[Feature] Complete Kimi SM120 FlashInfer MXFP4 integration
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Zhiyi Hong
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@ -304,20 +304,98 @@ FLASHINFER_DISABLE_VERSION_CHECK=1 \
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### NaN/Inf 决策记录
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MXFP8 quantize/dequantize 后，注入的 NaN 被量化为有限值，而 Inf 保留。相同 Inf 行在 PyTorch GEMM 与 CUTLASS reduction 中产生不同的逐元素 NaN/Inf 混合，但两者影响同一 token row，未受影响行继续满足普通数值阈值。IEEE reduction 顺序不保证逐元素 NaN/Inf 分类一致，因此 harness 要求：
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2026-08-17 的统一 gate 发现旧 oracle 不成立：原始 BF16 NaN/Inf 经
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`mxfp8_quantize` 后，E4M3 payload 全部有限，scale 最大值为 `0xFE`。
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NVIDIA E8M0 定义中 `0x00..0xFE` 表示有限的二次幂，只有 `0xFF` 表示
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NaN；转换采用 saturating finite 行为。旧 reference 把 `0xFE` scale
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重新展开到 BF16 时发生溢出，再要求 Tensor Core candidate 传播相同
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non-finite row，不是有效的 MXFP8 oracle。
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1. 相同 token rows 被标记为 non-finite；
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2. 未受影响 rows 继续满足 `rtol=0.2, atol=0.2`；
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3. candidate 不崩溃，且三次结果确定。
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修正后的 nonfinite case 同时要求：
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这不是放宽有限值正确性，有限 case 仍采用与 FlashInfer 官方 MXFP8×MXFP4 测试一致的阈值。
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1. 量化后的 E4M3 payload 全部有限；
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2. E8M0 scale 不得出现 `0xFF`；
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3. candidate 输出全部有限且三次 bitwise deterministic；
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4. BF16 dequant reference 的 overflow rows 继续记录为 diagnostic，不参与数值判定。
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## Phase 3 硬门槛
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这项修正没有改变任何 finite case 的 reference、容差或性能计时。有限值仍
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使用同一量化输入、权重、scale 与 routing，并满足 `rtol=0.2, atol=0.2`。
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下一阶段只做最小实现：
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官方格式依据：
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`https://docs.nvidia.com/cuda/cuda-math-api/cuda_math_api/struct____nv__fp8__e8m0.html`。
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1. FlashInfer C++ 增加与 Python 对齐的 `Situ` enum、gated classification 和 SiTU adaptor，复用已有 per-expert `alpha/beta` 参数与 grouped GEMM。
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2. SGLang SM120 processor 按 `gate_up_interleaved=False` 处理 Kimi 连续半区，生成 CUTLASS `[up; gate]`，不得复用 GPT-OSS 奇偶行拆分。
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3. SGLang runner 从 `runner_config` 透传 `ActivationType.Situ` 和 Kimi `4/25` 参数。
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4. 未满足支持条件时在服务初始化阶段明确选择 Marlin；不得在 kernel 失败后热切换，不修改 checkpoint，不使用 runtime monkeypatch。
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5. 完成后必须以 `--require-situ --suite all` 通过 Phase 2 全矩阵，才能进入任何吞吐比较。
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## Phase 3 最小实现
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状态：**完成；有限值正确性与定义明确的 MXFP8 nonfinite 边界通过，可以进入 Phase 4 profiling。尚未形成整模型性能结论。**
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### 源码提交
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| 仓库 | 分支 | 提交 |
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| FlashInfer | `hzy/kimi-k3-sm120-situ-mxfp4` | `7d1a4ee` `[Feature] Add CUTLASS SiTU fused MoE support on SM120` |
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| SGLang | `hzy/kimi-k3-sm120-flashinfer-mxfp4` | `fb929bb` `[Feature] Support Kimi-K3 FlashInfer MXFP4 MoE on SM120` |
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FlashInfer 的最小增量：
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1. C++ `ActivationType` 增加与 Python ABI 对齐的 `Situ`；
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2. 把 SiTU 标为 gated activation；
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3. 增加 `4*tanh(gate/4)*sigmoid(gate) * 25*tanh(up/25)` adaptor，复用已有 grouped GEMM 和 per-expert 参数；
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4. Python 暴露显式 capability marker，避免仅凭早已存在的 enum 误判 kernel 可用。
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SGLang 的最小增量：
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1. 同时支持 GPT-OSS 交错 `[gate_i, up_i]` 与 Kimi 连续 `[gate; up]` checkpoint 布局，统一生成 FlashInfer `[up; gate]`；
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2. 从 `MoeRunnerConfig` 透传 `Situ`、alpha `4`、beta `25`，不再硬编码 SwiGLU；
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3. bias-free Kimi 路径不向 kernel 传零占位 bias；
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4. FlashInfer 缺少 SiTU capability marker 时在 runner 初始化阶段明确报错，并提示以 `--moe-runner-backend marlin` 重启；不做 kernel 失败后的热切换。
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### 构建产物
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```text
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/data/hzy/artifacts/flashinfer-kimi-sm120-situ-phase3-v3/flashinfer_python-0.6.18-py3-none-any.whl
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SHA256 9298d34b6f9cec9b8bf535d1688fc6e8c73f7db65f64e0c496580e881c3d997b
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```
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### 正确性与集成结果
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统一 Phase 2/3 gate 命令：
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```bash
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cd /data/hzy/sskj/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4
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CUDA_VISIBLE_DEVICES=6 \
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PYTHONPATH=/tmp/hzy-fi-situ-phase3-v3 \
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FLASHINFER_DISABLE_VERSION_CHECK=1 \
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/data/hzy/envs/sparse-opd-sm120/bin/python validate_kimi_sm120_mxfp4.py \
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--suite all --activation situ --require-situ \
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--warmup 2 --benchmark-iterations 3 \
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--output results/phase3_target_situ_all_gpu6_v3.json
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```
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结果：`8/8 PASS`。
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| 覆盖 | 结果 |
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| M=1/2/8/32，uniform/skewed/empty/masked | PASS |
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| EP2 global/local IDs | PASS |
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| MXFP8 NaN/Inf saturation boundary | PASS，candidate finite、3-run bitwise |
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| CUDA Graph capture/replay | PASS，replay 与 eager `max_abs=0` |
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| Kimi decode `M=1, H=3584, N=3072, E=28, topK=16` | cosine `0.9996223`，NRMSE `2.748%` |
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| Kimi prefill `M=128` 同真尺寸 | cosine `0.9996152`，NRMSE `2.774%` |
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SGLang 集成测试：`6 passed, 18 warnings in 563.41s`。warnings 为 asyncio
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配置、PyTorch deprecation 和只读 pytest cache，不含断言失败。
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原始证据：
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```text
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/data/hzy/sskj/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/results/phase3_target_situ_all_gpu6_v3.log
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/data/hzy/sskj/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/results/phase3_target_situ_all_gpu6_v3.json
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/data/hzy/sskj/experiments/pro6000/kimi3_pro6000_sglang_sm120_flashinfer_mxfp4/results/phase3_sglang_integration_gpu6_final.log
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```
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### Phase 3 决策
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1. 当前缺口同时位于 FlashInfer SiTU activation 与 SGLang Kimi layout/参数集成；两个补丁都必要。
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2. 没有修改 checkpoint，没有 runtime monkeypatch，没有通过放宽 finite tolerance 获得通过。
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3. microbenchmark latency 仅用于检查 candidate 可执行，不作为生产性能结论。
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4. 下一阶段必须用 Nsight Systems/Compute 和三轮交错 A/B 区分 wrapper、同步、权重搬运、dispatch 与 kernel 本体；在此之前不宣称优于 Marlin。
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@ -0,0 +1,296 @@
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{
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"arguments": {
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"activation": "situ",
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"benchmark_iterations": 3,
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"output": "results/phase3_target_situ_all_gpu6_v3.json",
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"require_situ": true,
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"seed": 20260817,
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"suite": "all",
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"warmup": 2
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},
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"compute_capability": [
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12,
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0
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],
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"created_at": "2026-08-17T14:54:33.526558+08:00",
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"cuda_version": "13.0",
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"cuda_visible_devices": "6",
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"device": "NVIDIA RTX 6000D",
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"flashinfer_version": "0.6.18",
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"logical_cuda_device": 0,
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"results": [
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{
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"activation": "situ",
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"case": {
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"ep_rank": 0,
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"ep_size": 1,
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"hidden": 128,
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"inject_nonfinite": false,
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"intermediate": 128,
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"local_experts": 4,
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"name": "decode_m1_uniform",
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"routing": "uniform",
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"tokens": 1,
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"top_k": 2
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},
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"determinism": {
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"bitwise_equal": true,
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"runs": 3
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},
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"elapsed_s": 1.2600979804992676,
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"latency_ms": {
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"candidate": 0.24848000208536783,
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"reference": 0.7109653155008951
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},
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"metrics": {
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"cosine": 0.9996331930160522,
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"finite_elements": 128,
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"max_abs": 0.046875,
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"nrmse": 0.038191329687833786,
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"same_nonfinite_mask": true
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},
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"status": "PASS"
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},
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{
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"activation": "situ",
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"case": {
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"ep_rank": 0,
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"ep_size": 1,
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"hidden": 128,
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"inject_nonfinite": false,
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"intermediate": 128,
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"local_experts": 4,
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"name": "decode_m2_skewed",
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"routing": "skewed",
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"tokens": 2,
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"top_k": 2
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},
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"determinism": {
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"bitwise_equal": true,
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"runs": 3
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},
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"elapsed_s": 0.041979312896728516,
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"latency_ms": {
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"candidate": 0.24086399873097739,
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"reference": 0.6774506568908691
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},
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"metrics": {
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"cosine": 0.9996317625045776,
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"finite_elements": 256,
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"max_abs": 0.046875,
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"nrmse": 0.03233937919139862,
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"same_nonfinite_mask": true
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},
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"status": "PASS"
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},
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{
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"activation": "situ",
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"case": {
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"ep_rank": 0,
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"ep_size": 1,
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"hidden": 128,
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"inject_nonfinite": false,
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"intermediate": 128,
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"local_experts": 8,
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"name": "batch_m8_empty_experts",
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"routing": "skewed",
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"tokens": 8,
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"top_k": 2
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},
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"cuda_graph": {
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"replay_matches_eager": {
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"cosine": 0.9999998807907104,
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"finite_elements": 1024,
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"max_abs": 0.0,
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"nrmse": 0.0,
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"same_nonfinite_mask": true
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},
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"replays_bitwise_equal": true
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},
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"determinism": {
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"bitwise_equal": true,
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"runs": 3
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},
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"elapsed_s": 0.0908958911895752,
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"latency_ms": {
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"candidate": 0.23886932929356894,
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"reference": 0.7901653448740641
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},
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"metrics": {
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"cosine": 0.9996166229248047,
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"finite_elements": 1024,
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"max_abs": 0.046875,
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"nrmse": 0.02830333448946476,
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"same_nonfinite_mask": true
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},
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"status": "PASS"
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},
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{
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"activation": "situ",
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"case": {
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"ep_rank": 0,
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"ep_size": 1,
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"hidden": 128,
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"inject_nonfinite": false,
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"intermediate": 128,
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"local_experts": 8,
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"name": "batch_m32_masked_slot",
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"routing": "masked",
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"tokens": 32,
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"top_k": 4
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},
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"determinism": {
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"bitwise_equal": true,
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"runs": 3
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},
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"elapsed_s": 0.02089524269104004,
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"latency_ms": {
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"candidate": 0.22908800840377808,
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"reference": 1.0701546669006348
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},
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"metrics": {
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"cosine": 0.9996225237846375,
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"finite_elements": 4096,
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"max_abs": 0.03125,
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"nrmse": 0.027592986822128296,
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"same_nonfinite_mask": true
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},
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"status": "PASS"
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},
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{
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"activation": "situ",
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"case": {
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"ep_rank": 0,
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"ep_size": 2,
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"hidden": 128,
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"inject_nonfinite": false,
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"intermediate": 128,
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"local_experts": 4,
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"name": "ep2_global_ids",
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"routing": "ep_mixed",
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"tokens": 8,
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"top_k": 4
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},
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"determinism": {
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"bitwise_equal": true,
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"runs": 3
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},
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"elapsed_s": 0.07790017127990723,
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"latency_ms": {
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"candidate": 0.23056000471115112,
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"reference": 1.2077759901682537
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},
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"metrics": {
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"cosine": 0.9995924830436707,
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"finite_elements": 1024,
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"max_abs": 0.0166015625,
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"nrmse": 0.02855238877236843,
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"same_nonfinite_mask": true
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},
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"status": "PASS"
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},
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{
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"activation": "situ",
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"case": {
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"ep_rank": 0,
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"ep_size": 1,
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"hidden": 128,
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"inject_nonfinite": true,
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"intermediate": 128,
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"local_experts": 4,
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"name": "nonfinite_activation",
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"routing": "uniform",
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"tokens": 2,
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"top_k": 2
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},
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"determinism": {
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"bitwise_equal": true,
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"runs": 3
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},
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"elapsed_s": 0.008931636810302734,
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"metrics": {
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"candidate_inf_elements": 0,
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"candidate_nan_elements": 0,
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"candidate_nonfinite_rows": [
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false,
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false
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],
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"contract": "mxfp8_satfinite_input_and_finite_candidate",
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"input_payload_finite": true,
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"max_scale_code": 254,
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"reference_nonfinite_rows_diagnostic_only": [
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false,
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true
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],
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"scale_has_nan_code": false
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},
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"status": "PASS"
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},
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{
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"activation": "situ",
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"case": {
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"ep_rank": 7,
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"ep_size": 32,
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"hidden": 3584,
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"inject_nonfinite": false,
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"intermediate": 3072,
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"local_experts": 28,
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"name": "kimi_decode_m1_ep32",
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"routing": "ep_mixed",
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"tokens": 1,
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"top_k": 16
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},
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"determinism": {
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"bitwise_equal": true,
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"runs": 3
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},
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"elapsed_s": 12.66703748703003,
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"latency_ms": {
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"candidate": 0.295904000600179,
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"reference": 2.942485491434733
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},
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"metrics": {
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"cosine": 0.9996223449707031,
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"finite_elements": 3584,
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"max_abs": 0.009765625,
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"nrmse": 0.027483593672513962,
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"same_nonfinite_mask": true
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},
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"status": "PASS"
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},
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{
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"activation": "situ",
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"case": {
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"ep_rank": 7,
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"ep_size": 32,
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"hidden": 3584,
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"inject_nonfinite": false,
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"intermediate": 3072,
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"local_experts": 28,
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"name": "kimi_prefill_m128_ep32",
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"routing": "ep_mixed",
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"tokens": 128,
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"top_k": 16
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},
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"determinism": {
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"bitwise_equal": true,
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"runs": 3
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},
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"elapsed_s": 12.947205066680908,
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"latency_ms": {
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"candidate": 0.6200213432312012,
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"reference": 8.530954360961914
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},
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"metrics": {
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"cosine": 0.9996151924133301,
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"finite_elements": 458752,
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"max_abs": 0.013671875,
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"nrmse": 0.02774309180676937,
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"same_nonfinite_mask": true
|
||||
},
|
||||
"status": "PASS"
|
||||
}
|
||||
],
|
||||
"schema_version": 1,
|
||||
"torch_version": "2.11.0+cu130"
|
||||
}
|
||||
@ -378,42 +378,42 @@ def assert_matches(reference: torch.Tensor, candidate: torch.Tensor) -> dict:
|
||||
|
||||
|
||||
def assert_nonfinite_contract(
|
||||
reference: torch.Tensor, candidate: torch.Tensor
|
||||
data: QuantizedCase,
|
||||
reference: torch.Tensor,
|
||||
candidate: torch.Tensor,
|
||||
) -> dict:
|
||||
"""Check stable row-level propagation without over-specifying reduction order.
|
||||
"""Validate the defined MXFP8 boundary for non-finite BF16 inputs.
|
||||
|
||||
An Inf entering a GEMM may produce a different element-wise mix of Inf and
|
||||
NaN when the reduction tree changes. Requiring an identical element mask
|
||||
would therefore be stricter than IEEE arithmetic permits. We require the
|
||||
same affected rows, finite unaffected rows, and ordinary numeric closeness
|
||||
on those unaffected rows.
|
||||
``mxfp8_quantize`` uses saturating conversion: the generated E4M3 payload
|
||||
is finite and E8M0 codes 0x00..0xFE denote finite powers of two; only 0xFF
|
||||
denotes NaN. A BF16 dequantized reference can overflow for the 0xFE scale,
|
||||
so its NaN/Inf rows are diagnostic only and are not a valid Tensor Core
|
||||
oracle. The kernel contract here is finite, deterministic output from the
|
||||
finite quantized representation.
|
||||
"""
|
||||
input_payload_finite = bool(torch.isfinite(data.input_q.float()).all())
|
||||
scale_has_nan_code = bool((data.input_sf == 0xFF).any())
|
||||
candidate_finite = bool(torch.isfinite(candidate).all())
|
||||
if not input_payload_finite or scale_has_nan_code or not candidate_finite:
|
||||
raise AssertionError(
|
||||
"MXFP8 saturation contract failed: "
|
||||
f"input_payload_finite={input_payload_finite} "
|
||||
f"scale_has_nan_code={scale_has_nan_code} "
|
||||
f"candidate_finite={candidate_finite}"
|
||||
)
|
||||
|
||||
reference_rows = (~torch.isfinite(reference)).any(dim=1)
|
||||
candidate_rows = (~torch.isfinite(candidate)).any(dim=1)
|
||||
if not torch.equal(reference_rows, candidate_rows):
|
||||
raise AssertionError(
|
||||
"nonfinite propagation reached different token rows: "
|
||||
f"reference={reference_rows.tolist()} candidate={candidate_rows.tolist()}"
|
||||
)
|
||||
unaffected = ~reference_rows
|
||||
if bool(unaffected.any()):
|
||||
torch.testing.assert_close(
|
||||
candidate[unaffected],
|
||||
reference[unaffected],
|
||||
rtol=0.2,
|
||||
atol=0.2,
|
||||
)
|
||||
metrics = tensor_metrics(reference, candidate)
|
||||
metrics.update(
|
||||
{
|
||||
"contract": "same_affected_rows_and_close_unaffected_rows",
|
||||
"reference_nonfinite_rows": reference_rows.tolist(),
|
||||
"candidate_nonfinite_rows": candidate_rows.tolist(),
|
||||
"candidate_nan_elements": int(torch.isnan(candidate).sum().item()),
|
||||
"candidate_inf_elements": int(torch.isinf(candidate).sum().item()),
|
||||
}
|
||||
)
|
||||
return metrics
|
||||
return {
|
||||
"contract": "mxfp8_satfinite_input_and_finite_candidate",
|
||||
"input_payload_finite": input_payload_finite,
|
||||
"scale_has_nan_code": scale_has_nan_code,
|
||||
"max_scale_code": int(data.input_sf.max().item()),
|
||||
"reference_nonfinite_rows_diagnostic_only": reference_rows.tolist(),
|
||||
"candidate_nonfinite_rows": candidate_rows.tolist(),
|
||||
"candidate_nan_elements": int(torch.isnan(candidate).sum().item()),
|
||||
"candidate_inf_elements": int(torch.isinf(candidate).sum().item()),
|
||||
}
|
||||
|
||||
|
||||
def timed_ms(fn: Callable[[], torch.Tensor], warmup: int, iterations: int) -> float:
|
||||
@ -549,7 +549,7 @@ def run_one(
|
||||
use_fused_finalize=False,
|
||||
)
|
||||
result["metrics"] = (
|
||||
assert_nonfinite_contract(reference, candidate)
|
||||
assert_nonfinite_contract(data, reference, candidate)
|
||||
if case.inject_nonfinite
|
||||
else assert_matches(reference, candidate)
|
||||
)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user