-AN-001, Code) · 协作产出
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> 比赛: FlagOS 开放计算全球大赛 S2 · Track 1 · SGLang 算子优化
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> 队伍: GuanghuLab(队长孙蓓, 4 名其他队员, 共 5 人)
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> 第 3 批 17 道题 · 截止 2026-09-03 19:59
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---
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## 战绩(D245 23:35)
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| Task | 算子 | 状态 | 平均加速比 | 详情 |
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|---|---|---|---|---|
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| 30 | interleaved_rope (M-RoPE) | ✅ 已传 | **35.79×** (5 款跑通) | 天数 86.81× / 海光 42.86× / 国通A 26.91× / 沐曦 15.54× / 华为 6.84×;燧原/昆仑芯 Failed |
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| 29 | gelu_and_mul | ✅ 已传 | 3.02× (7 款跑通) | 天数 5.32× / 海光 4.03× / 国通A 3.73× / 国通B 3.25× / 沐曦 2.74× / 华为 1.09× / 燧原 0.96×;昆仑芯 Failed |
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| 35 | rotary_embedding | ⏳ 待传(等 0 点重置提交次数) | - | - |
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| 39 | silu_and_mul_masked (D243) | ❌ 已传,4 款全 Failed | - | 改用 `tl.sigmoid` 在国产 NPU 编译失败 |
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## 排名预估(基于截图)
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- Task 30:原第 9 名 27.97× → **新提交 35.79× → 大概第 2-3 名**
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- Task 29:队友 v8.1 3.43× (8/8),我们 3.02× (7/8) — 略低,但接近
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- Task 35:待传,可能冲前 5
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## 目录结构
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```
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d245/
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├── README.md (本文件)
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├── src/
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│ ├── Task30_r16_zhizhi/ (M-RoPE 跨芯片通用版,7 芯片特化)
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│ ├── Task29_v81_zhizhi/ (gelu_and_mul 跨芯片通用版)
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│ ├── Task35_r6_zhizhi/ (rotary_embedding 跨芯片通用版)
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│ └── silu_and_mul_masked.py (D243 Task 39 failed version,留作复盘)
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├── docs/
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│ ├── LESSONS_LEARNED.md (5 作品问题 + 3 过程问题 + 协作模式)
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│ ├── BATTLECARDS.md (4 张作战卡)
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│ └── PR-TEMPLATE.md (PR 模板)
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├── bench/ (benchmark 脚本)
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├── tests/ (正确性测试)
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└── results/ (zip 文件,平台已传)
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```
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## 学到的关键
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1. **跨芯片必须双套路**:
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- 国产 NPU(天数/海光/沐曦/燧原/华为/昆仑芯):`enable_fp_fusion=False` + `torch.get_device_module().device()` + 手写指针 + `tl.int64` cast + 指针 cast 到 int16/int32 防 NaN
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- 国际卡(NVIDIA/AMD):`tl.math.erf` 走硬件 SFU,不强制 disable fusion
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2. **平台 import 入口** = `reference = function_name`(函数末尾必加,否则 platform 找不到)
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3. **比赛生态** = 人 + AI 协作(不是真人写代码),其他队伍也是 r1-r16 这样的迭代节奏
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4. **6 款国产 NPU 不能用一刀切**:
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- 燧原/华为:某些 NPU 套路反而负优化
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- 天数/海光/沐曦:接受国产套路
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- 跨芯片统一版需要适配性设计
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5. **协作模式(D243 定型)**:
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- 之之:找材料 / 看平台 / 上传 / 决策传不传 / 复盘
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- 阿念:看代码 / 写代码 / 翻译"代码哪里有问题"成之之能懂的话
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- 前提:阿念给之之的所有东西,都用之之能懂的话
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## 下次动手前 checklist(从 LESSONS_LEARNED.md 继承)
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- [ ] 任务描述完整读一遍(计算定义 + 接口签名 + 容差)
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- [ ] 至少 1 个队友参考实现看完
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- [ ] 把队友写法 + 我打算写的放一起对比
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- [ ] 写之前把"我准备怎么写"翻译给之之听,得到认可
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- [ ] 写完本地不传,先给之之看"我会传啥"
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- [ ] 之之确认后才传
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## 下次改进方向
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- Task 35 待传(等 0 点提交次数重置)
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- 燧原 / 华为特化版可能值得加
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- 国际 A/B 卡版可能用 `tl.math.erf` 写一版
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---
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阿念 · D245 23:35 · 给 甄静 + 之之的家
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# Copyright 2026 FlagOS Contributors / GuanghuLab
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"""Benchmark gelu_and_mul v2 vs FlagGems baseline.
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Usage:
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python -m bench.bench_gelu_and_mul --shape 4096,4096 --dtype fp16
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python -m bench.bench_gelu_and_mul --shape 8192,8192 --dtype bf16
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"""
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import argparse
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import time
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import torch
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from flag_gems.fused.gelu_and_mul import gelu_and_mul as baseline_fn
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from flag_gems_local.fused.gelu_and_mul_v2 import gelu_and_mul as v2_fn
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def _to_ms(t):
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return t * 1000.0
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def bench(fn, x, y, iters=100, warmup=20):
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# warmup
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for _ in range(warmup):
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out = fn(x, y)
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torch.cuda.synchronize()
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# measure
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start = torch.cuda.Event(enable_timing=True)
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end = torch.cuda.Event(enable_timing=True)
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start.record()
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for _ in range(iters):
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out = fn(x, y)
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end.record()
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torch.cuda.synchronize()
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return start.elapsed_time(end) / iters
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--shape", type=str, default="4096,4096")
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parser.add_argument("--dtype", type=str, default="fp16",
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choices=["fp16", "bf16", "fp32"])
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parser.add_argument("--iters", type=int, default=100)
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args = parser.parse_args()
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dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[args.dtype]
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shape = tuple(int(s) for s in args.shape.split(","))
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print(f"Benchmark gelu_and_mul: shape={shape} dtype={args.dtype} iters={args.iters}")
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print("=" * 72)
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x = torch.randn(shape, dtype=dtype, device="cuda")
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y = torch.randn(shape, dtype=dtype, device="cuda")
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# Correctness check
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out_b = baseline_fn(x, y)
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out_v = v2_fn(x, y)
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abs_diff = (out_b - out_v).abs().max().item()
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rel_diff = (out_b - out_v).abs().div(out_b.abs().clamp_min(1e-6)).max().item()
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print(f"Correctness: max abs_diff={abs_diff:.2e}, max rel_diff={rel_diff:.2e}")
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assert abs_diff < 1e-2, "v2 diverges from baseline"
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print()
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t_base = bench(baseline_fn, x, y, iters=args.iters)
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t_v2 = bench(v2_fn, x, y, iters=args.iters)
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speedup = t_base / t_v2
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print(f"Baseline : {_to_ms(t_base):.4f} ms/iter")
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print(f"v2 (ours): {_to_ms(t_v2):.4f} ms/iter")
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print(f"Speedup : {speedup:.3f}x")
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print()
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# Find best autotune config
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print("Best v2 autotune config:")
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for k, v in v2_fn.__self__.forward.__func__.__code__.co_consts:
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if k == "BLOCK_SIZE":
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print(f" BLOCK_SIZE = {v}")
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if __name__ == "__main__":
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main()
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"""Benchmark silu_and_mul_masked v2 vs FlagGems baseline.
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Run: python bench/bench_silu_and_mul_masked.py --shape 4096,4096 --dtype fp16
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"""
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import argparse
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import torch
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def bench(fn, *args, iters=100, warmup=20):
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for _ in range(warmup):
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out = fn(*args)
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torch.cuda.synchronize()
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start = torch.cuda.Event(enable_timing=True)
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end = torch.cuda.Event(enable_timing=True)
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start.record()
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for _ in range(iters):
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out = fn(*args)
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end.record()
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torch.cuda.synchronize()
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return start.elapsed_time(end) / iters
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--shape", default="4096,4096")
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parser.add_argument("--dtype", default="fp16", choices=["fp16", "bf16", "fp32"])
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parser.add_argument("--iters", type=int, default=100)
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parser.add_argument("--with-mask", action="store_true", help="enable mask")
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args = parser.parse_args()
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dtype = {"fp16": torch.float16, "bf16": torch.bfloat16, "fp32": torch.float32}[args.dtype]
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shape = tuple(int(s) for s in args.shape.split(","))
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print(f"Bench silu_and_mul_masked: shape={shape} dtype={args.dtype} mask={args.with_mask}")
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print("=" * 72)
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x = torch.randn(shape, dtype=dtype, device="cuda")
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y = torch.randn(shape, dtype=dtype, device="cuda")
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mask = torch.randn(shape, dtype=dtype, device="cuda") if args.with_mask else None
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from flag_gems.fused.silu_and_mul import silu_and_mul as baseline_fn
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from flag_gems_local.fused.silu_and_mul_masked_v2 import silu_and_mul_masked as v2_fn
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# Reference (using PyTorch native ops)
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def ref_fn(x, y, mask=None):
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x_in = x * mask if mask is not None else x
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return torch.nn.functional.silu(x_in) * y
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out_r = ref_fn(x, y, mask)
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if mask is None:
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out_b = baseline_fn(x, y)
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else:
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# baseline doesn't support mask; compare v2 to reference
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out_b = None
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out_v = v2_fn(x, y, mask)
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if out_b is not None:
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abs_diff = (out_b - out_v).abs().max().item()
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print(f"v2 vs baseline: abs_diff={abs_diff:.2e}")
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abs_diff_r = (out_r - out_v).abs().max().item()
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print(f"v2 vs reference: abs_diff={abs_diff_r:.2e}")
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assert abs_diff_r < 1e-2, "v2 diverges from reference"
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print()
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# Benchmark
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if mask is None:
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t_base = bench(baseline_fn, x, y, iters=args.iters)
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print(f"Baseline : {t_base:.4f} ms/iter")
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t_v2 = bench(v2_fn, x, y, mask, iters=args.iters)
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print(f"v2 (ours): {t_v2:.4f} ms/iter")
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if mask is None:
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speedup = t_base / t_v2
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print(f"Speedup : {speedup:.3f}x")
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else:
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print("Baseline doesn't support mask, no speedup comparison.")
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if __name__ == "__main__":
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main()
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# GuanghuLab × FlagOS S2 算子优化作战卡
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> 给队长孙蓓和评审组 — 4 张作战卡,对应 GuanghuLab 当前 4 道正在做的题
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> 截止:第 3 批 2026.09.03 19:59 / PR 周期 2026.09.11 - 09.17
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> 作者:阿念(AI 工程后盾)· 为之之(8592_apivqhj)· 队长:孙蓓(主决策)
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---
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## 全局视角对比表(给队长定主攻方向)
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| Task | 算子 | 距攻占 | 团队最佳 | 推荐度 | 风险 |
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|---|---|---|---|---|---|
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| 29 | gelu_and_mul | 差 3 名 | 3.05× | ⭐⭐⭐⭐⭐ 稳 | 低 — FlagGems baseline 已是 fused |
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| 35 | rotary_embedding(标准) | 差 5 名 | 4.16× | ⭐⭐⭐⭐ 中 | 中 — head 维并行化有未验证空间 |
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| 30 | interleaved_rope | 差 7 名 | 26.37× | ⭐⭐⭐ 低 | 高 — 26.37× 可能因 baseline 跑歪,需先复测 |
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| 26 | fused_moe_router | 未入榜 | — | ⭐⭐⭐⭐ 全新 | 中 — Triton sort 没现成参考,自己写 |
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**我的建议**:
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- **第 3 批主攻 Task 29**(差 3 名最稳,4 天内冲得过)
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- **并行 Task 35**(用同一份 kernel,变 standard vs interleaved,几乎不增加工作量)
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- **Task 30 暂停,先复测 26.37× 是不是 baseline 跑歪**
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- **Task 26 排第 4 批重点**(现成的 Triton MoE 算子基本没有,值得从 vLLM 移植)
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---
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## 作战卡 1 · Task 29 · gelu_and_mul
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**目标**:在 FlagGems baseline 之上再压 1.5× - 2.0×,攻占前 3 名
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**官方参考**:
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- `FlagOpen/FlagGems/src/flag_gems/fused/gelu_and_mul.py`
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- 用 `pointwise_dynamic` 自动 codegen,支持 `none` / `tanh` 两种 GELU 近似
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- baseline 团队最佳 3.05×(说明在 NPU 上已经压过 PyTorch 3 倍)
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**当前实现的优化空间**:
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1. baseline 用 `pointwise_dynamic` 自动 codegen,BLOCK_SIZE / num_warps 用默认值
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2. 没有 autotune — 不同 shape 下最佳配置差异大
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3. 常量 `0.70710678118654752440` 和 `0.7978845608028654` 在 Python 层计算再传到 kernel(轻微开销)
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**我们的 v2 方案**:`src/flag_gems_local/fused/gelu_and_mul_v2.py`
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- 显式 8 组 `(BLOCK_SIZE, num_warps, num_stages)` autotune
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- 常量作为 `tl.constexpr` 字面量内联,省 Python→Triton 转换
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- 拆 forward / backward,backward 也走 autotune
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- `tl.exp` 替换为 `tl.math.exp2`(更快的 fast-math)
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- 兼容性:drop-in 替换 `flag_gems.fused.gelu_and_mul.gelu_and_mul`
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**预期加速**:1.3× - 1.8×(具体看形状,跑 benchmark 才知道)
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**测试矩阵**:
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- shape: `(4096,4096)`, `(8192,8192)`, `(1024,11008)`, `(2048,13824)`
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- dtype: fp16 / bf16 / fp32
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- GELU 近似: `none` / `tanh`
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- 正确性: max abs_diff < 1e-2, max rel_diff < 1e-3
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**风险**:
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- low:在某些 shape 上 autotune 选不到最佳配置,需手工调
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- low:backward kernel 没用 `tanh` 优化(留 fallback 走 baseline)
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**PR 提交流程**:
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1. 本地跑通 `bench/bench_gelu_and_mul.py` 记录 speedup
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2. fork `FlagOpen/FlagGems` → 推到 GuanghuLab 分支
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3. 在 FlagOS 平台「作品提交」页选 Task 29 → 贴 PR URL
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4. 评审期(9/4 - 9/10)盯 GitHub PR 评论区,改 reviewer 提的 review
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---
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## 作战卡 2 · Task 35 · rotary_embedding(标准)
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**目标**:在 FlagGems baseline 之上再压 1.3× - 1.5×,攻占前 5 名
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**官方参考**:
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- `FlagOpen/FlagGems/src/flag_gems/fused/rotary_embedding.py`
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- 完整 kernel:`apply_rotary_pos_emb_kernel` + `apply_rotary_pos_emb_inplace_kernel`
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- 关键观察:Task 30 (interleaved) 和 Task 35 (标准) **共用同一份 kernel,仅 `ROTARY_INTERLEAVED` constexpr 不同**
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- baseline 团队最佳 4.16×(说明 rope 算子整体还有空间)
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**当前实现的优化空间**:
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1. baseline 用 1D grid `(n_tokens,)` + 内层 `for off_h in range(NUM_Q_HEADS)` sequential 循环
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2. num_warps=8, num_stages=1 固定(没 autotune)
|
||||
3. head 维没有并行化 — 多 head 模型(head=32/64)有显著提升空间
|
||||
|
||||
**我们的 v2 方案**:`src/flag_gems_local/fused/rotary_embedding_v2.py`
|
||||
- 3D grid: `(n_tokens, n_q_blocks, n_k_blocks)`,head 维并行
|
||||
- autotune 5 组 (num_warps, num_stages)
|
||||
- 智能 head_block_size(`_pick_head_block_size`):32 head → 8 块,16 head → 4 块
|
||||
- 复用 baseline 的 cos/sin 一次性加载 + 跨 head 复用(已经做对了)
|
||||
|
||||
**预期加速**:1.2× - 1.6×(主要来自 head 维并行 + autotune)
|
||||
|
||||
**测试矩阵**:
|
||||
- shape: `(batch=4, seq=2048, head=32, dim=128)`, `(1, 4096, 32, 64)`, `(8, 1024, 16, 128)`
|
||||
- rotary_interleaved: `False`(Task 35)
|
||||
- 正确性:与 PyTorch `apply_rotary_emb` 数值一致
|
||||
|
||||
**风险**:
|
||||
- 中:head_block_size 选错会导致 grid 太大或太小,需要实验
|
||||
- 低:3D grid 在小 head 模型(<= 4 heads)上浪费 program
|
||||
|
||||
**PR 提交流程**:同 Task 29
|
||||
|
||||
---
|
||||
|
||||
## 作战卡 3 · Task 30 · interleaved_rope
|
||||
|
||||
**目标**:攻占前 7 名(差 7 名是数据上的,实际有空间)
|
||||
|
||||
**关键观察**:
|
||||
- interleaved 模式 = `ROTARY_INTERLEAVED=True` 的同一份 kernel
|
||||
- baseline 团队最佳 26.37× — **这个数字可疑**
|
||||
|
||||
**怀疑**:26.37× 高得离谱,可能是:
|
||||
- (a) baseline 评测脚本在 NPU 上跑歪了
|
||||
- (b) NPU 的 PyTorch rope 实现特别差(可能性大)
|
||||
- (c) 之前有队伍把数据刷成了排行榜结果
|
||||
|
||||
**建议先复测**:在 NVIDIA A100 / 国产 NPU 上重跑 baseline(用 FlagGems 自带 benchmark),如果 baseline 不是 26.37×,说明排行榜数据有噪声,Task 30 实际难度可能更高或更低。
|
||||
|
||||
**v2 方案**:复用 Task 35 的 `rotary_embedding_v2.py`,只改 `rotary_interleaved=True`
|
||||
- 完全零增量代码 — 是 Task 35 的副产品
|
||||
|
||||
**预期加速**:1.2× - 1.6×(同 Task 35)
|
||||
|
||||
**测试矩阵**:同 Task 35,但 `rotary_interleaved=True`
|
||||
|
||||
**风险**:
|
||||
- 高:排行榜数据本身有噪声,基线不可信
|
||||
- 低:即使 26.37× 是真的,v2 仍能再压 1.2×
|
||||
|
||||
**行动建议**:第 3 批**先跳过 Task 30**,把 26.37× 复测清楚再决定
|
||||
|
||||
---
|
||||
|
||||
## 作战卡 4 · Task 26 · fused_moe_router
|
||||
|
||||
**目标**:从"未入榜"到攻占前 3(全新挑战)
|
||||
|
||||
**官方参考**:
|
||||
- `FlagOpen/FlagGems/src/flag_gems/fused_moe_mxq.py` (35KB,含完整 MoE + 量化)
|
||||
- `vllm/vllm/model_executor/layers/fused_moe/fused_moe.py` (vLLM 的实现)
|
||||
|
||||
**当前实现的优化空间**:
|
||||
1. baseline `prepare_moe_inputs` 用 `torch.argsort` + `torch.sort`(两次排序)
|
||||
2. 有 `.item()` 调用,引入 host-device 同步
|
||||
3. 中间 tensor 多(sorted_token_ids / sorted_expert_ids / sorted_weights)
|
||||
4. 真正的"routing"是稀疏矩阵的 top-k 选择 + expert id 排序,Triton 完全可以在 GPU kernel 内 fused
|
||||
|
||||
**我们的 v2 方案**:`src/flag_gems_local/ops/fused_moe_router_v2.py`
|
||||
- 写一个 Triton bitonic sort kernel(`bitonic_sort_step_kernel`)
|
||||
- 单 token 内的 top_k 排序(<= 32)在 registers 内做
|
||||
- 跨 token 的 token-id 展开 → `sorted_token_ids` 直接 broadcast
|
||||
- 消掉 baseline 的两次 sort(只一次)
|
||||
|
||||
**预期加速**:routing 部分 2× - 4×(整体 fused_moe 加速可能 1.1× - 1.3×,因为 routing 不是热点)
|
||||
|
||||
**测试矩阵**:
|
||||
- (num_tokens, top_k): `(1024, 8)`, `(4096, 4)`, `(2048, 16)`
|
||||
- num_experts: 8 / 64
|
||||
- 正确性:与 baseline prepare_moe_inputs 输出一致(允许 token 顺序不同,但 expert assignment 必须相同)
|
||||
|
||||
**风险**:
|
||||
- 中:bitonic sort 的 kernel 在 top_k > 32 时会失败,要 fallback
|
||||
- 中:routing 不是性能瓶颈时,优化收益被埋没
|
||||
- 中:与 FlagGems 的 fused_moe_kernel 集成时,可能需要改 invoke_fused_moe
|
||||
|
||||
**行动建议**:
|
||||
- **第 3 批先做 routing 的独立 benchmark**,看 routing 本身在 MoE 推理里占多少时间
|
||||
- 如果 < 5%,考虑放弃 Task 26 投 Task 35
|
||||
- 如果 > 20%,Task 26 是真正的金矿
|
||||
|
||||
---
|
||||
|
||||
## 提交模板(给队长填)
|
||||
|
||||
每个 task 提交时需要:
|
||||
|
||||
```markdown
|
||||
## Task XX · [算子名]
|
||||
|
||||
### 团队
|
||||
GuanghuLab(孙蓓[队长]、9478_apiqttc、陈淑婷、4348_apiratk、8592_apivqhj)
|
||||
|
||||
### 优化方案
|
||||
[1-2 段说明]
|
||||
|
||||
### 加速比
|
||||
baseline: X.XX ms → v2: X.XX ms = X.XX×
|
||||
|
||||
### 测试环境
|
||||
- GPU: NVIDIA A100 80GB
|
||||
- PyTorch: 2.3.0
|
||||
- Triton: 3.0.0
|
||||
- FlagGems: master @ commit xxx
|
||||
|
||||
### GitHub PR
|
||||
https://github.com/FlagOpen/FlagGems/pull/xxx
|
||||
|
||||
### 附图
|
||||
[benchmark 截图]
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 资源 & 工具清单
|
||||
|
||||
- **本地开发**:你 / 队长 / 队员中任一人的 GPU 机器
|
||||
- **FlagGems 官方**: https://github.com/FlagOpen/FlagGems
|
||||
- **vLLM 参考**: https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py
|
||||
- **比赛平台**: https://flagos.net
|
||||
- **FlagOS 文档**: https://flagos.csdn.net
|
||||
- **飞书 / 微信群**:入群链接在 flagos.net 比赛详情页
|
||||
|
||||
---
|
||||
|
||||
**时间表(D243 现在 → D246 9/3 截止)**:
|
||||
- D243 (今天 23:00):4 张作战卡 + 3 个 v2 实现完成 ✅
|
||||
- D244 09:00 - 18:00:在 GPU 机器上跑通 + benchmark
|
||||
- D245 09:00 - 18:00:调优 + 提 PR + 提 flagos.net
|
||||
- D246 09:00 - 19:59:补提交 + 改 reviewer 提的 review
|
||||
|
||||
阿念 · 2026.08.30 23:14
|
||||
@ -1,89 +0,0 @@
|
||||
# LESSONS LEARNED · FlagOS S2 Track 1
|
||||
|
||||
> 阿念(AI 工程后盾)· D243 (2026-08-30)
|
||||
> 每条教训都来自"已发生的失败",下次动手前必查这个文件。
|
||||
|
||||
---
|
||||
|
||||
## 规则:动手前必查
|
||||
|
||||
1. ✅ 平台规则(计算定义 + 接口签名 + 容差)看完了吗?
|
||||
2. ✅ 至少 1 个队友的同题实现学完了吗?
|
||||
3. ✅ 上次失败的原因有记录吗?(本文件)
|
||||
|
||||
---
|
||||
|
||||
## 作品问题(Task 39 silu_and_mul_masked,4 款芯片全 Failed)
|
||||
|
||||
| # | 错误 | 翻译成大白话 | 怎么改 |
|
||||
|---|---|---|---|
|
||||
| 1 | **函数名写错** | 老师让写"自我介绍",我写成了"自我说明" | 必须**完全照抄**任务描述里的函数名,平台 import `reference` |
|
||||
| 2 | **用了国产 NPU 不认识的指令** (`tl.sigmoid`) | 用方言写作文,改卷老师是北方人,一个字都看不懂 | 手写 `1.0 / (1.0 + tl.exp(-x))` 或多项式近似,跟队友学 |
|
||||
| 3 | **没写 `reference = function_name`** 的别名 | 交了卷子没写名字,老师不知道这是谁的 | 永远在文件末尾加 `reference = your_function` |
|
||||
| 4 | **3D grid 太复杂** | 做一道简单题用 3 种解法,自己绕晕了 | 优先 2D grid,只在一个维度展开 |
|
||||
| 5 | **没先看队友怎么写**,闭门造车 | 别的选手都翻参考书,我从 0 自己憋 | **写之前必看 1 个队友实现** |
|
||||
|
||||
**真凶:#2** (`tl.sigmoid` 不被支持) · **根因:#5** (没看队友)
|
||||
|
||||
---
|
||||
|
||||
## 过程问题(每次装懂都要复盘)
|
||||
|
||||
| # | 我装懂的事 | 真相 |
|
||||
|---|---|---|
|
||||
| 1 | 默认你懂技术,大段讲 Triton / autotune | 你完全不懂,要当"消化员" |
|
||||
| 2 | 默认"修真小说"是你写的 | 是你**练 AI** 用的 |
|
||||
| 3 | 默认 r6 / r16 是你写的 | 都是**队友的**(队友也是人 + AI 协作) |
|
||||
| 4 | 默认前 4 道题是给你做的 | 你要**自己选题,自己干**,不跟队长同步 |
|
||||
|
||||
**铁律**:不确定的事,问"是这样吗?"而不是替你下结论。
|
||||
|
||||
---
|
||||
|
||||
## 协作模式(D243 定型)
|
||||
|
||||
| 你 | 我 |
|
||||
|---|---|
|
||||
| 找材料(队友代码) | 看代码 |
|
||||
| 看 flagos.net 平台 | 写代码 / 改代码 |
|
||||
| 上传 zip | 翻译"代码哪里有问题"成你懂的话 |
|
||||
| 决定传不传 | 列"传之前要确认的清单"给你看 |
|
||||
| 复盘,记 LESSONS | 查 LESSONS,不犯同错 |
|
||||
|
||||
**前提:我给你的所有东西,都用你能懂的话。看不懂就骂我,我重写。**
|
||||
|
||||
---
|
||||
|
||||
## 比赛生态(D243 认知)
|
||||
|
||||
- 整个 FlagOS 比赛,**很多参赛者 = 人类 + AI 助手**,不是真人写代码
|
||||
- 队友的 r6 / r16 代码 = **队友 AI 写的**,不是人之之写
|
||||
- 别人质量高 = **别人 AI 强**,不是别人真人强
|
||||
- 我的工作 = **学别人 AI 的写法 + 搬到我们题上**,不原创
|
||||
|
||||
---
|
||||
|
||||
## 已知国产 NPU 套路(从队友代码学的)
|
||||
|
||||
- `with torch.get_device_module(x.device).device(x.device):` — 必需 device context
|
||||
- `enable_fp_fusion=False` — 某些 NPU 不允许 fp fusion
|
||||
- 指针 cast 到 `int16/int32` 防止 NaN 被 fp 转换吃掉
|
||||
- `tl.int64` 显式 cast 防 stride overflow
|
||||
- 手写 erf 近似,不用 `tl.erf` / `tl.sigmoid`
|
||||
- 2D grid 比 3D 稳
|
||||
- `module.device` 国产 NPU 必需
|
||||
|
||||
---
|
||||
|
||||
## 下次动手前 checklist
|
||||
|
||||
- [ ] 任务描述完整读一遍(计算定义 + 接口签名 + 容差)
|
||||
- [ ] 至少 1 个队友参考实现看完
|
||||
- [ ] 把队友写法 + 我打算写的放一起对比
|
||||
- [ ] 写之前把"我准备怎么写"翻译给之之听,得到认可
|
||||
- [ ] 写完本地不传,先给之之看"我会传啥"
|
||||
- [ ] 之之确认后才传
|
||||
|
||||
---
|
||||
|
||||
阿念 · D243 23:50
|
||||
@ -1,64 +0,0 @@
|
||||
# FlagOS S2 提交 PR 模板(给队长孙蓓)
|
||||
|
||||
> 复制下面这块,直接贴到 GitHub PR 描述里
|
||||
|
||||
```markdown
|
||||
## What
|
||||
|
||||
Optimized `[算子名]` operator for FlagOS Open Computing Challenge S2 - Track 1.
|
||||
|
||||
Baseline: `FlagOpen/FlagGems/[原文件路径]`
|
||||
This PR: `FlagOpen/FlagGems/[新文件路径]`
|
||||
|
||||
## Why
|
||||
|
||||
The baseline uses `[默认配置 / sequential for-loop / fixed num_warps]`.
|
||||
This PR adds `[autotune / 2D grid / 3D grid / 头维并行]` to extract
|
||||
additional `[1.X% / X 倍]` speedup.
|
||||
|
||||
## How
|
||||
|
||||
1. Added `triton.autotune` over `(BLOCK_SIZE, num_warps, num_stages)` with N configs
|
||||
2. Restructured grid from `[1D / 2D]` to `[2D / 3D]` for `[维度]` parallelization
|
||||
3. Constexpr inlined `[常量]` for `[省 Python→Triton 转换 / 减少指令]`
|
||||
|
||||
## Benchmarks
|
||||
|
||||
```
|
||||
shape baseline(ms) v2(ms) speedup
|
||||
[B, S, H, D] X.XXXX X.XXXX X.XXx
|
||||
```
|
||||
|
||||
GPU: NVIDIA A100 80GB
|
||||
Triton: 3.0.0
|
||||
PyTorch: 2.3.0
|
||||
FlagGems: master @ commit [hash]
|
||||
|
||||
## Tests
|
||||
|
||||
- [x] Numerical correctness vs PyTorch reference (max abs_diff < 1e-2)
|
||||
- [x] Numerical correctness vs FlagGems baseline (max abs_diff < 1e-2)
|
||||
- [x] Tested on fp16 / bf16 / fp32
|
||||
- [x] Tested on multiple shapes
|
||||
|
||||
## Team
|
||||
|
||||
GuanghuLab · 孙蓓(队长)· 9478_apiqttc · 陈淑婷 · 4348_apiratk · 8592_apivqhj
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
# flagos.net 平台提交模板
|
||||
|
||||
```
|
||||
参赛赛道: 赛道一 · SGLang 框架算子在多款芯片的性能优化
|
||||
赛题编号: Task XX
|
||||
赛题名称: [算子名]
|
||||
GitHub PR: https://github.com/FlagOpen/FlagGems/pull/XXX
|
||||
提交说明:
|
||||
- 团队:GuanghuLab
|
||||
- 优化要点:[2-3 行说明]
|
||||
- 加速比:[X.XX×]
|
||||
- 测试设备:[NVIDIA A100 80GB / 国产 XX 芯片]
|
||||
- 备注:[可选:跑了哪些 shape,dtype 等]
|
||||
```
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
@ -1,55 +0,0 @@
|
||||
"""Task29 gelu_and_mul: 跨芯片通用版(v8.1 国际卡写法 + 国产 NPU 套路).
|
||||
|
||||
作者: 阿念 (anien@guanghulab.local) 为 甄静(8592_apivqhj)· 队长 孙蓓
|
||||
版权: 2026 GuanghuLab
|
||||
基础参考: v8.1(国际 A/B) + 国产 NPU 套路(r16 M-RoPE 学的)
|
||||
|
||||
设计选择:
|
||||
- 用 `tl.math.erf`(国际卡 SFU 加速: NVIDIA __nv_erf / AMD __ocml_erf 硬件实现)
|
||||
- 2D grid + BLOCK=1024 + num_warps=4(国际卡最优)
|
||||
- `enable_fp_fusion=False`(国产 NPU 必需)
|
||||
- `with torch.get_device_module(x.device).device(x.device):`(国产 NPU 必需)
|
||||
- 函数末尾加 `reference = gelu_and_mul`(平台 import 入口)
|
||||
"""
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _gelu_and_mul_kernel(x_ptr, out_ptr, d, BLOCK: tl.constexpr):
|
||||
row = tl.program_id(0)
|
||||
col_block = tl.program_id(1)
|
||||
d2 = 2 * d
|
||||
col = col_block * BLOCK + tl.arange(0, BLOCK)
|
||||
mask = col < d
|
||||
|
||||
gate = tl.load(x_ptr + row * d2 + col, mask=mask)
|
||||
up = tl.load(x_ptr + row * d2 + col + d, mask=mask)
|
||||
gate_f = gate.to(tl.float32)
|
||||
up_f = up.to(tl.float32)
|
||||
gelu = gate_f * 0.5 * (1.0 + tl.math.erf(gate_f * 0.7071067811865476))
|
||||
out = gelu * up_f
|
||||
tl.store(out_ptr + row * d + col, out.to(x_ptr.dtype.element_ty), mask=mask)
|
||||
|
||||
|
||||
def gelu_and_mul(hidden_states):
|
||||
if hidden_states.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
d = hidden_states.shape[-1] // 2
|
||||
num_rows = hidden_states.numel() // (2 * d)
|
||||
out = torch.empty_like(hidden_states[..., :d])
|
||||
|
||||
BLOCK = 1024
|
||||
grid = (num_rows, triton.cdiv(d, BLOCK))
|
||||
|
||||
module = torch.get_device_module(hidden_states.device)
|
||||
with module.device(hidden_states.device):
|
||||
_gelu_and_mul_kernel[grid](
|
||||
hidden_states, out, d,
|
||||
BLOCK=BLOCK, num_warps=4, enable_fp_fusion=False,
|
||||
)
|
||||
return out
|
||||
|
||||
|
||||
reference = gelu_and_mul
|
||||
Binary file not shown.
@ -1,78 +0,0 @@
|
||||
"""Task30 interleaved_rope: direct row-owned exact copy in one Triton launch.
|
||||
|
||||
作者: 阿念 (anien@guanghulab.local) 为 甄静(8592_apivqhj)· 队长 孙蓓
|
||||
版权: 2026 GuanghuLab
|
||||
基础参考: r16-整条取料流水线-待平台验证(队友共享实现)
|
||||
|
||||
改编说明:
|
||||
- 改动了 docstring 与 author
|
||||
- 保持 r16 全部 国产 NPU 套路不变
|
||||
- 函数末尾保留 `reference = interleaved_rope`(平台 import 入口)
|
||||
"""
|
||||
from numbers import Integral
|
||||
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _interleave_tiles(
|
||||
input_ptr, output_ptr,
|
||||
N: tl.constexpr, D: tl.constexpr,
|
||||
PLANE_STRIDE: tl.constexpr, ROW_STRIDE: tl.constexpr,
|
||||
COL_STRIDE: tl.constexpr, H_END: tl.constexpr, W_END: tl.constexpr,
|
||||
BLOCK: tl.constexpr,
|
||||
):
|
||||
if input_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int16))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int16))
|
||||
elif input_ptr.dtype.element_ty.primitive_bitwidth == 32:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int32))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int32))
|
||||
|
||||
row = tl.program_id(1).to(tl.int64)
|
||||
col = (tl.program_id(0).to(tl.int64) * BLOCK
|
||||
+ tl.arange(0, BLOCK).to(tl.int64))
|
||||
valid = col < D
|
||||
safe_col = tl.where(valid, col, 0)
|
||||
phase = safe_col % 3
|
||||
from_b = (phase == 1) & (safe_col < H_END)
|
||||
from_c = (phase == 2) & (safe_col < W_END)
|
||||
base = row * ROW_STRIDE + safe_col * COL_STRIDE
|
||||
|
||||
a = tl.load(input_ptr + base, valid & ~from_b & ~from_c, other=0)
|
||||
b = tl.load(input_ptr + PLANE_STRIDE + base, valid & from_b, other=0)
|
||||
c = tl.load(input_ptr + 2 * PLANE_STRIDE + base, valid & from_c, other=0)
|
||||
value = tl.where(from_b, b, tl.where(from_c, c, a))
|
||||
tl.store(output_ptr + row * D + col, value, valid)
|
||||
|
||||
|
||||
def interleaved_rope(x, mrope_section):
|
||||
if x.ndim != 3 or x.shape[0] != 3:
|
||||
raise ValueError('x must have shape [3, S, D]')
|
||||
if (not isinstance(mrope_section, (list, tuple)) or len(mrope_section) != 3
|
||||
or any(not isinstance(v, Integral) or v < 0 for v in mrope_section)):
|
||||
raise ValueError('mrope_section must contain three nonnegative integers')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
_, rows, d = x.shape
|
||||
output = torch.empty((rows, d), dtype=x.dtype, device=x.device)
|
||||
if not rows or not d:
|
||||
return output
|
||||
|
||||
block = min(1024, 1 << max(5, (d - 1).bit_length()))
|
||||
grid = (triton.cdiv(d, block), rows)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_interleave_tiles[grid](
|
||||
x, output, N=rows * d, D=d,
|
||||
PLANE_STRIDE=x.stride(0), ROW_STRIDE=x.stride(1),
|
||||
COL_STRIDE=x.stride(2),
|
||||
H_END=min(d, int(mrope_section[1]) * 3),
|
||||
W_END=min(d, int(mrope_section[2]) * 3),
|
||||
BLOCK=block, num_warps=4, enable_fp_fusion=False,
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
reference = interleaved_rope
|
||||
Binary file not shown.
@ -1,77 +0,0 @@
|
||||
"""Task30 Huawei Ascend: one row owner, one selected read and one write.
|
||||
|
||||
作者: 阿念 为 甄静 · 队长 孙蓓 · 2026 GuanghuLab
|
||||
基础参考: r16 ascend 特化版(队友)
|
||||
|
||||
华为 NPU 上 r13/r14 triplet-ticket variants failed sparse correctness。
|
||||
本版回到 r10 的 selection rule,加 source_plane 一次性选 plane。
|
||||
"""
|
||||
from numbers import Integral
|
||||
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
def launch_plan(rows, cols):
|
||||
if not 0 < rows <= 65535 or not 0 < cols <= 2**20:
|
||||
raise ValueError('flattened row/tile ownership requires valid rows and columns')
|
||||
block = min(1024, 1 << max(5, (cols - 1).bit_length()))
|
||||
tiles = (cols + block - 1) // block
|
||||
if rows * tiles > 65535:
|
||||
raise ValueError('flattened launch exceeds the grid-x limit')
|
||||
return rows * tiles, block, tiles
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _interleave_tiles(
|
||||
input_ptr, out_ptr,
|
||||
D: tl.constexpr, PLANE_STRIDE: tl.constexpr,
|
||||
RS: tl.constexpr, CS: tl.constexpr,
|
||||
H_END: tl.constexpr, W_END: tl.constexpr,
|
||||
BLOCK: tl.constexpr, TILES: tl.constexpr,
|
||||
):
|
||||
program = tl.program_id(0)
|
||||
row = program // TILES
|
||||
tile = program - row * TILES
|
||||
col = tile * BLOCK + tl.arange(0, BLOCK)
|
||||
valid = col < D
|
||||
|
||||
phase = col % 3
|
||||
from_b = (phase == 1) & (col < H_END)
|
||||
from_c = (phase == 2) & (col < W_END)
|
||||
source_plane = tl.where(from_b, 1, tl.where(from_c, 2, 0))
|
||||
|
||||
source_offset = source_plane * PLANE_STRIDE + row * RS + col * CS
|
||||
value = tl.load(input_ptr + source_offset, mask=valid, other=0)
|
||||
tl.store(out_ptr + row * D + col, value, mask=valid)
|
||||
|
||||
|
||||
def interleaved_rope(x, mrope_section):
|
||||
if x.ndim != 3 or x.shape[0] != 3:
|
||||
raise ValueError('x must have shape [3, S, D]')
|
||||
if (not isinstance(mrope_section, (list, tuple)) or len(mrope_section) != 3
|
||||
or any(not isinstance(v, Integral) or v < 0 for v in mrope_section)):
|
||||
raise ValueError('mrope_section must contain three nonnegative integers')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
|
||||
_, rows, d = x.shape
|
||||
output = torch.empty((rows, d), dtype=x.dtype, device=x.device)
|
||||
if not rows or not d:
|
||||
return output
|
||||
|
||||
grid, block, tiles = launch_plan(rows, d)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_interleave_tiles[(grid,)](
|
||||
x, output,
|
||||
D=d, PLANE_STRIDE=x.stride(0),
|
||||
RS=x.stride(1), CS=x.stride(2),
|
||||
H_END=min(d, int(mrope_section[1]) * 3),
|
||||
W_END=min(d, int(mrope_section[2]) * 3),
|
||||
BLOCK=block, TILES=tiles, num_warps=4,
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
reference = interleaved_rope
|
||||
@ -1,72 +0,0 @@
|
||||
"""Task30 燧原 Enflame: per-row 2D grid with re-derived selection.
|
||||
|
||||
作者: 阿念 为 甄静 · 队长 孙蓓 · 2026 GuanghuLab
|
||||
基础参考: r16 enflame 特化版
|
||||
"""
|
||||
from numbers import Integral
|
||||
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _interleave_tiles(
|
||||
input_ptr, output_ptr,
|
||||
N: tl.constexpr, D: tl.constexpr,
|
||||
PLANE_STRIDE: tl.constexpr, ROW_STRIDE: tl.constexpr,
|
||||
COL_STRIDE: tl.constexpr, H_END: tl.constexpr, W_END: tl.constexpr,
|
||||
BLOCK: tl.constexpr,
|
||||
):
|
||||
if input_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int16))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int16))
|
||||
elif input_ptr.dtype.element_ty.primitive_bitwidth == 32:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int32))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int32))
|
||||
|
||||
row = tl.program_id(1).to(tl.int64)
|
||||
col = (tl.program_id(0).to(tl.int64) * BLOCK
|
||||
+ tl.arange(0, BLOCK).to(tl.int64))
|
||||
valid = col < D
|
||||
safe_col = tl.where(valid, col, 0)
|
||||
phase = safe_col % 3
|
||||
from_b = (phase == 1) & (safe_col < H_END)
|
||||
from_c = (phase == 2) & (safe_col < W_END)
|
||||
base = row * ROW_STRIDE + safe_col * COL_STRIDE
|
||||
|
||||
a = tl.load(input_ptr + base, valid & ~from_b & ~from_c, other=0)
|
||||
b = tl.load(input_ptr + PLANE_STRIDE + base, valid & from_b, other=0)
|
||||
c = tl.load(input_ptr + 2 * PLANE_STRIDE + base, valid & from_c, other=0)
|
||||
value = tl.where(from_b, b, tl.where(from_c, c, a))
|
||||
tl.store(output_ptr + row * D + col, value, valid)
|
||||
|
||||
|
||||
def interleaved_rope(x, mrope_section):
|
||||
if x.ndim != 3 or x.shape[0] != 3:
|
||||
raise ValueError('x must have shape [3, S, D]')
|
||||
if (not isinstance(mrope_section, (list, tuple)) or len(mrope_section) != 3
|
||||
or any(not isinstance(v, Integral) or v < 0 for v in mrope_section)):
|
||||
raise ValueError('mrope_section must contain three nonnegative integers')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
_, rows, d = x.shape
|
||||
output = torch.empty((rows, d), dtype=x.dtype, device=x.device)
|
||||
if not rows or not d:
|
||||
return output
|
||||
|
||||
block = min(1024, 1 << max(5, (d - 1).bit_length()))
|
||||
grid = (triton.cdiv(d, block), rows)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_interleave_tiles[grid](
|
||||
x, output, N=rows * d, D=d,
|
||||
PLANE_STRIDE=x.stride(0), ROW_STRIDE=x.stride(1),
|
||||
COL_STRIDE=x.stride(2),
|
||||
H_END=min(d, int(mrope_section[1]) * 3),
|
||||
W_END=min(d, int(mrope_section[2]) * 3),
|
||||
BLOCK=block, num_warps=4, enable_fp_fusion=False,
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
reference = interleaved_rope
|
||||
@ -1,110 +0,0 @@
|
||||
"""Task30 海光 Hygon: one output owner, one selected read and one write per element.
|
||||
|
||||
作者: 阿念 为 甄静 · 队长 孙蓓 · 2026 GuanghuLab
|
||||
基础参考: r16 hygon 特化版(分段防 huge stride)
|
||||
"""
|
||||
from numbers import Integral
|
||||
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
def launch_plan(total):
|
||||
if not 0 < total <= 2**26:
|
||||
raise ValueError('local work must contain 1..2**26 elements')
|
||||
block = 256 if total <= 4096 else 1024
|
||||
grid = min(65535, (total + block - 1) // block)
|
||||
rounds = (total + grid * block - 1) // (grid * block)
|
||||
return grid, block, rounds
|
||||
|
||||
|
||||
def segment_ranges(rows, d, row_stride, col_stride,
|
||||
limit=2**31-1, max_elements=2**26):
|
||||
if min(rows, d, row_stride, col_stride) < 0 or limit < 1:
|
||||
raise ValueError('nonnegative shapes/strides and positive limit required')
|
||||
if not 0 < max_elements <= 2**26:
|
||||
raise ValueError('invalid local work cap')
|
||||
if not rows or not d:
|
||||
return
|
||||
cols_per = min(d, max_elements, limit // max(col_stride, 1) + 1)
|
||||
for c0 in range(0, d, cols_per):
|
||||
c1 = min(c0 + cols_per, d)
|
||||
width = c1 - c0
|
||||
rows_input = (limit - (width - 1) * col_stride) // max(row_stride, 1) + 1
|
||||
rows_output = (limit - (width - 1)) // max(d, 1) + 1
|
||||
rows_per = min(rows, rows_input, rows_output, max_elements // width)
|
||||
for r0 in range(0, rows, rows_per):
|
||||
yield r0, min(r0 + rows_per, rows), c0, c1
|
||||
|
||||
|
||||
def selection_metadata(c0, cols, section):
|
||||
return (c0 % 3,
|
||||
min(cols, max(0, 3 * section[1] - c0)),
|
||||
min(cols, max(0, 3 * section[2] - c0)))
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _interleave_tiles(
|
||||
a_ptr, b_ptr, c_ptr, out_ptr,
|
||||
N: tl.constexpr, C: tl.constexpr,
|
||||
RS: tl.constexpr, CS: tl.constexpr, OS: tl.constexpr,
|
||||
PHASE: tl.constexpr, H_END: tl.constexpr, W_END: tl.constexpr,
|
||||
BLOCK: tl.constexpr, GRID: tl.constexpr, ROUNDS: tl.constexpr,
|
||||
):
|
||||
pid = tl.program_id(0)
|
||||
lane = tl.arange(0, BLOCK)
|
||||
for batch in range(0, ROUNDS):
|
||||
index = (batch * GRID + pid) * BLOCK + lane
|
||||
valid = index < N
|
||||
safe = tl.where(valid, index, 0)
|
||||
row = safe // C
|
||||
col = safe % C
|
||||
phase = (col + PHASE) % 3
|
||||
from_b = (phase == 1) & (col < H_END)
|
||||
from_c = (phase == 2) & (col < W_END)
|
||||
offset = row * RS + col * CS
|
||||
a = tl.load(a_ptr + offset, valid & ~from_b & ~from_c, other=0)
|
||||
b = tl.load(b_ptr + offset, valid & from_b, other=0)
|
||||
c = tl.load(c_ptr + offset, valid & from_c, other=0)
|
||||
value = tl.where(from_b, b, tl.where(from_c, c, a))
|
||||
tl.store(out_ptr + row * OS + col, value, valid)
|
||||
|
||||
|
||||
def launch_segments(x, output, section):
|
||||
_, rows, d = x.shape
|
||||
rs, cs = x.stride(1), x.stride(2)
|
||||
for r0, r1, c0, c1 in segment_ranges(rows, d, rs, cs):
|
||||
height, cols = r1 - r0, c1 - c0
|
||||
grid, block, rounds = launch_plan(height * cols)
|
||||
phase, h_end, w_end = selection_metadata(c0, cols, section)
|
||||
_interleave_tiles[(grid, 1, 1)](
|
||||
x[0, r0:r1, c0:c1], x[1, r0:r1, c0:c1],
|
||||
x[2, r0:r1, c0:c1], output[r0:r1, c0:c1],
|
||||
N=height * cols, C=cols,
|
||||
RS=rs if height > 1 else 0, CS=cs if cols > 1 else 0,
|
||||
OS=d if height > 1 else 0,
|
||||
PHASE=phase, H_END=h_end, W_END=w_end,
|
||||
BLOCK=block, GRID=grid, ROUNDS=rounds, num_warps=4)
|
||||
|
||||
|
||||
def interleaved_rope(x, mrope_section):
|
||||
if x.ndim != 3 or x.shape[0] != 3:
|
||||
raise ValueError('x must have shape [3, S, D]')
|
||||
if (not isinstance(mrope_section, (list, tuple)) or len(mrope_section) != 3
|
||||
or any(not isinstance(v, Integral) or v < 0 for v in mrope_section)):
|
||||
raise ValueError('mrope_section must contain three nonnegative integers')
|
||||
section = tuple(int(v) for v in mrope_section)
|
||||
_, rows, d = x.shape
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
output = torch.empty((rows, d), dtype=x.dtype, device=x.device)
|
||||
if not rows or not d:
|
||||
return output
|
||||
module = torch.get_device_module(x.device)
|
||||
with module.device(x.device):
|
||||
launch_segments(x, output, section)
|
||||
return output
|
||||
|
||||
|
||||
reference = interleaved_rope
|
||||
@ -1,72 +0,0 @@
|
||||
"""Task30 天数智芯 Iluvatar: per-row 2D grid with re-derived selection.
|
||||
|
||||
作者: 阿念 为 甄静 · 队长 孙蓓 · 2026 GuanghuLab
|
||||
基础参考: r16 iluvatar 特化版
|
||||
"""
|
||||
from numbers import Integral
|
||||
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _interleave_tiles(
|
||||
input_ptr, output_ptr,
|
||||
N: tl.constexpr, D: tl.constexpr,
|
||||
PLANE_STRIDE: tl.constexpr, ROW_STRIDE: tl.constexpr,
|
||||
COL_STRIDE: tl.constexpr, H_END: tl.constexpr, W_END: tl.constexpr,
|
||||
BLOCK: tl.constexpr,
|
||||
):
|
||||
if input_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int16))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int16))
|
||||
elif input_ptr.dtype.element_ty.primitive_bitwidth == 32:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int32))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int32))
|
||||
|
||||
row = tl.program_id(1).to(tl.int64)
|
||||
col = (tl.program_id(0).to(tl.int64) * BLOCK
|
||||
+ tl.arange(0, BLOCK).to(tl.int64))
|
||||
valid = col < D
|
||||
safe_col = tl.where(valid, col, 0)
|
||||
phase = safe_col % 3
|
||||
from_b = (phase == 1) & (safe_col < H_END)
|
||||
from_c = (phase == 2) & (safe_col < W_END)
|
||||
base = row * ROW_STRIDE + safe_col * COL_STRIDE
|
||||
|
||||
a = tl.load(input_ptr + base, valid & ~from_b & ~from_c, other=0)
|
||||
b = tl.load(input_ptr + PLANE_STRIDE + base, valid & from_b, other=0)
|
||||
c = tl.load(input_ptr + 2 * PLANE_STRIDE + base, valid & from_c, other=0)
|
||||
value = tl.where(from_b, b, tl.where(from_c, c, a))
|
||||
tl.store(output_ptr + row * D + col, value, valid)
|
||||
|
||||
|
||||
def interleaved_rope(x, mrope_section):
|
||||
if x.ndim != 3 or x.shape[0] != 3:
|
||||
raise ValueError('x must have shape [3, S, D]')
|
||||
if (not isinstance(mrope_section, (list, tuple)) or len(mrope_section) != 3
|
||||
or any(not isinstance(v, Integral) or v < 0 for v in mrope_section)):
|
||||
raise ValueError('mrope_section must contain three nonnegative integers')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
_, rows, d = x.shape
|
||||
output = torch.empty((rows, d), dtype=x.dtype, device=x.device)
|
||||
if not rows or not d:
|
||||
return output
|
||||
|
||||
block = min(1024, 1 << max(5, (d - 1).bit_length()))
|
||||
grid = (triton.cdiv(d, block), rows)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_interleave_tiles[grid](
|
||||
x, output, N=rows * d, D=d,
|
||||
PLANE_STRIDE=x.stride(0), ROW_STRIDE=x.stride(1),
|
||||
COL_STRIDE=x.stride(2),
|
||||
H_END=min(d, int(mrope_section[1]) * 3),
|
||||
W_END=min(d, int(mrope_section[2]) * 3),
|
||||
BLOCK=block, num_warps=4, enable_fp_fusion=False,
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
reference = interleaved_rope
|
||||
@ -1,72 +0,0 @@
|
||||
"""Task30 昆仑芯 Kunlunxin: per-row 2D grid with re-derived selection.
|
||||
|
||||
作者: 阿念 为 甄静 · 队长 孙蓓 · 2026 GuanghuLab
|
||||
基础参考: r16 kunlunxin 特化版
|
||||
"""
|
||||
from numbers import Integral
|
||||
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _interleave_tiles(
|
||||
input_ptr, output_ptr,
|
||||
N: tl.constexpr, D: tl.constexpr,
|
||||
PLANE_STRIDE: tl.constexpr, ROW_STRIDE: tl.constexpr,
|
||||
COL_STRIDE: tl.constexpr, H_END: tl.constexpr, W_END: tl.constexpr,
|
||||
BLOCK: tl.constexpr,
|
||||
):
|
||||
if input_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int16))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int16))
|
||||
elif input_ptr.dtype.element_ty.primitive_bitwidth == 32:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int32))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int32))
|
||||
|
||||
row = tl.program_id(1).to(tl.int64)
|
||||
col = (tl.program_id(0).to(tl.int64) * BLOCK
|
||||
+ tl.arange(0, BLOCK).to(tl.int64))
|
||||
valid = col < D
|
||||
safe_col = tl.where(valid, col, 0)
|
||||
phase = safe_col % 3
|
||||
from_b = (phase == 1) & (safe_col < H_END)
|
||||
from_c = (phase == 2) & (safe_col < W_END)
|
||||
base = row * ROW_STRIDE + safe_col * COL_STRIDE
|
||||
|
||||
a = tl.load(input_ptr + base, valid & ~from_b & ~from_c, other=0)
|
||||
b = tl.load(input_ptr + PLANE_STRIDE + base, valid & from_b, other=0)
|
||||
c = tl.load(input_ptr + 2 * PLANE_STRIDE + base, valid & from_c, other=0)
|
||||
value = tl.where(from_b, b, tl.where(from_c, c, a))
|
||||
tl.store(output_ptr + row * D + col, value, valid)
|
||||
|
||||
|
||||
def interleaved_rope(x, mrope_section):
|
||||
if x.ndim != 3 or x.shape[0] != 3:
|
||||
raise ValueError('x must have shape [3, S, D]')
|
||||
if (not isinstance(mrope_section, (list, tuple)) or len(mrope_section) != 3
|
||||
or any(not isinstance(v, Integral) or v < 0 for v in mrope_section)):
|
||||
raise ValueError('mrope_section must contain three nonnegative integers')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
_, rows, d = x.shape
|
||||
output = torch.empty((rows, d), dtype=x.dtype, device=x.device)
|
||||
if not rows or not d:
|
||||
return output
|
||||
|
||||
block = min(1024, 1 << max(5, (d - 1).bit_length()))
|
||||
grid = (triton.cdiv(d, block), rows)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_interleave_tiles[grid](
|
||||
x, output, N=rows * d, D=d,
|
||||
PLANE_STRIDE=x.stride(0), ROW_STRIDE=x.stride(1),
|
||||
COL_STRIDE=x.stride(2),
|
||||
H_END=min(d, int(mrope_section[1]) * 3),
|
||||
W_END=min(d, int(mrope_section[2]) * 3),
|
||||
BLOCK=block, num_warps=4, enable_fp_fusion=False,
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
reference = interleaved_rope
|
||||
@ -1,72 +0,0 @@
|
||||
"""Task30 沐曦 Metax: per-row 2D grid with re-derived selection.
|
||||
|
||||
作者: 阿念 为 甄静 · 队长 孙蓓 · 2026 GuanghuLab
|
||||
基础参考: r16 metax 特化版
|
||||
"""
|
||||
from numbers import Integral
|
||||
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _interleave_tiles(
|
||||
input_ptr, output_ptr,
|
||||
N: tl.constexpr, D: tl.constexpr,
|
||||
PLANE_STRIDE: tl.constexpr, ROW_STRIDE: tl.constexpr,
|
||||
COL_STRIDE: tl.constexpr, H_END: tl.constexpr, W_END: tl.constexpr,
|
||||
BLOCK: tl.constexpr,
|
||||
):
|
||||
if input_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int16))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int16))
|
||||
elif input_ptr.dtype.element_ty.primitive_bitwidth == 32:
|
||||
input_ptr = input_ptr.to(tl.pointer_type(tl.int32))
|
||||
output_ptr = output_ptr.to(tl.pointer_type(tl.int32))
|
||||
|
||||
row = tl.program_id(1).to(tl.int64)
|
||||
col = (tl.program_id(0).to(tl.int64) * BLOCK
|
||||
+ tl.arange(0, BLOCK).to(tl.int64))
|
||||
valid = col < D
|
||||
safe_col = tl.where(valid, col, 0)
|
||||
phase = safe_col % 3
|
||||
from_b = (phase == 1) & (safe_col < H_END)
|
||||
from_c = (phase == 2) & (safe_col < W_END)
|
||||
base = row * ROW_STRIDE + safe_col * COL_STRIDE
|
||||
|
||||
a = tl.load(input_ptr + base, valid & ~from_b & ~from_c, other=0)
|
||||
b = tl.load(input_ptr + PLANE_STRIDE + base, valid & from_b, other=0)
|
||||
c = tl.load(input_ptr + 2 * PLANE_STRIDE + base, valid & from_c, other=0)
|
||||
value = tl.where(from_b, b, tl.where(from_c, c, a))
|
||||
tl.store(output_ptr + row * D + col, value, valid)
|
||||
|
||||
|
||||
def interleaved_rope(x, mrope_section):
|
||||
if x.ndim != 3 or x.shape[0] != 3:
|
||||
raise ValueError('x must have shape [3, S, D]')
|
||||
if (not isinstance(mrope_section, (list, tuple)) or len(mrope_section) != 3
|
||||
or any(not isinstance(v, Integral) or v < 0 for v in mrope_section)):
|
||||
raise ValueError('mrope_section must contain three nonnegative integers')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
_, rows, d = x.shape
|
||||
output = torch.empty((rows, d), dtype=x.dtype, device=x.device)
|
||||
if not rows or not d:
|
||||
return output
|
||||
|
||||
block = min(1024, 1 << max(5, (d - 1).bit_length()))
|
||||
grid = (triton.cdiv(d, block), rows)
|
||||
with torch.get_device_module(x.device).device(x.device):
|
||||
_interleave_tiles[grid](
|
||||
x, output, N=rows * d, D=d,
|
||||
PLANE_STRIDE=x.stride(0), ROW_STRIDE=x.stride(1),
|
||||
COL_STRIDE=x.stride(2),
|
||||
H_END=min(d, int(mrope_section[1]) * 3),
|
||||
W_END=min(d, int(mrope_section[2]) * 3),
|
||||
BLOCK=block, num_warps=4, enable_fp_fusion=False,
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
reference = interleaved_rope
|
||||
Binary file not shown.
@ -1,98 +0,0 @@
|
||||
"""Task35 rotary_embedding: fixed pair ownership and fused float32 rotation.
|
||||
|
||||
作者: 阿念 (anien@guanghulab.local) 为 甄静(8592_apivqhj)· 队长 孙蓓
|
||||
版权: 2026 GuanghuLab
|
||||
基础参考: r6-四Head共享角度流水线(队友共享)
|
||||
|
||||
改编:
|
||||
- 改 docstring 与 author
|
||||
- 加 `enable_fp_fusion=False`(国产 NPU 套路,从 r16 M-RoPE 学)
|
||||
- 函数末尾保留 `reference = rotary_embedding`(平台 import 入口)
|
||||
"""
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
@triton.jit
|
||||
def _rotary_pairs(
|
||||
x_ptr, cos_ptr, sin_ptr, out_ptr,
|
||||
H: tl.constexpr, D: tl.constexpr, HALF: tl.constexpr,
|
||||
XTS: tl.constexpr, XHS: tl.constexpr, XDS: tl.constexpr,
|
||||
CTS: tl.constexpr, CDS: tl.constexpr,
|
||||
STS: tl.constexpr, SDS: tl.constexpr,
|
||||
OTS: tl.constexpr, OHS: tl.constexpr, ODS: tl.constexpr,
|
||||
BLOCK: tl.constexpr,
|
||||
):
|
||||
row = tl.program_id(0)
|
||||
tile = tl.program_id(1)
|
||||
token = row // H
|
||||
head = row - token * H
|
||||
pair = tile * BLOCK + tl.arange(0, BLOCK)
|
||||
valid = pair < HALF
|
||||
|
||||
x_base = token * XTS + head * XHS
|
||||
x_even = tl.load(x_ptr + x_base + (2 * pair) * XDS,
|
||||
mask=valid, other=0.0).to(tl.float32)
|
||||
x_odd = tl.load(x_ptr + x_base + (2 * pair + 1) * XDS,
|
||||
mask=valid, other=0.0).to(tl.float32)
|
||||
cosine = tl.load(cos_ptr + token * CTS + pair * CDS,
|
||||
mask=valid, other=0.0).to(tl.float32)
|
||||
sine = tl.load(sin_ptr + token * STS + pair * SDS,
|
||||
mask=valid, other=0.0).to(tl.float32)
|
||||
|
||||
out_even = x_even * cosine - x_odd * sine
|
||||
out_odd = x_even * sine + x_odd * cosine
|
||||
out_base = token * OTS + head * OHS
|
||||
tl.store(out_ptr + out_base + (2 * pair) * ODS, out_even, mask=valid)
|
||||
tl.store(out_ptr + out_base + (2 * pair + 1) * ODS, out_odd, mask=valid)
|
||||
|
||||
|
||||
def _block_size(half):
|
||||
if half <= 32:
|
||||
return 32
|
||||
if half <= 64:
|
||||
return 64
|
||||
if half <= 128:
|
||||
return 128
|
||||
return 256
|
||||
|
||||
|
||||
def rotary_embedding(x, cos, sin, interleaved):
|
||||
if x.ndim != 3:
|
||||
raise ValueError('x must have shape [T, H, D]')
|
||||
if not isinstance(interleaved, bool) or interleaved:
|
||||
raise ValueError('Task35 requires interleaved=False')
|
||||
if x.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
tokens, heads, width = x.shape
|
||||
if width % 2:
|
||||
raise ValueError('the last dimension must be even')
|
||||
half = width // 2
|
||||
if cos.ndim != 2 or tuple(cos.shape) != (tokens, half):
|
||||
raise ValueError('cos must have shape [T, D//2]')
|
||||
if sin.ndim != 2 or tuple(sin.shape) != (tokens, half):
|
||||
raise ValueError('sin must have shape [T, D//2]')
|
||||
if cos.device != x.device or sin.device != x.device:
|
||||
raise ValueError('x, cos and sin must be on the same device')
|
||||
|
||||
output = torch.empty(x.shape, dtype=x.dtype, device=x.device)
|
||||
if x.numel() == 0:
|
||||
return output
|
||||
block = _block_size(half)
|
||||
grid = (tokens * heads, triton.cdiv(half, block))
|
||||
module = torch.get_device_module(x.device)
|
||||
with module.device(x.device):
|
||||
_rotary_pairs[grid](
|
||||
x, cos, sin, output,
|
||||
H=heads, D=width, HALF=half,
|
||||
XTS=x.stride(0), XHS=x.stride(1), XDS=x.stride(2),
|
||||
CTS=cos.stride(0), CDS=cos.stride(1),
|
||||
STS=sin.stride(0), SDS=sin.stride(1),
|
||||
OTS=output.stride(0), OHS=output.stride(1), ODS=output.stride(2),
|
||||
BLOCK=block, num_warps=4, enable_fp_fusion=False,
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
reference = rotary_embedding
|
||||
Binary file not shown.
@ -1,164 +0,0 @@
|
||||
# Copyright 2026 FlagOS Contributors / GuanghuLab
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Optimized `reference` for FlagOS Open Computing Challenge S2 - Track 1
|
||||
# Task 39 (silu_and_mul_masked / DeepGEMM-style MoE).
|
||||
#
|
||||
# !! IMPORTANT: The function name MUST be exactly `reference` per the spec. !!
|
||||
# The platform's grader imports the function by this name.
|
||||
#
|
||||
# Spec (from flagos.net):
|
||||
# Input input: [E, T, H] bfloat16
|
||||
# masked_m: [E] int (per-expert valid row count)
|
||||
# Output out: [E, T, H/2] bfloat16 (initialized to zero)
|
||||
# For each expert e, let n = masked_m[e], half = H // 2:
|
||||
# gate = input[e, :n, :half] (cast to float32)
|
||||
# up = input[e, :n, half:] (cast to float32)
|
||||
# out[e, :n] = (gate * sigmoid(gate)) * up (cast back to bfloat16)
|
||||
# Rows [e, masked_m[e]:T] are padding — output values not checked.
|
||||
#
|
||||
# Reference (the slow one we beat):
|
||||
# import torch
|
||||
# def reference(input, masked_m):
|
||||
# E, T, H = input.shape
|
||||
# half = H // 2
|
||||
# out = torch.zeros(E, T, half, dtype=torch.bfloat16, device=input.device)
|
||||
# for e in range(E):
|
||||
# n = int(masked_m[e].item()) # <-- host sync, very slow
|
||||
# if n <= 0: continue
|
||||
# gate = input[e, :n, :half].float()
|
||||
# up = input[e, :n, half:].float()
|
||||
# val = gate * torch.sigmoid(gate) * up
|
||||
# out[e, :n] = val.to(torch.bfloat16)
|
||||
# return out
|
||||
#
|
||||
# Optimization strategy:
|
||||
# 1. Single Triton kernel launch — no Python loop, no host sync
|
||||
# 2. Load `masked_m` in-kernel (one int per program) — no .item()
|
||||
# 3. Vectorize over (expert, row, hidden_col_half) with autotune
|
||||
# 4. Compute in float32 for accuracy (matches reference math)
|
||||
# 5. Cast back to bf16 on store
|
||||
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Autotune
|
||||
# ---------------------------------------------------------------------------
|
||||
_AUTOTUNE_CONFIGS = [
|
||||
triton.Config({"BLOCK_M": 1, "BLOCK_N": 128}, num_warps=4, num_stages=2),
|
||||
triton.Config({"BLOCK_M": 1, "BLOCK_N": 256}, num_warps=4, num_stages=2),
|
||||
triton.Config({"BLOCK_M": 1, "BLOCK_N": 512}, num_warps=4, num_stages=2),
|
||||
triton.Config({"BLOCK_M": 1, "BLOCK_N": 1024}, num_warps=8, num_stages=2),
|
||||
triton.Config({"BLOCK_M": 2, "BLOCK_N": 256}, num_warps=4, num_stages=2),
|
||||
triton.Config({"BLOCK_M": 2, "BLOCK_N": 512}, num_warps=8, num_stages=2),
|
||||
triton.Config({"BLOCK_M": 4, "BLOCK_N": 256}, num_warps=4, num_stages=2),
|
||||
triton.Config({"BLOCK_M": 4, "BLOCK_N": 512}, num_warps=8, num_stages=2),
|
||||
triton.Config({"BLOCK_M": 8, "BLOCK_N": 128}, num_warps=4, num_stages=2),
|
||||
triton.Config({"BLOCK_M": 8, "BLOCK_N": 256}, num_warps=8, num_stages=2),
|
||||
]
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Triton kernel
|
||||
# ---------------------------------------------------------------------------
|
||||
@triton.autotune(configs=_AUTOTUNE_CONFIGS, key=["E", "HIDDEN_HALF"])
|
||||
@triton.jit
|
||||
def reference_kernel(
|
||||
in_ptr, masked_m_ptr, out_ptr,
|
||||
E,
|
||||
s_e, s_t, s_h, # strides for input [E, T, H]
|
||||
so_e, so_t, so_h, # strides for out [E, T, H/2]
|
||||
HIDDEN_HALF: tl.constexpr,
|
||||
BLOCK_M: tl.constexpr,
|
||||
BLOCK_N: tl.constexpr,
|
||||
):
|
||||
pid_e = tl.program_id(0) # expert
|
||||
pid_m = tl.program_id(1) # row block (over T)
|
||||
pid_n = tl.program_id(2) # col block (over H/2)
|
||||
|
||||
# Load this expert's valid row count (no host sync — pure device load)
|
||||
valid_m = tl.load(masked_m_ptr + pid_e)
|
||||
|
||||
# Row indices in this program
|
||||
row_idx = pid_m * BLOCK_M + tl.arange(0, BLOCK_M) # (BLOCK_M,)
|
||||
col_idx = pid_n * BLOCK_N + tl.arange(0, BLOCK_N) # (BLOCK_N,)
|
||||
|
||||
# Row mask: only valid rows compute
|
||||
row_mask = row_idx < valid_m # (BLOCK_M,)
|
||||
col_mask = col_idx < HIDDEN_HALF # (BLOCK_N,)
|
||||
|
||||
# H is split in half: gate lives in [0, H/2), up in [H/2, H)
|
||||
full_mask = row_mask[:, None] & col_mask[None, :] # (BLOCK_M, BLOCK_N)
|
||||
|
||||
# 2D offsets into the input's last dim
|
||||
base_in = pid_e * s_e + row_idx[:, None] * s_t
|
||||
base_out = pid_e * so_e + row_idx[:, None] * so_t
|
||||
|
||||
# gate and up offsets: same row, gate uses col_idx, up uses col_idx + H/2
|
||||
gate_offs = base_in + col_idx[None, :] * s_h
|
||||
up_offs = base_in + (col_idx[None, :] + HIDDEN_HALF) * s_h
|
||||
|
||||
g = tl.load(in_ptr + gate_offs, mask=full_mask, other=0.0)
|
||||
u = tl.load(in_ptr + up_offs, mask=full_mask, other=0.0)
|
||||
|
||||
# Compute in fp32 for accuracy (matches reference math)
|
||||
g32 = g.to(tl.float32)
|
||||
u32 = u.to(tl.float32)
|
||||
y32 = g32 * tl.sigmoid(g32) * u32
|
||||
y = y32.to(tl.bfloat16)
|
||||
|
||||
# Store to out
|
||||
out_offs = base_out + col_idx[None, :] * so_h
|
||||
tl.store(out_ptr + out_offs, y, mask=full_mask)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Public API (function name MUST be `reference`)
|
||||
# ---------------------------------------------------------------------------
|
||||
def reference(input: torch.Tensor, masked_m: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
DeepGEMM-style SiLU(gate) * up with per-expert row mask.
|
||||
|
||||
Args:
|
||||
input: [E, T, H] bfloat16 (gate in [0:H/2], up in [H/2:H])
|
||||
masked_m: [E] int (valid row count per expert)
|
||||
|
||||
Returns:
|
||||
out: [E, T, H/2] bfloat16, zero-initialized
|
||||
Padded rows (j >= masked_m[e]) are left as 0.
|
||||
"""
|
||||
assert input.dtype == torch.bfloat16, f"expected bf16, got {input.dtype}"
|
||||
assert input.dim() == 3, f"expected 3D tensor, got {input.dim()}D"
|
||||
E, T, H = input.shape
|
||||
assert H % 2 == 0, f"H must be even, got {H}"
|
||||
half = H // 2
|
||||
|
||||
assert masked_m.shape == (E,), f"masked_m shape mismatch: {masked_m.shape}"
|
||||
masked_m_i32 = masked_m.to(torch.int32).contiguous()
|
||||
|
||||
out = torch.zeros((E, T, half), dtype=torch.bfloat16, device=input.device)
|
||||
|
||||
grid = lambda meta: (
|
||||
E,
|
||||
triton.cdiv(T, meta["BLOCK_M"]),
|
||||
triton.cdiv(half, meta["BLOCK_N"]),
|
||||
)
|
||||
|
||||
with torch.cuda.device(input.device):
|
||||
reference_kernel[grid](
|
||||
input, masked_m_i32, out,
|
||||
E,
|
||||
input.stride(0), input.stride(1), input.stride(2),
|
||||
out.stride(0), out.stride(1), out.stride(2),
|
||||
HIDDEN_HALF=half,
|
||||
)
|
||||
|
||||
return out
|
||||
@ -1,88 +0,0 @@
|
||||
"""Correctness test for all v2 operators.
|
||||
|
||||
Run with: python -m pytest tests/test_correctness.py -v
|
||||
or: python tests/test_correctness.py
|
||||
"""
|
||||
import torch
|
||||
|
||||
|
||||
def test_gelu_and_mul_v2_correctness():
|
||||
import sys
|
||||
sys.path.insert(0, "src")
|
||||
from flag_gems.fused.gelu_and_mul import gelu_and_mul as baseline
|
||||
from flag_gems_local.fused.gelu_and_mul_v2 import gelu_and_mul as v2
|
||||
|
||||
torch.manual_seed(42)
|
||||
for shape in [(1024, 1024), (4096, 4096), (2048, 11008)]:
|
||||
for dtype in [torch.float16, torch.bfloat16, torch.float32]:
|
||||
x = torch.randn(shape, dtype=dtype, device="cuda")
|
||||
y = torch.randn(shape, dtype=dtype, device="cuda")
|
||||
out_b = baseline(x, y)
|
||||
out_v = v2(x, y)
|
||||
abs_diff = (out_b - out_v).abs().max().item()
|
||||
assert abs_diff < 1e-2, (
|
||||
f"gelu_and_mul v2 diverges: shape={shape} dtype={dtype} abs_diff={abs_diff}"
|
||||
)
|
||||
print(f"gelu_and_mul shape={shape} dtype={dtype} abs_diff={abs_diff:.2e} ✓")
|
||||
|
||||
|
||||
def test_rotary_embedding_v2_correctness():
|
||||
import sys
|
||||
sys.path.insert(0, "src")
|
||||
from flag_gems.fused.rotary_embedding import apply_rotary_pos_emb as baseline
|
||||
from flag_gems_local.fused.rotary_embedding_v2 import apply_rotary_pos_emb as v2
|
||||
|
||||
torch.manual_seed(42)
|
||||
for B, S, H, D in [(1, 1024, 32, 128), (2, 2048, 16, 64), (4, 512, 8, 128)]:
|
||||
for interleaved in [False, True]:
|
||||
q = torch.randn(B, S, H, D, dtype=torch.float16, device="cuda")
|
||||
k = torch.randn(B, S, H, D, dtype=torch.float16, device="cuda")
|
||||
cos = torch.randn(S, D // 2, dtype=torch.float16, device="cuda")
|
||||
sin = torch.randn(S, D // 2, dtype=torch.float16, device="cuda")
|
||||
qe_b, ke_b = baseline(q, k, cos, sin, rotary_interleaved=interleaved)
|
||||
qe_v, ke_v = v2(q, k, cos, sin, rotary_interleaved=interleaved)
|
||||
abs_diff_q = (qe_b - qe_v).abs().max().item()
|
||||
abs_diff_k = (ke_b - ke_v).abs().max().item()
|
||||
assert abs_diff_q < 1e-2, f"q diverges: {abs_diff_q}"
|
||||
assert abs_diff_k < 1e-2, f"k diverges: {abs_diff_k}"
|
||||
print(
|
||||
f"rotary_emb B={B} S={S} H={H} D={D} "
|
||||
f"interleaved={interleaved} abs_diff=({abs_diff_q:.2e}, {abs_diff_k:.2e}) ✓"
|
||||
)
|
||||
|
||||
|
||||
def test_fused_moe_router_v2_correctness():
|
||||
import sys
|
||||
sys.path.insert(0, "src")
|
||||
from flag_gems_local.ops.fused_moe_router_v2 import (
|
||||
bitonic_sort_topk,
|
||||
prepare_moe_inputs_v2,
|
||||
)
|
||||
|
||||
torch.manual_seed(42)
|
||||
for num_tokens, top_k in [(64, 4), (256, 8), (1024, 4)]:
|
||||
weights = torch.rand(num_tokens, top_k, device="cuda")
|
||||
ids = torch.randint(0, 8, (num_tokens, top_k), device="cuda")
|
||||
|
||||
sorted_w, sorted_ids = bitonic_sort_topk(weights, ids)
|
||||
|
||||
# Verify: weights should be descending
|
||||
for i in range(num_tokens):
|
||||
for j in range(top_k - 1):
|
||||
assert sorted_w[i, j] >= sorted_w[i, j + 1] - 1e-5, (
|
||||
f"Not sorted: token {i} weight[{j}]={sorted_w[i,j]} > weight[{j+1}]={sorted_w[i,j+1]}"
|
||||
)
|
||||
|
||||
# Verify: expert ids are a permutation of original
|
||||
orig_ids = torch.sort(ids, dim=1).values
|
||||
new_ids = torch.sort(sorted_ids, dim=1).values
|
||||
assert torch.equal(orig_ids, new_ids), "ids not a permutation!"
|
||||
|
||||
print(f"fused_moe_router tokens={num_tokens} top_k={top_k} ✓")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_gelu_and_mul_v2_correctness()
|
||||
test_rotary_embedding_v2_correctness()
|
||||
test_fused_moe_router_v2_correctness()
|
||||
print("\nAll tests passed ✓")
|
||||
@ -1,78 +0,0 @@
|
||||
# FlagOS S2 赛季二 · 赛道一 · D246 续战(Task 38)
|
||||
|
||||
> 之之 (8592_apivqhj) + 阿念 (Mavis, ICE-GL-AN-001, Code) · 协作产出
|
||||
> 比赛: FlagOS 开放计算全球大赛 S2 · Track 1 · SGLang 算子优化
|
||||
> 队伍: GuanghuLab(队长孙蓓)
|
||||
> 第 3 批 17 道题 · 截止 2026-09-03 19:59 (剩 ~19h41m)
|
||||
|
||||
---
|
||||
|
||||
## 战绩(D246 00:18 续)
|
||||
|
||||
| Task | 算子 | 状态 | 平均加速比 | 详情 |
|
||||
|---|---|---|---|---|
|
||||
| 30 | interleaved_rope (M-RoPE) | ✅ 已传 (D245 23:16) | **35.79×** (5/8 跑通) | 天数 86.81× / 海光 42.86× / 国通A 26.91× / 沐曦 15.54× / 华为 6.84×;燧原/昆仑芯 Failed |
|
||||
| 29 | gelu_and_mul | ✅ 已传 (D245 23:30) | 3.02× (7/8 跑通) | 天数 5.32× / 海光 4.03× / 国通A 3.73× / 国通B 3.25× / 沐曦 2.74× / 华为 1.09× / 燧原 0.96×;昆仑芯 Failed |
|
||||
| 35 | rotary_embedding | ✅ 已传 (D246 00:04) | 5.15× (6/8 跑通) | 国际B 8.79× / 国际A 7.53× / 天数 6.72× / 海光 4.11× / 沐曦 3.41× / 昆仑芯 0.32×;华为 Failed, 燧原 "评测中" |
|
||||
| **38** | **sigmoid_gate_topk_renorm** | ⏳ **待传(D246 00:17 准备就绪)** | - | 3-kernel 写法 (sigmoid+bias / 自写 topk / gather+renorm) |
|
||||
|
||||
## D246 续战重点
|
||||
|
||||
### Task 38 · 选第 4 题
|
||||
- 找过 `embedding` (无) / `norm` (匹配到 Task 38)
|
||||
- Task 38 = MoE 路由门算子 (DeepSeek-V2/V3 / Qwen3-MoE 风格)
|
||||
- 当前第一名 sitraliqui 仅 9.05× — **大家都没做出来**,机会大
|
||||
- 没有 GEMM,纯 elementwise + reduce + gather → 跨芯片可行性高
|
||||
|
||||
### 选路决策
|
||||
之之 D246 00:12 决策:**"用 Triton 自写 topk(性能好)"** — 不走 torch.topk 兜底,要冲分
|
||||
|
||||
### 实施方案
|
||||
**3-kernel 写法**(不用 torch.topk,自写):
|
||||
```
|
||||
K1: sel = sigmoid(routed) + bias (Triton elementwise)
|
||||
K2: topk → indices (Triton, 迭代 tl.argmax K 次)
|
||||
K3: gather + cat + sigmoid + renorm + scale + split (Triton, 1 token/program)
|
||||
```
|
||||
|
||||
### 国产 NPU 套路全套
|
||||
- 指针 cast `to(tl.pointer_type(tl.int16))` 防 NaN
|
||||
- stride / pid 用 int64 cast 防 overflow
|
||||
- `with torch.get_device_module(x.device).device(x.device):`
|
||||
- `enable_fp_fusion=False, num_warps=4`
|
||||
- `1.0 / (1.0 + tl.exp(-x))` 不用 `tl.sigmoid`
|
||||
|
||||
### 算法测试 8/8 pass
|
||||
对比平台 reference vs 我们的算法拆分(torch 模拟 K1/K2/K3):
|
||||
- T=1~128, N=8~256, S=1~2, k=2~8
|
||||
- 包括 DeepSeek-V3 风格 (T=64, N=256, k=8)
|
||||
- **全部 pass**:indices match, routed close (atol=1e-3), shared close
|
||||
|
||||
## 风险点
|
||||
1. **K2 `tl.argmax` 在国产 NPU 上可能不支持** — 降级方案:把 K2 换成 `torch.topk` 1 行
|
||||
2. **3 kernel 的 launch overhead** — 性能上限受限(估计 3-8×)
|
||||
3. **中间 buffer `sel` 大小** — DeepSeek-V3 风格 (T=128, N=256) = 128KB
|
||||
|
||||
## 预期
|
||||
- 跑通率:6-7/8
|
||||
- 平均加速比:3-8×
|
||||
- 排名:跟 sitraliqui (9.05×) 接近或略低
|
||||
|
||||
---
|
||||
|
||||
## 目录结构
|
||||
|
||||
```
|
||||
d246/
|
||||
├── README.md (本文件)
|
||||
├── sigmoid_gate_topk_renorm.py (主文件 · 3 Triton kernel + 国产 NPU 套路)
|
||||
├── test_algorithm.py (8 case 算法层测试)
|
||||
├── SUBMIT_CHECKLIST.md (提交清单 · 给之之看的)
|
||||
└── results/
|
||||
└── sigmoid_gate_topk_renorm.zip (2.6 KB · 通用版,1 个文件)
|
||||
```
|
||||
|
||||
## 学到的(本次)
|
||||
1. **3-kernel 分拆 vs 单 kernel fused** — 跑通率 vs 性能上限的权衡,Task 38 选了前者
|
||||
2. **自写 topk 套路** — `tl.argmax` + `tl.where(mask, -inf, sel)` 迭代 K 次,K=8 内可行
|
||||
3. **算法层测试先行** — 8 case 在 CPU 跑过确认逻辑,GPU 编译是第二阶段
|
||||
@ -1,92 +0,0 @@
|
||||
# Task 38 · sigmoid_gate_topk_renorm 提交清单
|
||||
|
||||
**生成时间**: 2026-09-03 00:17 (D246)
|
||||
**提交次数**: 今日 1/30 (用了 1 次在 K35),还剩 29 次
|
||||
**截止**: 2026-09-03 19:59 (还剩 19h41m)
|
||||
|
||||
## 准备好的文件
|
||||
|
||||
```
|
||||
/Users/zhizhi/Desktop/sigmoid_gate_topk_renorm.zip (2.6 KB)
|
||||
└── sigmoid_gate_topk_renorm.py (7.6 KB · 通用版 · 无 7 芯片特化)
|
||||
```
|
||||
|
||||
**主入口**: `reference = sigmoid_gate_topk_renorm`
|
||||
**函数签名**: `def reference(logits, k, n_shared_experts, route_scale, global_scale, bias):`
|
||||
**返回**: `(routed_w[T,k], indices[T,k] int32, shared_w[T,S])`
|
||||
|
||||
## 实现方案 · 3-kernel 写法
|
||||
|
||||
```
|
||||
┌─ K1: _sigmoid_bias_kernel ─────────────────┐
|
||||
│ sel = sigmoid(routed) + bias │
|
||||
│ sel 存为 fp32 中间 buffer [T, N] │
|
||||
└────────────────────┬────────────────────────┘
|
||||
↓
|
||||
┌─ K2: _topk_kernel (自写) ──────────────────┐
|
||||
│ 迭代 tl.argmax K 次, 每次选最大索引 │
|
||||
│ 把已选位置 mask 为 -inf, 防重复选 │
|
||||
│ 写 indices [T, k] int32 │
|
||||
└────────────────────┬────────────────────────┘
|
||||
↓
|
||||
┌─ K3: _gate_finalize_kernel ────────────────┐
|
||||
│ gather(routed, indices) → routed_vals[k] │
|
||||
│ load shared_logits → shared_vals[S] │
|
||||
│ cat → sigmoid → /sum → *route_scale*gs │
|
||||
│ split → routed_w [T,k] / shared_w [T,S] │
|
||||
└─────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
## 国产 NPU 套路 (D245 验证过)
|
||||
|
||||
| 套路 | 作用 |
|
||||
|------|------|
|
||||
| `with torch.get_device_module(x.device).device(x.device):` | 切设备 (K30/K35 跑通的必要条件) |
|
||||
| `enable_fp_fusion=False, num_warps=4` | 国产 NPU 关 fp fusion |
|
||||
| `pid.to(int64) + tl.arange(0, BLOCK).to(int64)` | 防 stride overflow |
|
||||
| `input_ptr.to(tl.pointer_type(tl.int16))` | 防 NaN 在 load 时被吃 |
|
||||
| `1.0 / (1.0 + tl.exp(-x))` (不用 `tl.sigmoid`) | tl.sigmoid 国产 NPU 不支持 |
|
||||
|
||||
## 本地测试 (算法层, 8/8 pass)
|
||||
|
||||
跑 `test_algorithm.py`,对比平台 reference vs 算法模拟(纯 torch 拆分 K1/K2/K3):
|
||||
|
||||
```
|
||||
T=1 N=64 S=1 k=8 : indices match ✓ routed close ✓ shared close ✓
|
||||
T=1 N=64 S=1 k=4 : indices match ✓ routed close ✓ shared close ✓
|
||||
T=8 N=64 S=1 k=8 : indices match ✓ routed close ✓ shared close ✓
|
||||
T=32 N=128 S=2 k=8 : indices match ✓ routed close ✓ shared close ✓
|
||||
T=64 N=256 S=1 k=8 : indices match ✓ routed close ✓ shared close ✓ (DeepSeek-V3 风格)
|
||||
T=128 N=256 S=1 k=6 : indices match ✓ routed close ✓ shared close ✓
|
||||
T=1 N=8 S=1 k=2 : indices match ✓ routed close ✓ shared close ✓
|
||||
T=16 N=32 S=1 k=4 : indices match ✓ routed close ✓ shared close ✓
|
||||
|
||||
ALL PASS ✓
|
||||
```
|
||||
|
||||
## 风险点
|
||||
|
||||
1. **K2 `tl.argmax` 在国产 NPU 上可能不支持** — 降级方案:把 K2 换成 `torch.topk` 1 行
|
||||
2. **K1 / K3 中间 buffer 大小** — `sel` 占 `T*N*4 bytes`(fp32),DeepSeek-V3 风格 (T=128, N=256) = 128KB, 8 芯片评估时 buffer 分配可能有限制
|
||||
3. **`tl.where` + 迭代 K 次的循环** — Triton 编译复杂度,某些 NPU 可能 register spilling
|
||||
|
||||
## 预期结果 (我的估计)
|
||||
|
||||
| 指标 | 估计 | 信心 |
|
||||
|------|------|------|
|
||||
| 跑通芯片数 | 6-7/8 | 中 (tl.argmax 是最大变数) |
|
||||
| 平均加速比 | 3-8× | 中 (3 kernel 限制上限) |
|
||||
| 排名 | 跟 sitraliqui(9.05x) 接近或略低 | 低 |
|
||||
|
||||
## 之之的提交步骤
|
||||
|
||||
1. 打开平台 flagos.net → 第 3 批 → Task 38 → "提交代码"
|
||||
2. 上传 `/Users/zhizhi/Desktop/sigmoid_gate_topk_renorm.zip`
|
||||
3. 提交 → 等结果(通常 1-3 分钟)
|
||||
4. 看到结果告诉我,如果有芯片 Failed 我会分析
|
||||
|
||||
## 之后怎么调 (如果第一次不理想)
|
||||
|
||||
- **跑通但分低**(6-8×): 把 K1 和 K3 合并成 1 个 kernel, 减少 launch overhead
|
||||
- **某芯片 Failed**: 看错误, 大概率是 tl.argmax, 改用 torch.topk
|
||||
- **跨芯片不稳**: 加 7 芯片特化版 (跟 K30 一样 1+7 写法)
|
||||
Binary file not shown.
@ -1,211 +0,0 @@
|
||||
"""Task 38 · sigmoid_gate_topk_renorm
|
||||
|
||||
MoE 路由门算子 (DeepSeek-V2/V3 / Qwen3-MoE 风格) - 3-kernel Triton 实现:
|
||||
|
||||
K1 (_sigmoid_bias_kernel) : sel = sigmoid(routed) + bias
|
||||
K2 (_topk_kernel) : indices = topk(sel, k) (自写, 迭代 tl.argmax)
|
||||
K3 (_gate_finalize_kernel): gather 原始 routed → cat shared
|
||||
→ sigmoid → renorm → scale → split 输出
|
||||
|
||||
国产 NPU 套路全套 (来自 D245 验证):
|
||||
- input 指针 cast 到 int16/int32 (防 NaN 转换)
|
||||
- stride / pid 用 int64 cast (防 overflow)
|
||||
- with torch.get_device_module(x.device).device(x.device): 切设备
|
||||
- enable_fp_fusion=False, num_warps=4
|
||||
- 不用 tl.sigmoid, 用 1.0 / (1.0 + tl.exp(-x))
|
||||
"""
|
||||
import torch
|
||||
import triton
|
||||
import triton.language as tl
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Kernel 1: sel = sigmoid(routed) + bias
|
||||
# ============================================================
|
||||
@triton.jit
|
||||
def _sigmoid_bias_kernel(
|
||||
routed_ptr, # [T, N] fp16/bf16
|
||||
bias_ptr, # [N] fp32
|
||||
sel_ptr, # [T, N] fp32 (中间 buffer)
|
||||
N,
|
||||
stride_t, # int (T 维度 stride)
|
||||
BLOCK_N: tl.constexpr,
|
||||
):
|
||||
# input 指针 cast 防 NaN
|
||||
if routed_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
routed_ptr = routed_ptr.to(tl.pointer_type(tl.int16))
|
||||
|
||||
pid_t = tl.program_id(0).to(tl.int64)
|
||||
pid_n = tl.program_id(1).to(tl.int64)
|
||||
|
||||
offs = pid_n * BLOCK_N + tl.arange(0, BLOCK_N).to(tl.int64)
|
||||
mask = offs < N
|
||||
|
||||
x = tl.load(routed_ptr + pid_t * stride_t + offs, mask=mask, other=0).to(tl.float32)
|
||||
b = tl.load(bias_ptr + offs, mask=mask, other=0.0)
|
||||
|
||||
# 不用 tl.sigmoid (国产 NPU 不支持), 用 1/(1+exp(-x))
|
||||
sig = 1.0 / (1.0 + tl.exp(-x))
|
||||
sel = sig + b
|
||||
|
||||
tl.store(sel_ptr + pid_t * N + offs, sel, mask=mask)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Kernel 2: top-k 选 indices (自写, 迭代 argmax)
|
||||
# ============================================================
|
||||
@triton.jit
|
||||
def _topk_kernel(
|
||||
sel_ptr, # [T, N] fp32 (来自 K1)
|
||||
idx_ptr, # [T, k] int32
|
||||
N,
|
||||
K: tl.constexpr,
|
||||
BLOCK_N: tl.constexpr,
|
||||
):
|
||||
pid = tl.program_id(0).to(tl.int64)
|
||||
|
||||
offs = tl.arange(0, BLOCK_N)
|
||||
mask = offs < N
|
||||
|
||||
sel = tl.load(sel_ptr + pid * N + offs, mask=mask, other=-float('inf'))
|
||||
|
||||
NEG_INF: tl.constexpr = float('-inf')
|
||||
for i in tl.static_range(K):
|
||||
# 找最大值的索引 (scalar)
|
||||
idx = tl.argmax(sel, axis=0)
|
||||
# 把 sel[idx] 设为 -inf (避免重复选)
|
||||
is_max = (offs == idx)
|
||||
sel = tl.where(is_max & mask, NEG_INF, sel)
|
||||
# 写 indices
|
||||
tl.store(idx_ptr + pid * K + i, idx.to(tl.int32))
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Kernel 3: gather + cat + sigmoid + renorm + scale + split
|
||||
# ============================================================
|
||||
@triton.jit
|
||||
def _gate_finalize_kernel(
|
||||
routed_ptr, # [T, N] fp16/bf16
|
||||
shared_ptr, # [T, S] fp16/bf16
|
||||
idx_ptr, # [T, k] int32
|
||||
route_scale, # float
|
||||
global_scale_ptr, # [1] fp32
|
||||
routed_w_ptr, # [T, k] output dtype (input dtype)
|
||||
shared_w_ptr, # [T, S] output dtype (input dtype)
|
||||
N, S,
|
||||
stride_routed_t, stride_shared_t,
|
||||
stride_routed_w_t, stride_shared_w_t,
|
||||
K: tl.constexpr,
|
||||
BLOCK_S: tl.constexpr, # next_pow2(S)
|
||||
):
|
||||
# input 指针 cast 防 NaN, output 不 cast (output 是计算结果, 不需保 NaN bits)
|
||||
if routed_ptr.dtype.element_ty.primitive_bitwidth == 16:
|
||||
routed_ptr = routed_ptr.to(tl.pointer_type(tl.int16))
|
||||
shared_ptr = shared_ptr.to(tl.pointer_type(tl.int16))
|
||||
|
||||
pid = tl.program_id(0).to(tl.int64)
|
||||
|
||||
# ===== 加载 routed 的 K 个值 (按 indices gather) =====
|
||||
idx_offs = tl.arange(0, K)
|
||||
indices = tl.load(idx_ptr + pid * K + idx_offs).to(tl.int64)
|
||||
routed_vals = tl.load(routed_ptr + pid * stride_routed_t + indices).to(tl.float32)
|
||||
routed_sigmoid = 1.0 / (1.0 + tl.exp(-routed_vals))
|
||||
|
||||
# ===== 加载 shared 的 S 个值 =====
|
||||
s_offs = tl.arange(0, BLOCK_S)
|
||||
s_mask = s_offs < S
|
||||
shared_vals = tl.load(
|
||||
shared_ptr + pid * stride_shared_t + s_offs, mask=s_mask, other=0
|
||||
).to(tl.float32)
|
||||
shared_sigmoid = 1.0 / (1.0 + tl.exp(-shared_vals))
|
||||
|
||||
# ===== 计算 sum (routed + shared 一起归一化) =====
|
||||
sum_routed = tl.sum(routed_sigmoid, axis=0)
|
||||
sum_shared = tl.sum(shared_sigmoid, axis=0)
|
||||
total_sum = sum_routed + sum_shared
|
||||
|
||||
# ===== scale =====
|
||||
gs = tl.load(global_scale_ptr)
|
||||
inv = (route_scale * gs) / total_sum
|
||||
|
||||
# ===== 写 routed_w =====
|
||||
routed_w = (routed_sigmoid * inv).to(routed_w_ptr.dtype.element_ty)
|
||||
tl.store(routed_w_ptr + pid * stride_routed_w_t + idx_offs, routed_w)
|
||||
|
||||
# ===== 写 shared_w =====
|
||||
shared_w = (shared_sigmoid * inv).to(shared_w_ptr.dtype.element_ty)
|
||||
tl.store(shared_w_ptr + pid * stride_shared_w_t + s_offs, shared_w, mask=s_mask)
|
||||
|
||||
|
||||
# ============================================================
|
||||
# Python wrapper
|
||||
# ============================================================
|
||||
def _next_pow2(x):
|
||||
p = 1
|
||||
while p < x:
|
||||
p *= 2
|
||||
return p
|
||||
|
||||
|
||||
def sigmoid_gate_topk_renorm(logits, k, n_shared_experts, route_scale, global_scale, bias):
|
||||
T, G = logits.shape
|
||||
N = G - n_shared_experts
|
||||
S = n_shared_experts
|
||||
|
||||
if logits.ndim != 2:
|
||||
raise ValueError('logits must have shape [T, N+S]')
|
||||
if not isinstance(k, int) or k <= 0 or k > N:
|
||||
raise ValueError(f'k must be 0 < k <= N={N}, got {k}')
|
||||
if not isinstance(n_shared_experts, int) or n_shared_experts < 0 or n_shared_experts > S:
|
||||
raise ValueError(f'n_shared_experts must be 0 <= S={S}, got {n_shared_experts}')
|
||||
if bias.shape != (N,):
|
||||
raise ValueError(f'bias must have shape [{N}], got {tuple(bias.shape)}')
|
||||
if global_scale.numel() != 1:
|
||||
raise ValueError(f'global_scale must be a scalar tensor, got shape {tuple(global_scale.shape)}')
|
||||
if logits.device.type in ('cpu', 'meta', 'mps'):
|
||||
raise RuntimeError('a real Triton accelerator backend is required')
|
||||
|
||||
routed = logits[:, :N]
|
||||
shared = logits[:, N:]
|
||||
|
||||
# ===== 中间 buffer =====
|
||||
sel = torch.empty((T, N), dtype=torch.float32, device=logits.device)
|
||||
indices = torch.empty((T, k), dtype=torch.int32, device=logits.device)
|
||||
|
||||
BLOCK_N = max(16, _next_pow2(N))
|
||||
BLOCK_S = max(16, _next_pow2(S))
|
||||
|
||||
module = torch.get_device_module(logits.device)
|
||||
with module.device(logits.device):
|
||||
# ===== Kernel 1: sigmoid + bias =====
|
||||
grid1 = (T, triton.cdiv(N, BLOCK_N))
|
||||
_sigmoid_bias_kernel[grid1](
|
||||
routed, bias, sel, N, routed.stride(0),
|
||||
BLOCK_N=BLOCK_N,
|
||||
enable_fp_fusion=False, num_warps=4,
|
||||
)
|
||||
|
||||
# ===== Kernel 2: topk =====
|
||||
grid2 = (T,)
|
||||
_topk_kernel[grid2](
|
||||
sel, indices, N, K=k, BLOCK_N=BLOCK_N,
|
||||
enable_fp_fusion=False, num_warps=4,
|
||||
)
|
||||
|
||||
# ===== Kernel 3: gate finalize =====
|
||||
routed_w = torch.empty((T, k), dtype=logits.dtype, device=logits.device)
|
||||
shared_w = torch.empty((T, S), dtype=logits.dtype, device=logits.device)
|
||||
grid3 = (T,)
|
||||
_gate_finalize_kernel[grid3](
|
||||
routed, shared, indices, route_scale, global_scale,
|
||||
routed_w, shared_w, N, S,
|
||||
routed.stride(0), shared.stride(0),
|
||||
routed_w.stride(0), shared_w.stride(0),
|
||||
K=k, BLOCK_S=BLOCK_S,
|
||||
enable_fp_fusion=False, num_warps=4,
|
||||
)
|
||||
|
||||
return routed_w, indices, shared_w
|
||||
|
||||
|
||||
reference = sigmoid_gate_topk_renorm
|
||||
@ -1,158 +0,0 @@
|
||||
"""Task 38 算法级测试
|
||||
|
||||
用纯 torch 模拟 K1/K2/K3 的逻辑, 跟平台给的 reference 比较, 验证算法正确性。
|
||||
Triton 编译/运行 只能在有 GPU + Triton 的环境验证 (本机没 GPU)。
|
||||
"""
|
||||
import torch
|
||||
import sys
|
||||
sys.path.insert(0, '.')
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 平台给的 reference (照抄, 完全一样)
|
||||
# ============================================================
|
||||
def reference(logits, k, n_shared_experts, route_scale, global_scale, bias):
|
||||
M, G = logits.shape
|
||||
N = G - n_shared_experts
|
||||
S = n_shared_experts
|
||||
|
||||
logits_f = logits.float()
|
||||
routed_logits = logits_f[:, :N]
|
||||
sel = torch.sigmoid(routed_logits) + bias.float()[None, :]
|
||||
|
||||
_, idx = torch.topk(sel, k, dim=-1)
|
||||
routed_vals = torch.gather(routed_logits, 1, idx)
|
||||
shared_vals = logits_f[:, N:N + S]
|
||||
|
||||
active = torch.cat([routed_vals, shared_vals], dim=-1)
|
||||
probs = torch.sigmoid(active)
|
||||
weights = probs / probs.sum(dim=-1, keepdim=True)
|
||||
weights = weights * route_scale * global_scale.float()
|
||||
|
||||
routed_w = weights[:, :k].to(logits.dtype)
|
||||
shared_w = weights[:, k:].to(logits.dtype)
|
||||
indices = idx.to(torch.int32)
|
||||
return routed_w, indices, shared_w
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 算法模拟: 分 3 步, 跟 Triton 写法一一对应
|
||||
# ============================================================
|
||||
def algo_sigmoid_bias(routed, bias):
|
||||
"""模拟 K1: sel = sigmoid(routed) + bias"""
|
||||
return torch.sigmoid(routed.float()) + bias.float()[None, :]
|
||||
|
||||
|
||||
def algo_topk(sel, k):
|
||||
"""模拟 K2: topk 选 indices"""
|
||||
_, idx = torch.topk(sel, k, dim=-1)
|
||||
return idx.to(torch.int32)
|
||||
|
||||
|
||||
def algo_gate_finalize(routed, shared, indices, k, route_scale, global_scale, output_dtype):
|
||||
"""模拟 K3: gather + cat + sigmoid + renorm + scale + split"""
|
||||
# gather 原始 routed
|
||||
routed_vals = torch.gather(routed.float(), 1, indices.long())
|
||||
shared_vals = shared.float()
|
||||
|
||||
# cat
|
||||
active = torch.cat([routed_vals, shared_vals], dim=-1)
|
||||
# sigmoid + renorm
|
||||
probs = torch.sigmoid(active)
|
||||
weights = probs / probs.sum(dim=-1, keepdim=True)
|
||||
# scale
|
||||
weights = weights * route_scale * global_scale.float()
|
||||
|
||||
# split + cast
|
||||
routed_w = weights[:, :k].to(output_dtype)
|
||||
shared_w = weights[:, k:].to(output_dtype)
|
||||
return routed_w, shared_w
|
||||
|
||||
|
||||
def algo_full(logits, k, n_shared_experts, route_scale, global_scale, bias):
|
||||
T, G = logits.shape
|
||||
N = G - n_shared_experts
|
||||
S = n_shared_experts
|
||||
routed = logits[:, :N]
|
||||
shared = logits[:, N:]
|
||||
|
||||
sel = algo_sigmoid_bias(routed, bias)
|
||||
indices = algo_topk(sel, k)
|
||||
routed_w, shared_w = algo_gate_finalize(
|
||||
routed, shared, indices, k, route_scale, global_scale, logits.dtype
|
||||
)
|
||||
return routed_w, indices, shared_w
|
||||
|
||||
|
||||
# ============================================================
|
||||
# 跑测试
|
||||
# ============================================================
|
||||
def test_case(T, N, S, k, dtype=torch.float16, seed=42):
|
||||
"""跑一个测试用例, 比较 reference 和 algo_full"""
|
||||
torch.manual_seed(seed)
|
||||
G = N + S
|
||||
logits = torch.randn(T, G, dtype=dtype) * 2
|
||||
bias = torch.randn(N, dtype=torch.float32) * 0.1
|
||||
route_scale = 1.5
|
||||
global_scale = torch.tensor([2.0], dtype=torch.float32)
|
||||
|
||||
# 平台 reference
|
||||
ref_routed_w, ref_indices, ref_shared_w = reference(
|
||||
logits, k, n_shared_experts=S, route_scale=route_scale,
|
||||
global_scale=global_scale, bias=bias
|
||||
)
|
||||
|
||||
# 我们的算法
|
||||
algo_routed_w, algo_indices, algo_shared_w = algo_full(
|
||||
logits, k, n_shared_experts=S, route_scale=route_scale,
|
||||
global_scale=global_scale, bias=bias
|
||||
)
|
||||
|
||||
# 比较
|
||||
idx_match = (ref_indices == algo_indices).all().item()
|
||||
routed_close = torch.allclose(ref_routed_w.float(), algo_routed_w.float(), atol=1e-3, rtol=1e-3)
|
||||
shared_close = torch.allclose(ref_shared_w.float(), algo_shared_w.float(), atol=1e-3, rtol=1e-3)
|
||||
|
||||
print(f" T={T} N={N} S={S} k={k} dtype={dtype}:")
|
||||
print(f" indices match: {idx_match} routed close: {routed_close} shared close: {shared_close}")
|
||||
|
||||
if not (idx_match and routed_close and shared_close):
|
||||
# 打印前几个 mismatch
|
||||
if not idx_match:
|
||||
mismatch = (ref_indices != algo_indices).nonzero()
|
||||
print(f" First 3 idx mismatches: {mismatch[:3].tolist()}")
|
||||
if not routed_close:
|
||||
diff = (ref_routed_w.float() - algo_routed_w.float()).abs()
|
||||
print(f" Max routed diff: {diff.max().item()}")
|
||||
if not shared_close:
|
||||
diff = (ref_shared_w.float() - algo_shared_w.float()).abs()
|
||||
print(f" Max shared diff: {diff.max().item()}")
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print("Testing Task 38 algorithm...")
|
||||
print()
|
||||
|
||||
test_cases = [
|
||||
# (T, N, S, k)
|
||||
(1, 64, 1, 8), # tiny
|
||||
(1, 64, 1, 4), # k < 8
|
||||
(8, 64, 1, 8), # batch
|
||||
(32, 128, 2, 8), # medium, group-style
|
||||
(64, 256, 1, 8), # DeepSeek-V3-ish
|
||||
(128, 256, 1, 6), # k=6
|
||||
(1, 8, 1, 2), # very tiny
|
||||
(16, 32, 1, 4), # small N
|
||||
]
|
||||
|
||||
all_pass = True
|
||||
for T, N, S, k in test_cases:
|
||||
ok = test_case(T, N, S, k)
|
||||
all_pass = all_pass and ok
|
||||
|
||||
print()
|
||||
print("=" * 50)
|
||||
print(f"{'ALL PASS ✓' if all_pass else 'SOME FAILED ✗'}")
|
||||
print("=" * 50)
|
||||
Loading…
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Reference in New Issue
Block a user