From 584f7e9e7334d338c4be8dbba36712ef746c80b3 Mon Sep 17 00:00:00 2001 From: dela Date: Tue, 25 Aug 2026 14:43:17 +0800 Subject: [PATCH] Initial K3 snapshot: 0.5B KDA/MLA/MoE train path Standalone tree split from LLMRL/projects/kda. Includes Triton dt_bias backward fix, train_k3 --preset 0.5b, SFT, Docker runtime, and tests. --- .dockerignore | 15 + .gitignore | 23 + Dockerfile | 48 + README.md | 295 ++ compose.yaml | 29 + data/eval/.gitkeep | 1 + data/eval/en2zh.ref.txt | 20 + data/eval/en2zh.src.txt | 20 + data/eval/zh2en.ref.txt | 20 + data/eval/zh2en.src.txt | 20 + data/pretrain/.gitkeep | 1 + data/sft/.gitkeep | 2 + data/sft/toy.jsonl | 32 + inspect_tensors.py | 124 + kda/__init__.py | 14 + kda/_fla/LICENSE | 21 + kda/_fla/README.md | 9 + kda/_fla/__init__.py | 7 + kda/_fla/modules/__init__.py | 1 + kda/_fla/modules/backends/__init__.py | 3 + kda/_fla/modules/l2norm.py | 299 ++ kda/_fla/ops/__init__.py | 1 + kda/_fla/ops/backends/__init__.py | 34 + kda/_fla/ops/common/__init__.py | 1 + kda/_fla/ops/common/chunk_delta_h.py | 806 ++++ kda/_fla/ops/common/chunk_h.py | 432 ++ kda/_fla/ops/common/gate.py | 110 + kda/_fla/ops/cp/__init__.py | 13 + kda/_fla/ops/cp/chunk_delta_h.py | 11 + kda/_fla/ops/gla/__init__.py | 1 + kda/_fla/ops/gla/chunk.py | 1530 ++++++ kda/_fla/ops/kda/__init__.py | 7 + kda/_fla/ops/kda/chunk.py | 443 ++ kda/_fla/ops/kda/chunk_bwd.py | 651 +++ kda/_fla/ops/kda/chunk_fwd.py | 134 + kda/_fla/ops/kda/chunk_intra.py | 962 ++++ .../ops/kda/chunk_intra_token_parallel.py | 182 + kda/_fla/ops/kda/fused_recurrent.py | 491 ++ kda/_fla/ops/kda/gate.py | 514 ++ kda/_fla/ops/kda/wy_fast.py | 369 ++ kda/_fla/ops/utils/__init__.py | 14 + kda/_fla/ops/utils/cache.py | 449 ++ kda/_fla/ops/utils/constant.py | 10 + kda/_fla/ops/utils/cumsum.py | 468 ++ kda/_fla/ops/utils/index.py | 183 + kda/_fla/ops/utils/op.py | 101 + kda/_fla/ops/utils/softplus.py | 115 + kda/_fla/utils/__init__.py | 92 + kda/_fla/utils/_compat.py | 65 + kda/_fla/utils/_config.py | 17 + kda/_fla/utils/_decorators.py | 336 ++ kda/_fla/utils/_device.py | 245 + kda/_fla/utils/_testing.py | 41 + kda/layers/__init__.py | 23 + kda/layers/attn_res.py | 519 +++ kda/layers/block.py | 51 + kda/layers/kda_attn.py | 99 + kda/layers/latent_moe.py | 118 + kda/layers/mla.py | 97 + kda/layers/rmsnorm.py | 17 + kda/layers/swiglu.py | 20 + kda/models/__init__.py | 7 + kda/models/causal_lm.py | 123 + kda/models/config.py | 62 + kda/models/k3_config.py | 128 + kda/ops/__init__.py | 5 + kda/ops/api.py | 167 + kda/ops/recurrent/__init__.py | 5 + kda/ops/recurrent/fused.py | 73 + kda/ops/reference/__init__.py | 13 + kda/ops/reference/chunkwise.py | 155 + kda/ops/reference/gate.py | 47 + kda/ops/reference/recurrent.py | 298 ++ kda/ops/triton/__init__.py | 7 + kda/ops/triton/chunk.py | 5 + kda/ops/triton/chunk_bwd.py | 5 + kda/ops/triton/chunk_fwd.py | 37 + kda/ops/triton/gate.py | 36 + kda/ops/triton/wy_fast.py | 5 + kda/training/__init__.py | 5 + kda/training/data.py | 355 ++ kda/training/eval_mt.py | 139 + kda/training/prompts.py | 7 + kda/training/schedule.py | 47 + kda/training/success.py | 86 + kda/training/toy.py | 110 + kda/training/train_tokenizer.py | 51 + notes/ledger.yaml | 187 + notes/notes-macros.tex | 94 + notes/notes.pdf | Bin 0 -> 572085 bytes notes/notes.tex | 39 + notes/sections/sec-01.tex | 127 + notes/sections/sec-02.tex | 111 + notes/sections/sec-03.tex | 148 + notes/sections/sec-04.tex | 114 + notes/sections/sec-05.tex | 75 + notes/sections/sec-06.tex | 173 + notes/sections/sec-07.tex | 158 + notes/sections/sec-08.tex | 162 + notes/sections/sec-09.tex | 379 ++ notes/sections/sec-10.tex | 157 + notes/sections/sec-11.tex | 183 + notes/verify_gref_overflow.py | 106 + notes/verify_l2norm_stability.py | 83 + notes/verify_severity_sweep.py | 72 + pyproject.toml | 43 + pyrightconfig.json | 17 + scripts/container_help.py | 43 + scripts/export_flores.py | 61 + tests/__init__.py | 1 + tests/correctness/__init__.py | 1 + tests/correctness/test_api.py | 107 + tests/correctness/test_chunkwise.py | 47 + tests/correctness/test_decay_span.py | 99 + tests/correctness/test_gate.py | 52 + tests/correctness/test_recurrent.py | 34 + tests/inference/__init__.py | 1 + tests/inference/test_recurrent_decode.py | 30 + tests/integration/__init__.py | 1 + tests/integration/test_attn_res.py | 176 + tests/integration/test_causal_checkpoint.py | 64 + tests/integration/test_causal_lm.py | 78 + tests/integration/test_ckpt_compat.py | 56 + tests/integration/test_eval_files.py | 23 + tests/integration/test_eval_mt.py | 44 + tests/integration/test_k3_arch.py | 145 + tests/integration/test_pretrain_data.py | 53 + tests/integration/test_schedule.py | 21 + tests/integration/test_sft_data.py | 52 + tests/integration/test_tensorlens.py | 193 + tests/integration/test_torchlens.py | 83 + tests/integration/test_train_overfit.py | 32 + tests/kernels/__init__.py | 1 + tests/kernels/test_gate.py | 20 + tests/kernels/test_triton_bwd.py | 67 + tests/kernels/test_triton_fwd.py | 29 + train.py | 6 + train_k3.py | 461 ++ train_sft.py | 196 + uv.lock | 4133 +++++++++++++++++ 140 files changed, 21592 insertions(+) create mode 100644 .dockerignore create mode 100644 .gitignore create mode 100644 Dockerfile create mode 100644 README.md create mode 100644 compose.yaml create mode 100644 data/eval/.gitkeep create mode 100644 data/eval/en2zh.ref.txt create mode 100644 data/eval/en2zh.src.txt create mode 100644 data/eval/zh2en.ref.txt create mode 100644 data/eval/zh2en.src.txt create mode 100644 data/pretrain/.gitkeep create mode 100644 data/sft/.gitkeep create mode 100644 data/sft/toy.jsonl create mode 100644 inspect_tensors.py create mode 100644 kda/__init__.py create mode 100644 kda/_fla/LICENSE create mode 100644 kda/_fla/README.md create mode 100644 kda/_fla/__init__.py create mode 100644 kda/_fla/modules/__init__.py create mode 100644 kda/_fla/modules/backends/__init__.py create mode 100644 kda/_fla/modules/l2norm.py create mode 100644 kda/_fla/ops/__init__.py create mode 100644 kda/_fla/ops/backends/__init__.py create mode 100644 kda/_fla/ops/common/__init__.py create mode 100644 kda/_fla/ops/common/chunk_delta_h.py create mode 100644 kda/_fla/ops/common/chunk_h.py create mode 100644 kda/_fla/ops/common/gate.py create mode 100644 kda/_fla/ops/cp/__init__.py create mode 100644 kda/_fla/ops/cp/chunk_delta_h.py create mode 100644 kda/_fla/ops/gla/__init__.py create mode 100644 kda/_fla/ops/gla/chunk.py create mode 100644 kda/_fla/ops/kda/__init__.py create mode 100644 kda/_fla/ops/kda/chunk.py create mode 100644 kda/_fla/ops/kda/chunk_bwd.py create mode 100644 kda/_fla/ops/kda/chunk_fwd.py create mode 100644 kda/_fla/ops/kda/chunk_intra.py create mode 100644 kda/_fla/ops/kda/chunk_intra_token_parallel.py create mode 100644 kda/_fla/ops/kda/fused_recurrent.py create mode 100644 kda/_fla/ops/kda/gate.py create mode 100644 kda/_fla/ops/kda/wy_fast.py create mode 100644 kda/_fla/ops/utils/__init__.py create mode 100644 kda/_fla/ops/utils/cache.py create mode 100644 kda/_fla/ops/utils/constant.py create mode 100644 kda/_fla/ops/utils/cumsum.py create mode 100644 kda/_fla/ops/utils/index.py create mode 100644 kda/_fla/ops/utils/op.py create mode 100644 kda/_fla/ops/utils/softplus.py create mode 100644 kda/_fla/utils/__init__.py create mode 100644 kda/_fla/utils/_compat.py create mode 100644 kda/_fla/utils/_config.py create mode 100644 kda/_fla/utils/_decorators.py create mode 100644 kda/_fla/utils/_device.py create mode 100644 kda/_fla/utils/_testing.py create mode 100644 kda/layers/__init__.py create mode 100644 kda/layers/attn_res.py create mode 100644 kda/layers/block.py create mode 100644 kda/layers/kda_attn.py create mode 100644 kda/layers/latent_moe.py create mode 100644 kda/layers/mla.py create mode 100644 kda/layers/rmsnorm.py create mode 100644 kda/layers/swiglu.py create mode 100644 kda/models/__init__.py create mode 100644 kda/models/causal_lm.py create mode 100644 kda/models/config.py create mode 100644 kda/models/k3_config.py create mode 100644 kda/ops/__init__.py create mode 100644 kda/ops/api.py create mode 100644 kda/ops/recurrent/__init__.py create mode 100644 kda/ops/recurrent/fused.py create mode 100644 kda/ops/reference/__init__.py create mode 100644 kda/ops/reference/chunkwise.py create mode 100644 kda/ops/reference/gate.py create mode 100644 kda/ops/reference/recurrent.py create mode 100644 kda/ops/triton/__init__.py create mode 100644 kda/ops/triton/chunk.py create mode 100644 kda/ops/triton/chunk_bwd.py create mode 100644 kda/ops/triton/chunk_fwd.py create mode 100644 kda/ops/triton/gate.py create mode 100644 kda/ops/triton/wy_fast.py create mode 100644 kda/training/__init__.py create mode 100644 kda/training/data.py create mode 100644 kda/training/eval_mt.py create mode 100644 kda/training/prompts.py create mode 100644 kda/training/schedule.py create mode 100644 kda/training/success.py create mode 100644 kda/training/toy.py create mode 100644 kda/training/train_tokenizer.py create mode 100644 notes/ledger.yaml create mode 100644 notes/notes-macros.tex create mode 100644 notes/notes.pdf create mode 100644 notes/notes.tex create mode 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100644 tests/correctness/test_decay_span.py create mode 100644 tests/correctness/test_gate.py create mode 100644 tests/correctness/test_recurrent.py create mode 100644 tests/inference/__init__.py create mode 100644 tests/inference/test_recurrent_decode.py create mode 100644 tests/integration/__init__.py create mode 100644 tests/integration/test_attn_res.py create mode 100644 tests/integration/test_causal_checkpoint.py create mode 100644 tests/integration/test_causal_lm.py create mode 100644 tests/integration/test_ckpt_compat.py create mode 100644 tests/integration/test_eval_files.py create mode 100644 tests/integration/test_eval_mt.py create mode 100644 tests/integration/test_k3_arch.py create mode 100644 tests/integration/test_pretrain_data.py create mode 100644 tests/integration/test_schedule.py create mode 100644 tests/integration/test_sft_data.py create mode 100644 tests/integration/test_tensorlens.py create mode 100644 tests/integration/test_torchlens.py create mode 100644 tests/integration/test_train_overfit.py create mode 100644 tests/kernels/__init__.py create mode 100644 tests/kernels/test_gate.py create mode 100644 tests/kernels/test_triton_bwd.py create mode 100644 tests/kernels/test_triton_fwd.py create mode 100644 train.py create mode 100644 train_k3.py create mode 100644 train_sft.py create mode 100644 uv.lock diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..9c188af --- /dev/null +++ b/.dockerignore @@ -0,0 +1,15 @@ +# Build context is projects/kda/. Keep the image a runtime, not a data dump. +.venv/ +**/__pycache__/ +**/*.pyc +**/*.pyo +**/.pytest_cache/ +**/.ruff_cache/ +ckpts/ +*.pt +notes/ +.git/ +.gitignore +uv.lock +pyrightconfig.json +inspect_tensors.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5442a06 --- /dev/null +++ b/.gitignore @@ -0,0 +1,23 @@ +.venv/ +swanlog/ +.pytest_cache/ +__pycache__/ +*.py[cod] +ckpts/ +data/spm_4k.* +data/pretrain/* +!data/pretrain/.gitkeep +data/sft/* +!data/sft/.gitkeep +!data/sft/toy.jsonl +data/eval/flores* + +# LaTeX build output +*.aux +*.log +*.out +*.toc +*.fls +*.fdb_latexmk +*.xdv +wiki jsonl diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..af01994 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,48 @@ +# syntax=docker/dockerfile:1 +# +# Single GPU runtime for train + SwanLab client + eval. +# Build: docker build -t kda: . +# +# Does not bake data, checkpoints, or API keys. Mount them at run time. +# Host: NVIDIA driver >= 570, nvidia-container-toolkit. See README. + +FROM pytorch/pytorch:2.9.0-cuda12.8-cudnn9-devel + +ENV DEBIAN_FRONTEND=noninteractive \ + PIP_NO_CACHE_DIR=1 \ + PYTHONUNBUFFERED=1 \ + PYTHONPATH=/workspace/kda \ + HF_HOME=/cache/huggingface \ + HUGGINGFACE_HUB_CACHE=/cache/huggingface \ + HF_HUB_DISABLE_TELEMETRY=1 + +RUN apt-get update && apt-get install -y --no-install-recommends \ + git \ + ca-certificates \ + && rm -rf /var/lib/apt/lists/* + +WORKDIR /workspace/kda + +# Layer cache: install deps from pyproject before the rest of the tree. +COPY pyproject.toml ./ +COPY kda ./kda +# Base image already has torch/cuda/triton; do not let pip re-resolve torch. +RUN pip install --no-cache-dir --no-deps -e . && \ + pip install --no-cache-dir \ + "einops>=0.7.0" \ + "packaging>=23.0" \ + "sentencepiece>=0.2.0" \ + "datasets>=3.0.0" \ + "transformers>=4.51.0" \ + "swanlab>=0.6.0" \ + "sacrebleu>=2.4.0" \ + "langdetect>=1.0.9" \ + "pytest>=7.0" + +COPY . /workspace/kda +RUN pip install --no-cache-dir --no-deps -e . && \ + mkdir -p /cache/huggingface /workspace/kda/ckpts /workspace/kda/swanlog \ + /data/pretrain /data/eval /data/sft + +# Require an explicit entry (train / eval / pytest / swanlab ping). +CMD ["python", "scripts/container_help.py"] diff --git a/README.md b/README.md new file mode 100644 index 0000000..b431165 --- /dev/null +++ b/README.md @@ -0,0 +1,295 @@ +# KDA 训练 → 推理 手写实现 + +从 naive recurrent 到 fused Triton kernel + 训练 + 推理，逐层手写实现。 +对拍时复用上游 `flash-linear-attention` 的 `naive_recurrent_kda` / `naive_chunk_kda` 作为参考。 + +## 目录结构 + +``` +kda/ +├── pyproject.toml +├── README.md +├── train.py # KDA-only toy overfit +├── train_k3.py # K3-like 双语 wiki 预训练 +├── train_sft.py # zh↔en 指令 SFT（模板与 eval_mt 相同） +├── Dockerfile # 训练 / SwanLab 客户端 / 评估 同一 GPU 镜像 +├── compose.yaml +├── scripts/container_help.py # 镜像默认入口（拒绝无命令启动） +├── kda/ # 可安装包 `from kda import ...` +│ ├── _fla/ # FLA NVIDIA-Triton KDA 子集（不 import 上游 fla 包） +│ ├── ops/ +│ │ ├── api.py # 显式选择 reference / triton / fla +│ │ ├── reference/ # L1/L2 + gate 的 PyTorch 正确性实现 +│ │ ├── triton/ # vendored FLA chunk/gate/wy wrappers +│ │ └── recurrent/ # L6 fused recurrent decode + state cache +│ ├── layers/ # kda_attn / mla / swiglu / latent_moe / block +│ │ # attn_res.py = 深度残差 mixer（config.attnres） +│ ├── models/ # CausalLM + KDAConfig / K3Config +│ └── training/ # toy、双语 wiki、SFT、eval_mt / success +└── tests/ + ├── correctness/ # recurrent、chunkwise、gate reference + ├── kernels/ # Triton / FLA 对拍 + ├── inference/ # recurrent decode + └── integration/ # Causal LM toy overfit、K3 架构 +``` + +## 实现路线表 + +| # | 层级 | 文件 | 核心公式 / 关键操作 | 需手写的梯度 | 验证方法 | atol | +|---|------|------|---------------------|--------------|----------|------| +| L1 | Naive recurrent fwd+bwd | `ops/reference/recurrent.py` | `S_t = exp(g_t)⊙S + (β_t⊙k_t)⊗(v_t − k_t·S)`
`o_t = (q_t·scale)·S` | `dq,dk,dv,dg,dβ` | `torch.autograd.gradcheck` (float64) | 1e-4 | +| L2 | Naive chunked fwd+bwd | `ops/reference/chunkwise.py` | ① g→cumsum in chunk
② 构造下三角系统
③ triangular solve
④ chunk 间状态传递 | PyTorch autograd | recurrent 对拍 + gradcheck | 1e-4 | +| L3 | Triton fused fwd | `ops/triton/chunk_fwd.py` | chunk 内与 chunk 间并行计算 | — | Triton out vs L2 | 1e-4 | +| L4 | Triton fused bwd | `ops/triton/chunk_bwd.py` | dAv、状态反传、dqkg、intra 修正 | 端到端梯度 | gradcheck + L2 bwd 对拍 | 1e-3 | +| L5 | Gate reference / fusion | `ops/reference/gate.py`, `ops/triton/gate.py` | standard gate 与 safe gate 使用官方语义 | `dA_log,d_dt_bias,dg` | 公式对拍 + gradcheck | 1e-4 | +| L6 | recurrent decode | `ops/recurrent/fused.py` | `KDAState [B,HV,K,V]` | — | 逐 token vs L1 | 1e-5 | +| L7 | Model + training | `layers/`, `models/`, `training/` | KDA Causal LM + shifted CE + toy overfit | AdamW autograd | loss < 0.1 | — | + +## 关键约束 / 风险点 + +| 项 | 约定 | +|---|---| +| **GVA (G=HV/H)** | `repeat_interleave(G, dim=2)` 把 q/k 从 H 扩到 HV；bwd 的 `dq/dk` 在 HV 维算完后 **必须 `view(B,T,H,G,K).sum(dim=3)` 回到 H 维** | +| **scale** | `1/√K`，在 forward 入口乘入 q（不要散布到 kernel 内部） | +| **gradcheck** | 强制 `dtype=torch.float64`；`eps=1e-6, atol=1e-4`；inputs 显式 `requires_grad_(True)` | +| **chunk_size** | 默认 64；必须 `T % BT == 0`；H100 可上 128 | +| **exp 下溢** | g 应用前 clamp；gate 路径 `lower_bound=−5.0`；显式 exp 路径用 `−softplus(−g)` 防大量 0 | +| **L4 dxs 落位** | `dq, dk` 在 HV 维算完后归约到 H；`dv/dg/dβ` 在 HV 维直接输出 | +| **naive 测试 shape** | T=16, B=2, H=2, HV=4, K=V=8 | +| **fused_whole 测试 shape** | T=128/512, B=2/4, H=4, HV=8, K=V=64 | + +## 建议执行顺序 + +1. **L1 → L2**：先纯 PyTorch 把 fwd+bwd 写对，gradcheck 双保险 +2. **L3 → L4**：写 Triton kernel 时，L2 当 reference，逐项 diff +3. **L5**：和 L3/L4 **解耦测试**（用 PyTorch 等价 naive gate 当参考） +4. **L6**：复用 L1 单步公式，独立测试 +5. **L7**：把 L3+L4+L5 黏到 layer 里，配 SwiGLU + RMSNorm + CE 训练 + +## 测试状态 + +``` +| Layer | Test | Status | max_diff | atol | Notes | +|-------|----------------------|----------|--------------|--------|--------------| +| L1/L2 | reference correctness | PASSED | | | recurrent + chunkwise | +| L3/L4 | vendored FLA Triton chunk | PASSED | | | CUDA, chunk_size 32/64, q/k L2-norm | +| L5 reference | gate formula + gradcheck | PASSED | | | standard + safe gate | +| L5 Triton | fused gate | PASSED | | | vendored FLA kda_gate_* | +| L6 | recurrent decode | PASSED | | | vendored FLA fused_recurrent_kda | +| L7 | train overfit | PASSED | | | reference backend | +| L7 | TorchLens trace/extract | PASSED | | | 计算图展开 + 激活提取 | +| L7 | TensorLens viewer API | PASSED | | | trace/normalize/Flask 端点 | +| K3 | hybrid + MLA + MoE | PASSED | | | `test_k3_arch.py` 6 项 | +| AttnRes | 深度残差 mixer | PASSED | | | `test_attn_res.py` 14 项 | +``` + +## 参考资源 + +- 上游 naive 实现 (`对拍用`): `(optional sibling) flash-linear-attention/fla/ops/kda/naive.py` +- 上游 fused kernel (`读源码用`): `(optional sibling) flash-linear-attention/fla/ops/kda/chunk_{fwd,bwd,intra}.py` +- KDA 笔记: `(optional sibling) flash-linear-attention/KDA_学习笔记.md` +- KDA paper: https://arxiv.org/abs/2510.26692 +- AttnRes paper: https://arxiv.org/abs/2603.15031 +- 本项目完整笔记（LaTeX）: `notes/notes.tex` → `notes/notes.pdf` + +## 当前进度 + +- [x] L1 — recurrent PyTorch reference + gradcheck +- [x] L2 — chunked PyTorch reference + recurrent 对拍 + gradcheck +- [x] L7 — 可训练 CausalLM、toy overfit、checkpoint round-trip +- [x] 统一 `ops.api.chunk_kda` 接口：`reference` / `triton` / `fla` 显式选择 +- [x] K3-like — Gated MLA + LatentMoE + Hybrid 3:1，`train_k3.py` 双语 wiki 预训练 +- [x] L3/L4 — vendored FLA Triton chunk fwd/bwd（`kda/_fla`，不静默 import 上游 `fla`） +- [x] L5 — vendored FLA fused gate +- [x] L6 — vendored FLA fused recurrent decode +- [x] AttnRes — 深度残差 mixer 接入 `CausalLM`（`config.attnres = off | full | block`） +- [x] 翻译训练环 — `--max-tokens` / `--resume` / held-out / cosine、`train_sft.py`、冻结 `data/eval` + +当前可直接运行小模型训练： + +```bash +PYTHONPATH=. python train.py +``` + +默认 `reference` 始终使用本仓库 PyTorch 实现。`backend="triton"` 走 `kda/_fla` +里搬运的 FLA NVIDIA Triton kernel（CUDA，`chunk_size` 为 32 或 64）。`backend="fla"` +仍要求完整安装上游 `flash-linear-attention`，只用于显式对拍。 + +## Kimi K3 小规模复现（KDA + Gated MLA + Stable LatentMoE） + +按 `learning/kimi-k3-notes` 的架构笔记，复现 K3 的**核心三件套**（Hybrid Attention +三选一 + MoE），规模受 6GB 显存限制缩小约 30 倍： + +| K3 组件 | 真实 K3 | 本复现 (toy) | 实现 | +|---|---|---|---| +| KDA | safe gate, NoPE, H=HV=96 | H=HV=8, K=V=16 | `kda/ops/`（已验证）| +| Hybrid | 每 4 层 1×Gated MLA，末层 MLA | 同 pattern (L=4) | `kda/layers/mla.py` | +| Gated MLA | kv_lora 512, NoPE, 矩阵吸收 | kv_lora 32, 吸收版 | 同上 | +| Stable LatentMoE | ℓ=d/2=3584, 896/16, shared 2 | ℓ=d/2=128, 16/2, shared 2 | `kda/layers/latent_moe.py` | +| SiTU-GLU | β1=4, β2=25 | 同 | 同上 | +| AttnRes | Block, S≈L/8 个 DecoderBlock | 同（`--attnres block`，默认 off）| `kda/layers/attn_res.py` | + +验证（`tests/integration/test_k3_arch.py`，6 项全过）： + +- MLA 矩阵吸收版 **fwd+bwd 均与解压版逐位一致** +- LatentMoE Top-k 路由权重正确、shared 全宽贡献 +- Hybrid pattern（每 4 层 1 MLA + 末层强制） +- K3 模型因果性 + 小模型单 batch 收敛 + +### 复现训练 + +目标是 **~0.5B zh↔en 指令翻译模型**（`K3Config.preset("0.5b")` = 482M，tied Qwen3 词表）。本机 RTX 3060 6GB 只跑 8M 全流程孪生；0.5B 预训练需要 **32–40GB Ampere bf16**。 + +成功标准是冻结集上的 `translation_success()`，**不是** wiki train loss。wiki 预训练没见过 `Translate to English:\n...`，预训练阶段 `eval_mt` 的 success_rate 预期 ≈0。 + +```bash +# 8M 孪生（自训 8k SentencePiece；默认 zh+en wiki，缓存 data/pretrain/） +uv run python kda/training/train_tokenizer.py --out data/spm_4k --vocab-size 8192 --limit 20000 +uv run python train_k3.py --preset toy --limit 8000 --steps 800 --batch 4 --seq-len 256 +uv run python train_sft.py --ckpt ckpts/k3_wiki.pt --data data/sft/toy.jsonl \ + --src data/eval/zh2en.src.txt --ref data/eval/zh2en.ref.txt --target-lang en + +# AttnRes 对照（默认 off，块大小不给则自动 ≈ L/8 个 DecoderBlock） +uv run python train_k3.py --preset toy --attnres block --attnres-block-size 2 + +# ~0.5B 冒烟（默认 --steps 2000 ≈ 8.2M token，不是语言模型） +uv run python train_k3.py --preset 0.5b --attnres block + +# 32–40GB Ampere：1B-token 双语预训练（可 --resume ckpts/k3_0.5b_last.pt 续到 2–5B） +uv run python train_k3.py --preset 0.5b --attnres block \ + --max-tokens 1000000000 --warmup 2000 +``` + +`0.5b` 预设：`d=768`，`L=24`（6×3 KDA + 1 MLA），`H=12`，`head=64`，`chunk=64`，LatentMoE `ℓ=384` / 16 routed / Top-2 / shared 2，tied Qwen3 embedding，activation checkpoint 默认开。训练默认 seq 2048、micro-batch 2、grad-acc 8、lr 3e-4、warmup **64 optimizer steps**。checkpoint：`ckpts/k3_0.5b.pt`，另写 `_last` / `_best`（best 按 held-out CE）。 + +Token 会计：`step` 仍是 micro-batch；有效 token = `batch × seq_len × micro_steps`。默认 0.5b 冒烟是 **8.2M token ≈ 0.017 tok/param**。翻译前置 LM 的最低有意义预算是 **1B token**（`--max-tokens`），不是 2000 step。 + +训练指标参考（800 步 @ 8M 参数，RTX 3060，bf16 + grad clip 1.0）： + +- loss：9.0（ln 8192 均匀起步）→ best 3.81，无 nan +- 生成：乱码 → 高频中文片段 → 短句雏形 +- fp16 AMP 会在 KDA `exp(g)/cumsum` 上发散，必须 bf16 或 fp32 + +入口：`train.py` 用 `KDAConfig`；`train_k3.py` / `train_sft.py` 用 checkpoint 里的 `K3Config`（或 KDA）。SFT 指令模板与 `eval_mt` 相同，见 `kda/training/prompts.py`。冻结评测句在 `data/eval/`（不要拿去训练）。FLORES 导出：`uv run python scripts/export_flores.py --out data/eval`。 + +## Docker 训练镜像 + +一个 GPU 运行时覆盖 **训练、SwanLab 客户端监控、冻结集评估**。构建上下文 = 本目录。 +默认 `CMD` 只打印用法并退出码 2，必须显式传入口，避免误跑 toy overfit。 + +语料、checkpoint、`SWANLAB_API_KEY`、HF 词表缓存 **不打进镜像**，运行时挂载。 + +### 版本 / CUDA 矩阵（与本地开发环境一致，已实测） + +| 组件 | 版本 | 来源 | +|------|------|------| +| 基础镜像 | `pytorch/pytorch:2.9.0-cuda12.8-cudnn9-devel` | Docker Hub（~9.6GB）| +| Python | 3.12 | 基础镜像自带 | +| torch | 2.9.0+cu128 | 基础镜像自带 | +| CUDA | 12.8 | 基础镜像 runtime | +| cuDNN | 9.x | 基础镜像自带 | +| triton | 3.5.0 | torch 附带 | +| numpy | 2.3.x | torch 附带 | +| 训练依赖 | einops / sentencepiece / datasets / transformers | `pyproject.toml` | +| 监控 / 评估 | swanlab / sacrebleu / langdetect | extra `train` | + +宿主要求： + +- **NVIDIA driver >= 570.00**（CUDA 12.8 最低要求）。本机 driver 610.57 ✅ +- **nvidia-container-toolkit**（容器内用 GPU 的前置，见下） +- GPU 架构：cu128 完整支持 Ampere 及以后（RTX 3060 / sm_86 ✅） +- 显存注意：RTX 3060 只有 6GB，toy 训练无压力；大 batch / `chunk_size=128` + 可能 OOM，按需调小 + +### 宿主机一次性配置：nvidia-container-toolkit + +当前机器**未安装**，`docker run --gpus all` 会报 +`failed to discover GPU vendor from CDI`。安装并配置： + +```bash +# Arch Linux +sudo pacman -S nvidia-container-toolkit +sudo nvidia-ctk runtime configure --runtime=docker +sudo systemctl restart docker + +# Ubuntu / Debian（若换机器） +curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey \ + | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg +curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list \ + | sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' \ + | sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list +sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit +sudo nvidia-ctk runtime configure --runtime=docker +sudo systemctl restart docker +``` + +验证： + +```bash +docker run --rm --gpus all nvidia/cuda:12.8.0-base-ubuntu24.04 nvidia-smi +# 应能看到 RTX 3060 与 driver 610.57 +``` + +### 构建 + +```bash +docker build -t kda:latest . +``` + +> `--network=host` 必要：本机 docker daemon 配置了代理 `http://127.0.0.1:7890`， +> 构建容器内的 `127.0.0.1` 指向容器自身而非宿主，不走 host 网络时 pip 下载会失败。 + +### 运行 + +无命令时只打印用法（退出码 2）。正式跑必须写入口。 + +```bash +# 用法 +docker run --rm kda:latest + +# 监控连通（训练机出网到 api.swanlab.cn） +docker run --rm --gpus all -e SWANLAB_API_KEY kda:latest swanlab ping + +# toy 训练 + 云端监控；ckpt 落在宿主 +mkdir -p ckpts +docker run --rm --gpus all \ + -e SWANLAB_API_KEY \ + -v "$PWD/ckpts:/workspace/kda/ckpts" \ + -v "$PWD/data:/workspace/kda/data" \ + kda:latest python train_k3.py --preset toy --attnres off + +# 0.5b 冒烟（词表走 HF 缓存卷）。真正的 1B-token 预训练加 --max-tokens 1000000000 +docker run --rm --gpus all \ + -e SWANLAB_API_KEY \ + -v "$PWD/ckpts:/workspace/kda/ckpts" \ + -v "$PWD/data:/workspace/kda/data" \ + -v hf-cache:/cache/huggingface \ + kda:latest python train_k3.py --preset 0.5b --attnres block + +# 评估（src/ref 一行一句，挂冻结集） +docker run --rm --gpus all \ + -v "$PWD/ckpts:/workspace/kda/ckpts" \ + -v "$PWD/data/eval:/data/eval:ro" \ + kda:latest python -m kda.training.eval_mt \ + --ckpt /workspace/kda/ckpts/k3_wiki.pt \ + --src /data/eval/zh2en.src.txt --ref /data/eval/zh2en.ref.txt \ + --target-lang en + +# 镜像内测试 +docker run --rm --gpus all kda:latest python -m pytest -q \ + tests/correctness tests/integration/test_causal_lm.py \ + tests/integration/test_k3_arch.py tests/integration/test_attn_res.py \ + tests/integration/test_eval_mt.py + +# 交互 +docker run --rm -it --gpus all --entrypoint bash kda:latest +``` + +也可用 `docker compose run --rm train python train_k3.py --preset toy`（`compose.yaml`）。 + +### 镜像内验证 + +```bash +docker run --rm kda:latest python -c "import torch, einops, swanlab, sacrebleu; print(torch.__version__, torch.version.cuda)" +# 预期: 2.9.0+cu128 12.8 +``` diff --git a/compose.yaml b/compose.yaml new file mode 100644 index 0000000..f88d50a --- /dev/null +++ b/compose.yaml @@ -0,0 +1,29 @@ +# GPU train / eval against the image in Dockerfile. +# docker compose run --rm train python train_k3.py --preset toy +# docker compose run --rm train swanlab ping +# docker compose run --rm train python -m kda.training.eval_mt --ckpt ... +# +# Secrets and corpora stay on the host. + +services: + train: + build: . + image: kda:latest + gpus: all + ipc: host + shm_size: "2gb" + working_dir: /workspace/kda + environment: + SWANLAB_API_KEY: ${SWANLAB_API_KEY:-} + HF_HOME: /cache/huggingface + HUGGINGFACE_HUB_CACHE: /cache/huggingface + volumes: + - ./ckpts:/workspace/kda/ckpts + - ./data:/workspace/kda/data + - ${PRETRAIN_DATA:-./data/pretrain}:/data/pretrain:ro + - ${EVAL_DATA:-./data/eval}:/data/eval:ro + - ${SFT_DATA:-./data/sft}:/data/sft:ro + - hf-cache:/cache/huggingface + +volumes: + hf-cache: diff --git a/data/eval/.gitkeep b/data/eval/.gitkeep new file mode 100644 index 0000000..83d2af2 --- /dev/null +++ b/data/eval/.gitkeep @@ -0,0 +1 @@ +# Mount as /data/eval: one sentence per line, e.g. zh2en.src.txt + zh2en.ref.txt diff --git a/data/eval/en2zh.ref.txt b/data/eval/en2zh.ref.txt new file mode 100644 index 0000000..3403a96 --- /dev/null +++ b/data/eval/en2zh.ref.txt @@ -0,0 +1,20 @@ +今天天气很好。 +请把窗户打开。 +猫坐在垫子上。 +这本书值得一读。 +他昨天去了北京。 +我们需要更多的训练数据。 +太阳从东边升起。 +她正在学习线性代数。 +不要把评测集拿去训练。 +河对面有一座旧桥。 +科学是对自然的系统探索。 +他们在公园里散步。 +这台电脑的内存是十六吉字节。 +翻译时不要照抄原文。 +春天的风很温和。 +我把钥匙放在桌子上了。 +火车中午到达。 +水在一百摄氏度沸腾。 +小模型仍然可以学会狭窄的任务。 +冻结测试文件保持只读。 diff --git a/data/eval/en2zh.src.txt b/data/eval/en2zh.src.txt new file mode 100644 index 0000000..f0c03be --- /dev/null +++ b/data/eval/en2zh.src.txt @@ -0,0 +1,20 @@ +The weather is very nice today. +Please open the window. +The cat is sitting on the mat. +This book is worth reading. +He went to Beijing yesterday. +We need more training data. +The sun rises in the east. +She is studying linear algebra. +Do not train on the evaluation set. +There is an old bridge across the river. +Science is the systematic exploration of nature. +They are walking in the park. +This computer has sixteen gigabytes of memory. +Do not copy the source text when translating. +The spring wind is gentle. +I left the keys on the table. +The train arrives at noon. +Water boils at one hundred degrees Celsius. +A small model can still learn a narrow task. +Keep the frozen test files read-only. diff --git a/data/eval/zh2en.ref.txt b/data/eval/zh2en.ref.txt new file mode 100644 index 0000000..93b3860 --- /dev/null +++ b/data/eval/zh2en.ref.txt @@ -0,0 +1,20 @@ +The weather is very nice today. +The development of artificial intelligence has changed the world. +Please open the window. +The cat is sitting on the mat. +This book is worth reading. +The meeting will start at three in the afternoon. +He went to Beijing yesterday. +We need more training data. +The sun rises in the east. +This question still has no answer. +She is studying linear algebra. +Do not train on the evaluation set. +There is an old bridge across the river. +Please wait a moment, I will be right back. +Science is the systematic exploration of nature. +They are walking in the park. +This computer has sixteen gigabytes of memory. +Do not copy the source text when translating. +The spring wind is gentle. +I left the keys on the table. diff --git a/data/eval/zh2en.src.txt b/data/eval/zh2en.src.txt new file mode 100644 index 0000000..75e32ed --- /dev/null +++ b/data/eval/zh2en.src.txt @@ -0,0 +1,20 @@ +今天天气很好。 +人工智能的发展改变了世界。 +请把窗户打开。 +猫坐在垫子上。 +这本书值得一读。 +会议将在下午三点开始。 +他昨天去了北京。 +我们需要更多的训练数据。 +太阳从东边升起。 +这个问题还没有答案。 +她正在学习线性代数。 +不要把评测集拿去训练。 +河对面有一座旧桥。 +请稍等，我马上回来。 +科学是对自然的系统探索。 +他们在公园里散步。 +这台电脑的内存是十六吉字节。 +翻译时不要照抄原文。 +春天的风很温和。 +我把钥匙放在桌子上了。 diff --git a/data/pretrain/.gitkeep b/data/pretrain/.gitkeep new file mode 100644 index 0000000..fb73d36 --- /dev/null +++ b/data/pretrain/.gitkeep @@ -0,0 +1 @@ +# Mount bilingual pretrain shards here (not baked into the image). diff --git a/data/sft/.gitkeep b/data/sft/.gitkeep new file mode 100644 index 0000000..3965022 --- /dev/null +++ b/data/sft/.gitkeep @@ -0,0 +1,2 @@ +SFT bitext (jsonl or tsv) is mounted here. toy.jsonl is a tiny in-repo twin set. +OPUS / WMT dumps stay out of git. diff --git a/data/sft/toy.jsonl b/data/sft/toy.jsonl new file mode 100644 index 0000000..4905ded --- /dev/null +++ b/data/sft/toy.jsonl @@ -0,0 +1,32 @@ +{"src": "你好。", "tgt": "Hello.", "target_lang": "en"} +{"src": "Hello.", "tgt": "你好。", "target_lang": "zh"} +{"src": "谢谢。", "tgt": "Thank you.", "target_lang": "en"} +{"src": "Thank you.", "tgt": "谢谢。", "target_lang": "zh"} +{"src": "我爱北京。", "tgt": "I love Beijing.", "target_lang": "en"} +{"src": "I love Beijing.", "tgt": "我爱北京。", "target_lang": "zh"} +{"src": "现在几点？", "tgt": "What time is it now?", "target_lang": "en"} +{"src": "What time is it now?", "tgt": "现在几点？", "target_lang": "zh"} +{"src": "水是透明的。", "tgt": "Water is transparent.", "target_lang": "en"} +{"src": "Water is transparent.", "tgt": "水是透明的。", "target_lang": "zh"} +{"src": "他是一名教师。", "tgt": "He is a teacher.", "target_lang": "en"} +{"src": "He is a teacher.", "tgt": "他是一名教师。", "target_lang": "zh"} +{"src": "明天会下雨。", "tgt": "It will rain tomorrow.", "target_lang": "en"} +{"src": "It will rain tomorrow.", "tgt": "明天会下雨。", "target_lang": "zh"} +{"src": "请坐。", "tgt": "Please sit down.", "target_lang": "en"} +{"src": "Please sit down.", "tgt": "请坐。", "target_lang": "zh"} +{"src": "这是一只狗。", "tgt": "This is a dog.", "target_lang": "en"} +{"src": "This is a dog.", "tgt": "这是一只狗。", "target_lang": "zh"} +{"src": "大门在左边。", "tgt": "The gate is on the left.", "target_lang": "en"} +{"src": "The gate is on the left.", "tgt": "大门在左边。", "target_lang": "zh"} +{"src": "我们走吧。", "tgt": "Let's go.", "target_lang": "en"} +{"src": "Let's go.", "tgt": "我们走吧。", "target_lang": "zh"} +{"src": "夜空中有星星。", "tgt": "There are stars in the night sky.", "target_lang": "en"} +{"src": "There are stars in the night sky.", "tgt": "夜空中有星星。", "target_lang": "zh"} +{"src": "面包放在厨房里。", "tgt": "The bread is in the kitchen.", "target_lang": "en"} +{"src": "The bread is in the kitchen.", "tgt": "面包放在厨房里。", "target_lang": "zh"} +{"src": "孩子在睡觉。", "tgt": "The child is sleeping.", "target_lang": "en"} +{"src": "The child is sleeping.", "tgt": "孩子在睡觉。", "target_lang": "zh"} +{"src": "这座山很高。", "tgt": "This mountain is very high.", "target_lang": "en"} +{"src": "This mountain is very high.", "tgt": "这座山很高。", "target_lang": "zh"} +{"src": "请关上门。", "tgt": "Please close the door.", "target_lang": "en"} +{"src": "Please close the door.", "tgt": "请关上门。", "target_lang": "zh"} diff --git a/inspect_tensors.py b/inspect_tensors.py new file mode 100644 index 0000000..96098fa --- /dev/null +++ b/inspect_tensors.py @@ -0,0 +1,124 @@ +"""Inspect KDA activations: print stats, TorchLens extract, or TensorLens web UI. + + uv run python inspect_tensors.py # shape / min / max / nan + uv run python inspect_tensors.py --lens torch # named activations + uv run python inspect_tensors.py --lens web # http://127.0.0.1:8000 +""" +from __future__ import annotations + +import argparse + +import torch + +from kda.models.causal_lm import CausalLM +from kda.models.config import KDAConfig + +MODULES = ( + "embedding", + "blocks.0.attn.q_proj", + "blocks.0.attn.k_proj", + "blocks.0.attn.v_proj", + "blocks.0.attn", + "blocks.0.ffn", + "blocks.0", + "norm", +) + + +def _model() -> CausalLM: + torch.manual_seed(51) + return CausalLM( + KDAConfig( + hidden_size=16, + num_hidden_layers=1, + num_heads=2, + num_value_heads=2, + head_dim=4, + chunk_size=4, + vocab_size=32, + intermediate_size=32, + kda_backend="reference", + ) + ).eval() + + +def _tokens() -> torch.Tensor: + return torch.tensor([[1, 2, 3, 4]]) + + +def _named_modules(model: torch.nn.Module) -> dict[str, torch.nn.Module]: + return dict(model.named_modules()) + + +def capture_activations(model: CausalLM, x: torch.Tensor) -> dict[str, torch.Tensor]: + captured: dict[str, torch.Tensor] = {} + hooks = [] + modules = _named_modules(model) + for name in MODULES: + module = modules[name] + + def _hook(_module, _inp, out, key=name): + captured[key] = out.detach() + + hooks.append(module.register_forward_hook(_hook)) + with torch.no_grad(): + captured["logits"] = model(x).detach() + for hook in hooks: + hook.remove() + return captured + + +def print_stats(tensors: dict[str, torch.Tensor]) -> None: + print(f"{'name':28} {'shape':18} {'dtype':10} {'min':>10} {'max':>10} {'mean':>10} nan/inf") + for name, tensor in tensors.items(): + finite = torch.isfinite(tensor) + n_bad = int((~finite).sum()) + stats = tensor.float() if tensor.is_floating_point() else tensor + print( + f"{name:28} {str(tuple(tensor.shape)):18} {str(tensor.dtype):10} " + f"{stats.min().item():10.4f} {stats.max().item():10.4f} " + f"{stats.float().mean().item():10.4f} {n_bad}" + ) + + +def inspect_torchlens(model: CausalLM, x: torch.Tensor) -> None: + import torchlens as tl + + names = [*MODULES, "output"] + with torch.no_grad(): + acts = tl.extract(model, x, names) + print_stats(acts) + + +def inspect_web(model: CausalLM, x: torch.Tensor, host: str, port: int) -> None: + from tensorlens.tensorlens import trace + from tensorlens.web.server import app + + acts = capture_activations(model, x) + for name, tensor in acts.items(): + trace(name, tensor.cpu().float().numpy(), normalization="minmax") + trace("lm_head.weight", model.lm_head.weight.detach().cpu().float().numpy(), normalization="minmax") + print(f"TensorLens: http://{host}:{port} (Ctrl-C to stop)") + app.run(host=host, port=port, debug=False, use_reloader=False) + + +def main() -> None: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--lens", choices=("print", "torch", "web"), default="print") + parser.add_argument("--host", default="127.0.0.1") + parser.add_argument("--port", type=int, default=8000) + args = parser.parse_args() + + model = _model() + x = _tokens() + if args.lens == "torch": + inspect_torchlens(model, x) + return + if args.lens == "web": + inspect_web(model, x, args.host, args.port) + return + print_stats(capture_activations(model, x)) + + +if __name__ == "__main__": + main() diff --git a/kda/__init__.py b/kda/__init__.py new file mode 100644 index 0000000..2de8c74 --- /dev/null +++ b/kda/__init__.py @@ -0,0 +1,14 @@ +"""KDA operators, composable layers, and CausalLM.""" + +from .models.causal_lm import CausalLM +from .models.config import KDAConfig +from .models.k3_config import K3Config +from .ops import chunk_kda + +__all__ = [ + "CausalLM", + "KDAConfig", + "K3Config", + "chunk_kda", +] +__version__ = "0.0.1" diff --git a/kda/_fla/LICENSE b/kda/_fla/LICENSE new file mode 100644 index 0000000..7ced501 --- /dev/null +++ b/kda/_fla/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2023-2026 Songlin Yang, Yu Zhang, Zhiyuan Li + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/kda/_fla/README.md b/kda/_fla/README.md new file mode 100644 index 0000000..955438c --- /dev/null +++ b/kda/_fla/README.md @@ -0,0 +1,9 @@ +# Vendored FLA KDA kernels + +Subset of [flash-linear-attention](https://github.com/fla-org/flash-linear-attention) +used by `kda.ops` `backend="triton"`. + +- License: MIT (see `LICENSE`) +- Upstream version tag in `__init__.py` +- Import path is `kda._fla.*`, not `fla.*` +- Not included: context parallel, Ascend, TileLang, `flash_kda`, non-KDA ops diff --git a/kda/_fla/__init__.py b/kda/_fla/__init__.py new file mode 100644 index 0000000..0d5715c --- /dev/null +++ b/kda/_fla/__init__.py @@ -0,0 +1,7 @@ +"""Vendored NVIDIA-Triton KDA path from flash-linear-attention (MIT). + +This package is imported as ``kda._fla``, never as the upstream ``fla`` +distribution. Context-parallel, Ascend, and TileLang backends are omitted. +""" + +__version__ = "0.5.2" diff --git a/kda/_fla/modules/__init__.py b/kda/_fla/modules/__init__.py new file mode 100644 index 0000000..be8b25b --- /dev/null +++ b/kda/_fla/modules/__init__.py @@ -0,0 +1 @@ +# Vendored FLA modules used by KDA (l2norm). diff --git a/kda/_fla/modules/backends/__init__.py b/kda/_fla/modules/backends/__init__.py new file mode 100644 index 0000000..24050e4 --- /dev/null +++ b/kda/_fla/modules/backends/__init__.py @@ -0,0 +1,3 @@ +from kda._fla.ops.backends import BackendRegistry, BaseBackend, dispatch + +__all__ = ["BackendRegistry", "BaseBackend", "dispatch"] diff --git a/kda/_fla/modules/l2norm.py b/kda/_fla/modules/l2norm.py new file mode 100644 index 0000000..06dc2b6 --- /dev/null +++ b/kda/_fla/modules/l2norm.py @@ -0,0 +1,299 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch +import torch.nn as nn +import triton +import triton.language as tl + +from kda._fla.modules.backends import dispatch +from kda._fla.ops.utils.cache import fla_cache_autotune +from kda._fla.utils import IS_AMD, autotune_cache_kwargs, input_guard + +BT_LIST = [8, 16, 32, 64, 128] +NUM_WARPS_AUTOTUNE = [1, 2, 4, 8, 16] if IS_AMD else [1, 2, 4, 8, 16, 32] + + +@triton.autotune( + configs=[triton.Config({}, num_warps=num_warps) for num_warps in NUM_WARPS_AUTOTUNE], + key=["D"], + **autotune_cache_kwargs, +) +@triton.jit +def l2norm_fwd_kernel1( + x, + y, + rstd, + eps, + D, + BD: tl.constexpr, +): + i_t = tl.program_id(0).to(tl.int64) + x += i_t * D + y += i_t * D + # Compute mean and variance + cols = tl.arange(0, BD) + mask = cols < D + + b_x = tl.load(x + cols, mask=mask, other=0.0).to(tl.float32) + b_rstd = 1 / tl.sqrt(tl.sum(b_x * b_x) + eps) + b_y = b_x * b_rstd + tl.store(y + cols, b_y, mask=mask) + tl.store(rstd + i_t, b_rstd) + + +@triton.autotune( + configs=[triton.Config({}, num_warps=num_warps) for num_warps in NUM_WARPS_AUTOTUNE], + key=["D"], + **autotune_cache_kwargs, +) +@triton.jit +def l2norm_bwd_kernel1( + y, + rstd, + dy, + dx, + eps, + D, + BD: tl.constexpr, +): + i_t = tl.program_id(0).to(tl.int64) + y += i_t * D + dx += i_t * D + dy += i_t * D + + cols = tl.arange(0, BD) + mask = cols < D + b_y = tl.load(y + cols, mask=mask, other=0.0).to(tl.float32) + b_rstd = tl.load(rstd + i_t).to(tl.float32) + b_dy = tl.load(dy + cols, mask=mask, other=0.0).to(tl.float32) + b_dx = b_dy * b_rstd - tl.sum(b_dy * b_y) * b_y * b_rstd + tl.store(dx + cols, b_dx, mask=mask) + + +@fla_cache_autotune( + configs=[triton.Config({"BT": BT}, num_warps=num_warps) for num_warps in [1, 2, 4, 8, 16] for BT in BT_LIST], + key=["D", "NB"], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=["T"]) +def l2norm_fwd_kernel( + x, + y, + rstd, + eps, + T, + D: tl.constexpr, + BD: tl.constexpr, + NB: tl.constexpr, + BT: tl.constexpr, +): + i_t = tl.program_id(0).to(tl.int64) + o_t = i_t * BT + tl.arange(0, BT) + o_d = tl.arange(0, BD) + m_t = o_t < T + m_x = m_t[:, None] & (o_d[None, :] < D) + p_x = x + o_t[:, None] * D + o_d[None, :] + p_y = y + o_t[:, None] * D + o_d[None, :] + p_rstd = rstd + o_t + + b_x = tl.load(p_x, mask=m_x, other=0.0).to(tl.float32) + b_rstd = 1 / tl.sqrt(tl.sum(b_x * b_x, 1) + eps) + b_y = b_x * b_rstd[:, None] + + tl.store(p_y, b_y.to(p_y.dtype.element_ty), mask=m_x) + tl.store(p_rstd, b_rstd.to(p_rstd.dtype.element_ty), mask=m_t) + + +@fla_cache_autotune( + configs=[triton.Config({"BT": BT}, num_warps=num_warps) for num_warps in [1, 2, 4, 8, 16] for BT in BT_LIST], + key=["D", "NB"], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=["T"]) +def l2norm_bwd_kernel( + y, + rstd, + dy, + dx, + eps, + T, + D: tl.constexpr, + BD: tl.constexpr, + NB: tl.constexpr, + BT: tl.constexpr, +): + i_t = tl.program_id(0).to(tl.int64) + o_t = i_t * BT + tl.arange(0, BT) + o_d = tl.arange(0, BD) + m_t = o_t < T + m_x = m_t[:, None] & (o_d[None, :] < D) + p_y = y + o_t[:, None] * D + o_d[None, :] + p_rstd = rstd + o_t + p_dy = dy + o_t[:, None] * D + o_d[None, :] + p_dx = dx + o_t[:, None] * D + o_d[None, :] + + b_y = tl.load(p_y, mask=m_x, other=0.0).to(tl.float32) + b_rstd = tl.load(p_rstd, mask=m_t, other=0.0).to(tl.float32) + b_dy = tl.load(p_dy, mask=m_x, other=0.0).to(tl.float32) + b_dx = b_dy * b_rstd[:, None] - tl.sum(b_dy * b_y, 1)[:, None] * b_y * b_rstd[:, None] + tl.store(p_dx, b_dx.to(p_dx.dtype.element_ty), mask=m_x) + + +@dispatch('modules') +def l2norm_fwd( + x: torch.Tensor, + eps: float = 1e-6, + output_dtype: torch.dtype | None = None, +): + x_shape_og = x.shape + x = x.view(-1, x.shape[-1]) + # allocate output + if output_dtype is None: + y = torch.empty_like(x) + else: + y = torch.empty_like(x, dtype=output_dtype) + assert y.stride(-1) == 1 + T, D = x.shape[0], x.shape[-1] + # Less than 64KB per feature: enqueue fused kernel + MAX_FUSED_SIZE = 65536 // x.element_size() + BD = min(MAX_FUSED_SIZE, triton.next_power_of_2(D)) + if D > BD: + raise RuntimeError("This layer doesn't support feature dim >= 64KB.") + + rstd = torch.empty((T,), dtype=torch.float32, device=x.device) + if D <= 512: + # NOTE(tylerr): Avoid excessive recompilation and autotuning by tolerating a larger range + # of T before recompiling the kernel. + # NB = triton.cdiv(T, 2048) + NB = triton.cdiv(T, 2048 * 32) + + def grid(meta): + return (triton.cdiv(T, meta["BT"]),) + + l2norm_fwd_kernel[grid]( + x=x, + y=y, + rstd=rstd, + eps=eps, + T=T, + D=D, + BD=BD, + NB=NB, + ) + else: + l2norm_fwd_kernel1[(T,)]( + x=x, + y=y, + rstd=rstd, + eps=eps, + D=D, + BD=BD, + ) + return y.view(x_shape_og), rstd.view(x_shape_og[:-1]) + + +@dispatch('modules') +def l2norm_bwd( + y: torch.Tensor, + rstd: torch.Tensor, + dy: torch.Tensor, + eps: float = 1e-6, +): + y_shape_og = y.shape + y = y.view(-1, dy.shape[-1]) + dy = dy.view(-1, dy.shape[-1]) + assert dy.shape == y.shape + # allocate output + dx = torch.empty_like(y) + T, D = y.shape[0], y.shape[-1] + # Less than 64KB per feature: enqueue fused kernel + MAX_FUSED_SIZE = 65536 // y.element_size() + BD = min(MAX_FUSED_SIZE, triton.next_power_of_2(D)) + if D > BD: + raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.") + + if D <= 512: + # NOTE(tylerr): Avoid excessive recompilation and autotuning by tolerating a larger range + # of T before recompiling the kernel. + # NB = triton.cdiv(T, 2048) + NB = triton.cdiv(T, 2048 * 32) + + def grid(meta): + return (triton.cdiv(T, meta["BT"]),) + + l2norm_bwd_kernel[grid]( + y=y, + rstd=rstd, + dy=dy, + dx=dx, + eps=eps, + T=T, + D=D, + BD=BD, + NB=NB, + ) + else: + l2norm_bwd_kernel1[(T,)]( + y=y, + rstd=rstd, + dy=dy, + dx=dx, + eps=eps, + D=D, + BD=BD, + ) + + return dx.view(y_shape_og) + + +class L2NormFunction(torch.autograd.Function): + @staticmethod + @input_guard + def forward( + ctx, + x, + eps=1e-6, + output_dtype=None, + ): + y, rstd = l2norm_fwd(x, eps, output_dtype) + ctx.eps = eps + ctx.x_dtype = x.dtype + ctx.save_for_backward(y, rstd) + return y + + @staticmethod + @input_guard + def backward(ctx, dy): + y, rstd = ctx.saved_tensors + dx = l2norm_bwd(y, rstd, dy, ctx.eps) + return dx, None, None + + +def l2norm( + x: torch.Tensor, + eps: float = 1e-6, + output_dtype: torch.dtype | None = None, +) -> torch.Tensor: + return L2NormFunction.apply(x, eps, output_dtype) + + +l2_norm = l2norm + + +class L2Norm(nn.Module): + def __init__( + self, + eps: float = 1e-6, + output_dtype: torch.dtype | None = None, + ): + super().__init__() + self.eps = eps + self.output_dtype = output_dtype + + def forward(self, x: torch.Tensor) -> torch.Tensor: + return l2norm(x, self.eps, self.output_dtype) diff --git a/kda/_fla/ops/__init__.py b/kda/_fla/ops/__init__.py new file mode 100644 index 0000000..1869051 --- /dev/null +++ b/kda/_fla/ops/__init__.py @@ -0,0 +1 @@ +# Vendored FLA ops subset. diff --git a/kda/_fla/ops/backends/__init__.py b/kda/_fla/ops/backends/__init__.py new file mode 100644 index 0000000..386459f --- /dev/null +++ b/kda/_fla/ops/backends/__init__.py @@ -0,0 +1,34 @@ +"""Identity dispatch: keep the NVIDIA Triton implementation in this tree.""" + +from __future__ import annotations + +from collections.abc import Callable +from typing import TypeVar + +F = TypeVar("F", bound=Callable) + + +def dispatch(operation: str): + def decorator(func: F) -> F: + return func + + return decorator + + +class BaseBackend: + backend_type = "triton" + + def is_available(self) -> bool: + return True + + def is_enabled(self) -> bool: + return True + + +class BackendRegistry: + @classmethod + def ensure_initialized(cls, operation: str) -> None: + return None + + +__all__ = ["BackendRegistry", "BaseBackend", "dispatch"] diff --git a/kda/_fla/ops/common/__init__.py b/kda/_fla/ops/common/__init__.py new file mode 100644 index 0000000..9dd4499 --- /dev/null +++ b/kda/_fla/ops/common/__init__.py @@ -0,0 +1 @@ +# Vendored FLA common kernels used by KDA. diff --git a/kda/_fla/ops/common/chunk_delta_h.py b/kda/_fla/ops/common/chunk_delta_h.py new file mode 100644 index 0000000..6412161 --- /dev/null +++ b/kda/_fla/ops/common/chunk_delta_h.py @@ -0,0 +1,806 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.utils import prepare_chunk_indices, prepare_chunk_offsets +from kda._fla.ops.utils.cache import fla_cache_autotune +from kda._fla.ops.utils.op import exp2 +from kda._fla.utils import ( + IS_INTEL, + IS_NVIDIA_BLACKWELL, + IS_NVIDIA_HOPPER, + autotune_cache_kwargs, + check_shared_mem, +) + +NUM_WARPS = [2, 4] if IS_NVIDIA_HOPPER else [2, 4, 8, 16] + +# TODO: Triton mainline fixes a Blackwell tl.dot recurrence race. +# Keep this kernel on num_warps=2 for Blackwell until Triton 3.8 is released +# and we re-validate the wider config space. +# Intel needs more warps than NVIDIA here: 8 warps is ~1.5x faster than the best +# config reachable under the [2, 4] cap. +if IS_NVIDIA_BLACKWELL: + GATED_DELTA_RULE_FWD_H_NUM_WARPS = [2] +elif IS_INTEL: + GATED_DELTA_RULE_FWD_H_NUM_WARPS = [2, 4, 8, 16] +else: + GATED_DELTA_RULE_FWD_H_NUM_WARPS = [2, 4] + + +@triton.heuristics({ + 'USE_G': lambda args: args['g'] is not None, + 'USE_GK': lambda args: args['gk'] is not None, + 'USE_INITIAL_STATE': lambda args: args['h0'] is not None, + 'STORE_FINAL_STATE': lambda args: args['ht'] is not None, + 'SAVE_NEW_VALUE': lambda args: args['v_new'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BV': BV}, num_warps=num_warps, num_stages=num_stages) + for num_warps in GATED_DELTA_RULE_FWD_H_NUM_WARPS + for num_stages in ([2, 3, 4] if check_shared_mem('ampere') else [2, 1]) + for BV in ([32, 64] if check_shared_mem('ada') else [32]) + ], + key=['H', 'HV', 'K', 'V', 'BT', 'STATE_V_FIRST'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gated_delta_rule_fwd_kernel_h_blockdim64( + k, + v, + w, + v_new, + g, + gk, + h, + h0, + ht, + cu_seqlens, + chunk_offsets, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BV: tl.constexpr, + USE_G: tl.constexpr, + USE_GK: tl.constexpr, + USE_INITIAL_STATE: tl.constexpr, + STORE_FINAL_STATE: tl.constexpr, + SAVE_NEW_VALUE: tl.constexpr, + STATE_V_FIRST: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + pid = tl.program_id(0) + NV = tl.cdiv(V, BV) + i_v, i_nh = pid % NV, (pid // NV).to(tl.int64) + i_n, i_h = i_nh // HV, i_nh % HV + if IS_VARLEN: + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + NT = tl.cdiv(T, BT) + boh = tl.load(chunk_offsets + i_n).to(tl.int64) + else: + bos, eos = i_n * T, i_n * T + T + NT = tl.cdiv(T, BT) + boh = i_n * NT + + if STATE_V_FIRST: + b_h1 = tl.zeros([BV, 64], dtype=tl.float32) + if K > 64: + b_h2 = tl.zeros([BV, 64], dtype=tl.float32) + if K > 128: + b_h3 = tl.zeros([BV, 64], dtype=tl.float32) + if K > 192: + b_h4 = tl.zeros([BV, 64], dtype=tl.float32) + else: + b_h1 = tl.zeros([64, BV], dtype=tl.float32) + if K > 64: + b_h2 = tl.zeros([64, BV], dtype=tl.float32) + if K > 128: + b_h3 = tl.zeros([64, BV], dtype=tl.float32) + if K > 192: + b_h4 = tl.zeros([64, BV], dtype=tl.float32) + + # calculate offset + h += (boh * HV + i_h).to(tl.int64) * K*V + v += (bos * HV + i_h).to(tl.int64) * V + k += (bos * H + i_h // (HV // H)).to(tl.int64) * K + w += (bos * HV + i_h).to(tl.int64) * K + if SAVE_NEW_VALUE: + v_new += (bos * HV + i_h).to(tl.int64) * V + + if USE_INITIAL_STATE: + h0 = h0 + i_nh * K*V + if STORE_FINAL_STATE: + ht = ht + i_nh * K*V + + # load initial state + o_v = i_v * BV + tl.arange(0, BV) + m_v = o_v < V + o_k1 = tl.arange(0, 64) + m_k1 = o_k1 < K + o_k2 = 64 + o_k1 + m_k2 = o_k2 < K + o_k3 = 128 + o_k1 + m_k3 = o_k3 < K + o_k4 = 192 + o_k1 + m_k4 = o_k4 < K + if USE_INITIAL_STATE: + if STATE_V_FIRST: + p_h0_1 = h0 + o_v[:, None] * K + o_k1[None, :] + m_h0_1 = m_v[:, None] & m_k1[None, :] + else: + p_h0_1 = h0 + o_k1[:, None] * V + o_v[None, :] + m_h0_1 = m_k1[:, None] & m_v[None, :] + b_h1 += tl.load(p_h0_1, mask=m_h0_1, other=0.0).to(tl.float32) + if K > 64: + if STATE_V_FIRST: + p_h0_2 = h0 + o_v[:, None] * K + o_k2[None, :] + m_h0_2 = m_v[:, None] & m_k2[None, :] + else: + p_h0_2 = h0 + o_k2[:, None] * V + o_v[None, :] + m_h0_2 = m_k2[:, None] & m_v[None, :] + b_h2 += tl.load(p_h0_2, mask=m_h0_2, other=0.0).to(tl.float32) + if K > 128: + if STATE_V_FIRST: + p_h0_3 = h0 + o_v[:, None] * K + o_k3[None, :] + m_h0_3 = m_v[:, None] & m_k3[None, :] + else: + p_h0_3 = h0 + o_k3[:, None] * V + o_v[None, :] + m_h0_3 = m_k3[:, None] & m_v[None, :] + b_h3 += tl.load(p_h0_3, mask=m_h0_3, other=0.0).to(tl.float32) + if K > 192: + if STATE_V_FIRST: + p_h0_4 = h0 + o_v[:, None] * K + o_k4[None, :] + m_h0_4 = m_v[:, None] & m_k4[None, :] + else: + p_h0_4 = h0 + o_k4[:, None] * V + o_v[None, :] + m_h0_4 = m_k4[:, None] & m_v[None, :] + b_h4 += tl.load(p_h0_4, mask=m_h0_4, other=0.0).to(tl.float32) + + # main recurrence + for i_t in range(NT): + i_t_int64 = i_t.to(tl.int64) + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + if STATE_V_FIRST: + p_h1 = h + i_t_int64 * HV*K*V + o_v[:, None] * K + o_k1[None, :] + m_h1 = m_v[:, None] & m_k1[None, :] + else: + p_h1 = h + i_t_int64 * HV*K*V + o_k1[:, None] * V + o_v[None, :] + m_h1 = m_k1[:, None] & m_v[None, :] + tl.store(p_h1, b_h1.to(p_h1.dtype.element_ty), mask=m_h1) + if K > 64: + if STATE_V_FIRST: + p_h2 = h + i_t_int64 * HV*K*V + o_v[:, None] * K + o_k2[None, :] + m_h2 = m_v[:, None] & m_k2[None, :] + else: + p_h2 = h + i_t_int64 * HV*K*V + o_k2[:, None] * V + o_v[None, :] + m_h2 = m_k2[:, None] & m_v[None, :] + tl.store(p_h2, b_h2.to(p_h2.dtype.element_ty), mask=m_h2) + if K > 128: + if STATE_V_FIRST: + p_h3 = h + i_t_int64 * HV*K*V + o_v[:, None] * K + o_k3[None, :] + m_h3 = m_v[:, None] & m_k3[None, :] + else: + p_h3 = h + i_t_int64 * HV*K*V + o_k3[:, None] * V + o_v[None, :] + m_h3 = m_k3[:, None] & m_v[None, :] + tl.store(p_h3, b_h3.to(p_h3.dtype.element_ty), mask=m_h3) + if K > 192: + if STATE_V_FIRST: + p_h4 = h + i_t_int64 * HV*K*V + o_v[:, None] * K + o_k4[None, :] + m_h4 = m_v[:, None] & m_k4[None, :] + else: + p_h4 = h + i_t_int64 * HV*K*V + o_k4[:, None] * V + o_v[None, :] + m_h4 = m_k4[:, None] & m_v[None, :] + tl.store(p_h4, b_h4.to(p_h4.dtype.element_ty), mask=m_h4) + + p_w = w + o_t[:, None] * (HV*K) + o_k1[None, :] + b_w = tl.load(p_w, mask=m_t[:, None] & m_k1[None, :], other=0.0) + if STATE_V_FIRST: + b_v = tl.dot(b_w, tl.trans(b_h1).to(b_w.dtype)) + else: + b_v = tl.dot(b_w, b_h1.to(b_w.dtype)) + if K > 64: + p_w = w + o_t[:, None] * (HV*K) + o_k2[None, :] + b_w = tl.load(p_w, mask=m_t[:, None] & m_k2[None, :], other=0.0) + if STATE_V_FIRST: + b_v += tl.dot(b_w, tl.trans(b_h2).to(b_w.dtype)) + else: + b_v += tl.dot(b_w, b_h2.to(b_w.dtype)) + if K > 128: + p_w = w + o_t[:, None] * (HV*K) + o_k3[None, :] + b_w = tl.load(p_w, mask=m_t[:, None] & m_k3[None, :], other=0.0) + if STATE_V_FIRST: + b_v += tl.dot(b_w, tl.trans(b_h3).to(b_w.dtype)) + else: + b_v += tl.dot(b_w, b_h3.to(b_w.dtype)) + if K > 192: + p_w = w + o_t[:, None] * (HV*K) + o_k4[None, :] + b_w = tl.load(p_w, mask=m_t[:, None] & m_k4[None, :], other=0.0) + if STATE_V_FIRST: + b_v += tl.dot(b_w, tl.trans(b_h4).to(b_w.dtype)) + else: + b_v += tl.dot(b_w, b_h4.to(b_w.dtype)) + p_v = v + o_t[:, None] * (HV*V) + o_v[None, :] + b_v = tl.load(p_v, mask=m_t[:, None] & m_v[None, :], other=0.0) - b_v + + if SAVE_NEW_VALUE: + p_v = v_new + o_t[:, None] * (HV*V) + o_v[None, :] + tl.store(p_v, b_v.to(p_v.dtype.element_ty), mask=m_t[:, None] & m_v[None, :]) + + last_idx = min((i_t + 1) * BT, T) - 1 + if USE_G: + b_g_last = tl.load(g + (bos * HV + last_idx * HV + i_h).to(tl.int64)).to(tl.float32) + p_g = g + (bos * HV + i_h).to(tl.int64) + o_t * HV + b_g = tl.load(p_g, mask=m_t, other=0.0).to(tl.float32) + b_v = b_v * tl.where(m_t, exp2(b_g_last - b_g), 0)[:, None] + b_g_last = exp2(b_g_last) + b_h1 *= b_g_last + if K > 64: + b_h2 *= b_g_last + if K > 128: + b_h3 *= b_g_last + if K > 192: + b_h4 *= b_g_last + + if USE_GK: + o_k1 = tl.arange(0, 64) + b_gk_last1 = tl.load(gk + (bos + last_idx) * HV*K + i_h * K + o_k1, mask=(o_k1 < K), other=0.).to(tl.float32) + if STATE_V_FIRST: + b_h1 *= exp2(b_gk_last1)[None, :] + else: + b_h1 *= exp2(b_gk_last1)[:, None] + if K > 64: + o_k2 = 64 + o_k1 + b_gk_last2 = tl.load(gk + (bos + last_idx) * HV*K + i_h * K + o_k2, mask=(o_k2 < K), other=0.).to(tl.float32) + if STATE_V_FIRST: + b_h2 *= exp2(b_gk_last2)[None, :] + else: + b_h2 *= exp2(b_gk_last2)[:, None] + if K > 128: + o_k3 = 128 + o_k1 + b_gk_last3 = tl.load(gk + (bos + last_idx) * HV*K + i_h * K + o_k3, mask=(o_k3 < K), other=0.).to(tl.float32) + if STATE_V_FIRST: + b_h3 *= exp2(b_gk_last3)[None, :] + else: + b_h3 *= exp2(b_gk_last3)[:, None] + if K > 192: + o_k4 = 192 + o_k1 + b_gk_last4 = tl.load(gk + (bos + last_idx) * HV*K + i_h * K + o_k4, mask=(o_k4 < K), other=0.).to(tl.float32) + if STATE_V_FIRST: + b_h4 *= exp2(b_gk_last4)[None, :] + else: + b_h4 *= exp2(b_gk_last4)[:, None] + b_v = b_v.to(k.dtype.element_ty) + + p_k = k + o_k1[:, None] + o_t[None, :] * (H*K) + b_k = tl.load(p_k, mask=m_k1[:, None] & m_t[None, :], other=0.0) + if STATE_V_FIRST: + b_h1 += tl.trans(tl.dot(b_k, b_v)) + else: + b_h1 += tl.dot(b_k, b_v) + if K > 64: + p_k = k + o_k2[:, None] + o_t[None, :] * (H*K) + b_k = tl.load(p_k, mask=m_k2[:, None] & m_t[None, :], other=0.0) + if STATE_V_FIRST: + b_h2 += tl.trans(tl.dot(b_k, b_v)) + else: + b_h2 += tl.dot(b_k, b_v) + if K > 128: + p_k = k + o_k3[:, None] + o_t[None, :] * (H*K) + b_k = tl.load(p_k, mask=m_k3[:, None] & m_t[None, :], other=0.0) + if STATE_V_FIRST: + b_h3 += tl.trans(tl.dot(b_k, b_v)) + else: + b_h3 += tl.dot(b_k, b_v) + if K > 192: + p_k = k + o_k4[:, None] + o_t[None, :] * (H*K) + b_k = tl.load(p_k, mask=m_k4[:, None] & m_t[None, :], other=0.0) + if STATE_V_FIRST: + b_h4 += tl.trans(tl.dot(b_k, b_v)) + else: + b_h4 += tl.dot(b_k, b_v) + + if STORE_FINAL_STATE: + if STATE_V_FIRST: + p_ht = ht + o_v[:, None] * K + o_k1[None, :] + m_ht = m_v[:, None] & m_k1[None, :] + else: + p_ht = ht + o_k1[:, None] * V + o_v[None, :] + m_ht = m_k1[:, None] & m_v[None, :] + tl.store(p_ht, b_h1.to(p_ht.dtype.element_ty), mask=m_ht) + if K > 64: + if STATE_V_FIRST: + p_ht = ht + o_v[:, None] * K + o_k2[None, :] + m_ht = m_v[:, None] & m_k2[None, :] + else: + p_ht = ht + o_k2[:, None] * V + o_v[None, :] + m_ht = m_k2[:, None] & m_v[None, :] + tl.store(p_ht, b_h2.to(p_ht.dtype.element_ty), mask=m_ht) + if K > 128: + if STATE_V_FIRST: + p_ht = ht + o_v[:, None] * K + o_k3[None, :] + m_ht = m_v[:, None] & m_k3[None, :] + else: + p_ht = ht + o_k3[:, None] * V + o_v[None, :] + m_ht = m_k3[:, None] & m_v[None, :] + tl.store(p_ht, b_h3.to(p_ht.dtype.element_ty), mask=m_ht) + if K > 192: + if STATE_V_FIRST: + p_ht = ht + o_v[:, None] * K + o_k4[None, :] + m_ht = m_v[:, None] & m_k4[None, :] + else: + p_ht = ht + o_k4[:, None] * V + o_v[None, :] + m_ht = m_k4[:, None] & m_v[None, :] + tl.store(p_ht, b_h4.to(p_ht.dtype.element_ty), mask=m_ht) + + +@triton.heuristics({ + 'USE_G': lambda args: args['g'] is not None, + 'USE_GK': lambda args: args['gk'] is not None, + 'USE_INITIAL_STATE': lambda args: args['dh0'] is not None, + 'USE_FINAL_STATE_GRADIENT': lambda args: args['dht'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BV': BV}, num_warps=num_warps, num_stages=num_stages) + for num_warps in [2, 4] + for num_stages in ([2, 3, 4] if check_shared_mem('ampere') else [1]) + for BV in ([32, 64] if check_shared_mem('ada') else [32]) + ], + key=['H', 'HV', 'K', 'V', 'BT', 'BV', 'USE_G', 'STATE_V_FIRST'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gated_delta_rule_bwd_kernel_dhu_blockdim64( + q, + k, + w, + g, + gk, + dht, + dh0, + do, + dh, + dv, + dv2, + cu_seqlens, + chunk_offsets, + scale, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BV: tl.constexpr, + USE_G: tl.constexpr, + USE_GK: tl.constexpr, + USE_INITIAL_STATE: tl.constexpr, + USE_FINAL_STATE_GRADIENT: tl.constexpr, + STATE_V_FIRST: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + pid = tl.program_id(0) + NV = tl.cdiv(V, BV) + i_v, i_nh = pid % NV, (pid // NV).to(tl.int64) + i_n, i_h = i_nh // HV, i_nh % HV + if IS_VARLEN: + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + NT = tl.cdiv(T, BT) + boh = tl.load(chunk_offsets + i_n).to(tl.int64) + else: + bos, eos = i_n * T, i_n * T + T + NT = tl.cdiv(T, BT) + boh = i_n * NT + + if STATE_V_FIRST: + b_dh1 = tl.zeros([BV, 64], dtype=tl.float32) + if K > 64: + b_dh2 = tl.zeros([BV, 64], dtype=tl.float32) + if K > 128: + b_dh3 = tl.zeros([BV, 64], dtype=tl.float32) + if K > 192: + b_dh4 = tl.zeros([BV, 64], dtype=tl.float32) + else: + b_dh1 = tl.zeros([64, BV], dtype=tl.float32) + if K > 64: + b_dh2 = tl.zeros([64, BV], dtype=tl.float32) + if K > 128: + b_dh3 = tl.zeros([64, BV], dtype=tl.float32) + if K > 192: + b_dh4 = tl.zeros([64, BV], dtype=tl.float32) + + # calculate offset + q += (bos * H + i_h // (HV // H)).to(tl.int64) * K + k += (bos * H + i_h // (HV // H)).to(tl.int64) * K + w += (bos * HV + i_h).to(tl.int64) * K + do += (bos * HV + i_h).to(tl.int64) * V + dv += (bos * HV + i_h).to(tl.int64) * V + dv2 += (bos * HV + i_h).to(tl.int64) * V + dh += (boh * HV + i_h).to(tl.int64) * K*V + if USE_GK: + gk += (bos * HV + i_h).to(tl.int64) * K + + if USE_INITIAL_STATE: + dh0 += i_nh * K*V + if USE_FINAL_STATE_GRADIENT: + dht += i_nh * K*V + + o_v = i_v * BV + tl.arange(0, BV) + m_v = o_v < V + o_k1 = tl.arange(0, 64) + m_k1 = o_k1 < K + o_k2 = 64 + o_k1 + m_k2 = o_k2 < K + o_k3 = 128 + o_k1 + m_k3 = o_k3 < K + o_k4 = 192 + o_k1 + m_k4 = o_k4 < K + if USE_FINAL_STATE_GRADIENT: + if STATE_V_FIRST: + p_dht1 = dht + o_v[:, None] * K + o_k1[None, :] + m_dht1 = m_v[:, None] & m_k1[None, :] + else: + p_dht1 = dht + o_k1[:, None] * V + o_v[None, :] + m_dht1 = m_k1[:, None] & m_v[None, :] + b_dh1 += tl.load(p_dht1, mask=m_dht1, other=0.0) + if K > 64: + if STATE_V_FIRST: + p_dht2 = dht + o_v[:, None] * K + o_k2[None, :] + m_dht2 = m_v[:, None] & m_k2[None, :] + else: + p_dht2 = dht + o_k2[:, None] * V + o_v[None, :] + m_dht2 = m_k2[:, None] & m_v[None, :] + b_dh2 += tl.load(p_dht2, mask=m_dht2, other=0.0) + if K > 128: + if STATE_V_FIRST: + p_dht3 = dht + o_v[:, None] * K + o_k3[None, :] + m_dht3 = m_v[:, None] & m_k3[None, :] + else: + p_dht3 = dht + o_k3[:, None] * V + o_v[None, :] + m_dht3 = m_k3[:, None] & m_v[None, :] + b_dh3 += tl.load(p_dht3, mask=m_dht3, other=0.0) + if K > 192: + if STATE_V_FIRST: + p_dht4 = dht + o_v[:, None] * K + o_k4[None, :] + m_dht4 = m_v[:, None] & m_k4[None, :] + else: + p_dht4 = dht + o_k4[:, None] * V + o_v[None, :] + m_dht4 = m_k4[:, None] & m_v[None, :] + b_dh4 += tl.load(p_dht4, mask=m_dht4, other=0.0) + + for i_t in range(NT - 1, -1, -1): + i_t_int64 = i_t.to(tl.int64) + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + if STATE_V_FIRST: + p_dh1 = dh + i_t_int64*HV*K*V + o_v[:, None] * K + o_k1[None, :] + m_dh1 = m_v[:, None] & m_k1[None, :] + else: + p_dh1 = dh + i_t_int64*HV*K*V + o_k1[:, None] * V + o_v[None, :] + m_dh1 = m_k1[:, None] & m_v[None, :] + tl.store(p_dh1, b_dh1.to(p_dh1.dtype.element_ty), mask=m_dh1) + if K > 64: + if STATE_V_FIRST: + p_dh2 = dh + i_t_int64*HV*K*V + o_v[:, None] * K + o_k2[None, :] + m_dh2 = m_v[:, None] & m_k2[None, :] + else: + p_dh2 = dh + i_t_int64*HV*K*V + o_k2[:, None] * V + o_v[None, :] + m_dh2 = m_k2[:, None] & m_v[None, :] + tl.store(p_dh2, b_dh2.to(p_dh2.dtype.element_ty), mask=m_dh2) + if K > 128: + if STATE_V_FIRST: + p_dh3 = dh + i_t_int64*HV*K*V + o_v[:, None] * K + o_k3[None, :] + m_dh3 = m_v[:, None] & m_k3[None, :] + else: + p_dh3 = dh + i_t_int64*HV*K*V + o_k3[:, None] * V + o_v[None, :] + m_dh3 = m_k3[:, None] & m_v[None, :] + tl.store(p_dh3, b_dh3.to(p_dh3.dtype.element_ty), mask=m_dh3) + if K > 192: + if STATE_V_FIRST: + p_dh4 = dh + i_t_int64*HV*K*V + o_v[:, None] * K + o_k4[None, :] + m_dh4 = m_v[:, None] & m_k4[None, :] + else: + p_dh4 = dh + i_t_int64*HV*K*V + o_k4[:, None] * V + o_v[None, :] + m_dh4 = m_k4[:, None] & m_v[None, :] + tl.store(p_dh4, b_dh4.to(p_dh4.dtype.element_ty), mask=m_dh4) + + last_idx = min((i_t + 1) * BT, T) - 1 + if USE_G: + bg_last = tl.load(g + (bos + last_idx) * HV + i_h).to(tl.float32) + p_g = g + bos * HV + i_h + o_t * HV + b_g = tl.load(p_g, mask=m_t, other=0.0).to(tl.float32) + bg_last_exp = exp2(bg_last) + b_g_exp = exp2(b_g) + p_dv = dv + o_t[:, None] * (HV*V) + o_v[None, :] + p_dv2 = dv2 + o_t[:, None] * (HV*V) + o_v[None, :] + p_do = do + o_t[:, None] * (HV*V) + o_v[None, :] + + b_do = tl.load(p_do, mask=m_t[:, None] & m_v[None, :], other=0.0) + + # Update dv + p_k = k + o_t[:, None] * (H*K) + o_k1[None, :] + b_k = tl.load(p_k, mask=m_t[:, None] & m_k1[None, :], other=0.0) + if USE_GK: + o_k1 = tl.arange(0, 64) + b_gk_last1 = tl.load(gk + last_idx * HV*K + o_k1, mask=(o_k1 < K), other=0.).to(tl.float32) + if STATE_V_FIRST: + b_dv = tl.dot(b_k, tl.trans(b_dh1).to(b_k.dtype)) + else: + b_dv = tl.dot(b_k, b_dh1.to(b_k.dtype)) + + if K > 64: + p_k = k + o_t[:, None] * (H*K) + o_k2[None, :] + b_k = tl.load(p_k, mask=m_t[:, None] & m_k2[None, :], other=0.0) + if USE_GK: + b_gk_last2 = tl.load(gk + last_idx * HV*K + o_k2, mask=(o_k2 < K), other=0.).to(tl.float32) + if STATE_V_FIRST: + b_dv += tl.dot(b_k, tl.trans(b_dh2).to(b_k.dtype)) + else: + b_dv += tl.dot(b_k, b_dh2.to(b_k.dtype)) + + if K > 128: + p_k = k + o_t[:, None] * (H*K) + o_k3[None, :] + b_k = tl.load(p_k, mask=m_t[:, None] & m_k3[None, :], other=0.0) + if USE_GK: + b_gk_last3 = tl.load(gk + last_idx * HV*K + o_k3, mask=(o_k3 < K), other=0.).to(tl.float32) + if STATE_V_FIRST: + b_dv += tl.dot(b_k, tl.trans(b_dh3).to(b_k.dtype)) + else: + b_dv += tl.dot(b_k, b_dh3.to(b_k.dtype)) + + if K > 192: + p_k = k + o_t[:, None] * (H*K) + o_k4[None, :] + b_k = tl.load(p_k, mask=m_t[:, None] & m_k4[None, :], other=0.0) + if USE_GK: + b_gk_last4 = tl.load(gk + last_idx * HV*K + o_k4, mask=(o_k4 < K), other=0.).to(tl.float32) + if STATE_V_FIRST: + b_dv += tl.dot(b_k, tl.trans(b_dh4).to(b_k.dtype)) + else: + b_dv += tl.dot(b_k, b_dh4.to(b_k.dtype)) + + if USE_G: + b_dv *= tl.where(m_t, exp2(bg_last - b_g), 0)[:, None] + b_dv += tl.load(p_dv, mask=m_t[:, None] & m_v[None, :], other=0.0) + + tl.store(p_dv2, b_dv.to(p_dv.dtype.element_ty), mask=m_t[:, None] & m_v[None, :]) + # Update dh + p_w = w + o_k1[:, None] + o_t[None, :] * (HV*K) + p_q = q + o_k1[:, None] + o_t[None, :] * (H*K) + b_w = tl.load(p_w, mask=m_k1[:, None] & m_t[None, :], other=0.0) + b_q = tl.load(p_q, mask=m_k1[:, None] & m_t[None, :], other=0.0) + if USE_G: + b_dh1 *= bg_last_exp + b_q = b_q * b_g_exp[None, :] + if USE_GK: + if STATE_V_FIRST: + b_dh1 *= exp2(b_gk_last1)[None, :] + else: + b_dh1 *= exp2(b_gk_last1[:, None]) + if STATE_V_FIRST: + b_dh1 += tl.trans(tl.dot(b_q.to(b_q.dtype), b_do.to(b_q.dtype)) * scale - tl.dot(b_w, b_dv.to(b_w.dtype))) + else: + b_dh1 += tl.dot(b_q.to(b_q.dtype), b_do.to(b_q.dtype)) * scale - tl.dot(b_w, b_dv.to(b_w.dtype)) + if K > 64: + p_q = q + o_k2[:, None] + o_t[None, :] * (H*K) + p_w = w + o_k2[:, None] + o_t[None, :] * (HV*K) + b_q = tl.load(p_q, mask=m_k2[:, None] & m_t[None, :], other=0.0) + b_w = tl.load(p_w, mask=m_k2[:, None] & m_t[None, :], other=0.0) + if USE_G: + b_dh2 *= bg_last_exp + b_q = b_q * b_g_exp[None, :] + if USE_GK: + if STATE_V_FIRST: + b_dh2 *= exp2(b_gk_last2)[None, :] + else: + b_dh2 *= exp2(b_gk_last2[:, None]) + if STATE_V_FIRST: + b_dh2 += tl.trans(tl.dot(b_q.to(b_q.dtype), b_do.to(b_q.dtype)) * scale - tl.dot(b_w, b_dv.to(b_w.dtype))) + else: + b_dh2 += tl.dot(b_q.to(b_q.dtype), b_do.to(b_q.dtype)) * scale - tl.dot(b_w, b_dv.to(b_w.dtype)) + if K > 128: + p_q = q + o_k3[:, None] + o_t[None, :] * (H*K) + p_w = w + o_k3[:, None] + o_t[None, :] * (HV*K) + b_q = tl.load(p_q, mask=m_k3[:, None] & m_t[None, :], other=0.0) + b_w = tl.load(p_w, mask=m_k3[:, None] & m_t[None, :], other=0.0) + if USE_G: + b_dh3 *= bg_last_exp + b_q = b_q * b_g_exp[None, :] + if USE_GK: + if STATE_V_FIRST: + b_dh3 *= exp2(b_gk_last3)[None, :] + else: + b_dh3 *= exp2(b_gk_last3[:, None]) + if STATE_V_FIRST: + b_dh3 += tl.trans(tl.dot(b_q.to(b_q.dtype), b_do.to(b_q.dtype)) * scale - tl.dot(b_w, b_dv.to(b_w.dtype))) + else: + b_dh3 += tl.dot(b_q.to(b_q.dtype), b_do.to(b_q.dtype)) * scale - tl.dot(b_w, b_dv.to(b_w.dtype)) + if K > 192: + p_q = q + o_k4[:, None] + o_t[None, :] * (H*K) + p_w = w + o_k4[:, None] + o_t[None, :] * (HV*K) + b_q = tl.load(p_q, mask=m_k4[:, None] & m_t[None, :], other=0.0) + b_w = tl.load(p_w, mask=m_k4[:, None] & m_t[None, :], other=0.0) + if USE_G: + b_dh4 *= bg_last_exp + b_q = b_q * b_g_exp[None, :] + if USE_GK: + if STATE_V_FIRST: + b_dh4 *= exp2(b_gk_last4)[None, :] + else: + b_dh4 *= exp2(b_gk_last4[:, None]) + if STATE_V_FIRST: + b_dh4 += tl.trans(tl.dot(b_q.to(b_q.dtype), b_do.to(b_q.dtype)) * scale - tl.dot(b_w, b_dv.to(b_w.dtype))) + else: + b_dh4 += tl.dot(b_q.to(b_q.dtype), b_do.to(b_q.dtype)) * scale - tl.dot(b_w, b_dv.to(b_w.dtype)) + + if USE_INITIAL_STATE: + if STATE_V_FIRST: + p_dh0 = dh0 + o_v[:, None] * K + o_k1[None, :] + m_dh0 = m_v[:, None] & m_k1[None, :] + else: + p_dh0 = dh0 + o_k1[:, None] * V + o_v[None, :] + m_dh0 = m_k1[:, None] & m_v[None, :] + tl.store(p_dh0, b_dh1.to(p_dh0.dtype.element_ty), mask=m_dh0) + if K > 64: + if STATE_V_FIRST: + p_dh1 = dh0 + o_v[:, None] * K + o_k2[None, :] + m_dh1 = m_v[:, None] & m_k2[None, :] + else: + p_dh1 = dh0 + o_k2[:, None] * V + o_v[None, :] + m_dh1 = m_k2[:, None] & m_v[None, :] + tl.store(p_dh1, b_dh2.to(p_dh1.dtype.element_ty), mask=m_dh1) + if K > 128: + if STATE_V_FIRST: + p_dh2 = dh0 + o_v[:, None] * K + o_k3[None, :] + m_dh2 = m_v[:, None] & m_k3[None, :] + else: + p_dh2 = dh0 + o_k3[:, None] * V + o_v[None, :] + m_dh2 = m_k3[:, None] & m_v[None, :] + tl.store(p_dh2, b_dh3.to(p_dh2.dtype.element_ty), mask=m_dh2) + if K > 192: + if STATE_V_FIRST: + p_dh3 = dh0 + o_v[:, None] * K + o_k4[None, :] + m_dh3 = m_v[:, None] & m_k4[None, :] + else: + p_dh3 = dh0 + o_k4[:, None] * V + o_v[None, :] + m_dh3 = m_k4[:, None] & m_v[None, :] + tl.store(p_dh3, b_dh4.to(p_dh3.dtype.element_ty), mask=m_dh3) + + +@dispatch('common') +def chunk_gated_delta_rule_fwd_h( + k: torch.Tensor, + w: torch.Tensor, + u: torch.Tensor, + g: torch.Tensor | None = None, + gk: torch.Tensor | None = None, + initial_state: torch.Tensor | None = None, + output_final_state: bool = False, + chunk_size: int = 64, + save_new_value: bool = True, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + cu_seqlens_cpu: torch.LongTensor | None = None, + chunk_indices: torch.LongTensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None]: + B, T, H, K, V, HV = *k.shape, u.shape[-1], u.shape[2] + BT = chunk_size + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) + # N: the actual number of sequences in the batch with either equal or variable lengths + if cu_seqlens is None: + N, NT, chunk_offsets = B, triton.cdiv(T, BT), None + else: + N, NT, chunk_offsets = len(cu_seqlens) - 1, len(chunk_indices), prepare_chunk_offsets(cu_seqlens, BT) + assert K <= 256, "current kernel does not support head dimension larger than 256." + + if state_v_first: + h = k.new_empty(B, NT, HV, V, K) + final_state = k.new_zeros(N, HV, V, K, dtype=torch.float32) if output_final_state else None + else: + h = k.new_empty(B, NT, HV, K, V) + final_state = k.new_zeros(N, HV, K, V, dtype=torch.float32) if output_final_state else None + + v_new = torch.empty_like(u) if save_new_value else None + def grid(meta): return (triton.cdiv(V, meta['BV']) * N * HV, ) + chunk_gated_delta_rule_fwd_kernel_h_blockdim64[grid]( + k=k, + v=u, + w=w, + v_new=v_new, + g=g, + gk=gk, + h=h, + h0=initial_state, + ht=final_state, + cu_seqlens=cu_seqlens, + chunk_offsets=chunk_offsets, + T=T, + H=H, + HV=HV, + K=K, + V=V, + BT=BT, + STATE_V_FIRST=state_v_first, + ) + return h, v_new, final_state + + +@dispatch('common') +def chunk_gated_delta_rule_bwd_dhu( + q: torch.Tensor, + k: torch.Tensor, + w: torch.Tensor, + do: torch.Tensor, + dv: torch.Tensor, + g: torch.Tensor | None = None, + gk: torch.Tensor | None = None, + h0: torch.Tensor | None = None, + dht: torch.Tensor | None = None, + scale: float | None = None, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + B, T, H, K, V, HV = *q.shape, do.shape[-1], do.shape[2] + # N: the actual number of sequences in the batch with either equal or variable lengths + BT = chunk_size + assert K <= 256, "current kernel does not support head dimension being larger than 256." + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) + if cu_seqlens is None: + N, NT, chunk_offsets = B, triton.cdiv(T, BT), None + else: + N, NT, chunk_offsets = len(cu_seqlens) - 1, len(chunk_indices), prepare_chunk_offsets(cu_seqlens, BT) + + if state_v_first: + dh = q.new_empty(B, NT, HV, V, K) + else: + dh = q.new_empty(B, NT, HV, K, V) + dh0 = torch.empty_like(h0, dtype=torch.float32) if h0 is not None else None + dv2 = torch.empty_like(dv) + + def grid(meta): return (triton.cdiv(V, meta['BV']) * N * HV, ) + chunk_gated_delta_rule_bwd_kernel_dhu_blockdim64[grid]( + q=q, + k=k, + w=w, + g=g, + gk=gk, + dht=dht, + dh0=dh0, + do=do, + dh=dh, + dv=dv, + dv2=dv2, + cu_seqlens=cu_seqlens, + chunk_offsets=chunk_offsets, + scale=scale, + T=T, + H=H, + HV=HV, + K=K, + V=V, + BT=BT, + STATE_V_FIRST=state_v_first, + ) + return dh, dh0, dv2 diff --git a/kda/_fla/ops/common/chunk_h.py b/kda/_fla/ops/common/chunk_h.py new file mode 100644 index 0000000..b069976 --- /dev/null +++ b/kda/_fla/ops/common/chunk_h.py @@ -0,0 +1,432 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.utils import prepare_chunk_offsets +from kda._fla.ops.utils.op import exp2 +from kda._fla.utils import autotune_cache_kwargs, check_shared_mem + +BKV_LIST = [32, 64] if check_shared_mem() else [16, 32] + + +@triton.heuristics({ + 'USE_INITIAL_STATE': lambda args: args['h0'] is not None, + 'STORE_FINAL_STATE': lambda args: args['ht'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@triton.autotune( + configs=[ + triton.Config({'BK': BK, 'BV': BV}, num_warps=num_warps, num_stages=num_stages) + for BK in BKV_LIST + for BV in BKV_LIST + for num_warps in [1, 2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['BT', 'USE_G', 'USE_GK', 'USE_GV', 'STATE_V_FIRST'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_fwd_kernel_h( + k, + v, + h, + g, + g_gamma, + gk, + gv, + h0, + ht, + cu_seqlens, + split_offsets, + T, + H: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BS: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + USE_G: tl.constexpr, + USE_G_GAMMA: tl.constexpr, + USE_GK: tl.constexpr, + USE_GV: tl.constexpr, + USE_INITIAL_STATE: tl.constexpr, + STORE_FINAL_STATE: tl.constexpr, + IS_VARLEN: tl.constexpr, + STATE_V_FIRST: tl.constexpr, +): + i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2).to(tl.int64) + i_n, i_h = i_nh // H, i_nh % H + if IS_VARLEN: + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + NT, NS = tl.cdiv(T, BT), tl.cdiv(T, BS) + boh = tl.load(split_offsets + i_n).to(tl.int64) + else: + bos, eos = i_n * T, i_n * T + T + NT, NS = tl.cdiv(T, BT), tl.cdiv(T, BS) + boh = i_n * NS + NTS = BS // BT + + if USE_G_GAMMA: + # decay rate given the head index + b_gamma = tl.load(g_gamma + i_h) + b_g = b_gamma * (tl.arange(0, BT) + 1) + + # [BK, BV] accumulator; STATE_V_FIRST only flips the stored state's HBM layout to [V, K], applied at the load/store below. + b_h = tl.zeros([BK, BV], dtype=tl.float32) + o_k = i_k * BK + tl.arange(0, BK) + o_v = i_v * BV + tl.arange(0, BV) + if USE_INITIAL_STATE: + if STATE_V_FIRST: + p_h0 = h0 + i_nh * K*V + o_v[:, None] * K + o_k[None, :] + b_h = tl.trans(tl.load(p_h0, mask=(o_v[:, None] < V) & (o_k[None, :] < K), other=0.0)).to(tl.float32) + else: + p_h0 = h0 + i_nh * K*V + o_k[:, None] * V + o_v[None, :] + b_h = tl.load(p_h0, mask=(o_k[:, None] < K) & (o_v[None, :] < V), other=0.0).to(tl.float32) + + for i_t in range(NT): + i_s = i_t // NTS + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + p_k = k + (bos*H + i_h) * K + o_k[:, None] + o_t[None, :] * (H*K) + p_v = v + (bos*H + i_h) * V + o_t[:, None] * (H*V) + o_v[None, :] + + o_h = ((boh + i_s) * H + i_h).to(tl.int64) * K*V + if STATE_V_FIRST: + p_h = h + o_h + o_v[:, None] * K + o_k[None, :] + m_h = (o_v[:, None] < V) & (o_k[None, :] < K) + else: + p_h = h + o_h + o_k[:, None] * V + o_v[None, :] + m_h = (o_k[:, None] < K) & (o_v[None, :] < V) + + if i_t % NTS == 0: + tl.store(p_h, (tl.trans(b_h) if STATE_V_FIRST else b_h).to(p_h.dtype.element_ty), mask=m_h) + # [BK, BT] + b_k = tl.load(p_k, mask=(o_k[:, None] < K) & m_t[None, :], other=0.0) + # [BT, BV] + b_v = tl.load(p_v, mask=m_t[:, None] & (o_v < V)[None, :], other=0.0) + last_idx = min((i_t + 1) * BT, T) - 1 + + # scalar decay + if USE_G: + b_g_last = tl.load(g + bos * H + last_idx * H + i_h) + p_g = g + bos*H + (i_t * BT + tl.arange(0, BT)) * H + i_h + b_g = tl.load(p_g, mask=(i_t * BT + tl.arange(0, BT) < T), other=0.) + b_h *= exp2(b_g_last) + b_v = (b_v * exp2(b_g_last - b_g)[:, None]).to(b_v.dtype) + + if USE_G_GAMMA: + b_g_last = b_gamma * min(BT, T - i_t * BT) + b_h *= exp2(b_g_last) + b_v = (b_v * exp2(b_g_last - b_g)[:, None]).to(b_v.dtype) + + # vector decay, h = Diag(gk) @ h + if USE_GK: + p_gk = gk + (bos*H + i_h) * K + o_k[:, None] + o_t[None, :] * (H*K) + p_gk_last = gk + (bos + last_idx) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) + + b_gk_last = tl.load(p_gk_last, mask=(i_k * BK + tl.arange(0, BK) < K), other=0.) + b_gk = tl.load(p_gk, mask=(o_k[:, None] < K) & m_t[None, :], other=0.0) + b_h *= exp2(b_gk_last)[:, None] + b_k = (b_k * exp2(b_gk_last[:, None] - b_gk)).to(b_k.dtype) + + # vector decay, h = h @ Diag(gv) + if USE_GV: + p_gv = gv + (bos*H + i_h) * V + o_t[:, None] * (H*V) + o_v[None, :] + p_gv_last = gv + (bos + last_idx) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) + + b_gv_last = tl.load(p_gv_last, mask=(i_v * BV + tl.arange(0, BV) < V), other=0.) + b_gv = tl.load(p_gv, mask=m_t[:, None] & (o_v < V)[None, :], other=0.0) + b_h *= exp2(b_gv_last)[None, :] + b_v = (b_v * exp2(b_gv_last[None, :] - b_gv)).to(b_v.dtype) + + b_h += tl.dot(b_k, b_v) + + if STORE_FINAL_STATE: + if STATE_V_FIRST: + p_ht = ht + i_nh * K*V + o_v[:, None] * K + o_k[None, :] + tl.store(p_ht, tl.trans(b_h).to(p_ht.dtype.element_ty), mask=(o_v[:, None] < V) & (o_k[None, :] < K)) + else: + p_ht = ht + i_nh * K*V + o_k[:, None] * V + o_v[None, :] + tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=(o_k[:, None] < K) & (o_v[None, :] < V)) + + +@triton.heuristics({ + 'STORE_INITIAL_STATE_GRADIENT': lambda args: args['dh0'] is not None, + 'USE_FINAL_STATE_GRADIENT': lambda args: args['dht'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@triton.autotune( + configs=[ + triton.Config({'BK': BK, 'BV': BV}, num_warps=num_warps, num_stages=num_stages) + for BK in BKV_LIST + for BV in BKV_LIST + for num_warps in [1, 2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['BT', 'USE_G', 'USE_GK', 'USE_GV', 'STATE_V_FIRST'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_bwd_kernel_dh( + q, + g, + g_gamma, + gk, + gv, + do, + dh, + dht, + dh0, + cu_seqlens, + split_offsets, + scale, + T, + HQ: tl.constexpr, + H: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BS: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + NG: tl.constexpr, + USE_G: tl.constexpr, + USE_G_GAMMA: tl.constexpr, + USE_GK: tl.constexpr, + USE_GV: tl.constexpr, + STORE_INITIAL_STATE_GRADIENT: tl.constexpr, + USE_FINAL_STATE_GRADIENT: tl.constexpr, + IS_VARLEN: tl.constexpr, + STATE_V_FIRST: tl.constexpr, +): + i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2).to(tl.int64) + i_n, i_hq = i_nh // HQ, i_nh % HQ + i_h = i_hq // NG + if IS_VARLEN: + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + NT = tl.cdiv(T, BT) + NS = tl.cdiv(T, BS) + boh = tl.load(split_offsets + i_n).to(tl.int64) + else: + bos, eos = i_n * T, i_n * T + T + NT = tl.cdiv(T, BT) + NS = tl.cdiv(T, BS) + boh = i_n * NS + + if USE_G_GAMMA: + b_gamma = tl.load(g_gamma + i_h) + b_g = b_gamma * (tl.arange(0, BT) + 1) + + # [BK, BV] accumulator; STATE_V_FIRST only flips the stored state's HBM layout to [V, K], applied at the load/store below. + b_dh = tl.zeros([BK, BV], dtype=tl.float32) + o_k = i_k * BK + tl.arange(0, BK) + o_v = i_v * BV + tl.arange(0, BV) + if USE_FINAL_STATE_GRADIENT: + if STATE_V_FIRST: + p_dht = dht + i_nh * K*V + o_v[:, None] * K + o_k[None, :] + b_dh += tl.trans(tl.load(p_dht, mask=(o_v[:, None] < V) & (o_k[None, :] < K), other=0.0)).to(tl.float32) + else: + p_dht = dht + i_nh * K*V + o_k[:, None] * V + o_v[None, :] + b_dh += tl.load(p_dht, mask=(o_k[:, None] < K) & (o_v[None, :] < V), other=0.0).to(tl.float32) + + for i_t in range(NT - 1, -1, -1): + i_s = i_t // (BS // BT) + o_dh = ((boh + i_s) * H + i_h).to(tl.int64) * K*V + if STATE_V_FIRST: + p_dh = dh + o_dh + o_v[:, None] * K + o_k[None, :] + m_dh = (o_v[:, None] < V) & (o_k[None, :] < K) + else: + p_dh = dh + o_dh + o_k[:, None] * V + o_v[None, :] + m_dh = (o_k[:, None] < K) & (o_v[None, :] < V) + + if i_t % (BS // BT) == 0: + tl.store(p_dh, (tl.trans(b_dh) if STATE_V_FIRST else b_dh).to(p_dh.dtype.element_ty), mask=m_dh) + last_idx = min(i_t * BT + BT, T) - 1 + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + # [BK, BT] + p_q = q + (bos*HQ + i_hq) * K + o_k[:, None] + o_t[None, :] * (HQ*K) + p_do = do + (bos*HQ + i_hq) * V + o_t[:, None] * (HQ*V) + o_v[None, :] + b_q = tl.load(p_q, mask=(o_k[:, None] < K) & m_t[None, :], other=0.0) + b_q = (b_q * scale).to(b_q.dtype) + # [BT, BV] + b_do = tl.load(p_do, mask=m_t[:, None] & (o_v < V)[None, :], other=0.0) + + if USE_G: + p_g = g + (bos + i_t * BT + tl.arange(0, BT)) * H + i_h + b_g_last = tl.load(g + (bos + last_idx) * H + i_h) + b_g = tl.load(p_g, mask=(i_t * BT + tl.arange(0, BT) < T), other=0.) + b_q = (b_q * exp2(b_g)[None, :]).to(b_q.dtype) + b_dh *= exp2(b_g_last) + + if USE_G_GAMMA: + b_g_last = b_gamma * min(BT, T - i_t * BT) + b_q = (b_q * exp2(b_g)[None, :]).to(b_q.dtype) + b_dh *= exp2(b_g_last) + + if USE_GK: + p_gk = gk + (bos*H + i_h) * K + o_k[:, None] + o_t[None, :] * (H*K) + p_gk_last = gk + (bos + last_idx) * H*K + i_h * K + i_k * BK + tl.arange(0, BK) + + b_gk = tl.load(p_gk, mask=(o_k[:, None] < K) & m_t[None, :], other=0.0) + b_gk_last = tl.load(p_gk_last, mask=(i_k * BK + tl.arange(0, BK) < K), other=0.) + b_q = (b_q * exp2(b_gk)).to(b_q.dtype) + b_dh *= exp2(b_gk_last)[:, None] + + if USE_GV: + p_gv = gv + (bos*H + i_h) * V + o_t[:, None] * (H*V) + o_v[None, :] + p_gv_last = gv + (bos + last_idx) * H*V + i_h * V + i_v * BV + tl.arange(0, BV) + + b_gv = tl.load(p_gv, mask=m_t[:, None] & (o_v < V)[None, :], other=0.0) + b_gv_last = tl.load(p_gv_last, mask=(i_v * BV + tl.arange(0, BV) < V), other=0.) + b_do = (b_do * exp2(b_gv)) + b_dh *= exp2(b_gv_last)[None, :] + + b_dh += tl.dot(b_q, b_do.to(b_q.dtype)) + + if STORE_INITIAL_STATE_GRADIENT: + if STATE_V_FIRST: + p_dh0 = dh0 + i_nh * K*V + o_v[:, None] * K + o_k[None, :] + tl.store(p_dh0, tl.trans(b_dh).to(p_dh0.dtype.element_ty), mask=(o_v[:, None] < V) & (o_k[None, :] < K)) + else: + p_dh0 = dh0 + i_nh * K*V + o_k[:, None] * V + o_v[None, :] + tl.store(p_dh0, b_dh.to(p_dh0.dtype.element_ty), mask=(o_k[:, None] < K) & (o_v[None, :] < V)) + + +@dispatch('common') +def chunk_fwd_h( + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor | None = None, + g_gamma: torch.Tensor | None = None, + gk: torch.Tensor | None = None, + gv: torch.Tensor | None = None, + h0: torch.Tensor | None = None, + output_final_state: bool = False, + state_v_first: bool = False, + cu_seqlens: torch.Tensor | None = None, + chunk_size: int = 64, + split_size: int | None = None, + states_in_fp32: bool = False, +) -> tuple[torch.Tensor, torch.Tensor]: + B, T, H, K, V = *k.shape, v.shape[-1] + BT = chunk_size + BS = BT if split_size is None else split_size + assert BS % BT == 0, f"The `split_size` (got {BS}) must be a multiple of `chunk_size` {BT}" + # N: the actual number of sequences in the batch with either equal or variable lengths + if cu_seqlens is None: + N, NS, split_offsets = B, triton.cdiv(T, BS), None + else: + split_offsets = prepare_chunk_offsets(cu_seqlens, BS) + N, NS = len(cu_seqlens) - 1, split_offsets[-1].item() + + # `state_v_first` stores the states in V-first `[V, K]` layout instead of `[K, V]` + state_shape = (V, K) if state_v_first else (K, V) + h = k.new_empty(B, NS, H, *state_shape, dtype=k.dtype if not states_in_fp32 else torch.float) + ht = k.new_empty(N, H, *state_shape, dtype=torch.float) if output_final_state else None + def grid(meta): return (triton.cdiv(K, meta['BK']), triton.cdiv(V, meta['BV']), N * H) + chunk_fwd_kernel_h[grid]( + k=k, + v=v, + h=h, + g=g, + g_gamma=g_gamma, + gk=gk, + gv=gv, + h0=h0, + ht=ht, + cu_seqlens=cu_seqlens, + split_offsets=split_offsets, + T=T, + H=H, + K=K, + V=V, + BT=BT, + BS=BS, + USE_G=g is not None, + USE_G_GAMMA=g_gamma is not None, + USE_GK=gk is not None, + USE_GV=gv is not None, + STATE_V_FIRST=state_v_first, + ) + return h, ht + + +@dispatch('common') +def chunk_bwd_dh( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + do: torch.Tensor, + h0: torch.Tensor, + dht: torch.Tensor, + scale: float, + g: torch.Tensor | None = None, + g_gamma: torch.Tensor | None = None, + gk: torch.Tensor | None = None, + gv: torch.Tensor | None = None, + state_v_first: bool = False, + cu_seqlens: torch.Tensor | None = None, + chunk_size: int = 64, + split_size: int | None = None, + states_in_fp32: bool = False, +) -> tuple[torch.Tensor, torch.Tensor]: + B, T, H, K, V = *k.shape, v.shape[-1] + HQ = q.shape[2] + BT = chunk_size + BS = BT if split_size is None else split_size + assert BS % BT == 0, f"The `split_size` (got {BS}) must be a multiple of `chunk_size` {BT}" + # N: the actual number of sequences in the batch with either equal or variable lengths + # NG: number of groups in GQA + if cu_seqlens is None: + N, NS, split_offsets = B, triton.cdiv(T, BS), None + else: + split_offsets = prepare_chunk_offsets(cu_seqlens, BS) + N, NS = len(cu_seqlens) - 1, split_offsets[-1].item() + NG = HQ // H + + # `state_v_first` stores the states in V-first `[V, K]` layout instead of `[K, V]` + state_shape = (V, K) if state_v_first else (K, V) + dh = k.new_empty(B, NS, HQ, *state_shape, dtype=k.dtype if not states_in_fp32 else torch.float) + dh0 = torch.empty_like(h0, dtype=torch.float) if h0 is not None else None + + def grid(meta): return (triton.cdiv(K, meta['BK']), triton.cdiv(V, meta['BV']), N * H) + chunk_bwd_kernel_dh[grid]( + q=q, + g=g, + g_gamma=g_gamma, + gk=gk, + gv=gv, + do=do, + dh=dh, + dht=dht, + dh0=dh0, + cu_seqlens=cu_seqlens, + split_offsets=split_offsets, + scale=scale, + T=T, + HQ=HQ, + H=H, + K=K, + V=V, + BT=BT, + BS=BS, + NG=NG, + USE_G=g is not None, + USE_G_GAMMA=g_gamma is not None, + USE_GK=gk is not None, + USE_GV=gv is not None, + STATE_V_FIRST=state_v_first, + ) + return dh, dh0 diff --git a/kda/_fla/ops/common/gate.py b/kda/_fla/ops/common/gate.py new file mode 100644 index 0000000..d95849c --- /dev/null +++ b/kda/_fla/ops/common/gate.py @@ -0,0 +1,110 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +# Shared gate helpers reused across delta-rule family ops (KDA, GDN, ...). + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.utils import autocast_custom_bwd, autocast_custom_fwd, input_guard + + +@triton.jit +def fused_beta_sigmoid_fwd_kernel( + x, + y, + scale, + n_elements, + BLOCK_SIZE: tl.constexpr, +): + pid = tl.program_id(0).to(tl.int64) + offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE).to(tl.int64) + mask = offs < n_elements + b_x = tl.load(x + offs, mask=mask, other=0).to(tl.float32) + b_y = scale * tl.sigmoid(b_x) + tl.store(y + offs, b_y.to(y.dtype.element_ty), mask=mask) + + +@triton.jit +def fused_beta_sigmoid_bwd_kernel( + x, + dy, + dx, + scale, + n_elements, + BLOCK_SIZE: tl.constexpr, +): + pid = tl.program_id(0).to(tl.int64) + offs = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE).to(tl.int64) + mask = offs < n_elements + b_x = tl.load(x + offs, mask=mask, other=0).to(tl.float32) + b_dy = tl.load(dy + offs, mask=mask, other=0).to(tl.float32) + b_y = tl.sigmoid(b_x) + b_dx = b_dy * scale * b_y * (1.0 - b_y) + tl.store(dx + offs, b_dx.to(dx.dtype.element_ty), mask=mask) + + +_BETA_SIGMOID_BLOCK_SIZE = 2048 +_BETA_SIGMOID_NUM_WARPS = 8 + + +@dispatch('common') +def fused_beta_sigmoid_fwd(x: torch.Tensor, scale: float = 1.0) -> torch.Tensor: + y = torch.empty_like(x, dtype=torch.float32) + n_elements = x.numel() + grid = (triton.cdiv(n_elements, _BETA_SIGMOID_BLOCK_SIZE),) + fused_beta_sigmoid_fwd_kernel[grid]( + x, + y, + scale, + n_elements, + BLOCK_SIZE=_BETA_SIGMOID_BLOCK_SIZE, + num_warps=_BETA_SIGMOID_NUM_WARPS, + ) + return y + + +@dispatch('common') +def fused_beta_sigmoid_bwd(x: torch.Tensor, dy: torch.Tensor, scale: float = 1.0) -> torch.Tensor: + dx = torch.empty_like(x) + n_elements = x.numel() + grid = (triton.cdiv(n_elements, _BETA_SIGMOID_BLOCK_SIZE),) + fused_beta_sigmoid_bwd_kernel[grid]( + x, + dy, + dx, + scale, + n_elements, + BLOCK_SIZE=_BETA_SIGMOID_BLOCK_SIZE, + num_warps=_BETA_SIGMOID_NUM_WARPS, + ) + return dx + + +class BetaSigmoidFunction(torch.autograd.Function): + @staticmethod + @input_guard + @autocast_custom_fwd + def forward(ctx, x: torch.Tensor, scale: float = 1.0) -> torch.Tensor: + y = fused_beta_sigmoid_fwd(x, scale) + ctx.save_for_backward(x) + ctx.scale = scale + return y + + @staticmethod + @input_guard + @autocast_custom_bwd + def backward(ctx, dy: torch.Tensor): + (x,) = ctx.saved_tensors + dx = fused_beta_sigmoid_bwd(x, dy, ctx.scale) + return dx.type_as(x), None + + +def fused_beta_sigmoid(x: torch.Tensor, scale: float = 1.0) -> torch.Tensor: + return BetaSigmoidFunction.apply(x, scale) diff --git a/kda/_fla/ops/cp/__init__.py b/kda/_fla/ops/cp/__init__.py new file mode 100644 index 0000000..0febcba --- /dev/null +++ b/kda/_fla/ops/cp/__init__.py @@ -0,0 +1,13 @@ +"""Context-parallel stubs. Pass ``cp_context=None`` (the default).""" + + +class FLACPContext: + cu_seqlens = None + cu_seqlens_cpu = None + + +def build_cp_context(*args, **kwargs): + raise RuntimeError("Context parallel is not included in the vendored KDA kernels") + + +__all__ = ["FLACPContext", "build_cp_context"] diff --git a/kda/_fla/ops/cp/chunk_delta_h.py b/kda/_fla/ops/cp/chunk_delta_h.py new file mode 100644 index 0000000..ff9fc9e --- /dev/null +++ b/kda/_fla/ops/cp/chunk_delta_h.py @@ -0,0 +1,11 @@ +"""Context-parallel hooks referenced by KDA fwd/bwd. Not implemented here.""" + + +def _cp_unsupported(*args, **kwargs): + raise RuntimeError("Context parallel is not included in the vendored KDA kernels") + + +chunk_gated_delta_rule_fwd_h_pre_process = _cp_unsupported +compress_h0 = _cp_unsupported +chunk_gated_delta_rule_bwd_dhu_pre_process = _cp_unsupported +expand_h0 = _cp_unsupported diff --git a/kda/_fla/ops/gla/__init__.py b/kda/_fla/ops/gla/__init__.py new file mode 100644 index 0000000..735708b --- /dev/null +++ b/kda/_fla/ops/gla/__init__.py @@ -0,0 +1 @@ +# Vendored GLA chunk output kernel used by KDA. diff --git a/kda/_fla/ops/gla/chunk.py b/kda/_fla/ops/gla/chunk.py new file mode 100644 index 0000000..82b1d87 --- /dev/null +++ b/kda/_fla/ops/gla/chunk.py @@ -0,0 +1,1530 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.common.chunk_h import chunk_bwd_dh, chunk_fwd_h +from kda._fla.ops.utils import prepare_chunk_indices +from kda._fla.ops.utils.cache import fla_cache_autotune +from kda._fla.ops.utils.constant import RCP_LN2 +from kda._fla.ops.utils.cumsum import chunk_local_cumsum +from kda._fla.ops.utils.op import exp2 +from kda._fla.utils import autotune_cache_kwargs, check_shared_mem, input_guard + +BK_LIST = [32, 64] if check_shared_mem() else [16, 32] +BV_LIST = [64, 128] if check_shared_mem('ampere') else [16, 32] + + +def _prune_gla_bwd_configs(configs, nargs, **kwargs): + # Keep a tile only if it leaves headroom below its dim, or is the smallest + # option (so small dims still autotune); this avoids a software-pipelined + # block load prefetching past the tensor. K/V arrive as launch kwargs. + args = {**(nargs or {}), **kwargs} + K, V = args['K'], args['V'] + min_bk = min(c.kwargs['BK'] for c in configs) + min_bv = min(c.kwargs['BV'] for c in configs) + return [ + c for c in configs + if (c.kwargs['BK'] < K or c.kwargs['BK'] == min_bk) + and (c.kwargs['BV'] < V or c.kwargs['BV'] == min_bv) + ] + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BK': BK}, num_warps=num_warps, num_stages=num_stages) + for BK in [32, 64] + for num_warps in [1, 2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['BC'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gla_fwd_A_kernel_intra_sub_inter( + q, + k, + g, + A, + cu_seqlens, + chunk_indices, + scale, + T, + H: tl.constexpr, + K: tl.constexpr, + BT: tl.constexpr, + BC: tl.constexpr, + BK: tl.constexpr, + NC: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_t, i_c, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1), tl.program_id(2).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + i_i, i_j = i_c // NC, i_c % NC + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + if i_t * BT + i_i * BC >= T: + return + if i_i <= i_j: + return + + b_A = tl.zeros([BC, BC], dtype=tl.float32) + o_i = i_t * BT + i_i * BC + tl.arange(0, BC) + o_j = i_t * BT + i_j * BC + tl.arange(0, BC) + m_i = o_i < T + m_j = o_j < T + for i_k in range(tl.cdiv(K, BK)): + o_k = i_k * BK + tl.arange(0, BK) + m_k = o_k < K + m_qk = m_i[:, None] & m_k[None, :] + m_kj = m_k[:, None] & m_j[None, :] + + p_q = q + (bos*H+i_h)*K + o_i[:, None] * (H*K) + o_k[None, :] + p_g = g + (bos*H+i_h)*K + o_i[:, None] * (H*K) + o_k[None, :] + p_k = k + (bos*H+i_h)*K + o_k[:, None] + o_j[None, :] * (H*K) + p_gk = g + (bos*H+i_h)*K + o_k[:, None] + o_j[None, :] * (H*K) + p_gn = g + (bos + i_t * BT + i_i * BC) * H*K + i_h * K + o_k + + # [BK,] + b_gn = tl.load(p_gn, mask=m_k, other=0) + # [BC, BK] + b_q = tl.load(p_q, mask=m_qk, other=0.0) + b_g = tl.load(p_g, mask=m_qk, other=0.0) + b_qg = b_q * exp2(b_g - b_gn[None, :]) * scale + # [BK, BC] + b_k = tl.load(p_k, mask=m_kj, other=0.0) + b_gk = tl.load(p_gk, mask=m_kj, other=0.0) + b_kg = b_k * exp2(b_gn[:, None] - b_gk) + # [BC, BC] using tf32 to improve precision here. + b_A += tl.dot(b_qg, b_kg) + + o_jA = i_j * BC + tl.arange(0, BC) + m_A = m_i[:, None] & (o_jA[None, :] < BT) + p_A = A + (bos*H + i_h)*BT + o_i[:, None] * (H*BT) + o_jA[None, :] + tl.store(p_A, b_A.to(A.dtype.element_ty), mask=m_A) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps, num_stages=num_stages) + for num_warps in [1, 2, 4, 8] + for num_stages in [2, 3] + ], + key=['BK', 'BT'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gla_fwd_A_kernel_intra_sub_intra( + q, + k, + g, + A, + cu_seqlens, + chunk_indices, + scale, + T, + H: tl.constexpr, + K: tl.constexpr, + BT: tl.constexpr, + BC: tl.constexpr, + BK: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_t, i_i, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1), tl.program_id(2).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + i_j = i_i + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + if i_t * BT + i_i * BC >= T: + return + + o_i = tl.arange(0, BC) + o_k = tl.arange(0, BK) + o_A = (i_t * BT + i_i * BC + tl.arange(0, BC)) * H*BT + i_j * BC + m_k = o_k < K + m_A = (i_t * BT + i_i * BC + tl.arange(0, BC)) < T + + q += (bos * H + i_h) * K + k += (bos * H + i_h) * K + g += (bos * H + i_h) * K + A += (bos * H + i_h) * BT + + o_c = i_t * BT + i_i * BC + tl.arange(0, BC) + m_qk = m_A[:, None] & m_k[None, :] + p_q = q + o_c[:, None] * (H*K) + o_k[None, :] + p_g = g + o_c[:, None] * (H*K) + o_k[None, :] + b_q = tl.load(p_q, mask=m_qk, other=0.0) + b_g = tl.load(p_g, mask=m_qk, other=0.0) + + p_k = k + (i_t * BT + i_j * BC) * H*K + o_k + p_gk = g + (i_t * BT + i_j * BC) * H*K + o_k + + for j in range(0, min(BC, T - i_t * BT - i_i * BC)): + b_k = tl.load(p_k, mask=m_k, other=0).to(tl.float32) + b_gk = tl.load(p_gk, mask=m_k, other=0).to(tl.float32) + b_A = tl.sum(b_q * b_k[None, :] * exp2(b_g - b_gk[None, :]), 1) * scale + tl.store(A + o_A + j, b_A, mask=m_A) + p_k += H*K + p_gk += H*K + + tl.debug_barrier() + b_A = tl.zeros([BC, BC], dtype=tl.float32) + tl.store(A + o_A[:, None] + o_i, b_A, mask=m_A[:, None] & (o_i[:, None] < o_i)) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps) + for num_warps in [1, 2, 4, 8] + ], + key=['BC', 'BK'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gla_fwd_A_kernel_intra_sub_intra_split( + q, + k, + g, + A, + cu_seqlens, + chunk_indices, + scale, + T, + B: tl.constexpr, + H: tl.constexpr, + K: tl.constexpr, + BT: tl.constexpr, + BC: tl.constexpr, + BK: tl.constexpr, + NC: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_k, i_tc, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + i_t, i_i = (i_tc // NC).to(tl.int64), i_tc % NC + i_j = i_i + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + all = T + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + all = B * T + + if i_t * BT + i_i * BC >= T: + return + + o_i = tl.arange(0, BC) + o_k = i_k * BK + tl.arange(0, BK) + o_A = (i_t * BT + i_i * BC + tl.arange(0, BC)) * H*BC + m_k = o_k < K + m_A = (i_t * BT + i_i * BC + tl.arange(0, BC)) < T + + q += (bos * H + i_h) * K + k += (bos * H + i_h) * K + g += (bos * H + i_h) * K + A += ((i_k * all + bos) * H + i_h) * BC + + o_c = i_t * BT + i_i * BC + tl.arange(0, BC) + m_qk = m_A[:, None] & m_k[None, :] + p_q = q + o_c[:, None] * (H*K) + o_k[None, :] + p_g = g + o_c[:, None] * (H*K) + o_k[None, :] + b_q = tl.load(p_q, mask=m_qk, other=0.0) + b_g = tl.load(p_g, mask=m_qk, other=0.0) + + p_k = k + (i_t * BT + i_j * BC) * H*K + o_k + p_gk = g + (i_t * BT + i_j * BC) * H*K + o_k + for j in range(0, min(BC, T - i_t * BT - i_i * BC)): + b_k = tl.load(p_k, mask=m_k, other=0).to(tl.float32) + b_gk = tl.load(p_gk, mask=m_k, other=0).to(tl.float32) + b_A = tl.sum(b_q * b_k[None, :] * exp2(b_g - b_gk[None, :]), 1) * scale + tl.store(A + o_A + j, b_A, mask=m_A) + p_k += H*K + p_gk += H*K + + tl.debug_barrier() + b_A = tl.zeros([BC, BC], dtype=tl.float32) + tl.store(A + o_A[:, None] + o_i, b_A, mask=m_A[:, None] & (o_i[:, None] < o_i)) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=1), + triton.Config({}, num_warps=2), + triton.Config({}, num_warps=4), + triton.Config({}, num_warps=8), + ], + key=['BC'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gla_fwd_A_kernel_intra_sub_intra_merge( + A, + A2, + cu_seqlens, + chunk_indices, + T, + B: tl.constexpr, + H: tl.constexpr, + BT: tl.constexpr, + BC: tl.constexpr, + NK: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_t, i_c, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1), tl.program_id(2).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + all = T + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + all = B * T + + if i_t * BT + i_c * BC >= T: + return + + b_A = tl.zeros([BC, BC], dtype=tl.float32) + o_c = i_t * BT + i_c * BC + tl.arange(0, BC) + o_i = tl.arange(0, BC) + m_c = o_c < T + m_A = m_c[:, None] & (o_i[None, :] < BC) + m_A2 = m_c[:, None] & ((i_c * BC + o_i)[None, :] < BT) + for i_k in range(0, NK): + p_A = A + (i_k*all+bos)*H*BC+i_h*BC + o_c[:, None] * (H*BC) + o_i[None, :] + b_A += tl.load(p_A, mask=m_A, other=0.0) + p_A2 = A2 + (bos*H+i_h)*BT + o_c[:, None] * (H*BT) + (i_c * BC + o_i)[None, :] + tl.store(p_A2, b_A.to(A2.dtype.element_ty), mask=m_A2) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BK': BK, 'BV': BV}, num_warps=num_warps, num_stages=num_stages) + for BK in [32, 64] + for BV in [64, 128] + for num_warps in [2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['BT', 'HV', 'STATE_V_FIRST'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gla_fwd_kernel_o( + q, + v, + g, + h, + o, + A, + cu_seqlens, + chunk_indices, + scale, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + STATE_V_FIRST: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_v, i_t, i_bh = tl.program_id(0), tl.program_id(1).to(tl.int64), tl.program_id(2) + i_b, i_hv = i_bh // HV, i_bh % HV + i_h = i_hv // (HV // H) + if IS_VARLEN: + i_tg = i_t.to(tl.int64) + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + NT = tl.cdiv(T, BT) + else: + NT = tl.cdiv(T, BT) + i_tg = (i_b * NT + i_t).to(tl.int64) + bos, eos = (i_b * T).to(tl.int64), (i_b * T + T).to(tl.int64) + + m_s = tl.arange(0, BT)[:, None] >= tl.arange(0, BT)[None, :] + + q += (bos * H + i_h) * K + g += (bos * HV + i_hv) * K + v += (bos * HV + i_hv) * V + o += (bos * HV + i_hv) * V + h += (i_tg * HV + i_hv).to(tl.int64) * K * V + A += (bos * HV + i_hv) * BT + + b_o = tl.zeros([BT, BV], dtype=tl.float32) + o_t = i_t * BT + tl.arange(0, BT) + o_v = i_v * BV + tl.arange(0, BV) + o_i = tl.arange(0, BT) + m_t = o_t < T + m_v = o_v < V + m_tv = m_t[:, None] & m_v[None, :] + m_A = m_t[:, None] & (o_i[None, :] < BT) + for i_k in range(tl.cdiv(K, BK)): + o_k = i_k * BK + tl.arange(0, BK) + m_k = o_k < K + m_qk = m_t[:, None] & m_k[None, :] + p_q = q + o_t[:, None] * (H*K) + o_k[None, :] + p_g = g + o_t[:, None] * (HV*K) + o_k[None, :] + if STATE_V_FIRST: + p_h = h + o_v[:, None] * K + o_k[None, :] + m_h = m_v[:, None] & m_k[None, :] + else: + p_h = h + o_k[:, None] * V + o_v[None, :] + m_h = m_k[:, None] & m_v[None, :] + + # [BT, BK] + b_q = tl.load(p_q, mask=m_qk, other=0.0) + # [BT, BK] + b_g = tl.load(p_g, mask=m_qk, other=0.0).to(tl.float32) + # [BT, BK] + b_qg = (b_q * exp2(b_g)).to(b_q.dtype) + b_h = tl.load(p_h, mask=m_h, other=0.0) + if i_k >= 0: + if STATE_V_FIRST: + b_o += tl.dot(b_qg, tl.trans(b_h).to(b_qg.dtype)) + else: + b_o += tl.dot(b_qg, b_h.to(b_qg.dtype)) + b_o *= scale + p_v = v + o_t[:, None] * (HV*V) + o_v[None, :] + p_o = o + o_t[:, None] * (HV*V) + o_v[None, :] + p_A = A + o_t[:, None] * (HV*BT) + o_i[None, :] + # [BT, BV] + b_v = tl.load(p_v, mask=m_tv, other=0.0) + # [BT, BT] + b_A = tl.load(p_A, mask=m_A, other=0.0) + b_A = tl.where(m_s, b_A, 0.).to(b_v.dtype) + b_o += tl.dot(b_A, b_v) + tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_tv) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps, num_stages=num_stages) + for num_warps in [1, 2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['BK', 'NC', 'BT'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gla_bwd_kernel_intra( + q, + k, + g, + dA, + dq, + dk, + cu_seqlens, + chunk_indices, + T, + H: tl.constexpr, + K: tl.constexpr, + BT: tl.constexpr, + BC: tl.constexpr, + BK: tl.constexpr, + NC: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_kc, i_t, i_bh = tl.program_id(0), tl.program_id(1).to(tl.int64), tl.program_id(2).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + i_k, i_i = i_kc // NC, i_kc % NC + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + else: + bos, eos = i_b * T, i_b * T + T + T = eos - bos + if i_t * BT + i_i * BC >= T: + return + + o_k = i_k * BK + tl.arange(0, BK) + m_k = o_k < K + + o_c = i_t * BT + i_i * BC + tl.arange(0, BC) + m_c = o_c < T + m_ck = m_c[:, None] & m_k[None, :] + p_g = g + (bos*H + i_h) * K + o_c[:, None] * (H*K) + o_k[None, :] + # [BC, BK] + b_g = tl.load(p_g, mask=m_ck, other=0.0) + + b_dq = tl.zeros([BC, BK], dtype=tl.float32) + if i_i > 0: + p_gn = g + (bos + i_t * BT + i_i * BC) * H*K + i_h*K + o_k + # [BK,] + b_gn = tl.load(p_gn, mask=m_k, other=0) + for i_j in range(0, i_i): + o_j = i_t * BT + i_j * BC + tl.arange(0, BC) + o_jA = i_j * BC + tl.arange(0, BC) + m_jk = (o_j[:, None] < T) & m_k[None, :] + m_da = m_c[:, None] & (o_jA[None, :] < BT) + p_k = k+(bos*H+i_h)*K + o_j[:, None] * (H*K) + o_k[None, :] + p_gk = g+(bos*H+i_h)*K + o_j[:, None] * (H*K) + o_k[None, :] + p_dA = dA+(bos*H+i_h)*BT + o_c[:, None] * (H*BT) + o_jA[None, :] + # [BC, BK] + b_k = tl.load(p_k, mask=m_jk, other=0.0) + b_gk = tl.load(p_gk, mask=m_jk, other=0.0) + b_kg = b_k * exp2(b_gn[None, :] - b_gk) + # [BC, BC] + b_dA = tl.load(p_dA, mask=m_da, other=0.0) + + b_dq += tl.dot(b_dA, b_kg) + b_dq *= exp2(b_g - b_gn[None, :]) + o_i = tl.arange(0, BC) + m_dA = (i_t * BT + i_i * BC + tl.arange(0, BC)) < T + o_dA = bos*H*BT + (i_t * BT + i_i * BC + tl.arange(0, BC)) * H*BT + i_h * BT + i_i * BC + p_kj = k + (bos + i_t * BT + i_i * BC) * H*K + i_h * K + o_k + p_gkj = g + (bos + i_t * BT + i_i * BC) * H*K + i_h * K + o_k + p_dq = dq + (bos*H + i_h) * K + o_c[:, None] * (H*K) + o_k[None, :] + + for j in range(0, min(BC, T - i_t * BT - i_i * BC)): + # [BC,] + b_dA = tl.load(dA + o_dA + j, mask=m_dA, other=0) + # [BK,] + b_kj = tl.load(p_kj, mask=m_k, other=0).to(tl.float32) + b_gkj = tl.load(p_gkj, mask=m_k, other=0).to(tl.float32) + # [BC, BK] + m_i = o_i[:, None] >= j + # [BC, BK] + # (SY 09/17) important to not use bf16 here to have a good precision. + b_dq += tl.where(m_i, b_dA[:, None] * b_kj[None, :] * exp2(b_g - b_gkj[None, :]), 0.) + p_kj += H*K + p_gkj += H*K + tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), mask=m_ck) + + tl.debug_barrier() + # [BC, BK] + b_dk = tl.zeros([BC, BK], dtype=tl.float32) + + NC = min(NC, tl.cdiv(T - i_t * BT, BC)) + if i_i < NC - 1: + p_gn = g + (bos + min(i_t * BT + i_i * BC + BC, T) - 1) * H*K + i_h * K + o_k + + # [BK,] + b_gn = tl.load(p_gn, mask=m_k, other=0) + for i_j in range(i_i + 1, NC): + o_j = i_t * BT + i_j * BC + o_i + o_iA = i_i * BC + tl.arange(0, BC) + m_j = o_j < T + m_jk = m_j[:, None] & m_k[None, :] + m_da = (o_iA[:, None] < BT) & m_j[None, :] + p_q = q + (bos*H+i_h)*K + o_j[:, None] * (H*K) + o_k[None, :] + p_gq = g + (bos*H+i_h)*K + o_j[:, None] * (H*K) + o_k[None, :] + p_dA = dA + (bos*H+i_h)*BT + o_iA[:, None] + o_j[None, :] * (H*BT) + # [BC, BK] + b_q = tl.load(p_q, mask=m_jk, other=0.0) + b_gq = tl.load(p_gq, mask=m_jk, other=0.0) + b_qg = b_q * tl.where(m_j[:, None], exp2(b_gq - b_gn[None, :]), 0) + # [BC, BC] + b_dA = tl.load(p_dA, mask=m_da, other=0.0) + # [BC, BK] + # (SY 09/17) important to not use bf16 here to have a good precision. + b_dk += tl.dot(b_dA, b_qg) + b_dk *= exp2(b_gn[None, :] - b_g) + o_dA = bos*H*BT + (i_t * BT + i_i * BC) * H*BT + i_h * BT + i_i * BC + tl.arange(0, BC) + p_qj = q + (bos + i_t * BT + i_i * BC) * H*K + i_h * K + o_k + p_gqj = g + (bos + i_t * BT + i_i * BC) * H*K + i_h * K + o_k + p_dk = dk + (bos*H+i_h)*K + o_c[:, None] * (H*K) + o_k[None, :] + for j in range(0, min(BC, T - i_t * BT - i_i * BC)): + # [BC,] + b_dA = tl.load(dA + o_dA + j * H*BT) + # [BK,] + b_qj = tl.load(p_qj, mask=m_k, other=0).to(tl.float32) + b_gqj = tl.load(p_gqj, mask=m_k, other=0).to(tl.float32) + # [BC, BK] + m_i = o_i[:, None] <= j + b_dk += tl.where(m_i, b_dA[:, None] * b_qj[None, :] * exp2(b_gqj[None, :] - b_g), 0.) + p_qj += H*K + p_gqj += H*K + tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), mask=m_ck) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps, num_stages=num_stages) + for num_warps in [1, 2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['BV', 'BT'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gla_bwd_kernel_dA( + v, + do, + dA, + cu_seqlens, + chunk_indices, + scale, + T, + H: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BV: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + else: + bos, eos = i_b * T, i_b * T + T + T = eos - bos + + b_dA = tl.zeros([BT, BT], dtype=tl.float32) + o_t = i_t * BT + tl.arange(0, BT) + o_i = tl.arange(0, BT) + m_t = o_t < T + m_A = m_t[:, None] & (o_i[None, :] < BT) + for i_v in range(tl.cdiv(V, BV)): + o_v = i_v * BV + tl.arange(0, BV) + m_v = o_v < V + m_tv = m_t[:, None] & m_v[None, :] + m_vt = m_v[:, None] & m_t[None, :] + p_do = do + (bos*H + i_h) * V + o_t[:, None] * (H*V) + o_v[None, :] + p_v = v + (bos*H + i_h) * V + o_v[:, None] + o_t[None, :] * (H*V) + b_v = tl.load(p_v, mask=m_vt, other=0.0) + b_do = tl.load(p_do, mask=m_tv, other=0.0) + + b_dA += tl.dot(b_do, b_v) + + p_dA = dA + (bos * H + i_h) * BT + o_t[:, None] * (H*BT) + o_i[None, :] + m_s = tl.arange(0, BT)[:, None] >= tl.arange(0, BT)[None, :] + b_dA = tl.where(m_s, b_dA * scale, 0.) + tl.store(p_dA, b_dA.to(p_dA.dtype.element_ty), mask=m_A) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BK': BK, 'BV': BV}, num_warps=num_warps, num_stages=num_stages) + for BK in BK_LIST + for BV in BV_LIST + for num_warps in [2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['BT', 'STATE_V_FIRST', 'K', 'V'], + prune_configs_by={'early_config_prune': _prune_gla_bwd_configs}, + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gla_bwd_kernel_dv( + k, + g, + A, + do, + dh, + dv, + cu_seqlens, + chunk_indices, + T, + H: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + IS_VARLEN: tl.constexpr, + STATE_V_FIRST: tl.constexpr, +): + i_v, i_t, i_bh = tl.program_id(0), tl.program_id(1).to(tl.int64), tl.program_id(2).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + if IS_VARLEN: + i_tg = i_t + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + NT = tl.cdiv(T, BT) + else: + NT = tl.cdiv(T, BT) + i_tg = i_b * NT + i_t + bos, eos = i_b * T, i_b * T + T + + o_t = i_t * BT + tl.arange(0, BT) + o_v = i_v * BV + tl.arange(0, BV) + o_i = tl.arange(0, BT) + m_t = o_t < T + m_v = o_v < V + m_A = (o_i[:, None] < BT) & m_t[None, :] + m_tv = m_t[:, None] & m_v[None, :] + p_A = A + (bos * H + i_h) * BT + o_i[:, None] + o_t[None, :] * (H*BT) + p_do = do + (bos * H + i_h) * V + o_t[:, None] * (H*V) + o_v[None, :] + p_dv = dv + (bos * H + i_h) * V + o_t[:, None] * (H*V) + o_v[None, :] + b_A = tl.load(p_A, mask=m_A, other=0.0) + b_do = tl.load(p_do, mask=m_tv, other=0.0) + + b_A = tl.where(tl.arange(0, BT)[:, None] <= tl.arange(0, BT)[None, :], b_A, 0.) + # (SY 09/17) important to disallow tf32 here to maintain a good precision. + b_dv = tl.dot(b_A, b_do.to(b_A.dtype), allow_tf32=False) + + for i_k in range(tl.cdiv(K, BK)): + o_k = i_k * BK + tl.arange(0, BK) + m_k = o_k < K + m_tk = m_t[:, None] & m_k[None, :] + m_kvd = m_k[:, None] & m_v[None, :] + + p_k = k + (bos * H + i_h) * K + o_t[:, None] * (H*K) + o_k[None, :] + p_gk = g + (bos * H + i_h) * K + o_t[:, None] * (H*K) + o_k[None, :] + p_gn = g + (bos + min(i_t * BT + BT, T) - 1)*H*K + i_h * K + o_k + if STATE_V_FIRST: + # dh stored as [V, K]; read a logical [BK, BV] tile via on-the-fly transpose + p_dh = dh + (i_tg * H + i_h) * K*V + o_k[:, None] + o_v[None, :] * K + else: + p_dh = dh + (i_tg * H + i_h) * K*V + o_k[:, None] * V + o_v[None, :] + + b_k = tl.load(p_k, mask=m_tk, other=0.0) + b_gk = tl.load(p_gk, mask=m_tk, other=0.0) + b_dh = tl.load(p_dh, mask=m_kvd, other=0.0) + + b_gn = exp2(tl.load(p_gn, mask=m_k, other=0)[None, :] - b_gk) + b_k = (b_k * b_gn).to(b_k.dtype) + # [BT, BV] + # (SY 09/17) it is ok to have bf16 interchunk gradient contribution here + b_dv += tl.dot(b_k, b_dh.to(b_k.dtype)) + + tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), mask=m_tv) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BK': BK, 'BV': BV}, num_warps=num_warps, num_stages=num_stages) + for BK in BK_LIST + for BV in BV_LIST + for num_warps in [2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['BT', 'STATE_V_FIRST', 'K', 'V'], + prune_configs_by={'early_config_prune': _prune_gla_bwd_configs}, + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_gla_bwd_kernel_inter( + q, + k, + v, + g, + h, + do, + dh, + dq, + dk, + dq2, + dk2, + dg, + cu_seqlens, + chunk_indices, + scale, + T, + H: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + IS_VARLEN: tl.constexpr, + STATE_V_FIRST: tl.constexpr, +): + i_k, i_t, i_bh = tl.program_id(0), tl.program_id(1).to(tl.int64), tl.program_id(2).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + if IS_VARLEN: + i_tg = i_t + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + NT = tl.cdiv(T, BT) + else: + NT = tl.cdiv(T, BT) + i_tg = i_b * NT + i_t + bos, eos = i_b * T, i_b * T + T + o_k = i_k * BK + tl.arange(0, BK) + m_k = o_k < K + + q += (bos * H + i_h) * K + k += (bos * H + i_h) * K + v += (bos * H + i_h) * V + g += (bos * H + i_h) * K + h += (i_tg * H + i_h) * K*V + do += (bos * H + i_h) * V + dh += (i_tg * H + i_h) * K*V + dq += (bos * H + i_h) * K + dk += (bos * H + i_h) * K + dq2 += (bos * H + i_h) * K + dk2 += (bos * H + i_h) * K + dg += (bos * H + i_h) * K + + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + m_tk = m_t[:, None] & m_k[None, :] + p_gk = g + o_t[:, None] * (H*K) + o_k[None, :] + b_gk = tl.load(p_gk, mask=m_tk, other=0.0) + p_gn = g + (min(T, i_t * BT + BT) - 1) * H*K + o_k + b_gn = tl.load(p_gn, mask=m_k, other=0) + b_dq = tl.zeros([BT, BK], dtype=tl.float32) + b_dk = tl.zeros([BT, BK], dtype=tl.float32) + b_dgk = tl.zeros([BK], dtype=tl.float32) + + for i_v in range(tl.cdiv(V, BV)): + o_v = i_v * BV + tl.arange(0, BV) + m_v = o_v < V + m_tv = m_t[:, None] & m_v[None, :] + m_vk = m_v[:, None] & m_k[None, :] + p_v = v + o_t[:, None] * (H*V) + o_v[None, :] + p_do = do + o_t[:, None] * (H*V) + o_v[None, :] + if STATE_V_FIRST: + # h / dh stored as [V, K] -- the [BV, BK] tile is now a contiguous read + p_h = h + o_v[:, None] * K + o_k[None, :] + p_dh = dh + o_v[:, None] * K + o_k[None, :] + else: + p_h = h + o_v[:, None] + o_k[None, :] * V + p_dh = dh + o_v[:, None] + o_k[None, :] * V + # [BT, BV] + b_v = tl.load(p_v, mask=m_tv, other=0.0) + b_do = tl.load(p_do, mask=m_tv, other=0.0) + # [BV, BK] + b_h = tl.load(p_h, mask=m_vk, other=0.0) + b_dh = tl.load(p_dh, mask=m_vk, other=0.0) + + # [BK] + b_dgk += tl.sum(b_h * b_dh, axis=0) + # [BT, BK] + b_dq += tl.dot(b_do, b_h.to(b_do.dtype)) + b_dk += tl.dot(b_v, b_dh.to(b_v.dtype)) + + b_dgk *= exp2(b_gn) + b_dq *= scale + b_dq = b_dq * exp2(b_gk) + b_dk = b_dk * exp2(b_gn[None, :] - b_gk) + p_q = q + o_t[:, None] * (H*K) + o_k[None, :] + p_k = k + o_t[:, None] * (H*K) + o_k[None, :] + p_dq = dq + o_t[:, None] * (H*K) + o_k[None, :] + p_dk = dk + o_t[:, None] * (H*K) + o_k[None, :] + b_q = tl.load(p_q, mask=m_tk, other=0.0) + b_k = tl.load(p_k, mask=m_tk, other=0.0) + b_dgk += tl.sum(b_dk * b_k, axis=0) + b_dq += tl.load(p_dq, mask=m_tk, other=0.0) + b_dk += tl.load(p_dk, mask=m_tk, other=0.0) + b_dg = b_q * b_dq - b_k * b_dk + # tl.debug_barrier() + b_dg = b_dg - tl.cumsum(b_dg, axis=0) + tl.sum(b_dg, axis=0)[None, :] + b_dgk[None, :] + # Buggy due to strange triton compiler issue. + # m_s = tl.where(tl.arange(0, BT)[:, None] <= tl.arange(0, BT)[None, :], 1., 0.) + # b_dg = tl.dot(m_s, b_dg, allow_tf32=False) + b_dgk[None, :] + p_dq = dq2 + o_t[:, None] * (H*K) + o_k[None, :] + p_dk = dk2 + o_t[:, None] * (H*K) + o_k[None, :] + p_dg = dg + o_t[:, None] * (H*K) + o_k[None, :] + tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), mask=m_tk) + tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), mask=m_tk) + tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), mask=m_tk) + + +@dispatch('gla') +def chunk_gla_fwd_intra_gk( + q: torch.Tensor, + k: torch.Tensor, + g: torch.Tensor, + scale: float, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +): + B, T, H, K = k.shape + BT = chunk_size + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + BC = min(16, BT) + NC = triton.cdiv(BT, BC) + + A = q.new_empty(B, T, H, BT, dtype=torch.float) + grid = (NT, NC * NC, B * H) + chunk_gla_fwd_A_kernel_intra_sub_inter[grid]( + q=q, + k=k, + g=g, + A=A, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + scale=scale, + T=T, + H=H, + K=K, + BT=BT, + BC=BC, + NC=NC, + ) + + grid = (NT, NC, B * H) + # load the entire [BC, K] blocks into SRAM at once + if K <= 256: + BK = max(triton.next_power_of_2(K), 16) + chunk_gla_fwd_A_kernel_intra_sub_intra[grid]( + q=q, + k=k, + g=g, + A=A, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + scale=scale, + T=T, + H=H, + K=K, + BT=BT, + BC=BC, + BK=BK, + ) + # split then merge + else: + BK = min(128, triton.next_power_of_2(K)) + NK = triton.cdiv(K, BK) + A_intra = q.new_empty(NK, B, T, H, BC, dtype=torch.float) + + grid = (NK, NT * NC, B * H) + chunk_gla_fwd_A_kernel_intra_sub_intra_split[grid]( + q=q, + k=k, + g=g, + A=A_intra, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + scale=scale, + T=T, + B=B, + H=H, + K=K, + BT=BT, + BC=BC, + BK=BK, + NC=NC, + ) + + grid = (NT, NC, B * H) + chunk_gla_fwd_A_kernel_intra_sub_intra_merge[grid]( + A=A_intra, + A2=A, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + T=T, + B=B, + H=H, + BT=BT, + BC=BC, + NK=NK, + ) + return A + + +@dispatch('gla') +def chunk_gla_fwd_o_gk( + q: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + A: torch.Tensor, + h: torch.Tensor, + scale: float, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +): + B, T, H, K, HV, V = *q.shape, v.shape[2], v.shape[-1] + BT = chunk_size + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + + # Please ensure zeros, since vllm will use padding v + o = torch.zeros_like(v) + def grid(meta): return (triton.cdiv(V, meta['BV']), NT, B * HV) + chunk_gla_fwd_kernel_o[grid]( + q=q, + v=v, + g=g, + h=h, + o=o, + A=A, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + scale=scale, + T=T, + H=H, + HV=HV, + K=K, + V=V, + BT=BT, + STATE_V_FIRST=state_v_first, + ) + return o + + +@dispatch('gla') +def chunk_gla_bwd_dA( + v: torch.Tensor, + do: torch.Tensor, + scale: float, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +): + B, T, H, V = v.shape + BT = chunk_size + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + BV = min(64, triton.next_power_of_2(V)) + + dA = v.new_empty(B, T, H, BT, dtype=torch.float) + grid = (NT, B * H) + chunk_gla_bwd_kernel_dA[grid]( + v=v, + do=do, + dA=dA, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + scale=scale, + T=T, + H=H, + V=V, + BT=BT, + BV=BV, + ) + return dA + + +@dispatch('gla') +def chunk_gla_bwd_dv( + k: torch.Tensor, + g: torch.Tensor, + A: torch.Tensor, + do: torch.Tensor, + dh: torch.Tensor, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +): + B, T, H, K, V = *k.shape, do.shape[-1] + BT = chunk_size + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + + dv = torch.empty_like(do) + def grid(meta): return (triton.cdiv(V, meta['BV']), NT, B * H) + chunk_gla_bwd_kernel_dv[grid]( + k=k, + g=g, + A=A, + do=do, + dh=dh, + dv=dv, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + T=T, + H=H, + K=K, + V=V, + BT=BT, + STATE_V_FIRST=state_v_first, + ) + return dv + + +@dispatch('gla') +def chunk_gla_bwd_dqk_intra( + q: torch.Tensor, + k: torch.Tensor, + g: torch.Tensor, + dA: torch.Tensor, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +): + B, T, H, K = q.shape + BT = chunk_size + BC = min(16, BT) + BK = min(64, triton.next_power_of_2(K)) + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + NC = triton.cdiv(BT, BC) + NK = triton.cdiv(K, BK) + + dq = torch.empty_like(q, dtype=torch.float) + dk = torch.empty_like(k, dtype=torch.float) + grid = (NK * NC, NT, B * H) + chunk_gla_bwd_kernel_intra[grid]( + q=q, + k=k, + g=g, + dA=dA, + dq=dq, + dk=dk, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + T=T, + H=H, + K=K, + BT=BT, + BC=BC, + BK=BK, + NC=NC, + ) + return dq, dk + + +@dispatch('gla') +def chunk_gla_bwd_dqkg( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + h: torch.Tensor, + g: torch.Tensor, + do: torch.Tensor, + dh: torch.Tensor, + dq: torch.Tensor, + dk: torch.Tensor, + scale: float | None = None, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +): + B, T, H, K, V = *k.shape, v.shape[-1] + BT = chunk_size + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, chunk_size) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + + dg = torch.empty_like(g) + dq2 = torch.empty_like(dq) + dk2 = torch.empty_like(dk) + def grid(meta): return (triton.cdiv(K, meta['BK']), NT, B * H) + chunk_gla_bwd_kernel_inter[grid]( + q=q, + k=k, + v=v, + g=g, + h=h, + do=do, + dh=dh, + dq=dq, + dk=dk, + dq2=dq2, + dk2=dk2, + dg=dg, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + scale=scale, + T=T, + H=H, + K=K, + V=V, + BT=BT, + STATE_V_FIRST=state_v_first, + ) + return dq2, dk2, dg + + +def chunk_gla_fwd( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + g_cumsum: torch.Tensor | None, + scale: float, + initial_state: torch.Tensor, + output_final_state: bool, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + if g_cumsum is None: + g_cumsum = chunk_local_cumsum( + g, + chunk_size, + scale=RCP_LN2, + cu_seqlens=cu_seqlens, + ) + + h, ht = chunk_fwd_h( + k=k, + v=v, + g=None, + gk=g_cumsum, + gv=None, + h0=initial_state, + output_final_state=output_final_state, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + states_in_fp32=False, + state_v_first=state_v_first, + ) + + # the intra A is kept in fp32 + # the computation has very marginal effect on the entire throughput + A = chunk_gla_fwd_intra_gk( + q=q, + k=k, + g=g_cumsum, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + ) + o = chunk_gla_fwd_o_gk( + q=q, + v=v, + g=g_cumsum, + A=A, + h=h, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + state_v_first=state_v_first, + ) + return g_cumsum, A, h, ht, o + + +def chunk_gla_bwd( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + g_cumsum: torch.Tensor | None, + scale: float, + initial_state: torch.Tensor, + h: torch.Tensor, + A: torch.Tensor, + do: torch.Tensor, + dht: torch.Tensor, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +): + if g_cumsum is None: + g_cumsum = chunk_local_cumsum( + g, + chunk_size, + scale=RCP_LN2, + cu_seqlens=cu_seqlens, + ) + + if h is None: + h, _ = chunk_fwd_h( + k=k, + v=v, + g=None, + gk=g_cumsum, + gv=None, + h0=initial_state, + output_final_state=False, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + states_in_fp32=True, + state_v_first=state_v_first, + ) + dh, dh0 = chunk_bwd_dh( + q=q, + k=k, + v=v, + g=None, + gk=g_cumsum, + gv=None, + do=do, + h0=initial_state, + dht=dht, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + states_in_fp32=True, + state_v_first=state_v_first, + ) + + dv = chunk_gla_bwd_dv( + k=k, + g=g_cumsum, + A=A, + do=do, + dh=dh, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + state_v_first=state_v_first, + ) + + # dq dk in fp32 + dA = chunk_gla_bwd_dA( + v=v, + do=do, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + ) + dq, dk = chunk_gla_bwd_dqk_intra( + q=q, + k=k, + g=g_cumsum, + dA=dA, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + ) + dq, dk, dg = chunk_gla_bwd_dqkg( + q=q, + k=k, + v=v, + h=h, + g=g_cumsum, + do=do, + dh=dh, + dq=dq, + dk=dk, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + state_v_first=state_v_first, + ) + return dq, dk, dv, dg, dh0 + + +class ChunkGLAFunction(torch.autograd.Function): + + @staticmethod + @input_guard + def forward( + ctx, + q, + k, + v, + g, + scale, + initial_state, + output_final_state, + state_v_first, + cu_seqlens, + cu_seqlens_cpu, + ): + chunk_size = min(64, max(16, triton.next_power_of_2(q.shape[1]))) + if cu_seqlens is not None: + chunk_indices = prepare_chunk_indices( + cu_seqlens, + chunk_size, + cu_seqlens_cpu=cu_seqlens_cpu, + ) + else: + chunk_indices = None + + g_cumsum, A, _, ht, o = chunk_gla_fwd( + q=q, + k=k, + v=v, + g=g, + g_cumsum=None, + scale=scale, + initial_state=initial_state, + output_final_state=output_final_state, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + state_v_first=state_v_first, + ) + # recompute g_cumsum in bwd pass + if g.dtype != torch.float: + g_cumsum = None + else: + g = None + ctx.save_for_backward(q, k, v, g, g_cumsum, initial_state, A, chunk_indices) + ctx.chunk_size = chunk_size + ctx.scale = scale + ctx.cu_seqlens = cu_seqlens + ctx.state_v_first = state_v_first + return o, ht + + @staticmethod + @input_guard + def backward(ctx, do, dht): + q, k, v, g, g_cumsum, initial_state, A, chunk_indices = ctx.saved_tensors + chunk_size, scale, cu_seqlens = ctx.chunk_size, ctx.scale, ctx.cu_seqlens + dq, dk, dv, dg, dh0 = chunk_gla_bwd( + q=q, + k=k, + v=v, + g=g, + g_cumsum=g_cumsum, + scale=scale, + h=None, + A=A, + initial_state=initial_state, + do=do, + dht=dht, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + state_v_first=ctx.state_v_first, + ) + return dq.to(q), dk.to(k), dv.to(v), dg, None, dh0, None, None, None, None + + +@torch.compiler.disable +def chunk_gla( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + scale: int | None = None, + initial_state: torch.Tensor = None, + output_final_state: bool = False, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + cu_seqlens_cpu: torch.LongTensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + r""" + Args: + q (torch.Tensor): + queries of shape `[B, T, H, K]`. + k (torch.Tensor): + keys of shape `[B, T, H, K]`. + v (torch.Tensor): + values of shape `[B, T, H, V]`. + g (torch.Tensor): + Forget gates of shape `[B, T, H, K]`. + scale (Optional[float]): + Scale factor for the attention scores. + If not provided, it will default to `1 / sqrt(K)`. Default: `None`. + initial_state (Optional[torch.Tensor]): + Initial state of shape `[N, H, K, V]` (or `[N, H, V, K]` if `state_v_first=True`) + for `N` input sequences. + For equal-length input sequences, `N` equals the batch size `B`. + Default: `None`. + output_final_state (Optional[bool]): + Whether to output the final state of shape `[N, H, K, V]` + (or `[N, H, V, K]` if `state_v_first=True`). Default: `False`. + state_v_first (Optional[bool]): + Store the recurrent state in V-first `[V, K]` layout instead of the default `[K, V]`. Default: `False`. + cu_seqlens (torch.LongTensor): + Cumulative sequence lengths of shape `[N+1]` used for variable-length training, + consistent with the FlashAttention API. + + Returns: + o (torch.Tensor): + Outputs of shape `[B, T, H, V]`. + final_state (torch.Tensor): + Final state of shape `[N, H, K, V]` (or `[N, H, V, K]` if `state_v_first=True`) + if `output_final_state=True` else `None`. + + Examples:: + >>> import torch + >>> import torch.nn.functional as F + >>> from einops import rearrange + >>> from fla.ops.gla import chunk_gla + # inputs with equal lengths + >>> B, T, H, K, V = 4, 2048, 4, 512, 512 + >>> q = torch.randn(B, T, H, K, device='cuda') + >>> k = torch.randn(B, T, H, K, device='cuda') + >>> v = torch.randn(B, T, H, V, device='cuda') + >>> g = F.logsigmoid(torch.randn(B, T, H, K, device='cuda')) + >>> h0 = torch.randn(B, H, K, V, device='cuda') + >>> o, ht = chunk_gla( + q, k, v, g, + initial_state=h0, + output_final_state=True + ) + # for variable-length inputs, the batch size `B` is expected to be 1 and `cu_seqlens` is required + >>> q, k, v, g = map(lambda x: rearrange(x, 'b t h d -> 1 (b t) h d'), (q, k, v, g)) + # for a batch with 4 sequences, `cu_seqlens` with 5 start/end positions are expected + >>> cu_seqlens = q.new_tensor([0, 2048, 4096, 6144, 8192], dtype=torch.long) + >>> o, ht = chunk_gla( + q, k, v, g, + initial_state=h0, + output_final_state=True, + cu_seqlens=cu_seqlens + ) + """ + if cu_seqlens is not None: + if q.shape[0] != 1: + raise ValueError( + f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`." + f"Please flatten variable-length inputs before processing.", + ) + if initial_state is not None and initial_state.shape[0] != len(cu_seqlens) - 1: + raise ValueError( + f"The number of initial states is expected to be equal to the number of input sequences, " + f"i.e., {len(cu_seqlens) - 1} rather than {initial_state.shape[0]}.", + ) + if scale is None: + scale = q.shape[-1] ** -0.5 + if initial_state is not None: + assert initial_state.dtype == torch.float32, "initial_state must be in float32." + assert q.shape == k.shape == g.shape, "q, k, g must have the same shape." + assert v.shape == (*q.shape[:3], v.shape[-1]), "v must be of shape (batch size, seq len, num of head, head dim)." + o, final_state = ChunkGLAFunction.apply( + q, + k, + v, + g, + scale, + initial_state, + output_final_state, + state_v_first, + cu_seqlens, + cu_seqlens_cpu, + ) + return o, final_state diff --git a/kda/_fla/ops/kda/__init__.py b/kda/_fla/ops/kda/__init__.py new file mode 100644 index 0000000..483321f --- /dev/null +++ b/kda/_fla/ops/kda/__init__.py @@ -0,0 +1,7 @@ +from .chunk import chunk_kda +from .fused_recurrent import fused_recurrent_kda + +__all__ = [ + "chunk_kda", + "fused_recurrent_kda", +] diff --git a/kda/_fla/ops/kda/chunk.py b/kda/_fla/ops/kda/chunk.py new file mode 100644 index 0000000..0f99931 --- /dev/null +++ b/kda/_fla/ops/kda/chunk.py @@ -0,0 +1,443 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +# Related files are modified and supported by the Moonshot AI Team + +import warnings + +import torch + +from kda._fla.modules.l2norm import l2norm_bwd, l2norm_fwd +from kda._fla.ops.backends import dispatch +from kda._fla.ops.common.gate import fused_beta_sigmoid, fused_beta_sigmoid_bwd +from kda._fla.ops.cp import FLACPContext +from kda._fla.ops.kda.chunk_bwd import chunk_kda_bwd +from kda._fla.ops.kda.chunk_fwd import chunk_kda_fwd +from kda._fla.ops.utils.index import prepare_chunk_indices +from kda._fla.utils import autocast_custom_bwd, autocast_custom_fwd, input_guard + + +class ChunkKDAFunction(torch.autograd.Function): + @staticmethod + @input_guard + @autocast_custom_fwd + def forward( + ctx, + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + A_log: torch.Tensor, + dt_bias: torch.Tensor, + scale: float, + initial_state: torch.Tensor, + output_final_state: bool = False, + use_qk_l2norm_in_kernel: bool = False, + use_gate_in_kernel: bool = False, + use_beta_sigmoid_in_kernel: bool = False, + allow_neg_eigval: bool = False, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + cu_seqlens_cpu: torch.LongTensor | None = None, + safe_gate: bool = False, + lower_bound: float | None = None, + chunk_size: int = 64, + disable_recompute: bool = False, + return_intermediate_states: bool = False, + cp_context: FLACPContext | None = None, + ): + # Apply l2norm + q_rstd, k_rstd = None, None + if use_qk_l2norm_in_kernel: + q, q_rstd = l2norm_fwd(q) + k, k_rstd = l2norm_fwd(k) + + beta_raw = beta + if use_beta_sigmoid_in_kernel: + beta = fused_beta_sigmoid(beta_raw, scale=2.0 if allow_neg_eigval else 1.0) + + chunk_indices = None + if cu_seqlens is not None: + chunk_indices = prepare_chunk_indices( + cu_seqlens, + chunk_size, + cu_seqlens_cpu=cu_seqlens_cpu, + ) + + g_input = g + + (o, final_state, g_cumsum, Aqk, Akk, w, u, qg, kg, v_new, h, initial_state) = chunk_kda_fwd( + q=q, + k=k, + v=v, + g=g_input, + beta=beta, + scale=scale, + initial_state=initial_state, + output_final_state=output_final_state, + cu_seqlens=cu_seqlens, + cu_seqlens_cpu=cu_seqlens_cpu, + chunk_indices=chunk_indices, + safe_gate=safe_gate, + lower_bound=lower_bound, + use_gate_in_kernel=use_gate_in_kernel, + A_log=A_log, + dt_bias=dt_bias, + chunk_size=chunk_size, + disable_recompute=disable_recompute, + return_intermediate_states=return_intermediate_states, + cp_context=cp_context, + state_v_first=state_v_first, + ) + + if return_intermediate_states: + assert torch.is_inference_mode_enabled(), "return_intermediate_states is only allowed in inference mode" + assert disable_recompute is False, "return_intermediate_states must be used with disable_recompute=False" + return o.type_as(q), final_state, h + + ctx.save_for_backward( + q, q_rstd, k, k_rstd, v, g_cumsum, g_input, beta_raw, beta, A_log, dt_bias, Aqk, Akk, + w, u, qg, kg, v_new, h, + initial_state, cu_seqlens, chunk_indices + ) + ctx.chunk_size = chunk_size + ctx.safe_gate = safe_gate + ctx.scale = scale + ctx.lower_bound = lower_bound + ctx.use_qk_l2norm_in_kernel = use_qk_l2norm_in_kernel + ctx.use_gate_in_kernel = use_gate_in_kernel + ctx.use_beta_sigmoid_in_kernel = use_beta_sigmoid_in_kernel + ctx.allow_neg_eigval = allow_neg_eigval + ctx.disable_recompute = disable_recompute + ctx.cp_context = cp_context + ctx.state_v_first = state_v_first + return o.type_as(q), final_state + + @staticmethod + @input_guard + @autocast_custom_bwd + def backward( + ctx, + do: torch.Tensor, + dht: torch.Tensor, + ): + (q, q_rstd, k, k_rstd, v, g_cumsum, g_input, beta_raw, beta, A_log, dt_bias, Aqk, Akk, + w, u, qg, kg, v_new, h, + initial_state, cu_seqlens, chunk_indices) = ( + ctx.saved_tensors + ) + + dq, dk, dv, db, dg, dh0, dA, dbias = chunk_kda_bwd( + q=q, + k=k, + v=v, + beta=beta, + Aqk=Aqk, + Akk=Akk, + scale=ctx.scale, + initial_state=initial_state, + do=do, + dht=dht, + g=g_cumsum, + g_org=g_input if ctx.use_gate_in_kernel else None, + state_v_first=ctx.state_v_first, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + chunk_size=ctx.chunk_size, + safe_gate=ctx.safe_gate, + lower_bound=ctx.lower_bound, + use_gate_in_kernel=ctx.use_gate_in_kernel, + A_log=A_log, + dt_bias=dt_bias, + disable_recompute=ctx.disable_recompute, + cp_context=ctx.cp_context, + w=w, + u=u, + qg=qg, + kg=kg, + v_new=v_new, + h=h, + ) + if ctx.use_qk_l2norm_in_kernel: + dq = l2norm_bwd(q, q_rstd, dq) + dk = l2norm_bwd(k, k_rstd, dk) + if ctx.use_beta_sigmoid_in_kernel: + db = fused_beta_sigmoid_bwd(beta_raw, db, scale=2.0 if ctx.allow_neg_eigval else 1.0) + + return (dq.to(q), dk.to(k), dv.to(v), dg.to(g_input), db.to(beta_raw), dA, dbias, None, dh0, + None, None, None, None, None, None, None, None, None, None, None, None, None, None) + + +@dispatch('kda') +@torch.compiler.disable +def chunk_kda( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + scale: float | None = None, + initial_state: torch.Tensor | None = None, + output_final_state: bool = False, + use_qk_l2norm_in_kernel: bool = False, + use_gate_in_kernel: bool = False, + use_beta_sigmoid_in_kernel: bool = False, + allow_neg_eigval: bool = False, + safe_gate: bool = False, + lower_bound: float | None = None, + disable_recompute: bool = False, + return_intermediate_states: bool = False, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + cu_seqlens_cpu: torch.LongTensor | None = None, + cp_context: FLACPContext = None, + **kwargs, +): + r""" + Args: + q (torch.Tensor): + queries of shape ``[B, T, H, K]``. + k (torch.Tensor): + keys of shape ``[B, T, H, K]``. + v (torch.Tensor): + values of shape ``[B, T, HV, V]``. + GVA (Grouped Value Attention) is applied if ``HV > H``, where ``HV`` must be divisible by ``H``. + g (torch.Tensor): + (forget) gating tensor (in log space!) of shape ``[B, T, HV, K]``. + When ``use_gate_in_kernel=False`` (default), ``g`` should be the pre-computed decay value. + When ``use_gate_in_kernel=True``, ``g`` is the raw input before gate activation; + the kernel fuses ``-exp(A_log) * softplus(g + dt_bias)`` + chunk cumsum internally. + beta (torch.Tensor): + betas of shape ``[B, T, HV]``. + scale (Optional[float]): + Scale factor for the KDA attention scores. + If not provided, it will default to ``1 / sqrt(K)``. Default: ``None``. + initial_state (Optional[torch.Tensor]): + Initial state of shape ``[N, HV, K, V]`` for ``N`` input sequences. + For equal-length input sequences, ``N`` equals the batch size ``B``. + Default: ``None``. + output_final_state (Optional[bool]): + Whether to output the final state of shape ``[N, HV, K, V]``. Default: ``False``. + use_qk_l2norm_in_kernel (bool): + Whether to apply L2norm to the q,k tensor internally. Default: ``False``. + use_gate_in_kernel (bool): + Whether to compute the log-space KDA decay internally. + - If ``True``: + The passed ``g`` acts as the raw input for ``-exp(A_log) * softplus(g + dt_bias.view(HV, K))``. + Note that as part of the input arguments, + ``A_log`` (shape ``[HV]``) and the optional ``dt_bias`` (shape ``[HV * K]``) should be provided. + When ``lower_bound`` is set, ``A_log`` may be ``None``, + in which case the gate is ``lower_bound * sigmoid(g + dt_bias)``. + - If ``False``, ``g`` is expected to be the pre-computed decay value. + Default: ``False``. + use_beta_sigmoid_in_kernel (bool): + Whether to apply ``torch.sigmoid(beta)`` before launching the chunk kernel. + - If ``True``, the passed ``beta`` acts as the raw beta logits. + - If ``False``, ``beta`` is expected to already be in post-sigmoid space. + Default: ``False``. + allow_neg_eigval (bool): + Whether to allow negative eigenvalues by scaling ``beta`` to ``[0, 2)``. + Only takes effect together with ``use_beta_sigmoid_in_kernel=True``, in which case + the kernel computes ``2 * sigmoid(beta)`` instead of ``sigmoid(beta)``. + Default: ``False``. + safe_gate (bool): + Whether to clamp the gate to ``[lower_bound, 0)`` and enable M=16 TensorCore + acceleration for higher throughput. Requires ``lower_bound`` to be set. + Default: ``False``. + lower_bound (Optional[float]): + Lower bound for the forget gate (in log space). When set together with + ``safe_gate=True``, changes the gate activation from + ``-exp(A_log) * softplus(g + dt_bias)`` to + ``lower_bound * sigmoid(exp(A_log) * (g + dt_bias))``, + which naturally clamps the output to ``[lower_bound, 0)``. + Recommended value: ``-5`` (i.e., per-step decay ``exp(-5) ≈ 0.0067``). + Default: ``None``. + disable_recompute (bool): + Whether to disable gradient recomputation in the kernel. When ``True``, the kernel + will save all intermediate activations for backward pass, which is beneficial + for training small models at the cost of increased memory usage. Default: ``False``. + return_intermediate_states (bool): + If True, returns intermediate state ``h`` for inference scenarios (e.g., vLLM). + Must be used within ``torch.inference_mode()`` and will return a 3-tuple instead of 2-tuple. + This is not intended for training as it bypasses autograd. Default: ``False``. + state_v_first (Optional[bool]): + Store the recurrent state in V-first ``[V, K]`` layout instead of the default ``[K, V]``. Default: ``False``. + cu_seqlens (torch.LongTensor): + Cumulative sequence lengths of shape ``[N+1]`` used for variable-length training, + consistent with the FlashAttention API. + cu_seqlens_cpu (torch.LongTensor): + Cumulative sequence lengths of shape ``[N+1]`` used for variable-length training, + consistent with the FlashAttention API. + cp_context (Optional[FLACPContext]): + Context parallel context for distributed training across multiple devices. + When provided, ``initial_state`` and ``output_final_state`` are not supported, + and ``cu_seqlens`` will be overridden by the context. Default: ``None``. + + Returns: + - Normal mode (return_intermediate_states=False): A tuple (o, final_state) + o (torch.Tensor): + Outputs of shape ``[B, T, HV, V]``. + final_state (torch.Tensor): + Final state of shape ``[N, HV, K, V]`` if ``output_final_state=True`` else ``None``. + - Inference mode (return_intermediate_states=True): A tuple (o, final_state, h) + o (torch.Tensor): + Outputs of shape ``[B, T, HV, V]``. + final_state (torch.Tensor): + Final state of shape ``[N, HV, K, V]`` if ``output_final_state=True`` else ``None``. + h (torch.Tensor): + Intermediate states of shape ``[B, NT, HV, K, V]`` and dtype ``bfloat16``. + - For equal-length sequences: ``NT = ceil(T / chunk_size)`` + - For variable-length sequences (cu_seqlens): B is always 1 (flattened), + NT is the total number of chunks across all sequences. + + Examples:: + >>> import torch + >>> import torch.nn.functional as F + >>> from einops import rearrange + >>> from fla.ops.kda import chunk_kda + # inputs with equal lengths (no GVA, HV == H) + >>> B, T, H, K, V = 4, 2048, 4, 512, 512 + >>> q = torch.randn(B, T, H, K, dtype=torch.bfloat16, device='cuda') + >>> k = torch.randn(B, T, H, K, dtype=torch.bfloat16, device='cuda') + >>> v = torch.randn(B, T, H, V, dtype=torch.bfloat16, device='cuda') + >>> beta = torch.rand(B, T, H, dtype=torch.bfloat16, device='cuda') + >>> g = torch.rand(B, T, H, K, dtype=torch.bfloat16, device='cuda') + >>> h0 = torch.randn(B, H, K, V, dtype=torch.bfloat16, device='cuda') + >>> A_log = torch.randn(H, dtype=torch.float32, device='cuda') + >>> dt_bias = torch.randn(H * K, dtype=torch.float32, device='cuda') + >>> o, ht = chunk_kda( + q, k, v, g, beta, + A_log=A_log, + dt_bias=dt_bias, + use_qk_l2norm_in_kernel=True, + use_gate_in_kernel=True, + initial_state=h0, + output_final_state=True + ) + # GVA mode (HV > H) + >>> HV = 8 # 2x more value heads than qk heads + >>> v = torch.randn(B, T, HV, V, dtype=torch.bfloat16, device='cuda') + >>> g = torch.rand(B, T, HV, K, dtype=torch.bfloat16, device='cuda') + >>> beta = torch.rand(B, T, HV, dtype=torch.bfloat16, device='cuda') + >>> h0 = torch.randn(B, HV, K, V, dtype=torch.bfloat16, device='cuda') + >>> A_log = torch.randn(HV, dtype=torch.float32, device='cuda') + >>> dt_bias = torch.randn(HV * K, dtype=torch.float32, device='cuda') + >>> o, ht = chunk_kda( + q, k, v, g, beta, + A_log=A_log, + dt_bias=dt_bias, + use_qk_l2norm_in_kernel=True, + use_gate_in_kernel=True, + initial_state=h0, + output_final_state=True + ) + # for variable-length inputs, the batch size `B` is expected to be 1 and `cu_seqlens` is required + >>> q, k, v, beta, g = map(lambda x: rearrange(x, 'b t ... -> 1 (b t) ...'), (q, k, v, beta, g)) + # for a batch with 4 sequences, `cu_seqlens` with 5 start/end positions are expected + >>> cu_seqlens = q.new_tensor([0, 2048, 4096, 6144, 8192], dtype=torch.long) + >>> o, ht = chunk_kda( + q, k, v, g, beta, + A_log=A_log, + dt_bias=dt_bias, + use_qk_l2norm_in_kernel=True, + use_gate_in_kernel=True, + initial_state=h0, + output_final_state=True, + cu_seqlens=cu_seqlens + ) + """ + if 'transpose_state_layout' in kwargs: + if state_v_first: + raise ValueError("Cannot pass both `state_v_first` and the deprecated `transpose_state_layout`.") + warnings.warn( + "`transpose_state_layout` is deprecated and renamed to `state_v_first`.", + DeprecationWarning, + stacklevel=2, + ) + state_v_first = kwargs.pop('transpose_state_layout') + + if cp_context is not None: + assert initial_state is None, "Initial state is not supported for CP" + assert output_final_state is False, "Output final state is not supported for CP" + assert cp_context.cu_seqlens is not None, "cu_seqlens is required for CP" + # Override cu_seqlens and cu_seqlens_cpu with the ones from the context + cu_seqlens = cp_context.cu_seqlens + if cp_context.cu_seqlens_cpu is not None: + cu_seqlens_cpu = cp_context.cu_seqlens_cpu + + if cu_seqlens is not None: + if q.shape[0] != 1: + raise ValueError( + f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`." + f"Please flatten variable-length inputs before processing.", + ) + if initial_state is not None and initial_state.shape[0] != len(cu_seqlens) - 1: + raise ValueError( + f"The number of initial states is expected to be equal to the number of input sequences, " + f"i.e., {len(cu_seqlens) - 1} rather than {initial_state.shape[0]}.", + ) + if initial_state is not None: + assert initial_state.dtype == torch.float32, "initial_state must be in float32." + + A_log, dt_bias = None, None + if use_gate_in_kernel: + A_log, dt_bias = kwargs.get("A_log"), kwargs.get("dt_bias") + if A_log is None and lower_bound is None: + raise ValueError("`A_log` must be provided when `use_gate_in_kernel=True` and `lower_bound` is not set.") + + chunk_size = kwargs.pop("chunk_size", 64) + if chunk_size not in (32, 64): + raise ValueError(f"`chunk_size` must be either 32 or 64 for KDA, got {chunk_size}.") + + if safe_gate and use_gate_in_kernel: + if lower_bound is None: + raise ValueError("`lower_bound` must be specified when `safe_gate=True` and `use_gate_in_kernel=True`.") + if not (-5 <= lower_bound < 0): + raise ValueError(f"`lower_bound` must be in the safe range [-5, 0), got {lower_bound}.") + + if allow_neg_eigval and not use_beta_sigmoid_in_kernel: + raise ValueError("`allow_neg_eigval=True` requires `use_beta_sigmoid_in_kernel=True`.") + + # Validate head dimensions for GVA + B, T, H, K, HV = *q.shape, v.shape[2] + assert q.shape == k.shape, f"q and k must have the same shape, got q={q.shape} vs k={k.shape}" + assert K <= 256, f"Currently we only support key headdim <=256 for KDA, got {K}." + assert HV % H == 0, ( + f"For GVA, num_v_heads (HV={HV}) must be evenly divisible by num_qk_heads (H={H}), " + f"but got HV % H = {HV % H}" + ) + assert g.shape == (B, T, HV, K), f"g must have shape [B, T, HV, K]={[B, T, HV, K]}, got {list(g.shape)}" + assert beta.shape == (B, T, HV), f"beta must have shape [B, T, HV]={[B, T, HV]}, got {list(beta.shape)}" + + if scale is None: + scale = K ** -0.5 + return ChunkKDAFunction.apply( + q, + k, + v, + g, + beta, + A_log, + dt_bias, + scale, + initial_state, + output_final_state, + use_qk_l2norm_in_kernel, + use_gate_in_kernel, + use_beta_sigmoid_in_kernel, + allow_neg_eigval, + state_v_first, + cu_seqlens, + cu_seqlens_cpu, + safe_gate, + lower_bound, + chunk_size, + disable_recompute, + return_intermediate_states, + cp_context, + ) diff --git a/kda/_fla/ops/kda/chunk_bwd.py b/kda/_fla/ops/kda/chunk_bwd.py new file mode 100644 index 0000000..9b8a88a --- /dev/null +++ b/kda/_fla/ops/kda/chunk_bwd.py @@ -0,0 +1,651 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.common.chunk_delta_h import ( + chunk_gated_delta_rule_bwd_dhu, + chunk_gated_delta_rule_fwd_h, +) +from kda._fla.ops.cp import FLACPContext +from kda._fla.ops.cp.chunk_delta_h import ( + chunk_gated_delta_rule_bwd_dhu_pre_process, + expand_h0, +) +from kda._fla.ops.kda.chunk_intra import chunk_kda_bwd_intra +from kda._fla.ops.kda.gate import kda_gate_bwd, kda_gate_chunk_cumsum +from kda._fla.ops.kda.wy_fast import recompute_w_u_fwd +from kda._fla.ops.utils import chunk_local_cumsum, prepare_chunk_indices +from kda._fla.ops.utils.cache import fla_cache_autotune +from kda._fla.ops.utils.constant import RCP_LN2 +from kda._fla.ops.utils.op import exp2 +from kda._fla.utils import ( + IS_NVIDIA_HOPPER, + IS_NVIDIA_SM100, + autotune_cache_kwargs, + check_shared_mem, +) + +BK_LIST = [32, 64] if check_shared_mem() else [16, 32] +BV_LIST = [64, 128] if check_shared_mem("ampere") else [16, 32] +NUM_WARPS = [2, 4] if IS_NVIDIA_HOPPER else [2, 4, 8] + + +@triton.heuristics( + { + "IS_VARLEN": lambda args: args["cu_seqlens"] is not None, + } +) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps, num_stages=num_stages) + for num_warps in NUM_WARPS + for num_stages in [2, 3, 4] + ], + key=["H", "HV", "K", "V", "BT", "BK", "BV"], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=["T"]) +def chunk_kda_bwd_kernel_dAv( + q, + k, + v, + A, + do, + dv, + dA, + cu_seqlens, + chunk_indices, + scale, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64) + i_b, i_hv = i_bh // HV, i_bh % HV + i_h = i_hv // (HV // H) + if IS_VARLEN: + i_n, i_t = ( + tl.load(chunk_indices + i_t * 2).to(tl.int32), + tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64), + ) + bos, eos = ( + tl.load(cu_seqlens + i_n).to(tl.int64), + tl.load(cu_seqlens + i_n + 1).to(tl.int64), + ) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + # offset calculation + q += (bos * H + i_h) * K + k += (bos * H + i_h) * K + v += (bos * HV + i_hv) * V + do += (bos * HV + i_hv) * V + dv += (bos * HV + i_hv) * V + dA += (bos * HV + i_hv) * BT + + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + o_A = tl.arange(0, BT) + m_AT = (o_A[:, None] < BT) & m_t[None, :] + p_A = A + (bos * HV + i_hv) * BT + o_A[:, None] + o_t[None, :] * (HV * BT) + b_A = tl.load(p_A, mask=m_AT, other=0.0) + + m_A = (o_t[:, None] <= o_t[None, :]) & (m_t[:, None] & m_t) + b_A = tl.where(m_A, b_A, 0).to(do.dtype.element_ty) + + b_dA = tl.zeros([BT, BT], dtype=tl.float32) + for i_v in range(tl.cdiv(V, BV)): + o_v = i_v * BV + tl.arange(0, BV) + m_v = o_v < V + m_vT = m_v[:, None] & m_t[None, :] + m_tv = m_t[:, None] & m_v[None, :] + p_v = v + o_v[:, None] + o_t[None, :] * (HV * V) + p_do = do + o_t[:, None] * (HV * V) + o_v[None, :] + p_dv = dv + o_t[:, None] * (HV * V) + o_v[None, :] + # [BV, BT] + b_v = tl.load(p_v, mask=m_vT, other=0.0) + # [BT, BV] + b_do = tl.load(p_do, mask=m_tv, other=0.0) + # [BT, BT] + b_dA += tl.dot(b_do, b_v) + # [BT, BV] + b_dv = tl.dot(b_A.to(b_do.dtype), b_do) + tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), mask=m_tv) + + m_dA = m_t[:, None] & (o_A[None, :] < BT) + p_dA = dA + o_t[:, None] * (HV * BT) + o_A[None, :] + b_dA = tl.where(o_t[:, None] >= o_t, b_dA * scale, 0.0) + tl.store(p_dA, b_dA.to(p_dA.dtype.element_ty), mask=m_dA) + + +@triton.heuristics( + { + "IS_VARLEN": lambda args: args["cu_seqlens"] is not None, + } +) +@fla_cache_autotune( + configs=[ + triton.Config({"BK": BK, "BV": BV}, num_warps=num_warps, num_stages=num_stages) + for BK in BK_LIST + for BV in BV_LIST + for num_warps in NUM_WARPS + for num_stages in [2, 3, 4] + if not (IS_NVIDIA_HOPPER and BK == 32 and num_warps == 4) + if not (IS_NVIDIA_SM100 and BK == 32 and num_warps != 2) + ], + key=["BT", "HV", "STATE_V_FIRST"], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=["T"]) +def chunk_kda_bwd_kernel_wy_dqkg_fused( + q, + k, + v, + v_new, + g, + beta, + A, + h, + do, + dh, + dq, + dk, + dv, + dv2, + dg, + db, + dA, + cu_seqlens, + chunk_indices, + scale, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + STATE_V_FIRST: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1) + i_b, i_hv = i_bh // HV, i_bh % HV + i_h = i_hv // (HV // H) + + if IS_VARLEN: + i_tg = i_t.to(tl.int64) + i_n, i_t = ( + tl.load(chunk_indices + i_t * 2).to(tl.int32), + tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64), + ) + bos, eos = ( + tl.load(cu_seqlens + i_n).to(tl.int64), + tl.load(cu_seqlens + i_n + 1).to(tl.int64), + ) + T = (eos - bos).to(tl.int32) + NT = tl.cdiv(T, BT) + else: + NT = tl.cdiv(T, BT) + i_tg = (i_b * NT + i_t).to(tl.int64) + bos, eos = (i_b * T).to(tl.int64), (i_b * T + T).to(tl.int64) + + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + m_last = o_t == min(T, i_t * BT + BT) - 1 + + q += (bos * H + i_h) * K + k += (bos * H + i_h) * K + v += (bos * HV + i_hv) * V + v_new += (bos * HV + i_hv) * V + g += (bos * HV + i_hv) * K + beta += bos * HV + i_hv + A += (bos * HV + i_hv) * BT + h += (i_tg * HV + i_hv) * K * V + do += (bos * HV + i_hv) * V + dh += (i_tg * HV + i_hv) * K * V + dq += (bos * HV + i_hv) * K + dk += (bos * HV + i_hv) * K + dv += (bos * HV + i_hv) * V + dv2 += (bos * HV + i_hv) * V + dg += (bos * HV + i_hv) * K + db += bos * HV + i_hv + dA += (bos * HV + i_hv) * BT + + p_beta = beta + o_t * HV + b_beta = tl.load(p_beta, mask=m_t, other=0.0) + + o_A = tl.arange(0, BT) + m_AT = (o_A[:, None] < BT) & m_t[None, :] + p_A = A + o_A[:, None] + o_t[None, :] * (HV * BT) + b_A = tl.load(p_A, mask=m_AT, other=0.0) + + b_dA = tl.zeros([BT, BT], dtype=tl.float32) + b_db = tl.zeros([BT], dtype=tl.float32) + + for i_k in range(tl.cdiv(K, BK)): + o_k = i_k * BK + tl.arange(0, BK) + m_k = o_k < K + m_tk = m_t[:, None] & m_k[None, :] + + p_k = k + o_t[:, None] * (H * K) + o_k[None, :] + p_g = g + o_t[:, None] * (HV * K) + o_k[None, :] + b_k = tl.load(p_k, mask=m_tk, other=0.0) + b_g = tl.load(p_g, mask=m_tk, other=0.0).to(tl.float32) + + p_gn = g + (min(T, i_t * BT + BT) - 1).to(tl.int64) * HV * K + o_k + b_gn = tl.load(p_gn, mask=m_k, other=0).to(tl.float32) + + b_dq = tl.zeros([BT, BK], dtype=tl.float32) + b_dk = tl.zeros([BT, BK], dtype=tl.float32) + b_dw = tl.zeros([BT, BK], dtype=tl.float32) + b_dgk = tl.zeros([BK], dtype=tl.float32) + + for i_v in range(tl.cdiv(V, BV)): + o_v = i_v * BV + tl.arange(0, BV) + m_tv = m_t[:, None] & (o_v[None, :] < V) + m_h = (o_v[:, None] < V) & m_k[None, :] + p_v_new = v_new + o_t[:, None] * (HV * V) + o_v[None, :] + p_do = do + o_t[:, None] * (HV * V) + o_v[None, :] + if STATE_V_FIRST: + p_h = h + o_v[:, None] * K + o_k[None, :] + p_dh = dh + o_v[:, None] * K + o_k[None, :] + else: + p_h = h + o_v[:, None] + o_k[None, :] * V + p_dh = dh + o_v[:, None] + o_k[None, :] * V + p_dv = dv + o_t[:, None] * (HV * V) + o_v[None, :] + # [BT, BV] + b_v_new = tl.load(p_v_new, mask=m_tv, other=0.0) + b_do = tl.load(p_do, mask=m_tv, other=0.0) + # [BV, BK] + b_h = tl.load(p_h, mask=m_h, other=0.0) + b_dh = tl.load(p_dh, mask=m_h, other=0.0) + # [BT, BV] + b_dv = tl.load(p_dv, mask=m_tv, other=0.0) + + b_dgk += tl.sum(b_h * b_dh, axis=0) + b_dq += tl.dot(b_do, b_h.to(b_do.dtype)) + b_dk += tl.dot(b_v_new, b_dh.to(b_v_new.dtype)) + b_dw += tl.dot(b_dv.to(b_v_new.dtype), b_h.to(b_v_new.dtype)) + tl.debug_barrier() # DO NOT REMOVE THIS LINE! + if i_k == 0: + p_v = v + o_t[:, None] * (HV * V) + o_v[None, :] + p_dv2 = dv2 + o_t[:, None] * (HV * V) + o_v[None, :] + + b_v = tl.load(p_v, mask=m_tv, other=0.0) + + b_dA += tl.dot(b_dv, tl.trans(b_v)) + + b_dvb = tl.dot(b_A, b_dv) + b_dv2 = b_dvb * b_beta[:, None] + b_db += tl.sum(b_dvb * b_v, 1) + + tl.store(p_dv2, b_dv2.to(p_dv2.dtype.element_ty), mask=m_tv) + + b_gk_exp = exp2(b_g) + b_gb = b_gk_exp * b_beta[:, None] + b_dgk *= exp2(b_gn) + b_dq = b_dq * b_gk_exp * scale + b_dk = b_dk * tl.where(m_t[:, None], exp2(b_gn[None, :] - b_g), 0) + + b_kg = b_k * b_gk_exp + + b_dw = -b_dw.to(b_A.dtype) + b_dA += tl.dot(b_dw, tl.trans(b_kg.to(b_A.dtype))) + + b_dkgb = tl.dot(b_A, b_dw) + b_db += tl.sum(b_dkgb * b_kg, 1) + + p_q = q + o_t[:, None] * (H * K) + o_k[None, :] + b_q = tl.load(p_q, mask=m_tk, other=0.0) + b_kdk = b_k * b_dk + b_dgk += tl.sum(b_kdk, axis=0) + b_dg = ( + b_q * b_dq + - b_kdk + + m_last[:, None] * b_dgk + + b_kg * b_dkgb * b_beta[:, None] + ) + b_dk = b_dk + b_dkgb * b_gb + + p_dq = dq + o_t[:, None] * (HV * K) + o_k[None, :] + p_dk = dk + o_t[:, None] * (HV * K) + o_k[None, :] + p_dg = dg + o_t[:, None] * (HV * K) + o_k[None, :] + tl.store(p_dq, b_dq.to(p_dq.dtype.element_ty), mask=m_tk) + tl.store(p_dk, b_dk.to(p_dk.dtype.element_ty), mask=m_tk) + tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), mask=m_tk) + + m_A = (o_t[:, None] > o_t[None, :]) & (m_t[:, None] & m_t) + b_dA = tl.where(m_A, b_dA * b_beta[None, :], 0) + b_dA = tl.dot(b_dA.to(b_A.dtype), b_A) + b_dA = tl.dot(b_A, b_dA.to(b_A.dtype)) + b_dA = tl.where(m_A, -b_dA, 0) + + m_dA = m_t[:, None] & (o_A[None, :] < BT) + p_dA = dA + o_t[:, None] * (HV * BT) + o_A[None, :] + p_db = db + o_t * HV + tl.store(p_dA, b_dA.to(p_dA.dtype.element_ty), mask=m_dA) + tl.store(p_db, b_db.to(p_db.dtype.element_ty), mask=m_t) + + +@dispatch("kda") +def chunk_kda_bwd_dAv( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + do: torch.Tensor, + A: torch.Tensor | None = None, + scale: float = None, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor]: + B, T, H, K, HV, V = *k.shape, do.shape[2], do.shape[-1] + BT = chunk_size + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, BT) + # H100 can have larger block size + if check_shared_mem("hopper", k.device.index): + CONST_TILING = 128 + elif check_shared_mem: + CONST_TILING = 64 + else: + CONST_TILING = 32 + BK = min(max(triton.next_power_of_2(K), 16), CONST_TILING) + BV = min(max(triton.next_power_of_2(V), 16), CONST_TILING) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + + dA = v.new_empty(B, T, HV, BT, dtype=torch.float) + dv = torch.empty_like(do) + grid = (NT, B * HV) + chunk_kda_bwd_kernel_dAv[grid]( + q=q, + k=k, + v=v, + A=A, + do=do, + dv=dv, + dA=dA, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + scale=scale, + T=T, + H=H, + HV=HV, + K=K, + V=V, + BT=BT, + BK=BK, + BV=BV, + ) + return dA, dv + + +@dispatch("kda") +def chunk_kda_bwd_wy_dqkg_fused( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + v_new: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + A: torch.Tensor, + h: torch.Tensor, + do: torch.Tensor, + dh: torch.Tensor, + dv: torch.Tensor, + scale: float | None = None, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, +): + B, T, H, K, HV, V = *k.shape, v.shape[2], v.shape[-1] + BT = chunk_size + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, BT) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + + # dq, dk are allocated at HV dimension; caller reduces to H if GVA + dq = g.new_empty(B, T, HV, K, dtype=torch.float) + dk = g.new_empty(B, T, HV, K, dtype=torch.float) + dv2 = torch.empty_like(v) + dg = torch.empty_like(g, dtype=torch.float) + db = torch.empty_like(beta, dtype=torch.float) + dA = torch.empty_like(A, dtype=torch.float) + + grid = (NT, B * HV) + chunk_kda_bwd_kernel_wy_dqkg_fused[grid]( + q=q, + k=k, + v=v, + v_new=v_new, + g=g, + beta=beta, + A=A, + h=h, + do=do, + dh=dh, + dq=dq, + dk=dk, + dv=dv, + dv2=dv2, + dg=dg, + db=db, + dA=dA, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + scale=scale, + T=T, + H=H, + HV=HV, + K=K, + V=V, + BT=BT, + STATE_V_FIRST=state_v_first, + ) + dv = dv2 + return dq, dk, dv, db, dg, dA + + +def chunk_kda_bwd( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + beta: torch.Tensor, + Aqk: torch.Tensor, + Akk: torch.Tensor, + scale: float, + initial_state: torch.Tensor, + do: torch.Tensor, + dht: torch.Tensor, + g: torch.Tensor | None = None, + g_org: torch.Tensor | None = None, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + chunk_indices: torch.LongTensor | None = None, + chunk_size: int = 64, + safe_gate: bool = False, + lower_bound: float | None = None, + use_gate_in_kernel: bool = False, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + disable_recompute: bool = False, + cp_context: FLACPContext | None = None, + **kwargs, +): + H, HV = q.shape[2], v.shape[2] + G = HV // H + + if disable_recompute is False: + if use_gate_in_kernel: + g = kda_gate_chunk_cumsum( + g=g_org, + A_log=A_log, + dt_bias=dt_bias, + scale=RCP_LN2, + chunk_size=chunk_size, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + lower_bound=lower_bound, + ) + w, u, qg, kg = recompute_w_u_fwd( + q=q, + k=k, + v=v, + beta=beta, + A=Akk, + gk=g, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + ) + if cp_context is not None: + # Restore the full initial_state tensor from the compressed version. + # Only the first sequence's state is non-zero as it's the only one that could be cross-rank. + initial_state = expand_h0(initial_state, context=cp_context) + h, v_new, _ = chunk_gated_delta_rule_fwd_h( + k=kg, + w=w, + u=u, + gk=g, + initial_state=initial_state, + output_final_state=False, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + chunk_size=chunk_size, + state_v_first=state_v_first, + ) + else: + w, u, qg, kg, v_new, h = ( + kwargs["w"], + kwargs["u"], + kwargs["qg"], + kwargs["kg"], + kwargs["v_new"], + kwargs["h"], + ) + if cp_context is not None: + # Restore the full initial_state tensor from the compressed version. + # Only the first sequence's state is non-zero as it's the only one that could be cross-rank. + initial_state = expand_h0(initial_state, context=cp_context) + + # dAqk = do @ v.T + # dv = A @ do + dAqk, dv = chunk_kda_bwd_dAv( + q=q, + k=k, + v=v_new, + do=do, + A=Aqk, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + ) + + if cp_context is not None: + # initial_state is None in the CP mode + # We only need to compute dht of current rank and pass it to the backward kernel + dht, initial_state = chunk_gated_delta_rule_bwd_dhu_pre_process( + q=qg, + k=kg, + w=w, + do=do, + dv=dv, + gk=g, + scale=scale, + cu_seqlens=cu_seqlens, + dht=dht, + initial_state=initial_state, + context=cp_context, + chunk_size=chunk_size, + state_v_first=state_v_first, + ) + + dh, dh0, dv = chunk_gated_delta_rule_bwd_dhu( + q=qg, + k=kg, + w=w, + gk=g, + h0=initial_state, + dht=dht, + do=do, + dv=dv, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + state_v_first=state_v_first, + ) + + dq, dk, dv, db, dg, dAkk = chunk_kda_bwd_wy_dqkg_fused( + q=q, + k=k, + v=v, + v_new=v_new, + g=g, + beta=beta, + A=Akk, + h=h, + do=do, + dh=dh, + dv=dv, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + state_v_first=state_v_first, + ) + + dq, dk, db, dg = chunk_kda_bwd_intra( + q=q, + k=k, + g=g, + beta=beta, + dAqk=dAqk, + dAkk=dAkk, + dq=dq, + dk=dk, + db=db, + dg=dg, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + safe_gate=safe_gate, + ) + + # For GVA, reduce dq and dk from [B, T, HV, K] back to [B, T, H, K] + if HV > H: + dq = dq.view(*dq.shape[:2], H, G, dq.shape[-1]).sum(dim=3) + dk = dk.view(*dk.shape[:2], H, G, dk.shape[-1]).sum(dim=3) + + dA, dbias = None, None + dg = chunk_local_cumsum( + dg, + chunk_size=chunk_size, + reverse=True, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + ) + if use_gate_in_kernel: + dg, dA, dbias = kda_gate_bwd( + g=g_org, A_log=A_log, dt_bias=dt_bias, dyg=dg, lower_bound=lower_bound + ) + + return dq, dk, dv, db, dg, dh0, dA, dbias diff --git a/kda/_fla/ops/kda/chunk_fwd.py b/kda/_fla/ops/kda/chunk_fwd.py new file mode 100644 index 0000000..10a4b8e --- /dev/null +++ b/kda/_fla/ops/kda/chunk_fwd.py @@ -0,0 +1,134 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch + +from kda._fla.ops.common.chunk_delta_h import chunk_gated_delta_rule_fwd_h +from kda._fla.ops.cp import FLACPContext +from kda._fla.ops.cp.chunk_delta_h import chunk_gated_delta_rule_fwd_h_pre_process, compress_h0 +from kda._fla.ops.gla.chunk import chunk_gla_fwd_o_gk +from kda._fla.ops.kda.chunk_intra import chunk_kda_fwd_intra +from kda._fla.ops.kda.gate import kda_gate_chunk_cumsum +from kda._fla.ops.utils import chunk_local_cumsum +from kda._fla.ops.utils.constant import RCP_LN2 + + +def chunk_kda_fwd( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + scale: float, + initial_state: torch.Tensor, + output_final_state: bool, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + cu_seqlens_cpu: torch.LongTensor | None = None, + chunk_indices: torch.LongTensor | None = None, + chunk_size: int = 64, + safe_gate: bool = False, + lower_bound: float | None = None, + use_gate_in_kernel: bool = False, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + disable_recompute: bool = False, + return_intermediate_states: bool = False, + cp_context: FLACPContext | None = None, +): + # Apply gate activation + g_org = None + if use_gate_in_kernel: + g_org = g + g = kda_gate_chunk_cumsum( + g=g_org, + A_log=A_log, + dt_bias=dt_bias, + scale=RCP_LN2, + chunk_size=chunk_size, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + lower_bound=lower_bound, + ) + else: + g = chunk_local_cumsum( + g=g, + scale=RCP_LN2, + chunk_size=chunk_size, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices + ) + + # qg = None if disable_recompute is False + w, u, qg, kg, Aqk, Akk = chunk_kda_fwd_intra( + q=q, + k=k, + v=v, + gk=g, + beta=beta, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + safe_gate=safe_gate, + disable_recompute=disable_recompute + ) + + if cp_context is not None: + initial_state = chunk_gated_delta_rule_fwd_h_pre_process( + k=kg, + w=w, + u=u, + gk=g, + cu_seqlens=cu_seqlens, + initial_state=initial_state, + context=cp_context, + chunk_size=chunk_size, + state_v_first=state_v_first, + ) + + h, v_new, final_state = chunk_gated_delta_rule_fwd_h( + k=kg, + w=w, + u=u, + gk=g, + initial_state=initial_state, + output_final_state=output_final_state, + cu_seqlens=cu_seqlens, + cu_seqlens_cpu=cu_seqlens_cpu, + chunk_indices=chunk_indices, + chunk_size=chunk_size, + state_v_first=state_v_first, + ) + + if cp_context is not None: + # In Context Parallel (CP) mode, global initial states are not supported at the entry point. + # The `initial_state` here is computed internally via inter-rank communication. + # Since only the first sequence in the local batch can be a continuation of a cross-rank sequence, + # only the first state in the tensor is relevant. We compress it to optimize memory for `save_for_backward`. + initial_state = compress_h0(initial_state, context=cp_context) + + o = chunk_gla_fwd_o_gk( + q=q, + v=v_new, + g=g, + A=Aqk, + h=h, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=chunk_size, + chunk_indices=chunk_indices, + state_v_first=state_v_first, + ) + if disable_recompute is False: + # Delete to save memory + w, u, qg, kg, v_new = None, None, None, None, None + if not return_intermediate_states: + h = None + if use_gate_in_kernel: + g = None + return o, final_state, g, Aqk, Akk, w, u, qg, kg, v_new, h, initial_state diff --git a/kda/_fla/ops/kda/chunk_intra.py b/kda/_fla/ops/kda/chunk_intra.py new file mode 100644 index 0000000..e691aee --- /dev/null +++ b/kda/_fla/ops/kda/chunk_intra.py @@ -0,0 +1,962 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.kda.chunk_intra_token_parallel import chunk_kda_fwd_intra_token_parallel +from kda._fla.ops.kda.wy_fast import recompute_w_u_fwd +from kda._fla.ops.utils import prepare_chunk_indices +from kda._fla.ops.utils.cache import fla_cache_autotune +from kda._fla.ops.utils.op import exp2, gather +from kda._fla.utils import IS_GATHER_SUPPORTED, IS_TF32_SUPPORTED, autotune_cache_kwargs + +if IS_TF32_SUPPORTED: + SOLVE_TRIL_DOT_PRECISION = tl.constexpr('tf32') +else: + SOLVE_TRIL_DOT_PRECISION = tl.constexpr('ieee') + +################################################################################ +# Fused inter + solve_tril kernel: compute off-diagonal Akk and solve in one pass +################################################################################ + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BK': BK}, num_warps=num_warps) + for BK in [32, 64] + for num_warps in [1, 2, 4] + ], + key=["H", "HV", "K", "BT", "BC", "NC"], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_kda_fwd_kernel_inter_solve_fused( + q, + k, + g, + beta, + Aqk, + Akkd, + Akk, + scale, + cu_seqlens, + chunk_indices, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + BT: tl.constexpr, + BC: tl.constexpr, + NC: tl.constexpr, + BK: tl.constexpr, + IS_VARLEN: tl.constexpr, + USE_SAFE_GATE: tl.constexpr, +): + """ + Fused kernel: compute inter-subchunk Akk + solve_tril in one pass. + Prerequisite: token_parallel has already computed diagonal Akk blocks in Akkd. + + This kernel: + 1. Computes off-diagonal Aqk blocks -> writes to global + 2. Computes off-diagonal Akk blocks -> keeps in registers + 3. Loads diagonal Akk blocks from Akkd (fp32) + 4. Does forward substitution on diagonals + 5. Computes merged Akk_inv + 6. Writes Akk_inv to Akk + """ + i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64) + i_b, i_hv = i_bh // HV, i_bh % HV + i_h = i_hv // (HV // H) + + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + if i_t * BT >= T: + return + + i_tc0 = i_t * BT + i_tc1 = i_t * BT + BC + i_tc2 = i_t * BT + 2 * BC + i_tc3 = i_t * BT + 3 * BC + + q += (bos * H + i_h) * K + k += (bos * H + i_h) * K + g += (bos * HV + i_hv) * K + Aqk += (bos * HV + i_hv) * BT + Akk += (bos * HV + i_hv) * BT + Akkd += (bos * HV + i_hv) * BC + + o_i = tl.arange(0, BC) + m_tc1 = (i_tc1 + o_i) < T + m_tc2 = (i_tc2 + o_i) < T + m_tc3 = (i_tc3 + o_i) < T + o_c0 = i_tc0 + o_i + o_c1 = i_tc1 + o_i + o_c2 = i_tc2 + o_i + o_c3 = i_tc3 + o_i + m_tc0 = o_c0 < T + m_A0 = m_tc0[:, None] & (o_i[None, :] < BT) + m_A1 = m_tc1[:, None] & (o_i[None, :] < BT) + m_A2 = m_tc2[:, None] & (o_i[None, :] < BT) + m_A3 = m_tc3[:, None] & (o_i[None, :] < BT) + + b_Aqk10 = tl.zeros([BC, BC], dtype=tl.float32) + b_Akk10 = tl.zeros([BC, BC], dtype=tl.float32) + + b_Aqk20 = tl.zeros([BC, BC], dtype=tl.float32) + b_Akk20 = tl.zeros([BC, BC], dtype=tl.float32) + b_Aqk21 = tl.zeros([BC, BC], dtype=tl.float32) + b_Akk21 = tl.zeros([BC, BC], dtype=tl.float32) + + b_Aqk30 = tl.zeros([BC, BC], dtype=tl.float32) + b_Akk30 = tl.zeros([BC, BC], dtype=tl.float32) + b_Aqk31 = tl.zeros([BC, BC], dtype=tl.float32) + b_Akk31 = tl.zeros([BC, BC], dtype=tl.float32) + b_Aqk32 = tl.zeros([BC, BC], dtype=tl.float32) + b_Akk32 = tl.zeros([BC, BC], dtype=tl.float32) + + ################################################################################ + # off-diagonal blocks + ################################################################################ + for i_k in range(tl.cdiv(K, BK)): + o_k = i_k * BK + tl.arange(0, BK) + m_k = o_k < K + + m_ck0 = m_tc0[:, None] & m_k[None, :] + p_k0 = k + o_c0[:, None] * (H*K) + o_k[None, :] + p_g0 = g + o_c0[:, None] * (HV*K) + o_k[None, :] + b_k0 = tl.load(p_k0, mask=m_ck0, other=0.0).to(tl.float32) + b_g0 = tl.load(p_g0, mask=m_ck0, other=0.0).to(tl.float32) + + if i_tc1 < T: + m_ck1 = m_tc1[:, None] & m_k[None, :] + p_q1 = q + o_c1[:, None] * (H*K) + o_k[None, :] + p_k1 = k + o_c1[:, None] * (H*K) + o_k[None, :] + p_g1 = g + o_c1[:, None] * (HV*K) + o_k[None, :] + # [BC, BK] + b_q1 = tl.load(p_q1, mask=m_ck1, other=0.0).to(tl.float32) + b_k1 = tl.load(p_k1, mask=m_ck1, other=0.0).to(tl.float32) + b_g1 = tl.load(p_g1, mask=m_ck1, other=0.0).to(tl.float32) + # [BK] + b_gn1 = tl.load(g + i_tc1 * HV*K + o_k, mask=m_k, other=0).to(tl.float32) + # [BC, BK] + b_gqn = tl.where(m_tc1[:, None], exp2(b_g1 - b_gn1[None, :]), 0) + # [BK, BC] + b_kgt = tl.trans(b_k0 * exp2(b_gn1[None, :] - b_g0)) + # [BC, BC] + b_Aqk10 += tl.dot(b_q1 * b_gqn, b_kgt) + b_Akk10 += tl.dot(b_k1 * b_gqn, b_kgt) + + if NC >= 3 and i_tc2 < T: + m_ck2 = m_tc2[:, None] & m_k[None, :] + p_q2 = q + o_c2[:, None] * (H*K) + o_k[None, :] + p_k2 = k + o_c2[:, None] * (H*K) + o_k[None, :] + p_g2 = g + o_c2[:, None] * (HV*K) + o_k[None, :] + # [BC, BK] + b_q2 = tl.load(p_q2, mask=m_ck2, other=0.0).to(tl.float32) + b_k2 = tl.load(p_k2, mask=m_ck2, other=0.0).to(tl.float32) + b_g2 = tl.load(p_g2, mask=m_ck2, other=0.0).to(tl.float32) + # [BK] + b_gn2 = tl.load(g + i_tc2 * HV*K + o_k, mask=m_k, other=0).to(tl.float32) + # [BC, BK] + b_gqn2 = tl.where(m_tc2[:, None], exp2(b_g2 - b_gn2[None, :]), 0) + b_qg2 = b_q2 * b_gqn2 + b_kg2 = b_k2 * b_gqn2 + # [BK, BC] + b_kgt = tl.trans(b_k0 * exp2(b_gn2[None, :] - b_g0)) + b_Aqk20 += tl.dot(b_qg2, b_kgt) + b_Akk20 += tl.dot(b_kg2, b_kgt) + # [BC, BC] + b_kgt = tl.trans(b_k1 * exp2(b_gn2[None, :] - b_g1)) + # [BC, BC] + b_Aqk21 += tl.dot(b_qg2, b_kgt) + b_Akk21 += tl.dot(b_kg2, b_kgt) + + if NC >= 4 and i_tc3 < T: + m_ck3 = m_tc3[:, None] & m_k[None, :] + p_q3 = q + o_c3[:, None] * (H*K) + o_k[None, :] + p_k3 = k + o_c3[:, None] * (H*K) + o_k[None, :] + p_g3 = g + o_c3[:, None] * (HV*K) + o_k[None, :] + # [BC, BK] + b_q3 = tl.load(p_q3, mask=m_ck3, other=0.0).to(tl.float32) + b_k3 = tl.load(p_k3, mask=m_ck3, other=0.0).to(tl.float32) + b_g3 = tl.load(p_g3, mask=m_ck3, other=0.0).to(tl.float32) + # [BK] + b_gn3 = tl.load(g + i_tc3 * HV*K + o_k, mask=m_k, other=0).to(tl.float32) + # [BC, BK] + b_gqn3 = tl.where(m_tc3[:, None], exp2(b_g3 - b_gn3[None, :]), 0) + b_qg3 = b_q3 * b_gqn3 + b_kg3 = b_k3 * b_gqn3 + # [BK, BC] + b_kgt = tl.trans(b_k0 * exp2(b_gn3[None, :] - b_g0)) + # [BC, BC] + b_Aqk30 += tl.dot(b_qg3, b_kgt) + b_Akk30 += tl.dot(b_kg3, b_kgt) + # [BK, BC] + b_kgt = tl.trans(b_k1 * exp2(b_gn3[None, :] - b_g1)) + # [BC, BC] + b_Aqk31 += tl.dot(b_qg3, b_kgt) + b_Akk31 += tl.dot(b_kg3, b_kgt) + # [BK, BC] + b_kgt = tl.trans(b_k2 * exp2(b_gn3[None, :] - b_g2)) + # [BC, BC] + b_Aqk32 += tl.dot(b_qg3, b_kgt) + b_Akk32 += tl.dot(b_kg3, b_kgt) + + ################################################################################ + # save off-diagonal Aqk blocks and prepare Akk + ################################################################################ + if i_tc1 < T: + p_Aqk10 = Aqk + o_c1[:, None] * (HV*BT) + o_i[None, :] + tl.store(p_Aqk10, (b_Aqk10 * scale).to(Aqk.dtype.element_ty), mask=m_A1) + + p_b1 = beta + bos * HV + i_hv + o_c1 * HV + b_b1 = tl.load(p_b1, mask=m_tc1, other=0.0).to(tl.float32) + b_Akk10 = b_Akk10 * b_b1[:, None] + if NC >= 3 and i_tc2 < T: + p_Aqk20 = Aqk + o_c2[:, None] * (HV*BT) + o_i[None, :] + p_Aqk21 = Aqk + o_c2[:, None] * (HV*BT) + (o_i + BC)[None, :] + tl.store(p_Aqk20, (b_Aqk20 * scale).to(Aqk.dtype.element_ty), mask=m_A2) + tl.store(p_Aqk21, (b_Aqk21 * scale).to(Aqk.dtype.element_ty), mask=m_A2) + + p_b2 = beta + bos * HV + i_hv + o_c2 * HV + b_b2 = tl.load(p_b2, mask=m_tc2, other=0.0).to(tl.float32) + b_Akk20 = b_Akk20 * b_b2[:, None] + b_Akk21 = b_Akk21 * b_b2[:, None] + if NC >= 4 and i_tc3 < T: + p_Aqk30 = Aqk + o_c3[:, None] * (HV*BT) + o_i[None, :] + p_Aqk31 = Aqk + o_c3[:, None] * (HV*BT) + (o_i + BC)[None, :] + p_Aqk32 = Aqk + o_c3[:, None] * (HV*BT) + (o_i + 2*BC)[None, :] + tl.store(p_Aqk30, (b_Aqk30 * scale).to(Aqk.dtype.element_ty), mask=m_A3) + tl.store(p_Aqk31, (b_Aqk31 * scale).to(Aqk.dtype.element_ty), mask=m_A3) + tl.store(p_Aqk32, (b_Aqk32 * scale).to(Aqk.dtype.element_ty), mask=m_A3) + + p_b3 = beta + bos * HV + i_hv + o_c3 * HV + b_b3 = tl.load(p_b3, mask=m_tc3, other=0.0).to(tl.float32) + b_Akk30 = b_Akk30 * b_b3[:, None] + b_Akk31 = b_Akk31 * b_b3[:, None] + b_Akk32 = b_Akk32 * b_b3[:, None] + + p_Akk00 = Akkd + o_c0[:, None] * (HV*BC) + o_i[None, :] + p_Akk11 = Akkd + o_c1[:, None] * (HV*BC) + o_i[None, :] + b_Ai00 = tl.load(p_Akk00, mask=m_A0, other=0.0).to(tl.float32) + b_Ai11 = tl.load(p_Akk11, mask=m_A1, other=0.0).to(tl.float32) + if NC >= 3: + p_Akk22 = Akkd + o_c2[:, None] * (HV*BC) + o_i[None, :] + b_Ai22 = tl.load(p_Akk22, mask=m_A2, other=0.0).to(tl.float32) + if NC >= 4: + p_Akk33 = Akkd + o_c3[:, None] * (HV*BC) + o_i[None, :] + b_Ai33 = tl.load(p_Akk33, mask=m_A3, other=0.0).to(tl.float32) + + ################################################################################ + # forward substitution on diagonals + ################################################################################ + + if not USE_SAFE_GATE: + m_A = o_i[:, None] > o_i[None, :] + m_I = o_i[:, None] == o_i[None, :] + + b_Ai00 = -tl.where(m_A, b_Ai00, 0) + b_Ai11 = -tl.where(m_A, b_Ai11, 0) + if NC >= 3: + b_Ai22 = -tl.where(m_A, b_Ai22, 0) + if NC >= 4: + b_Ai33 = -tl.where(m_A, b_Ai33, 0) + + for i in range(2, min(BC, T - i_tc0)): + b_a00 = -tl.load(Akkd + (i_tc0 + i) * HV*BC + o_i) + b_a00 = tl.where(o_i < i, b_a00, 0.) + b_a00 += tl.sum(b_a00[:, None] * b_Ai00, 0) + b_Ai00 = tl.where((o_i == i)[:, None], b_a00, b_Ai00) + for i in range(BC + 2, min(2*BC, T - i_tc0)): + b_a11 = -tl.load(Akkd + (i_tc0 + i) * HV*BC + o_i) + b_a11 = tl.where(o_i < i - BC, b_a11, 0.) + b_a11 += tl.sum(b_a11[:, None] * b_Ai11, 0) + b_Ai11 = tl.where((o_i == i - BC)[:, None], b_a11, b_Ai11) + if NC >= 3: + for i in range(2*BC + 2, min(3*BC, T - i_tc0)): + b_a22 = -tl.load(Akkd + (i_tc0 + i) * HV*BC + o_i) + b_a22 = tl.where(o_i < i - 2*BC, b_a22, 0.) + b_a22 += tl.sum(b_a22[:, None] * b_Ai22, 0) + b_Ai22 = tl.where((o_i == i - 2*BC)[:, None], b_a22, b_Ai22) + if NC >= 4: + for i in range(3*BC + 2, min(4*BC, T - i_tc0)): + b_a33 = -tl.load(Akkd + (i_tc0 + i) * HV*BC + o_i) + b_a33 = tl.where(o_i < i - 3*BC, b_a33, 0.) + b_a33 += tl.sum(b_a33[:, None] * b_Ai33, 0) + b_Ai33 = tl.where((o_i == i - 3*BC)[:, None], b_a33, b_Ai33) + + b_Ai00 += m_I + b_Ai11 += m_I + if NC >= 3: + b_Ai22 += m_I + if NC >= 4: + b_Ai33 += m_I + + ################################################################################ + # compute merged inverse using off-diagonals + ################################################################################ + + # we used tf32 to maintain matrix inverse's precision whenever possible. + b_Ai10 = -tl.dot( + tl.dot(b_Ai11, b_Akk10, input_precision=SOLVE_TRIL_DOT_PRECISION), + b_Ai00, + input_precision=SOLVE_TRIL_DOT_PRECISION + ) + + if NC >= 3: + b_Ai21 = -tl.dot( + tl.dot(b_Ai22, b_Akk21, input_precision=SOLVE_TRIL_DOT_PRECISION), + b_Ai11, + input_precision=SOLVE_TRIL_DOT_PRECISION + ) + b_Ai20 = -tl.dot( + b_Ai22, + tl.dot(b_Akk20, b_Ai00, input_precision=SOLVE_TRIL_DOT_PRECISION) + + tl.dot(b_Akk21, b_Ai10, input_precision=SOLVE_TRIL_DOT_PRECISION), + input_precision=SOLVE_TRIL_DOT_PRECISION + ) + if NC >= 4: + b_Ai32 = -tl.dot( + tl.dot(b_Ai33, b_Akk32, input_precision=SOLVE_TRIL_DOT_PRECISION), + b_Ai22, + input_precision=SOLVE_TRIL_DOT_PRECISION + ) + b_Ai31 = -tl.dot( + b_Ai33, + tl.dot(b_Akk31, b_Ai11, input_precision=SOLVE_TRIL_DOT_PRECISION) + + tl.dot(b_Akk32, b_Ai21, input_precision=SOLVE_TRIL_DOT_PRECISION), + input_precision=SOLVE_TRIL_DOT_PRECISION + ) + b_Ai30 = -tl.dot( + b_Ai33, + tl.dot(b_Akk30, b_Ai00, input_precision=SOLVE_TRIL_DOT_PRECISION) + + tl.dot(b_Akk31, b_Ai10, input_precision=SOLVE_TRIL_DOT_PRECISION) + + tl.dot(b_Akk32, b_Ai20, input_precision=SOLVE_TRIL_DOT_PRECISION), + input_precision=SOLVE_TRIL_DOT_PRECISION + ) + + ################################################################################ + # store full Akk_inv to Akk + ################################################################################ + + p_Akk00 = Akk + o_c0[:, None] * (HV*BT) + o_i[None, :] + p_Akk10 = Akk + o_c1[:, None] * (HV*BT) + o_i[None, :] + p_Akk11 = Akk + o_c1[:, None] * (HV*BT) + (o_i + BC)[None, :] + + tl.store(p_Akk00, b_Ai00.to(Akk.dtype.element_ty), mask=m_A0) + tl.store(p_Akk10, b_Ai10.to(Akk.dtype.element_ty), mask=m_A1) + tl.store(p_Akk11, b_Ai11.to(Akk.dtype.element_ty), mask=m_A1) + if NC >= 3: + p_Akk20 = Akk + o_c2[:, None] * (HV*BT) + o_i[None, :] + p_Akk21 = Akk + o_c2[:, None] * (HV*BT) + (o_i + BC)[None, :] + p_Akk22 = Akk + o_c2[:, None] * (HV*BT) + (o_i + 2*BC)[None, :] + tl.store(p_Akk20, b_Ai20.to(Akk.dtype.element_ty), mask=m_A2) + tl.store(p_Akk21, b_Ai21.to(Akk.dtype.element_ty), mask=m_A2) + tl.store(p_Akk22, b_Ai22.to(Akk.dtype.element_ty), mask=m_A2) + if NC >= 4: + p_Akk30 = Akk + o_c3[:, None] * (HV*BT) + o_i[None, :] + p_Akk31 = Akk + o_c3[:, None] * (HV*BT) + (o_i + BC)[None, :] + p_Akk32 = Akk + o_c3[:, None] * (HV*BT) + (o_i + 2*BC)[None, :] + p_Akk33 = Akk + o_c3[:, None] * (HV*BT) + (o_i + 3*BC)[None, :] + tl.store(p_Akk30, b_Ai30.to(Akk.dtype.element_ty), mask=m_A3) + tl.store(p_Akk31, b_Ai31.to(Akk.dtype.element_ty), mask=m_A3) + tl.store(p_Akk32, b_Ai32.to(Akk.dtype.element_ty), mask=m_A3) + tl.store(p_Akk33, b_Ai33.to(Akk.dtype.element_ty), mask=m_A3) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps, num_stages=num_stages) + for num_warps in [1, 2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['BK', 'NC', 'BT', 'HV'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['B', 'T']) +def chunk_kda_bwd_kernel_intra( + q, + k, + g, + beta, + dAqk, + dAkk, + dq, + dq2, + dk, + dk2, + dg, + dg2, + db, + cu_seqlens, + chunk_indices, + B, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + BT: tl.constexpr, + BC: tl.constexpr, + BK: tl.constexpr, + NC: tl.constexpr, + IS_VARLEN: tl.constexpr, + SAFE_GATE: tl.constexpr, + USE_GATHER: tl.constexpr, +): + i_kc, i_t, i_bh = tl.program_id(0), tl.program_id(1).to(tl.int64), tl.program_id(2).to(tl.int64) + i_b, i_hv = i_bh // HV, i_bh % HV + i_h = i_hv // (HV // H) + i_k, i_i = i_kc // NC, i_kc % NC + + all = B * T + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + else: + bos, eos = i_b * T, i_b * T + T + T = eos - bos + + i_ti = i_t * BT + i_i * BC + if i_ti >= T: + return + + o_k = i_k * BK + tl.arange(0, BK) + m_k = o_k < K + + q += (bos * H + i_h) * K + k += (bos * H + i_h) * K + g += (bos * HV + i_hv) * K + beta += bos * HV + i_hv + + dAqk += (bos * HV + i_hv) * BT + dAkk += (bos * HV + i_hv) * BT + dq += (bos * HV + i_hv) * K + dq2 += (bos * HV + i_hv) * K + dk += (bos * HV + i_hv) * K + dk2 += (bos * HV + i_hv) * K + dg += (bos * HV + i_hv) * K + dg2 += (bos * HV + i_hv) * K + db += (i_k * all + bos) * HV + i_hv + + o_i = tl.arange(0, BC) + o_c = i_ti + o_i + m_c = o_c < T + m_ck = m_c[:, None] & m_k[None, :] + m_dAf = m_c[:, None] & (o_i[None, :] < BT) + m_dAt = (o_i[:, None] < BT) & m_c[None, :] + p_g = g + o_c[:, None] * (HV*K) + o_k[None, :] + b_g = tl.load(p_g, mask=m_ck, other=0.0).to(tl.float32) + + p_b = beta + o_c * HV + b_b = tl.load(p_b, mask=m_c, other=0.0) + + b_dq2 = tl.zeros([BC, BK], dtype=tl.float32) + b_dk2 = tl.zeros([BC, BK], dtype=tl.float32) + if i_i > 0: + p_gn = g + i_ti * HV*K + o_k + # [BK,] + b_gn = tl.load(p_gn, mask=m_k, other=0).to(tl.float32)[None, :] + for i_j in range(0, i_i): + o_j = i_t * BT + i_j * BC + o_i + m_jk = (o_j < T)[:, None] & m_k[None, :] + p_k = k + o_j[:, None] * (H*K) + o_k[None, :] + p_gk = g + o_j[:, None] * (HV*K) + o_k[None, :] + p_dAqk = dAqk + o_c[:, None] * (HV*BT) + (i_j * BC + o_i)[None, :] + p_dAkk = dAkk + o_c[:, None] * (HV*BT) + (i_j * BC + o_i)[None, :] + # [BC, BK] + b_k = tl.load(p_k, mask=m_jk, other=0.0) + b_gk = tl.load(p_gk, mask=m_jk, other=0.0) + b_kg = b_k * exp2(b_gn - b_gk) + # [BC, BC] + b_dAqk = tl.load(p_dAqk, mask=m_dAf, other=0.0) + b_dAkk = tl.load(p_dAkk, mask=m_dAf, other=0.0) + # [BC, BK] + b_dq2 += tl.dot(b_dAqk, b_kg) + b_dk2 += tl.dot(b_dAkk, b_kg) + b_gqn = exp2(b_g - b_gn) + b_dq2 *= b_gqn + b_dk2 *= b_gqn + + o_i = tl.arange(0, BC) + m_dA = (i_ti + o_i) < T + o_dA = (i_ti + o_i) * HV*BT + i_i * BC + p_kj = k + i_ti * H*K + o_k + p_gkj = g + i_ti * HV*K + o_k + + p_q = q + o_c[:, None] * (H*K) + o_k[None, :] + p_k = k + o_c[:, None] * (H*K) + o_k[None, :] + b_q = tl.load(p_q, mask=m_ck, other=0.0) + b_k = tl.load(p_k, mask=m_ck, other=0.0) + + if SAFE_GATE: + if USE_GATHER: + b_gn = gather(b_g, tl.full([1, BK], min(BC//2, T - i_ti - 1), dtype=tl.int16), axis=0) + else: + p_gn = g + (i_ti + min(BC // 2, T - i_ti - 1)) * HV*K + o_k + b_gn = tl.load(p_gn, mask=m_k, other=0)[None, :] + + p_dAqk = dAqk + o_c[:, None] * (HV*BT) + (i_i * BC + o_i)[None, :] + p_dAkk = dAkk + o_c[:, None] * (HV*BT) + (i_i * BC + o_i)[None, :] + b_dAqk_diag_qk = tl.load(p_dAqk, mask=m_dAf, other=0.0).to(tl.float32) + b_dAkk_diag_qk = tl.load(p_dAkk, mask=m_dAf, other=0.0).to(tl.float32) + + m_i_diag_qk = (o_i[:, None] >= o_i[None, :]) & ((i_ti + o_i[:, None]) < T) & ((i_ti + o_i[None, :]) < T) + m_j_diag_qk = (i_ti + o_i[:, None]) < T + + b_dAqk_diag_qk = tl.where(m_i_diag_qk, b_dAqk_diag_qk, 0.) + b_dAkk_diag_qk = tl.where(m_i_diag_qk, b_dAkk_diag_qk, 0.) + b_g_diag_qk = tl.where(m_j_diag_qk, b_g - b_gn, 0.) + exp_b_g_diag_qk = tl.where(m_j_diag_qk, exp2(b_g_diag_qk), 0.) + exp_neg_b_g_diag_qk = tl.where(m_j_diag_qk, exp2(-b_g_diag_qk), 0.) + + b_k_exp_diag_qk = b_k * exp_neg_b_g_diag_qk + b_dq2 += tl.dot(b_dAqk_diag_qk, b_k_exp_diag_qk) * exp_b_g_diag_qk + b_dk2 += tl.dot(b_dAkk_diag_qk, b_k_exp_diag_qk) * exp_b_g_diag_qk + else: + for j in range(0, min(BC, T - i_t * BT - i_i * BC)): + # [BC] + b_dAqk = tl.load(dAqk + o_dA + j, mask=m_dA, other=0) + b_dAkk = tl.load(dAkk + o_dA + j, mask=m_dA, other=0) + # [BK] + b_kj = tl.load(p_kj, mask=m_k, other=0).to(tl.float32) + b_gkj = tl.load(p_gkj, mask=m_k, other=0).to(tl.float32) + # [BC, BK] + m_i = o_i[:, None] >= j + # [BC, BK] + b_gqk = exp2(b_g - b_gkj[None, :]) + b_dq2 += tl.where(m_i, b_dAqk[:, None] * b_kj[None, :] * b_gqk, 0.) + b_dk2 += tl.where(m_i, b_dAkk[:, None] * b_kj[None, :] * b_gqk, 0.) + + p_kj += H*K + p_gkj += HV*K + + b_db = tl.sum(b_dk2 * b_k, 1) + b_dk2 *= b_b[:, None] + + p_dq = dq + o_c[:, None] * (HV*K) + o_k[None, :] + p_dq2 = dq2 + o_c[:, None] * (HV*K) + o_k[None, :] + p_db = db + o_c * HV + + b_dg2 = b_q * b_dq2 + b_dq2 = b_dq2 + tl.load(p_dq, mask=m_ck, other=0.0) + tl.store(p_dq2, b_dq2.to(p_dq2.dtype.element_ty), mask=m_ck) + tl.store(p_db, b_db.to(p_db.dtype.element_ty), mask=m_c) + + tl.debug_barrier() + b_dkt = tl.zeros([BC, BK], dtype=tl.float32) + + NC = min(NC, tl.cdiv(T - i_t * BT, BC)) + if i_i < NC - 1: + p_gn = g + (min(i_ti + BC, T) - 1) * HV*K + o_k + # [BK,] + b_gn = tl.load(p_gn, mask=m_k, other=0).to(tl.float32)[None, :] + for i_j in range(i_i + 1, NC): + o_j = i_t * BT + i_j * BC + o_i + m_j = o_j < T + m_jk = m_j[:, None] & m_k[None, :] + m_dAj = (o_i[:, None] < BT) & m_j[None, :] + p_q = q + o_j[:, None] * (H*K) + o_k[None, :] + p_k = k + o_j[:, None] * (H*K) + o_k[None, :] + p_gk = g + o_j[:, None] * (HV*K) + o_k[None, :] + p_b = beta + o_j * HV + p_dAqk = dAqk + (i_i * BC + o_i)[:, None] + o_j[None, :] * (HV*BT) + p_dAkk = dAkk + (i_i * BC + o_i)[:, None] + o_j[None, :] * (HV*BT) + # [BC] + b_b = tl.load(p_b, mask=m_j, other=0.0) + # [BC, BK] + b_q = tl.load(p_q, mask=m_jk, other=0.0) + b_kb = tl.load(p_k, mask=m_jk, other=0.0) * b_b[:, None] + b_gk = tl.load(p_gk, mask=m_jk, other=0.0).to(tl.float32) + # [BC, BC] + b_dAqk = tl.load(p_dAqk, mask=m_dAj, other=0.0) + b_dAkk = tl.load(p_dAkk, mask=m_dAj, other=0.0) + + # [BC, BK] + b_gkn = exp2(b_gk - b_gn) + b_qg = b_q * tl.where(m_j[:, None], b_gkn, 0) + b_kbg = b_kb * tl.where(m_j[:, None], b_gkn, 0) + # [BC, BK] + # (SY 09/17) important to not use bf16 here to have a good precision. + b_dkt += tl.dot(b_dAqk, b_qg) + b_dkt += tl.dot(b_dAkk, b_kbg) + b_dkt *= exp2(b_gn - b_g) + o_dA = i_ti * HV*BT + i_i * BC + o_i + p_qj = q + i_ti * H*K + o_k + p_kj = k + i_ti * H*K + o_k + p_gkj = g + i_ti * HV*K + o_k + p_bj = beta + i_ti * HV + + if SAFE_GATE: + if USE_GATHER: + b_gn = gather(b_g, tl.full([1, BK], min(BC//2, T - i_ti - 1), dtype=tl.int16), axis=0) + else: + p_gn = g + (i_ti + min(BC // 2, T - i_ti - 1)) * HV*K + o_k + b_gn = tl.load(p_gn, mask=m_k, other=0).to(tl.float32)[None, :] + p_q = q + o_c[:, None] * (H*K) + o_k[None, :] + b_q = tl.load(p_q, mask=m_ck, other=0.0) + p_b = beta + o_c * HV + b_b = tl.load(p_b, mask=m_c, other=0.0) + + p_dAqk = dAqk + (i_i * BC + o_i)[:, None] + o_c[None, :] * (HV*BT) + p_dAkk = dAkk + (i_i * BC + o_i)[:, None] + o_c[None, :] * (HV*BT) + b_dAqk_diag_kk = tl.load(p_dAqk, mask=m_dAt, other=0.0).to(tl.float32) + b_dAkk_diag_kk = tl.load(p_dAkk, mask=m_dAt, other=0.0).to(tl.float32) + + m_i_diag_kk = (o_i[:, None] <= o_i[None, :]) & ((i_ti + o_i[:, None]) < T) & ((i_ti + o_i[None, :]) < T) + m_j_diag_kk = (i_ti + o_i[:, None]) < T + + b_dAqk_diag_kk = tl.where(m_i_diag_kk, b_dAqk_diag_kk, 0.) + b_dAkk_diag_kk = tl.where(m_i_diag_kk, b_dAkk_diag_kk, 0.) + # ensure numerical stability + b_g_diag_kk = tl.where(m_j_diag_kk, b_g - b_gn, 0.) + exp_b_g_diag_kk = tl.where(m_j_diag_kk, exp2(b_g_diag_kk), 0.) + exp_neg_b_g_diag_kk = tl.where(m_j_diag_kk, exp2(-b_g_diag_kk), 0.) + + b_q_exp = b_q * exp_b_g_diag_kk + b_kb_exp = b_k * b_b[:, None] * exp_b_g_diag_kk + + b_dkt += tl.dot(b_dAqk_diag_kk, b_q_exp) * exp_neg_b_g_diag_kk + b_dkt += tl.dot(b_dAkk_diag_kk, b_kb_exp) * exp_neg_b_g_diag_kk + else: + for j in range(0, min(BC, T - i_t * BT - i_i * BC)): + # [BC,] + b_dAqk = tl.load(dAqk + o_dA + j * HV*BT) + b_dAkk = tl.load(dAkk + o_dA + j * HV*BT) + # [BK,] + b_qj = tl.load(p_qj, mask=m_k, other=0).to(tl.float32) + b_kbj = tl.load(p_kj, mask=m_k, other=0).to(tl.float32) * tl.load(p_bj) + b_gkj = tl.load(p_gkj, mask=m_k, other=0).to(tl.float32) + # [BC, BK] + m_i = o_i[:, None] <= j + b_gkq = exp2(b_gkj[None, :] - b_g) + b_dkt += tl.where(m_i, b_dAqk[:, None] * b_qj[None, :] * b_gkq, 0.) + b_dkt += tl.where(m_i, b_dAkk[:, None] * b_kbj[None, :] * b_gkq, 0.) + + p_qj += H*K + p_kj += H*K + p_gkj += HV*K + p_bj += HV + p_dk = dk + o_c[:, None] * (HV*K) + o_k[None, :] + p_dk2 = dk2 + o_c[:, None] * (HV*K) + o_k[None, :] + p_dg = dg + o_c[:, None] * (HV*K) + o_k[None, :] + p_dg2 = dg2 + o_c[:, None] * (HV*K) + o_k[None, :] + + b_dg2 += (b_dk2 - b_dkt) * b_k + tl.load(p_dg, mask=m_ck, other=0.0) + b_dk2 += tl.load(p_dk, mask=m_ck, other=0.0) + b_dk2 += b_dkt + + tl.store(p_dk2, b_dk2.to(p_dk2.dtype.element_ty), mask=m_ck) + tl.store(p_dg2, b_dg2.to(p_dg2.dtype.element_ty), mask=m_ck) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps, num_stages=num_stages) + for num_warps in [1, 2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=["BT", "BC", "HV"], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_kda_fwd_kernel_intra_sub_chunk( + q, + k, + g, + beta, + Aqk, + Akk, + scale, + cu_seqlens, + chunk_indices, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + BT: tl.constexpr, + BC: tl.constexpr, + BK: tl.constexpr, + IS_VARLEN: tl.constexpr, + USE_GATHER: tl.constexpr, +): + i_t, i_i, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1), tl.program_id(2).to(tl.int64) + i_b, i_hv = i_bh // HV, i_bh % HV + i_h = i_hv // (HV // H) + + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + i_ti = i_t * BT + i_i * BC + if i_ti >= T: + return + + o_c = i_ti + tl.arange(0, BC) + m_c = o_c < T + + q = q + (bos * H + i_h) * K + k = k + (bos * H + i_h) * K + g = g + (bos * HV + i_hv) * K + beta = beta + bos * HV + i_hv + Aqk = Aqk + (bos * HV + i_hv) * BT + Akk = Akk + (bos * HV + i_hv) * BC + + o_k = tl.arange(0, BK) + m_k = o_k < K + m_ck = m_c[:, None] & m_k[None, :] + p_q = q + o_c[:, None] * (H*K) + o_k[None, :] + p_k = k + o_c[:, None] * (H*K) + o_k[None, :] + p_g = g + o_c[:, None] * (HV*K) + o_k[None, :] + + p_beta = beta + o_c * HV + + b_q = tl.load(p_q, mask=m_ck, other=0.0) + b_k = tl.load(p_k, mask=m_ck, other=0.0) + b_g = tl.load(p_g, mask=m_ck, other=0.0) + b_beta = tl.load(p_beta, mask=m_c, other=0.0) + + if USE_GATHER: + b_gn = gather(b_g, tl.full([1, BK], min(BC//2, T - i_ti - 1), dtype=tl.int16), axis=0) + else: + # caculate offset + p_gn = g + (i_ti + min(BC // 2, T - i_ti - 1)) * HV*K + tl.arange(0, BK) + b_gn = tl.load(p_gn, mask=tl.arange(0, BK) < K, other=0.0) + b_gn = b_gn[None, :] + + # current block, keep numerical stability by subtracting the left boundary + # less than 85 to avoid overflow in exp2 + b_gm = (b_g - b_gn).to(tl.float32) + + b_gq = tl.where(m_c[:, None], exp2(b_gm), 0.) + b_gk = tl.where(m_c[:, None], exp2(-b_gm), 0.) + + b_kgt = tl.trans(b_k * b_gk) + + b_Aqk = tl.dot(b_q * b_gq, b_kgt) * scale + b_Akk = tl.dot(b_k * b_gq, b_kgt) * b_beta[:, None] + + o_i = tl.arange(0, BC) + m_Aqk = o_i[:, None] >= o_i[None, :] + m_Akk = o_i[:, None] > o_i[None, :] + m_I = o_i[:, None] == o_i[None, :] + + b_Aqk = tl.where(m_Aqk, b_Aqk, 0.0) + b_Akk = tl.where(m_Akk, b_Akk, 0.0) + + m_Aqk_st = m_c[:, None] & (o_i[None, :] < BT) + m_Akk_st = m_c[:, None] & (o_i[None, :] < BC) + p_Aqk = Aqk + o_c[:, None] * (HV*BT) + (i_i * BC + o_i)[None, :] + p_Akk = Akk + o_c[:, None] * (HV*BC) + o_i[None, :] + tl.store(p_Aqk, b_Aqk.to(Aqk.dtype.element_ty), mask=m_Aqk_st) + tl.store(p_Akk, b_Akk.to(Akk.dtype.element_ty), mask=m_Akk_st) + + tl.debug_barrier() + + ################################################################################ + # forward substitution + ################################################################################ + + b_Ai = -b_Akk + for i in range(2, min(BC, T - i_ti)): + b_a = -tl.load(Akk + (i_ti + i) * HV*BC + o_i) + b_a = tl.where(o_i < i, b_a, 0.) + b_a += tl.sum(b_a[:, None] * b_Ai, 0) + b_Ai = tl.where((o_i == i)[:, None], b_a, b_Ai) + b_Ai += m_I + tl.store(p_Akk, b_Ai.to(Akk.dtype.element_ty), mask=m_Akk_st) + + +@dispatch('kda') +def chunk_kda_fwd_intra( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + gk: torch.Tensor | None = None, + beta: torch.Tensor | None = None, + scale: float | None = None, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + chunk_indices: torch.LongTensor | None = None, + safe_gate: bool = False, + disable_recompute: bool = False, +): + B, T, H, K, HV = *k.shape, gk.shape[2] + BT = chunk_size + if BT not in (32, 64): + raise ValueError(f"KDA intra chunk kernel only supports chunk_size 32 or 64, got {BT}.") + BC = 16 + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, BT) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + NC = triton.cdiv(BT, BC) + + Aqk = torch.empty(B, T, HV, BT, device=k.device, dtype=k.dtype) + # Akk must be zero-initialized - kernel only writes lower triangular + Akk = torch.zeros(B, T, HV, BT, device=k.device, dtype=k.dtype) + # Separate fp32 buffer for diagonal 16x16 blocks (for precision in solve_tril) + Akkd = torch.empty(B, T, HV, BC, device=k.device, dtype=torch.float32) + + # Step 1: Run token_parallel first to compute diagonal blocks into Akkd (fp32) + # Step 1: compute diagonal blocks into Akk_diag (fp32) + if safe_gate: + grid = (NT, NC, B * HV) + BK = triton.next_power_of_2(K) + chunk_kda_fwd_kernel_intra_sub_chunk[grid]( + q=q, + k=k, + g=gk, + beta=beta, + Aqk=Aqk, + Akk=Akkd, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + T=T, + H=H, + HV=HV, + K=K, + BT=BT, + BC=BC, + BK=BK, + USE_GATHER=IS_GATHER_SUPPORTED, + ) + else: + Aqk, Akkd = chunk_kda_fwd_intra_token_parallel( + q=q, + k=k, + gk=gk, + beta=beta, + Aqk=Aqk, + Akk=Akkd, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_size=BT, + sub_chunk_size=BC, + ) + + # Step 2: Fused inter + solve_tril (works for both fixed-len and varlen) + grid = (NT, B * HV) + chunk_kda_fwd_kernel_inter_solve_fused[grid]( + q=q, + k=k, + g=gk, + beta=beta, + Aqk=Aqk, + Akkd=Akkd, + Akk=Akk, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + T=T, + H=H, + HV=HV, + K=K, + BT=BT, + BC=BC, + NC=NC, + USE_SAFE_GATE=safe_gate, + ) + w, u, qg, kg = recompute_w_u_fwd( + k=k, + v=v, + beta=beta, + A=Akk, + q=q if disable_recompute else None, + gk=gk, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + ) + return w, u, qg, kg, Aqk, Akk + + +@dispatch('kda') +def chunk_kda_bwd_intra( + q: torch.Tensor, + k: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + dAqk: torch.Tensor, + dAkk: torch.Tensor, + dq: torch.Tensor, + dk: torch.Tensor, + db: torch.Tensor, + dg: torch.Tensor, + cu_seqlens: torch.LongTensor | None = None, + chunk_indices: torch.LongTensor | None = None, + chunk_size: int = 64, + safe_gate: bool = False, +): + B, T, H, K, HV = *k.shape, g.shape[2] + BT = chunk_size + BC = min(16, BT) + BK = min(32, triton.next_power_of_2(K)) + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, BT) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + NC = triton.cdiv(BT, BC) + NK = triton.cdiv(K, BK) + + dq2 = torch.empty_like(dq) + dk2 = torch.empty_like(dk) + db2 = beta.new_empty(NK, *beta.shape, dtype=torch.float) + dg2 = torch.empty_like(dg, dtype=torch.float) + grid = (NK * NC, NT, B * HV) + chunk_kda_bwd_kernel_intra[grid]( + q=q, + k=k, + g=g, + beta=beta, + dAqk=dAqk, + dAkk=dAkk, + dq=dq, + dq2=dq2, + dk=dk, + dk2=dk2, + dg=dg, + dg2=dg2, + db=db2, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + B=B, + T=T, + H=H, + HV=HV, + K=K, + BT=BT, + BC=BC, + BK=BK, + NC=NC, + SAFE_GATE=safe_gate, + USE_GATHER=IS_GATHER_SUPPORTED, + ) + dq = dq2 + dk = dk2 + db = db2.sum(0).add_(db) + dg = dg2 + + return dq, dk, db, dg diff --git a/kda/_fla/ops/kda/chunk_intra_token_parallel.py b/kda/_fla/ops/kda/chunk_intra_token_parallel.py new file mode 100644 index 0000000..573f51b --- /dev/null +++ b/kda/_fla/ops/kda/chunk_intra_token_parallel.py @@ -0,0 +1,182 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +# Token-parallel implementation of KDA intra chunk kernel + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.utils.cache import fla_cache_autotune +from kda._fla.ops.utils.op import exp2 +from kda._fla.utils import autotune_cache_kwargs + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BH': BH}, num_warps=num_warps) + for BH in [1, 2, 4, 8] + for num_warps in [1, 2, 4, 8] + ], + key=["K", "H", "HV"], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T', 'N']) +def chunk_kda_fwd_kernel_intra_token_parallel( + q, + k, + g, + beta, + Aqk, + Akk, + scale, + cu_seqlens, + N, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + BK: tl.constexpr, + BT: tl.constexpr, + BC: tl.constexpr, + BH: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_tg, i_hg = tl.program_id(0).to(tl.int64), tl.program_id(1) + + if IS_VARLEN: + i_n = 0 + left, right = 0, N + + # Unrolled binary search (max B=2^32) + # We can limit iterations based on expected max batch size if needed + # 20 iterations covers B=1M, usually enough + for _ in range(20): + if left < right: + mid = (left + right) // 2 + if i_tg < tl.load(cu_seqlens + mid + 1).to(tl.int32): + right = mid + else: + left = mid + 1 + i_n = left + + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + i_t = i_tg - bos + else: + bos = (i_tg // T) * T + i_t = i_tg % T + + if i_t >= T: + return + + i_c = i_t // BT + i_s = (i_t % BT) // BC + i_tc = i_c * BT + i_ts = i_tc + i_s * BC + + G: tl.constexpr = HV // H + + q += bos * H*K + k += bos * H*K + g += bos * HV*K + Aqk += bos * HV*BT + Akk += bos * HV*BC + beta += bos * HV + + o_hv = i_hg * BH + tl.arange(0, BH) + o_h = o_hv // G + o_k = tl.arange(0, BK) + m_hv = o_hv < HV + m_k = o_k < K + m_hk = m_hv[:, None] & m_k[None, :] + + # q/k: [B, T, H, K], manual load via mapped qk head index + p_qk = o_h[:, None] * K + o_k[None, :] + b_q = tl.load(q + i_t * H * K + p_qk, mask=m_hk, other=0).to(tl.float32) + b_k = tl.load(k + i_t * H * K + p_qk, mask=m_hk, other=0).to(tl.float32) + + # g: [B, T, HV, K], beta: [B, T, HV] + p_g = g + i_t * HV * K + o_hv[:, None] * K + o_k[None, :] + p_beta = beta + i_t * HV + o_hv + b_g = tl.load(p_g, mask=m_hk, other=0.0).to(tl.float32) + b_k = b_k * tl.load(p_beta, mask=m_hv, other=0.0).to(tl.float32)[:, None] + + for j in range(i_ts, min(i_t + 1, min(T, i_ts + BC))): + b_kj = tl.load(k + j * H * K + p_qk, mask=m_hk, other=0).to(tl.float32) + p_gj = g + j * HV * K + o_hv[:, None] * K + o_k[None, :] + b_gj = tl.load(p_gj, mask=m_hk, other=0.0).to(tl.float32) + + b_kgj = tl.where(m_k[None, :], b_kj * exp2(b_g - b_gj), 0.0) + b_Aqk = tl.sum(b_q * b_kgj, axis=1) * scale + b_Akk = tl.sum(b_k * b_kgj, axis=1) * tl.where(j < i_t, 1.0, 0.0) + + tl.store(Aqk + i_t * HV * BT + o_hv * BT + j % BT, b_Aqk.to(Aqk.dtype.element_ty), mask=m_hv) + tl.store(Akk + i_t * HV * BC + o_hv * BC + j - i_ts, b_Akk.to(Akk.dtype.element_ty), mask=m_hv) + + +@dispatch('kda') +def chunk_kda_fwd_intra_token_parallel( + q: torch.Tensor, + k: torch.Tensor, + gk: torch.Tensor, + beta: torch.Tensor, + Aqk: torch.Tensor, + Akk: torch.Tensor, + scale: float, + cu_seqlens: torch.LongTensor | None = None, + chunk_size: int = 64, + sub_chunk_size: int = 16, +) -> None: + """ + Token-parallel implementation: each token gets its own thread block. + Supports both fixed-length and variable-length sequences. + Reduces wasted computation on padding. + + Writes directly to Aqk and Akk tensors (in-place). + + Args: + q: [B, T, H, K] + k: [B, T, H, K] + gk: [B, T, HV, K] cumsum of gates (HV >= H for GVA) + beta: [B, T, HV] + Aqk: [B, T, HV, BT] output tensor to write to + Akk: [B, T, HV, BC] output tensor for diagonal blocks (fp32) + scale: attention scale + chunk_size: BT (default 64) + sub_chunk_size: BC (default 16) + """ + B, T, H, K, HV = *q.shape, gk.shape[2] + N = len(cu_seqlens) - 1 if cu_seqlens is not None else B + BT = chunk_size + BC = sub_chunk_size + BK = triton.next_power_of_2(K) + + def grid(meta): return (B * T, triton.cdiv(HV, meta['BH'])) + chunk_kda_fwd_kernel_intra_token_parallel[grid]( + q=q, + k=k, + g=gk, + beta=beta, + Aqk=Aqk, + Akk=Akk, + scale=scale, + cu_seqlens=cu_seqlens, + N=N, + T=T, + H=H, + HV=HV, + K=K, + BK=BK, + BT=BT, + BC=BC, + ) + return Aqk, Akk diff --git a/kda/_fla/ops/kda/fused_recurrent.py b/kda/_fla/ops/kda/fused_recurrent.py new file mode 100644 index 0000000..d98ab89 --- /dev/null +++ b/kda/_fla/ops/kda/fused_recurrent.py @@ -0,0 +1,491 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +# This kernel is modified from the Decode kernel of the vllm gdn/kda model. + +import warnings + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.utils.op import exp +from kda._fla.ops.utils.softplus import softplus +from kda._fla.utils import input_guard + + +@triton.heuristics( + { + "USE_INITIAL_STATE": lambda args: args["h0"] is not None, + "STORE_FINAL_STATE": lambda args: args["ht"] is not None, + "IS_VARLEN": lambda args: args["cu_seqlens"] is not None, + "IS_CONTINUOUS_BATCHING": lambda args: args["ssm_state_indices"] is not None, + "IS_SPEC_DECODING": lambda args: args["num_accepted_tokens"] is not None, + "HAS_A": lambda args: args["A_log"] is not None, + "HAS_BIAS": lambda args: args["dt_bias"] is not None, + "USE_LOWER_BOUND": lambda args: args["lower_bound"] is not None, + } +) +@triton.jit(do_not_specialize=["N", "T"]) +def fused_recurrent_kda_fwd_kernel( + q, + k, + v, + g, + beta, + A_log, + dt_bias, + o, + h0, + ht, + cu_seqlens, + ssm_state_indices, + num_accepted_tokens, + lower_bound, + scale: tl.constexpr, + N: tl.int64, # num of sequences + T: tl.int64, # num of tokens + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + stride_init_state_token: tl.constexpr, + stride_final_state_token: tl.constexpr, + stride_indices_seq: tl.constexpr, + stride_indices_tok: tl.constexpr, + USE_INITIAL_STATE: tl.constexpr, # whether to use initial state + INPLACE_FINAL_STATE: tl.constexpr, # whether to store final state inplace + IS_BETA_HEADWISE: tl.constexpr, # whether beta is headwise vector or scalar, + USE_QK_L2NORM_IN_KERNEL: tl.constexpr, + IS_VARLEN: tl.constexpr, + IS_CONTINUOUS_BATCHING: tl.constexpr, + IS_SPEC_DECODING: tl.constexpr, + STORE_FINAL_STATE: tl.constexpr, + HAS_A: tl.constexpr, + HAS_BIAS: tl.constexpr, + USE_GATE_IN_KERNEL: tl.constexpr, + USE_LOWER_BOUND: tl.constexpr, + APPLY_BETA_SIGMOID: tl.constexpr, + ALLOW_NEG_EIGVAL: tl.constexpr, + STATE_V_FIRST: tl.constexpr, + num_stages: tl.constexpr, +): + pid = tl.program_id(0).to(tl.int64) + NV = tl.cdiv(V, BV) + NK = tl.cdiv(K, BK) + i_k = pid % NK + pid_rest = pid // NK + + i_v = pid_rest % NV + i_nh = pid_rest // NV + i_n, i_hv = i_nh // HV, i_nh % HV + i_h = i_hv // (HV // H) + if IS_VARLEN: + bos, eos = ( + tl.load(cu_seqlens + i_n).to(tl.int64), + tl.load(cu_seqlens + i_n + 1).to(tl.int64), + ) + T = eos - bos + else: + bos, eos = i_n * T, i_n * T + T + + if T == 0: + # no tokens to process for this sequence + return + + o_k = i_k * BK + tl.arange(0, BK) + o_v = i_v * BV + tl.arange(0, BV) + + p_q = q + (bos * H + i_h) * K + o_k + p_k = k + (bos * H + i_h) * K + o_k + p_v = v + (bos * HV + i_hv) * V + o_v + if IS_BETA_HEADWISE: + p_beta = beta + (bos * HV + i_hv) * V + o_v + else: + p_beta = beta + bos * HV + i_hv + + p_g = g + (bos * HV + i_hv) * K + o_k + p_o = o + (bos * HV + i_hv) * V + o_v + + mask_k = o_k < K + mask_v = o_v < V + if STATE_V_FIRST: + mask_h = mask_v[:, None] & mask_k[None, :] + else: + mask_h = mask_k[:, None] & mask_v[None, :] + + if STATE_V_FIRST: + b_h = tl.zeros([BV, BK], dtype=tl.float32) + else: + b_h = tl.zeros([BK, BV], dtype=tl.float32) + if USE_INITIAL_STATE: + if IS_CONTINUOUS_BATCHING: + if IS_SPEC_DECODING: + i_t = tl.load(num_accepted_tokens + i_n).to(tl.int64) - 1 + else: + i_t = 0 + p_h0 = ( + h0 + + tl.load(ssm_state_indices + i_n * stride_indices_seq + i_t).to( + tl.int64 + ) + * stride_init_state_token + ) + if STATE_V_FIRST: + p_h0 = p_h0 + i_hv * K * V + o_v[:, None] * K + o_k[None, :] + else: + p_h0 = p_h0 + i_hv * K * V + o_k[:, None] * V + o_v[None, :] + else: + if STATE_V_FIRST: + p_h0 = h0 + (i_n * HV + i_hv) * K * V + o_v[:, None] * K + o_k[None, :] + else: + p_h0 = h0 + (i_n * HV + i_hv) * K * V + o_k[:, None] * V + o_v[None, :] + b_h += tl.load(p_h0, mask=mask_h, other=0).to(tl.float32) + + for i_t in tl.range(0, T, num_stages=num_stages): + b_q = tl.load(p_q, mask=mask_k, other=0, eviction_policy='evict_last').to(tl.float32) + b_k = tl.load(p_k, mask=mask_k, other=0, eviction_policy='evict_last').to(tl.float32) + b_v = tl.load(p_v, mask=mask_v, other=0, eviction_policy='evict_first').to(tl.float32) + + if USE_QK_L2NORM_IN_KERNEL: + b_q = b_q / tl.sqrt(tl.sum(b_q * b_q) + 1e-6) + b_k = b_k / tl.sqrt(tl.sum(b_k * b_k) + 1e-6) + b_q = b_q * scale + b_g = tl.load(p_g, mask=mask_k, other=0, eviction_policy='evict_last').to(tl.float32) + + if USE_GATE_IN_KERNEL: + b_A = tl.load(A_log + i_hv).to(tl.float32) if HAS_A else 1.0 + + if HAS_BIAS: + b_bias = tl.load(dt_bias + i_hv * K + o_k, mask=mask_k, other=0).to(tl.float32) + b_g = b_g + b_bias + + if USE_LOWER_BOUND: + b_gk = lower_bound * tl.sigmoid((exp(b_A) if HAS_A else b_A) * b_g) + else: + b_gk = -exp(b_A) * softplus(b_g) + else: + b_gk = b_g + + if STATE_V_FIRST: + b_h *= exp(b_gk[None, :]) + else: + b_h *= exp(b_gk[:, None]) + + if STATE_V_FIRST: + b_v -= tl.sum(b_h * b_k[None, :], 1) + else: + b_v -= tl.sum(b_h * b_k[:, None], 0) + if IS_BETA_HEADWISE: + b_beta = tl.load(p_beta, mask=mask_v, other=0, eviction_policy='evict_first').to(tl.float32) + else: + b_beta = tl.load(p_beta, eviction_policy='evict_last').to(tl.float32) + if APPLY_BETA_SIGMOID: + b_beta = tl.sigmoid(b_beta) + if ALLOW_NEG_EIGVAL: + b_beta = b_beta * 2 + b_v *= b_beta + if STATE_V_FIRST: + b_h += b_v[:, None] * b_k[None, :] + b_o = tl.sum(b_h * b_q[None, :], 1) + else: + b_h += b_k[:, None] * b_v[None, :] + b_o = tl.sum(b_h * b_q[:, None], 0) + tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v, eviction_policy='evict_first') + + if IS_CONTINUOUS_BATCHING: + if INPLACE_FINAL_STATE: + p_ht = ( + ht + + tl.load(ssm_state_indices + i_n * stride_indices_seq + i_t).to( + tl.int64 + ) + * stride_final_state_token + ) + else: + p_ht = ht + (bos + i_t) * stride_final_state_token + if STATE_V_FIRST: + p_ht = p_ht + i_hv * K * V + o_v[:, None] * K + o_k[None, :] + else: + p_ht = p_ht + i_hv * K * V + o_k[:, None] * V + o_v[None, :] + tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h) + + p_q += H * K + p_k += H * K + p_o += HV * V + p_v += HV * V + p_g += HV * K + p_beta += HV * (V if IS_BETA_HEADWISE else 1) + + if not IS_CONTINUOUS_BATCHING: + if STORE_FINAL_STATE: + if STATE_V_FIRST: + p_ht = ht + (i_n * HV + i_hv) * K * V + o_v[:, None] * K + o_k[None, :] + else: + p_ht = ht + (i_n * HV + i_hv) * K * V + o_k[:, None] * V + o_v[None, :] + tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h) + + +@dispatch("kda") +def fused_recurrent_kda_fwd( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + initial_state: torch.Tensor | None = None, + scale: float | None = None, + output_final_state: bool = False, + inplace_final_state: bool = True, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + ssm_state_indices: torch.Tensor | None = None, + num_accepted_tokens: torch.Tensor | None = None, + use_qk_l2norm_in_kernel: bool = False, + use_gate_in_kernel: bool = False, + use_beta_sigmoid_in_kernel: bool = False, + allow_neg_eigval: bool = False, + lower_bound: float | None = None, + out: torch.Tensor | None = None, + **kwargs, +) -> tuple[torch.Tensor, torch.Tensor]: + if scale is None: + scale = k.shape[-1] ** -0.5 + + B, T, H, K, V = *k.shape, v.shape[-1] + HV = v.shape[2] + N = B if cu_seqlens is None else len(cu_seqlens) - 1 + BK = triton.next_power_of_2(K) + BV = 32 + + if out is None: + out = torch.zeros_like(v) + else: + assert out.shape == v.shape + if inplace_final_state: + assert initial_state is not None + final_state = initial_state + elif output_final_state: + if state_v_first: + final_state = q.new_empty(N, HV, V, K, dtype=torch.float32) + else: + final_state = q.new_empty(N, HV, K, V, dtype=torch.float32) + else: + final_state = None + + stride_init_state_token = initial_state.stride(0) if initial_state is not None else 1 + stride_final_state_token = final_state.stride(0) if final_state is not None else 1 + + if ssm_state_indices is None: + stride_indices_seq, stride_indices_tok = 1, 1 + elif ssm_state_indices.ndim == 1: + stride_indices_seq, stride_indices_tok = ssm_state_indices.stride(0), 1 + else: + stride_indices_seq, stride_indices_tok = ssm_state_indices.stride() + + grid = (triton.cdiv(V, BV) * N * HV, ) + fused_recurrent_kda_fwd_kernel[grid]( + q=q, + k=k, + v=v, + g=g, + beta=beta, + A_log=A_log, + dt_bias=dt_bias, + o=out, + h0=initial_state, + ht=final_state, + cu_seqlens=cu_seqlens, + ssm_state_indices=ssm_state_indices, + num_accepted_tokens=num_accepted_tokens, + lower_bound=lower_bound, + scale=scale, + N=N, + T=T, + H=H, + HV=HV, + K=K, + V=V, + BK=BK, + BV=BV, + stride_init_state_token=stride_init_state_token, + stride_final_state_token=stride_final_state_token, + stride_indices_seq=stride_indices_seq, + stride_indices_tok=stride_indices_tok, + IS_BETA_HEADWISE=beta.ndim == v.ndim, + USE_QK_L2NORM_IN_KERNEL=use_qk_l2norm_in_kernel, + INPLACE_FINAL_STATE=inplace_final_state, + USE_GATE_IN_KERNEL=use_gate_in_kernel, + APPLY_BETA_SIGMOID=use_beta_sigmoid_in_kernel, + ALLOW_NEG_EIGVAL=allow_neg_eigval, + STATE_V_FIRST=state_v_first, + num_warps=4, + num_stages=2, + ) + + return out, final_state + + +@input_guard +def fused_recurrent_kda( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + scale: float | None = None, + initial_state: torch.Tensor = None, + output_final_state: bool = False, + use_qk_l2norm_in_kernel: bool = False, + use_gate_in_kernel: bool = False, + use_beta_sigmoid_in_kernel: bool = False, + allow_neg_eigval: bool = False, + lower_bound: float | None = None, + state_v_first: bool = False, + cu_seqlens: torch.LongTensor | None = None, + **kwargs, +) -> tuple[torch.Tensor, torch.Tensor]: + r""" + Args: + q (torch.Tensor): + queries of shape `[B, T, H, K]`. + k (torch.Tensor): + keys of shape `[B, T, H, K]`. + v (torch.Tensor): + values of shape `[B, T, HV, V]`. + GVA is applied if `HV > H`. + g (torch.Tensor): + g (decays) of shape `[B, T, HV, K]`. + beta (torch.Tensor): + betas of shape `[B, T, HV]`. + A_log (Optional[torch.Tensor]): + Decay parameter of shape `[HV]`. + When `use_gate_in_kernel=True` together with `lower_bound`, + may be `None` to use `lower_bound * sigmoid(g + dt_bias)`. + dt_bias (Optional[torch.Tensor]): + Bias added to `g` before activation, of shape `[HV]`. Only used when `use_gate_in_kernel=True`. + scale (Optional[float]): + Scale factor for the RetNet attention scores. + If not provided, it will default to `1 / sqrt(K)`. Default: `None`. + initial_state (Optional[torch.Tensor]): + Initial state of shape `[N, HV, K, V]` for `N` input sequences. + For equal-length input sequences, `N` equals the batch size `B`. + Default: `None`. + output_final_state (Optional[bool]): + Whether to output the final state of shape `[N, HV, K, V]`. Default: `False`. + use_qk_l2norm_in_kernel (Optional[bool]): + Whether to use L2 normalization in the kernel. Default: `False`. + use_gate_in_kernel (Optional[bool]): + Whether to compute the log-space KDA decay internally. + When `True`, `g` is the raw input and the kernel fuses gate activation into the recurrence. + Default: `False`. + use_beta_sigmoid_in_kernel (Optional[bool]): + Whether to apply `torch.sigmoid(beta)` inside the kernel. + - If `True`, the passed `beta` acts as the raw beta logits. + - If `False`, `beta` is expected to already be in post-sigmoid space. + Default: `False`. + allow_neg_eigval (Optional[bool]): + Whether to allow negative eigenvalues by scaling `beta` to `[0, 2)`. + Only takes effect together with `use_beta_sigmoid_in_kernel=True`, in which case + the kernel computes `2 * sigmoid(beta)` instead of `sigmoid(beta)`. Default: `False`. + lower_bound (Optional[float]): + Lower bound for the forget gate (in log space). Only used when `use_gate_in_kernel=True`. Default: `None`. + state_v_first (Optional[bool]): + Store the recurrent state in V-first ``[V, K]`` layout instead of the default ``[K, V]``. Default: ``False``. + cu_seqlens (torch.LongTensor): + Cumulative sequence lengths of shape `[N+1]` used for variable-length training, + consistent with the FlashAttention API. + + Returns: + o (torch.Tensor): + Outputs of shape `[B, T, HV, V]`. + final_state (torch.Tensor): + Final state of shape `[N, HV, K, V]` if `output_final_state=True` else `None`. + + Examples:: + >>> import torch + >>> import torch.nn.functional as F + >>> from einops import rearrange + >>> from fla.ops.kda import fused_recurrent_kda + # inputs with equal lengths + >>> B, T, H, HV, K, V = 4, 2048, 4, 8, 512, 512 + >>> q = torch.randn(B, T, H, K, device='cuda') + >>> k = F.normalize(torch.randn(B, T, H, K, device='cuda'), p=2, dim=-1) + >>> v = torch.randn(B, T, HV, V, device='cuda') + >>> g = F.logsigmoid(torch.rand(B, T, HV, K, device='cuda')) + >>> beta = torch.rand(B, T, HV, device='cuda').sigmoid() + >>> h0 = torch.randn(B, HV, K, V, device='cuda') + >>> o, ht = fused_recurrent_kda( + q, k, v, g, beta, + initial_state=h0, + output_final_state=True + ) + # for variable-length inputs, the batch size `B` is expected to be 1 and `cu_seqlens` is required + >>> q, k, v, g, beta = map(lambda x: rearrange(x, 'b t ... -> 1 (b t) ...'), (q, k, v, g, beta)) + # for a batch with 4 sequences, `cu_seqlens` with 5 start/end positions are expected + >>> cu_seqlens = q.new_tensor([0, 2048, 4096, 6144, 8192], dtype=torch.long) + >>> o_var, ht_var = fused_recurrent_kda( + q, k, v, g, beta, + initial_state=h0, + output_final_state=True, + cu_seqlens=cu_seqlens + ) + """ + if 'transpose_state_layout' in kwargs: + if state_v_first: + raise ValueError("Cannot pass both `state_v_first` and the deprecated `transpose_state_layout`.") + warnings.warn( + "`transpose_state_layout` is deprecated and renamed to `state_v_first`.", + DeprecationWarning, + stacklevel=2, + ) + state_v_first = kwargs.pop('transpose_state_layout') + + if cu_seqlens is not None: + if q.shape[0] != 1: + raise ValueError( + f"The batch size is expected to be 1 rather than {q.shape[0]} when using `cu_seqlens`." + f"Please flatten variable-length inputs before processing.", + ) + if initial_state is not None and initial_state.shape[0] != len(cu_seqlens) - 1: + raise ValueError( + f"The number of initial states is expected to be equal to the number of input sequences, " + f"i.e., {len(cu_seqlens) - 1} rather than {initial_state.shape[0]}.", + ) + if scale is None: + scale = k.shape[-1] ** -0.5 + if allow_neg_eigval and not use_beta_sigmoid_in_kernel: + raise ValueError("`allow_neg_eigval=True` requires `use_beta_sigmoid_in_kernel=True`.") + + o, final_state = fused_recurrent_kda_fwd( + q=q, + k=k, + v=v, + g=g, + beta=beta, + A_log=A_log, + dt_bias=dt_bias, + scale=scale, + initial_state=initial_state, + inplace_final_state=False, + output_final_state=output_final_state, + use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel, + use_gate_in_kernel=use_gate_in_kernel, + use_beta_sigmoid_in_kernel=use_beta_sigmoid_in_kernel, + allow_neg_eigval=allow_neg_eigval, + lower_bound=lower_bound, + cu_seqlens=cu_seqlens, + state_v_first=state_v_first, + ) + return o, final_state diff --git a/kda/_fla/ops/kda/gate.py b/kda/_fla/ops/kda/gate.py new file mode 100644 index 0000000..a71b7fd --- /dev/null +++ b/kda/_fla/ops/kda/gate.py @@ -0,0 +1,514 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +# This file is modified and supported by the Moonshot AI Team + +import torch +import torch.nn.functional as F +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.utils.cache import fla_cache_autotune +from kda._fla.ops.utils.index import prepare_chunk_indices +from kda._fla.ops.utils.op import exp +from kda._fla.ops.utils.softplus import softplus +from kda._fla.utils import IS_AMD, autocast_custom_bwd, autocast_custom_fwd, autotune_cache_kwargs, check_shared_mem, input_guard + +BS_LIST = [32, 64] if check_shared_mem() else [16, 32] +BT_LIST_AUTOTUNE = [32, 64, 128] +NUM_WARPS_AUTOTUNE = [2, 4, 8, 16] if IS_AMD else [4, 8, 16, 32] + + +def naive_kda_gate( + g: torch.Tensor, + A_log: torch.Tensor, + dt_bias: torch.Tensor | None = None, + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + Torch reference implementation for KDA gate computation. + + Computes: g = -A_log.exp().unsqueeze(-1) * softplus(g + dt_bias.view(g.shape[-2:])) + + Args: + g (torch.Tensor): + Input tensor of shape `[..., H, K]`. + A_log (torch.Tensor): + Parameter tensor with `H` elements. + dt_bias (torch.Tensor | None): + Optional bias tensor added to `g` before activation, shape `[H * K]`. + + Returns: + Output tensor of shape `[..., H, K]` . + """ + H, _ = g.shape[-2:] + g = g.float() + if dt_bias is not None: + g = g + dt_bias.view(H, -1) + + g = (-A_log.view(H, 1).float().exp() * F.softplus(g.float())).to(output_dtype) + return g + + +def naive_kda_lowerbound_gate( + g: torch.Tensor, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + lower_bound: float = -5.0, + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + Torch reference implementation for KDA lowerbound gate computation. + + Computes: ``g = lower_bound * sigmoid(exp(A_log) * (g + dt_bias))``. + When ``A_log`` is ``None``: ``g = lower_bound * sigmoid(g + dt_bias)``. + + Args: + g (torch.Tensor): + Input tensor of shape `[..., H, K]`. + A_log (torch.Tensor | None): + Optional parameter tensor with `H` elements. + dt_bias (torch.Tensor | None): + Optional bias tensor added to `g` before activation, shape `[H * K]`. + lower_bound (float): + Lower bound for the gate output. Default: `-5.0`. + output_dtype (torch.dtype): + The dtype of the output tensor. Default: `torch.float32`. + + Returns: + Output tensor of shape `[..., H, K]`. + """ + H, _ = g.shape[-2:] + g = g.float() + if dt_bias is not None: + g = g + dt_bias.view(H, -1) + if A_log is not None: + g = A_log.view(H, 1).float().exp() * g + g = lower_bound * F.sigmoid(g) + return g.to(output_dtype) + + +@triton.heuristics({ + "HAS_A": lambda args: args["A_log"] is not None, + "HAS_BIAS": lambda args: args["dt_bias"] is not None, + "HAS_BETA": lambda args: args["beta"] is not None, + 'USE_LOWER_BOUND': lambda args: args['lower_bound'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({"BT": BT}, num_warps=num_warps, num_stages=num_stages) + for BT in BT_LIST_AUTOTUNE + for num_warps in NUM_WARPS_AUTOTUNE + for num_stages in [2, 3] + ], + key=["H", "D"], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def kda_gate_fwd_kernel( + g, + A_log, + dt_bias, + beta, + yg, + yb, + lower_bound, + T, + H: tl.constexpr, + D: tl.constexpr, + BT: tl.constexpr, + BD: tl.constexpr, + HAS_A: tl.constexpr, + HAS_BIAS: tl.constexpr, + HAS_BETA: tl.constexpr, + USE_LOWER_BOUND: tl.constexpr, +): + i_t, i_h = tl.program_id(0).to(tl.int64), tl.program_id(1) + + b_A = tl.load(A_log + i_h).to(tl.float32) if HAS_A else 1.0 + + o_t = i_t * BT + tl.arange(0, BT) + o_d = tl.arange(0, BD) + m_t = o_t < T + m_g = m_t[:, None] & (o_d[None, :] < D) + p_g = g + i_h * D + o_t[:, None] * (H * D) + o_d[None, :] + p_yg = yg + i_h * D + o_t[:, None] * (H * D) + o_d[None, :] + # [BT, BD] + b_g = tl.load(p_g, mask=m_g, other=0.0).to(tl.float32) + if HAS_BIAS: + o_b = i_h * D + tl.arange(0, BD) + b_g = b_g + tl.load(dt_bias + o_b, mask=o_b < H * D, other=0.0).to(tl.float32) + if not USE_LOWER_BOUND: + b_yg = -exp(b_A) * softplus(b_g) + else: + b_yg = lower_bound * tl.sigmoid((exp(b_A) if HAS_A else b_A) * b_g) + tl.store(p_yg, b_yg.to(p_yg.dtype.element_ty), mask=m_g) + + if HAS_BETA: + p_b = beta + i_h + o_t * H + p_yb = yb + i_h + o_t * H + b_yb = tl.sigmoid(tl.load(p_b, mask=m_t, other=0.0).to(tl.float32)) + tl.store(p_yb, b_yb.to(p_yb.dtype.element_ty), mask=m_t) + + +@triton.heuristics({ + "HAS_A": lambda args: args["A_log"] is not None, + "HAS_BIAS": lambda args: args["dt_bias"] is not None, + "HAS_BETA": lambda args: args["beta"] is not None, + 'USE_LOWER_BOUND': lambda args: args['lower_bound'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps, num_stages=num_stages) + for num_warps in NUM_WARPS_AUTOTUNE + for num_stages in [2, 3] + ], + key=["H", "D"], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def kda_gate_bwd_kernel( + g, + A_log, + dt_bias, + beta, + dyg, + dyb, + dg, + dA, + dbeta, + lower_bound, + T, + H: tl.constexpr, + D: tl.constexpr, + BT: tl.constexpr, + BD: tl.constexpr, + HAS_A: tl.constexpr, + HAS_BIAS: tl.constexpr, + HAS_BETA: tl.constexpr, + USE_LOWER_BOUND: tl.constexpr, +): + i_t, i_h = tl.program_id(0).to(tl.int64), tl.program_id(1) + + b_A = tl.load(A_log + i_h).to(tl.float32) if HAS_A else 1.0 + + o_t = i_t * BT + tl.arange(0, BT) + o_d = tl.arange(0, BD) + m_t = o_t < T + m_g = m_t[:, None] & (o_d[None, :] < D) + p_g = g + i_h * D + o_t[:, None] * (H * D) + o_d[None, :] + p_dg = dg + i_h * D + o_t[:, None] * (H * D) + o_d[None, :] + p_dyg = dyg + i_h * D + o_t[:, None] * (H * D) + o_d[None, :] + + # [BT, BD] + b_g = tl.load(p_g, mask=m_g, other=0.0).to(tl.float32) + b_dyg = tl.load(p_dyg, mask=m_g, other=0.0).to(tl.float32) + + if HAS_BIAS: + o_b = i_h * D + tl.arange(0, BD) + b_g = b_g + tl.load(dt_bias + o_b, mask=o_b < H * D, other=0.0).to(tl.float32) + + # [BT, BD] + if not USE_LOWER_BOUND: + b_A = -exp(b_A) + b_yg = b_A * softplus(b_g) + b_dg = b_A * (b_dyg * tl.sigmoid(b_g)) + b_dA = tl.sum(tl.sum(b_dyg * b_yg, 1), 0) + else: + b_A = exp(b_A) if HAS_A else b_A + b_inner = b_A * b_g + b_sig = tl.sigmoid(b_inner) + b_dsig = b_sig * (1.0 - b_sig) + # Common term: dy * (LB * dsig) + b_d_inner_term = b_dyg * (lower_bound * b_dsig) + # dg = d_inner_term * A + b_dg = b_d_inner_term * b_A + b_dA = tl.sum(tl.sum(b_dg * b_g, 1), 0) if HAS_A else 0.0 + + tl.store(p_dg, b_dg.to(p_dg.dtype.element_ty), mask=m_g) + if HAS_A: + tl.store(dA + i_t * H + i_h, b_dA) + + if HAS_BETA: + p_b = beta + i_h + o_t * H + p_db = dbeta + i_h + o_t * H + p_dyb = dyb + i_h + o_t * H + + b_b = tl.load(p_b, mask=m_t, other=0.0).to(tl.float32) + b_db = tl.load(p_dyb, mask=m_t, other=0.0).to(tl.float32) * b_b * (1.0 - b_b) + tl.store(p_db, b_db.to(p_db.dtype.element_ty), mask=m_t) + + +@dispatch('kda') +def kda_gate_fwd( + g: torch.Tensor, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + lower_bound: float | None = None, + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + H, K = g.shape[-2:] + T = g.numel() // (H * K) + + yg = torch.empty_like(g, dtype=output_dtype) + + def grid(meta): + return (triton.cdiv(T, meta["BT"]), H) + + kda_gate_fwd_kernel[grid]( + g=g, + A_log=A_log, + dt_bias=dt_bias, + beta=None, + yg=yg, + yb=None, + T=T, + H=H, + D=K, + BD=triton.next_power_of_2(K), + lower_bound=lower_bound, + ) + return yg + + +@dispatch('kda') +def kda_gate_bwd( + g: torch.Tensor, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + dyg: torch.Tensor | None = None, + lower_bound: float | None = None, +) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor | None]: + H, K = g.shape[-2:] + T = g.numel() // (H * K) + BT = 32 + NT = triton.cdiv(T, BT) + + dg = torch.empty_like(g, dtype=torch.float32) + dA = g.new_empty(NT, H, dtype=torch.float32) if A_log is not None else None + + grid = (triton.cdiv(T, BT), H) + kda_gate_bwd_kernel[grid]( + g=g, + A_log=A_log, + dt_bias=dt_bias, + beta=None, + dyg=dyg, + dyb=None, + dg=dg, + dA=dA, + dbeta=None, + T=T, + H=H, + D=K, + BT=BT, + BD=triton.next_power_of_2(K), + lower_bound=lower_bound, + ) + + dg = dg.view_as(g).type_as(g) + dA = dA.sum(0).view_as(A_log).type_as(A_log) if A_log is not None else None + # dt_bias is [HV, K] in KDAAttention and [HV*K] in some FLA call sites. + dbias = ( + dg.view(-1, H * K).sum(0).reshape_as(dt_bias).type_as(dt_bias) + if dt_bias is not None + else None + ) + + return dg, dA, dbias + + +class KDAGateFunction(torch.autograd.Function): + @staticmethod + @input_guard + @autocast_custom_fwd + def forward( + ctx, + g: torch.Tensor, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + lower_bound: float | None = None, + output_dtype: torch.dtype = torch.float32, + ) -> torch.Tensor: + yg = kda_gate_fwd( + g=g, + A_log=A_log, + dt_bias=dt_bias, + lower_bound=lower_bound, + output_dtype=output_dtype + ) + ctx.save_for_backward(g, A_log, dt_bias) + ctx.lower_bound = lower_bound + return yg + + @staticmethod + @input_guard + @autocast_custom_bwd + def backward(ctx, dyg: torch.Tensor): + g, A_log, dt_bias = ctx.saved_tensors + dg, dA, dbias = kda_gate_bwd( + g=g, + A_log=A_log, + dt_bias=dt_bias, + dyg=dyg, + lower_bound=ctx.lower_bound + ) + return dg, dA, dbias, None, None + + +@dispatch('kda') +@torch.compiler.disable +def fused_kda_gate( + g: torch.Tensor, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + lower_bound: float | None = None, + output_dtype: torch.dtype = torch.float32, +) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: + """ + Fused KDA gate computation with autograd support. + + Computes: g = -A_log.exp().unsqueeze(-1) * softplus(g + dt_bias.view(g.shape[-2:])) + When ``lower_bound`` is set: g = lower_bound * sigmoid(exp(A_log) * (g + dt_bias)). + When ``A_log`` is ``None`` (requires ``lower_bound``): g = lower_bound * sigmoid(g + dt_bias). + + Args: + g (torch.Tensor): + Input tensor of shape `[..., H, K]`. + A_log (torch.Tensor | None): + Optional parameter tensor with `H` elements. + When ``None``, the gate reduces to ``lower_bound * sigmoid(g + dt_bias)`` (requires ``lower_bound``). + dt_bias (torch.Tensor | None): + Optional bias tensor added to `g` before activation, shape `[H * K]`. + + Returns: + Output tensor of shape `[..., H, K]`. + """ + return KDAGateFunction.apply(g, A_log, dt_bias, lower_bound, output_dtype) + + +@triton.heuristics({ + "HAS_A": lambda args: args["A_log"] is not None, + "HAS_BIAS": lambda args: args["dt_bias"] is not None, + 'HAS_SCALE': lambda args: args['scale'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, + 'USE_LOWER_BOUND': lambda args: args['lower_bound'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BS': BS}, num_warps=num_warps) + for BS in BS_LIST + for num_warps in [2, 4, 8] + ], + key=['H', 'S', 'BT', 'IS_VARLEN', 'REVERSE'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def kda_gate_chunk_cumsum_vector_kernel( + s, + A_log, + dt_bias, + o, + scale, + cu_seqlens, + chunk_indices, + lower_bound, + T, + H: tl.constexpr, + S: tl.constexpr, + BT: tl.constexpr, + BS: tl.constexpr, + REVERSE: tl.constexpr, + HAS_A: tl.constexpr, + HAS_BIAS: tl.constexpr, + HAS_SCALE: tl.constexpr, + IS_VARLEN: tl.constexpr, + USE_LOWER_BOUND: tl.constexpr, +): + i_s, i_t, i_bh = tl.program_id(0), tl.program_id(1).to(tl.int64), tl.program_id(2).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + o_t = i_t * BT + tl.arange(0, BT) + o_s = i_s * BS + tl.arange(0, BS) + m_s = (o_t[:, None] < T) & (o_s[None, :] < S) + p_s = s + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] + p_o = o + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] + # [BT, BS] + b_s = tl.load(p_s, mask=m_s, other=0.0).to(tl.float32) + + # Apply dt_bias if exists + if HAS_BIAS: + b_bias = tl.load(dt_bias + i_h * S + o_s, mask=o_s < S, other=0.0).to(tl.float32) + b_s = b_s + b_bias[None, :] + + b_A = tl.load(A_log + i_h).to(tl.float32) if HAS_A else 1.0 + if not USE_LOWER_BOUND: + # Apply gate: -exp(A_log) * softplus(g + bias) + b_gate = -exp(b_A) * softplus(b_s) + else: + b_gate = lower_bound * tl.sigmoid((exp(b_A) if HAS_A else b_A) * b_s) + + # Apply chunk local cumsum + if REVERSE: + b_o = tl.cumsum(b_gate, axis=0, reverse=True) + else: + b_o = tl.cumsum(b_gate, axis=0) + + if HAS_SCALE: + b_o *= scale + tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_s) + + +@input_guard +@dispatch('kda') +def kda_gate_chunk_cumsum( + g: torch.Tensor, + A_log: torch.Tensor | None, + chunk_size: int, + scale: float = None, + dt_bias: torch.Tensor | None = None, + cu_seqlens: torch.Tensor | None = None, + output_dtype: torch.dtype | None = torch.float, + chunk_indices: torch.LongTensor | None = None, + lower_bound: float | None = None, + **kwargs, +) -> torch.Tensor: + if cu_seqlens is not None: + assert g.shape[0] == 1, "Only batch size 1 is supported when cu_seqlens are provided" + assert len(g.shape) == 4 + B, T, H, S = g.shape + BT = chunk_size + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, BT) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + assert chunk_size == 2**(chunk_size.bit_length()-1), "chunk_size must be a power of 2" + + g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype) + def grid(meta): return (triton.cdiv(meta['S'], meta['BS']), NT, B * H) + kda_gate_chunk_cumsum_vector_kernel[grid]( + s=g_org, + A_log=A_log, + dt_bias=dt_bias, + o=g, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + lower_bound=lower_bound, + T=T, + H=H, + S=S, + BT=BT, + REVERSE=False, + ) + return g diff --git a/kda/_fla/ops/kda/wy_fast.py b/kda/_fla/ops/kda/wy_fast.py new file mode 100644 index 0000000..e23b1ab --- /dev/null +++ b/kda/_fla/ops/kda/wy_fast.py @@ -0,0 +1,369 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.utils import prepare_chunk_indices +from kda._fla.ops.utils.cache import fla_cache_autotune +from kda._fla.ops.utils.op import exp2 +from kda._fla.utils import autotune_cache_kwargs, check_shared_mem + + +@triton.heuristics({ + 'STORE_QG': lambda args: args['qg'] is not None, + 'STORE_KG': lambda args: args['kg'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps, num_stages=num_stages) + for num_warps in [2, 4, 8] + for num_stages in [2, 3, 4] + ], + key=['H', 'HV', 'K', 'V', 'BT', 'BK', 'BV', 'IS_VARLEN'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def recompute_w_u_fwd_kda_kernel( + q, + k, + qg, + kg, + v, + beta, + w, + u, + A, + gk, + cu_seqlens, + chunk_indices, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + STORE_QG: tl.constexpr, + STORE_KG: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64) + i_b, i_hv = i_bh // HV, i_bh % HV + i_h = i_hv // (HV // H) + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + k += (bos * H + i_h) * K + v += (bos * HV + i_hv) * V + u += (bos * HV + i_hv) * V + w += (bos * HV + i_hv) * K + gk += (bos * HV + i_hv) * K + beta += bos * HV + i_hv + A += (bos * HV + i_hv) * BT + if STORE_QG: + q += (bos * H + i_h) * K + qg += (bos * HV + i_hv) * K + if STORE_KG: + kg += (bos * HV + i_hv) * K + + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + p_b = beta + o_t * HV + b_b = tl.load(p_b, mask=m_t, other=0.0) + + o_A = tl.arange(0, BT) + m_A = m_t[:, None] & (o_A[None, :] < BT) + p_A = A + o_t[:, None] * (HV*BT) + o_A[None, :] + b_A = tl.load(p_A, mask=m_A, other=0.0) + + for i_v in range(tl.cdiv(V, BV)): + o_v = i_v * BV + tl.arange(0, BV) + m_v = m_t[:, None] & (o_v[None, :] < V) + p_v = v + o_t[:, None] * (HV*V) + o_v[None, :] + p_u = u + o_t[:, None] * (HV*V) + o_v[None, :] + b_v = tl.load(p_v, mask=m_v, other=0.0) + b_vb = (b_v * b_b[:, None]).to(b_v.dtype) + b_u = tl.dot(b_A, b_vb) + tl.store(p_u, b_u.to(p_u.dtype.element_ty), mask=m_v) + + for i_k in range(tl.cdiv(K, BK)): + o_k = i_k * BK + tl.arange(0, BK) + m_k = o_k < K + m_tk = m_t[:, None] & m_k[None, :] + p_w = w + o_t[:, None] * (HV*K) + o_k[None, :] + p_k = k + o_t[:, None] * (H*K) + o_k[None, :] + b_k = tl.load(p_k, mask=m_tk, other=0.0) + b_kb = b_k * b_b[:, None] + + p_gk = gk + o_t[:, None] * (HV*K) + o_k[None, :] + b_gk = tl.load(p_gk, mask=m_tk, other=0.0).to(tl.float32) + b_kb *= exp2(b_gk) + if STORE_QG: + p_q = q + o_t[:, None] * (H*K) + o_k[None, :] + p_qg = qg + o_t[:, None] * (HV*K) + o_k[None, :] + b_q = tl.load(p_q, mask=m_tk, other=0.0) + b_qg = b_q * exp2(b_gk) + tl.store(p_qg, b_qg.to(p_qg.dtype.element_ty), mask=m_tk) + if STORE_KG: + last_idx = min(i_t * BT + BT, T) - 1 + b_gn = tl.load(gk + last_idx * HV*K + o_k, mask=m_k, other=0.).to(tl.float32) + b_kg = b_k * tl.where((i_t * BT + tl.arange(0, BT) < T)[:, None], exp2(b_gn[None, :] - b_gk), 0) + p_kg = kg + o_t[:, None] * (HV*K) + o_k[None, :] + tl.store(p_kg, b_kg.to(p_kg.dtype.element_ty), mask=m_tk) + + b_w = tl.dot(b_A, b_kb.to(b_k.dtype)) + tl.store(p_w, b_w.to(p_w.dtype.element_ty), mask=m_tk) + + +@triton.heuristics({ + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps, num_stages=num_stages) + for num_warps in [2, 4] + for num_stages in [2, 3, 4] + ], + key=['H', 'HV', 'K', 'V', 'BT', 'BK', 'BV', 'IS_VARLEN'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def prepare_wy_repr_bwd_kda_kernel( + k, + v, + beta, + gk, + A, + dA, + dw, + du, + dk, + dk2, + dv, + db, + dg, + dg2, + cu_seqlens, + chunk_indices, + T, + H: tl.constexpr, + HV: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + BT: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64) + i_b, i_hv = i_bh // HV, i_bh % HV + i_h = i_hv // (HV // H) + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + k += (bos * H + i_h) * K + v += (bos * HV + i_hv) * V + beta += bos * HV + i_hv + gk += (bos * HV + i_hv) * K + A += (bos * HV + i_hv) * BT + dA += (bos * HV + i_hv) * BT + dk += (bos * HV + i_hv) * K + dk2 += (bos * HV + i_hv) * K + dw += (bos * HV + i_hv) * K + du += (bos * HV + i_hv) * V + dv += (bos * HV + i_hv) * V + db += bos * HV + i_hv + dg += (bos * HV + i_hv) * K + dg2 += (bos * HV + i_hv) * K + + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + p_b = beta + o_t * HV + p_db = db + o_t * HV + o_A = tl.arange(0, BT) + m_AT = (o_A[:, None] < BT) & m_t[None, :] + p_A = A + o_A[:, None] + o_t[None, :] * (HV*BT) + + b_b = tl.load(p_b, mask=m_t, other=0.0) + b_db = tl.zeros([BT], dtype=tl.float32) + b_A = tl.load(p_A, mask=m_AT, other=0.0) + b_dA = tl.zeros([BT, BT], dtype=tl.float32) + + for i_k in range(tl.cdiv(K, BK)): + o_k = i_k * BK + tl.arange(0, BK) + m_k = m_t[:, None] & (o_k[None, :] < K) + p_k = k + o_t[:, None] * (H*K) + o_k[None, :] + p_dk = dk + o_t[:, None] * (HV*K) + o_k[None, :] + p_dk2 = dk2 + o_t[:, None] * (HV*K) + o_k[None, :] + p_dw = dw + o_t[:, None] * (HV*K) + o_k[None, :] + p_dg = dg + o_t[:, None] * (HV*K) + o_k[None, :] + p_dg2 = dg2 + o_t[:, None] * (HV*K) + o_k[None, :] + + # [BT, BK] + b_k = tl.load(p_k, mask=m_k, other=0.0) + p_gk = gk + o_t[:, None] * (HV*K) + o_k[None, :] + b_gk_exp = exp2(tl.load(p_gk, mask=m_k, other=0.0)) + b_kbg = b_k * b_b[:, None] * b_gk_exp + b_dw = tl.load(p_dw, mask=m_k, other=0.0) + + b_dA += tl.dot(b_dw, tl.trans(b_kbg).to(b_dw.dtype)) + b_dkbg = tl.dot(b_A, b_dw) + b_dk = b_dkbg * b_gk_exp * b_b[:, None] + tl.load(p_dk, mask=m_k, other=0.0) + b_db += tl.sum(b_dkbg * b_k * b_gk_exp, 1) + b_dg = b_kbg * b_dkbg + tl.load(p_dg, mask=m_k, other=0.0) + + tl.store(p_dk2, b_dk.to(p_dk2.dtype.element_ty), mask=m_k) + tl.store(p_dg2, b_dg.to(p_dg2.dtype.element_ty), mask=m_k) + + for i_v in range(tl.cdiv(V, BV)): + o_v = i_v * BV + tl.arange(0, BV) + m_v = m_t[:, None] & (o_v[None, :] < V) + p_v = v + o_t[:, None] * (HV*V) + o_v[None, :] + p_dv = dv + o_t[:, None] * (HV*V) + o_v[None, :] + p_du = du + o_t[:, None] * (HV*V) + o_v[None, :] + b_v = tl.load(p_v, mask=m_v, other=0.0) + b_vb = (b_v * b_b[:, None]).to(b_v.dtype) + b_du = tl.load(p_du, mask=m_v, other=0.0) + b_dA += tl.dot(b_du, tl.trans(b_vb)) + b_dvb = tl.dot(b_A, b_du) + b_dv = b_dvb * b_b[:, None] + b_db += tl.sum(b_dvb * b_v, 1) + tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), mask=m_v) + + m_A = (o_t[:, None] > o_t[None, :]) & (m_t[:, None] & m_t) + b_dA = tl.where(m_A, b_dA, 0) + b_dA = tl.dot(b_dA.to(b_A.dtype), b_A) + b_dA = tl.dot(b_A, b_dA.to(b_A.dtype)) + + b_dA = tl.where(m_A, -b_dA, 0) + + m_dA = m_t[:, None] & (o_A[None, :] < BT) + p_dA = dA + o_t[:, None] * (HV*BT) + o_A[None, :] + tl.store(p_dA, b_dA.to(p_dA.dtype.element_ty), mask=m_dA) + tl.store(p_db, b_db.to(p_db.dtype.element_ty), mask=m_t) + + +@dispatch('kda') +def recompute_w_u_fwd( + k: torch.Tensor, + v: torch.Tensor, + beta: torch.Tensor, + A: torch.Tensor, + gk: torch.Tensor, + q: torch.Tensor | None = None, + cu_seqlens: torch.LongTensor | None = None, + chunk_indices: torch.LongTensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor | None, torch.Tensor | None]: + B, T, H, K, V = *k.shape, v.shape[-1] + HV = v.shape[2] + BT = A.shape[-1] + BK = 64 + BV = 64 + + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, BT) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + + w = torch.empty(B, T, HV, K, device=k.device, dtype=k.dtype) + u = torch.empty_like(v) + qg = torch.empty(B, T, HV, K, device=k.device, dtype=k.dtype) if q is not None else None + kg = torch.empty(B, T, HV, K, device=k.device, dtype=k.dtype) + recompute_w_u_fwd_kda_kernel[(NT, B*HV)]( + q=q, + k=k, + qg=qg, + kg=kg, + v=v, + beta=beta, + w=w, + u=u, + A=A, + gk=gk, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + T=T, + H=H, + HV=HV, + K=K, + V=V, + BT=BT, + BK=BK, + BV=BV, + ) + return w, u, qg, kg + + +def prepare_wy_repr_bwd( + k: torch.Tensor, + v: torch.Tensor, + beta: torch.Tensor, + gk: torch.Tensor, + A: torch.Tensor, + dk: torch.Tensor, + dw: torch.Tensor, + du: torch.Tensor, + dg: torch.Tensor, + cu_seqlens: torch.LongTensor | None = None, + chunk_indices: torch.LongTensor | None = None, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + B, T, H, K, V = *k.shape, v.shape[-1] + HV = v.shape[2] + BT = A.shape[-1] + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, BT) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + CONST_TILING = 64 if check_shared_mem() else 32 + BK = min(max(triton.next_power_of_2(K), 16), CONST_TILING) + BV = min(max(triton.next_power_of_2(V), 16), CONST_TILING) + + dk2 = torch.empty_like(dk, dtype=torch.float) + dv = torch.empty_like(v) + dg2 = torch.empty_like(gk, dtype=torch.float) + dA = torch.empty_like(A, dtype=torch.float) + db = torch.empty_like(beta, dtype=torch.float) + prepare_wy_repr_bwd_kda_kernel[(NT, B * HV)]( + k=k, + v=v, + beta=beta, + gk=gk, + A=A, + dA=dA, + dw=dw, + du=du, + dk=dk, + dk2=dk2, + dv=dv, + db=db, + dg=dg, + dg2=dg2, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + T=T, + H=H, + HV=HV, + K=K, + V=V, + BT=BT, + BK=BK, + BV=BV, + ) + dk = dk2 + dg = dg2 + return dk, dv, db, dg, dA diff --git a/kda/_fla/ops/utils/__init__.py b/kda/_fla/ops/utils/__init__.py new file mode 100644 index 0000000..ebe88c4 --- /dev/null +++ b/kda/_fla/ops/utils/__init__.py @@ -0,0 +1,14 @@ +from .cumsum import ( + chunk_local_cumsum, + chunk_local_cumsum_scalar, + chunk_local_cumsum_vector, +) +from .index import prepare_chunk_indices, prepare_chunk_offsets + +__all__ = [ + "chunk_local_cumsum", + "chunk_local_cumsum_scalar", + "chunk_local_cumsum_vector", + "prepare_chunk_indices", + "prepare_chunk_offsets", +] diff --git a/kda/_fla/ops/utils/cache.py b/kda/_fla/ops/utils/cache.py new file mode 100644 index 0000000..40ab2db --- /dev/null +++ b/kda/_fla/ops/utils/cache.py @@ -0,0 +1,449 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import dataclasses +import enum +import json +import logging +import os +import re +from functools import cache, lru_cache +from pathlib import Path +from typing import Any + +import torch +import triton +from packaging import version +from triton.runtime.autotuner import Autotuner + +TRITON_ABOVE_3_5_1 = version.parse(triton.__version__) >= version.parse("3.5.1") +TRITON_ABOVE_3_4_0 = version.parse(triton.__version__) >= version.parse("3.4.0") + + +class FlaCacheMode(enum.Enum): + """Controls how FLA loads kernel configs from its config cache (FLA_CACHE_MODE env var). + + DISABLED — skip all cache lookups, always fall back to Triton autotune (default when FLA_CACHE_MODE is unset) + STRICT — exact key match only; falls back to Triton autotune if no match + FUZZY — exact key match → fuzzy key match; falls back to Triton autotune if no match + FULL — exact key match → fuzzy key match → default_config fallback + DEFAULT — use only the top-level default_config field, skip key-based lookup + ALWAYS — like DEFAULT, but re-reads config files on every kernel call; + useful for debugging: edit default_config in a JSON file and the next + kernel call picks it up without restarting the process + """ + DISABLED = "disabled" + STRICT = "strict" + FUZZY = "fuzzy" + FULL = "full" + DEFAULT = "default" + ALWAYS = "always" + + def uses_default_config(self) -> bool: + """Return True for modes that may fall back to default_config (FULL, DEFAULT, ALWAYS).""" + return self in (FlaCacheMode.FULL, FlaCacheMode.DEFAULT, FlaCacheMode.ALWAYS) + + @classmethod + def from_env(cls) -> "FlaCacheMode": + mode_str = os.environ.get("FLA_CACHE_MODE", cls.DISABLED.value) + try: + return cls(mode_str) + except ValueError: + valid = [m.value for m in cls] + raise ValueError( + f"Invalid FLA_CACHE_MODE={mode_str!r}. Valid values: {valid}" + ) from None + + +FLA_CACHE_MODE: FlaCacheMode = FlaCacheMode.from_env() +logger = logging.getLogger(__name__) + + +def sanitize_gpu_name(gpu_name: str) -> str: + sanitized = re.sub(r"[^0-9A-Za-z]+", "_", gpu_name) + sanitized = sanitized.strip("_") + return sanitized or "unknown_gpu" + + +@lru_cache(maxsize=1) +def get_gpu_info(): + """Get GPU model information. + + This function detects the GPU model and returns a sanitized string identifier. + It prioritizes FLA_GPU_NAME environment variable if set, then detects from + available hardware (CUDA, ROCm, Intel GPU, or CPU). + """ + # Check if GPU name is overridden via environment variable + gpu_name = None + # Check if GPU name is overridden via environment variable + if "FLA_GPU_NAME" in os.environ: + gpu_name = os.environ["FLA_GPU_NAME"] + # Try to get device name based on availability + elif torch.cuda.is_available(): + # Works for both NVIDIA and AMD GPUs (ROCm) + gpu_name = torch.cuda.get_device_name(0) + elif hasattr(torch, 'xpu') and torch.xpu.is_available(): + gpu_name = torch.xpu.get_device_name(0) + + if gpu_name: + return sanitize_gpu_name(gpu_name) + + # Default to CPU if no GPU available + return "cpu" + + +def get_fla_config_dir() -> Path: + """Get FLA's configs directory. + + The directory can be overridden by setting the FLA_CONFIG_DIR environment variable. + If set, configs will be loaded directly from $FLA_CONFIG_DIR/. Otherwise FLA + falls back to the default fla/configs/{GPU}/ directory in the project. + """ + # Check if custom config dir is set via environment variable + if "FLA_CONFIG_DIR" in os.environ: + return Path(os.environ["FLA_CONFIG_DIR"]) + + # Default: project_dir/fla/configs/{GPU}/ + project_dir = Path(__file__).parent.parent.parent + return project_dir / "configs" / get_gpu_info() + + +@dataclasses.dataclass(frozen=True) +class AutotuneKey: + """Autotune key with exact/fuzzy matching, serialization, and construction helpers.""" + autotune_key: tuple[Any, ...] + + @staticmethod + def normalize_autotune_key(value: Any) -> Any: + if isinstance(value, (list, tuple)): + return [AutotuneKey.normalize_autotune_key(v) for v in value] + if isinstance(value, dict): + return {k: AutotuneKey.normalize_autotune_key(v) for k, v in value.items()} + return value + + @staticmethod + def serialize(key: Any) -> str: + return json.dumps(AutotuneKey.normalize_autotune_key(key), separators=(",", ":"), sort_keys=True) + + @staticmethod + def key_hash(key: Any) -> str: + import hashlib + return hashlib.md5(AutotuneKey.serialize(key).encode()).hexdigest() + + @staticmethod + def is_numeric(value: Any) -> bool: + return isinstance(value, (int, float)) and not isinstance(value, bool) + + @staticmethod + def keys_fuzzy_match(cached_key: Any, requested_key: Any) -> bool: + # Fuzzy match: numeric leaves are compatible regardless of their actual numeric values + # (e.g. a config tuned for seq_len=1024 can apply to seq_len=2048). + # Structure (type, length, dict keys) must still match exactly. + if AutotuneKey.is_numeric(cached_key) and AutotuneKey.is_numeric(requested_key): + return True + if isinstance(cached_key, (list, tuple)) and isinstance(requested_key, (list, tuple)): + return len(cached_key) == len(requested_key) and all( + AutotuneKey.keys_fuzzy_match(c, r) for c, r in zip(cached_key, requested_key) + ) + if isinstance(cached_key, dict) and isinstance(requested_key, dict): + return cached_key.keys() == requested_key.keys() and all( + AutotuneKey.keys_fuzzy_match(cached_key[k], requested_key[k]) for k in cached_key + ) + return cached_key == requested_key + + @classmethod + def build( + cls, + arg_names: list[str], + key_names: list[str], + positional_args: tuple[Any, ...], + runtime_kwargs: dict[str, Any], + ) -> "AutotuneKey": + named_args = dict(zip(arg_names, positional_args)) + all_args = {**named_args, **runtime_kwargs} + tracked_args = {k: v for (k, v) in all_args.items() if k in arg_names} + tuning_key = [tracked_args[name] for name in key_names if name in tracked_args] + for arg in tracked_args.values(): + if hasattr(arg, "dtype"): + tuning_key.append(str(arg.dtype)) + return cls(autotune_key=tuple(tuning_key)) + + def exact_matches(self, entry_key: Any) -> bool: + return self.serialize(self.autotune_key) == self.serialize(entry_key) + + def fuzzy_matches(self, entry_key: Any) -> bool: + self_normalized = self.normalize_autotune_key(self.autotune_key) + entry_normalized = self.normalize_autotune_key(entry_key) + return ( + isinstance(self_normalized, list) + and isinstance(entry_normalized, list) + and len(self_normalized) == len(entry_normalized) + and AutotuneKey.keys_fuzzy_match(self_normalized, entry_normalized) + ) + + +@dataclasses.dataclass(frozen=True) +class KernelConfigFile: + """Validated in-memory representation of a {kernel_name}.json config file.""" + kernel_name: str | None + triton_version: str | None + autotune_entries: dict[str, dict[str, Any]] | None + default_config: dict[str, Any] | None + + @classmethod + def from_dict(cls, config_file: Path, data: Any) -> "KernelConfigFile | None": + """Parse and validate a raw JSON dict. Returns None (with a warning) if malformed.""" + def fail(msg, *args): + logger.warning(msg, *args) + raise ValueError + + try: + if not isinstance(data, dict): + fail("Malformed config %s: root is %s, expected dict", config_file, type(data).__name__) + raw_entries = data.get("autotune_entries") + entries: dict[str, dict[str, Any]] | None = None + if raw_entries is not None: + if not isinstance(raw_entries, dict): + fail("Malformed config %s: 'autotune_entries' is %s, expected dict", + config_file, type(raw_entries).__name__) + for h, entry in raw_entries.items(): + if not isinstance(entry, dict): + fail("Malformed config %s: autotune_entries[%r] is %s, expected dict", + config_file, h, type(entry).__name__) + if not isinstance(entry.get("config"), dict): + fail("Malformed config %s: autotune_entries[%r] missing valid 'config' field", config_file, h) + entries = raw_entries + default_config = data.get("default_config") + if default_config is not None and not isinstance(default_config, dict): + fail("Malformed config %s: 'default_config' is %s, expected dict", config_file, type(default_config).__name__) + return cls( + kernel_name=data.get("kernel_name"), + triton_version=data.get("triton_version"), + autotune_entries=entries, + default_config=default_config, + ) + except ValueError: + return None + + @classmethod + def from_file(cls, config_file: Path) -> "KernelConfigFile | None": + """Read and validate a config file. Returns None if the file is missing or malformed.""" + config_data = read_config_file(config_file) + if config_data is None: + return None + return cls.from_dict(config_file, config_data) + + def lookup_exact(self, key: AutotuneKey) -> dict[str, Any] | None: + if self.autotune_entries is None: + return None + return self.autotune_entries.get(AutotuneKey.key_hash(key.autotune_key)) + + def lookup_fuzzy(self, key: AutotuneKey) -> dict[str, Any] | None: + if self.autotune_entries is None: + return None + for entry in self.autotune_entries.values(): + if key.fuzzy_matches(entry.get("autotune_key")): + return entry + return None + + +@cache +def load_config_file(config_file: Path) -> dict[str, Any] | None: + try: + with open(config_file) as f: + return json.load(f) + except Exception as e: + logger.warning("Error reading config file %s: %s", config_file, e) + return None + + +def read_config_file(config_file: Path) -> dict[str, Any] | None: + """Read a config file, bypassing the in-process cache in ALWAYS mode.""" + if FLA_CACHE_MODE is FlaCacheMode.ALWAYS: + return load_config_file.__wrapped__(config_file) + return load_config_file(config_file) + + +def load_cached_config(kernel_name: str, autotune_key: AutotuneKey | None = None) -> dict[str, Any] | None: + """ + Load cached best config for a kernel from FLA configs directory. + + This function loads the cached best configuration for a given kernel name + from get_fla_config_dir()/{kernel_name}.json. + + Cache files may contain multiple autotune entries keyed by Triton's + runtime tuning key plus a top-level default config. + + If the config file is not found or cannot be loaded, a warning is printed + and None is returned, allowing fallback to Triton's autotune. + + The lookup mode is controlled by the FLA_CACHE_MODE environment variable (see FlaCacheMode). + + Args: + kernel_name: Name of the kernel (e.g., "causal_conv1d_fwd_kernel") + autotune_key: Triton autotune key for the current invocation + + Returns: + Best config dictionary or None if not found or disabled + """ + if FLA_CACHE_MODE is FlaCacheMode.DISABLED: + return None + + config_dir = get_fla_config_dir() + config_file = config_dir / f"{kernel_name}.json" + + if not config_file.exists(): + return None + + config_data = read_config_file(config_file) + if config_data is None: + return None + config = KernelConfigFile.from_dict(config_file, config_data) + if config is None: + return None + + if FLA_CACHE_MODE is FlaCacheMode.DEFAULT or FLA_CACHE_MODE is FlaCacheMode.ALWAYS: + return config.default_config + + # STRICT mode: exact match only, no fuzzy fallback + if FLA_CACHE_MODE is FlaCacheMode.STRICT: + if autotune_key is not None: + entry = config.lookup_exact(autotune_key) + if entry is not None: + return entry["config"] + return None + + # FULL and FUZZY modes: try exact key match first, then fuzzy match + if autotune_key is not None: + entry = config.lookup_exact(autotune_key) or config.lookup_fuzzy(autotune_key) + if entry is not None: + return entry["config"] + + if FLA_CACHE_MODE is FlaCacheMode.FUZZY: + return None + + # FULL mode: fall back to default_config, then legacy raw config (no autotune_entries) + if config.default_config is not None: + return config.default_config + if config.autotune_entries is not None: + return None + return config_data + + +class CachedAutotuner(Autotuner): + """ + A modified autotuner that loads best config from FLA's config directory. + + This class extends Triton's Autotuner but overrides the run method to + try loading cached configuration first before falling back to autotune. + """ + + def __init__(self, fn, arg_names, configs, key, reset_to_zero, restore_value, **kwargs): + super().__init__(fn, arg_names, configs, key, reset_to_zero, restore_value, **kwargs) + self.kernel_name = fn.fn.__name__ if hasattr(fn, 'fn') else fn.__name__ + + # None-safe pre/post hooks: Triton's defaults crash when a restore_value / reset_to_zero arg + # is None (idiomatic for optional pointers gated by a tl.constexpr flag). + # Fixed upstream in triton-lang/triton#10295 — remove this override once FLA's minimum Triton version has it. + if not self.user_defined_pre_hook and (self.reset_to_zero or self.restore_value): + def _pre_hook(kw, reset_only=False): + for n in self.reset_to_zero: + if kw[n] is not None: + kw[n].zero_() + if not reset_only: + self.restore_copies = {n: kw[n].clone() for n in self.restore_value if kw[n] is not None} + self.pre_hook = _pre_hook + if not self.user_defined_post_hook and self.restore_value: + def _post_hook(kw, exception): + for n, copy in self.restore_copies.items(): + kw[n].copy_(copy) + self.restore_copies = {} + self.post_hook = _post_hook + + def should_check_fla_cache(self, key: AutotuneKey) -> bool: + if FLA_CACHE_MODE is FlaCacheMode.DISABLED: + return False + if FLA_CACHE_MODE is FlaCacheMode.ALWAYS: + return True + return key.autotune_key not in self.cache + + def run(self, *args, **kwargs): + key = AutotuneKey.build(self.arg_names, self.keys, args, kwargs) + if self.should_check_fla_cache(key): + self.maybe_load_cached_config(key) + return super().run(*args, **kwargs) + + def maybe_load_cached_config(self, key: AutotuneKey): + best_config = load_cached_config(self.kernel_name, key) + + if best_config is not None: + kw = best_config["kwargs"] + num_warps = best_config["num_warps"] + num_stages = best_config["num_stages"] + + extra = { + "num_ctas": best_config["num_ctas"], + "maxnreg": best_config.get("maxnreg"), + "pre_hook": None, + "ir_override": best_config.get("ir_override"), + } if TRITON_ABOVE_3_5_1 else {} + cfg = triton.Config(kw, num_warps=num_warps, num_stages=num_stages, **extra) + + self.cache[key.autotune_key] = cfg + else: + logger.debug( + "No cached config found for kernel %s and key %s; falling back to Triton autotune", + self.kernel_name, + list(key.autotune_key), + ) + + +def fla_cache_autotune(configs, key=None, prune_configs_by=None, reset_to_zero=None, restore_value=None, + pre_hook=None, post_hook=None, warmup=None, rep=None, use_cuda_graph=False, + do_bench=None, cache_results=False): + """ + Decorator for auto-tuning a :code:`triton.jit`'d function with FLA config support. + + Extends Triton's autotune to load best configurations from FLA's config directory + (default: fla/configs/{GPU}/, or FLA_CONFIG_DIR/ when overridden), keyed by kernel + name from {kernel_name}.json. Lookup behaviour is controlled by FLA_CACHE_MODE. + Falls back to normal Triton autotuning when no cached config is found. + """ + # key can be None when we want to use cache only (no fallback autotune) + if key is None: + key = [] + + def decorator(fn): + kwargs = {} + if TRITON_ABOVE_3_4_0: + kwargs = {"cache_results": cache_results} + + return CachedAutotuner(fn, fn.arg_names, configs, key, reset_to_zero, restore_value, + pre_hook=pre_hook, post_hook=post_hook, + prune_configs_by=prune_configs_by, warmup=warmup, rep=rep, + use_cuda_graph=use_cuda_graph, do_bench=do_bench, + **kwargs, + ) + + return decorator + + +def configure_fla_cache_autotune(): + triton.autotune = fla_cache_autotune + logger.info( + "configure_fla_cache_autotune() is enabling FLA fla_cache_autotune; " + "triton.autotune will be replaced with fla_cache_autotune." + ) + + +def restore_autotune_backend(): + from triton.runtime.autotuner import autotune as original_autotune + triton.autotune = original_autotune + logger.info( + "restore_autotune_backend() is restoring Triton's original autotune; " + "triton.autotune will be replaced with triton.runtime.autotuner.autotune." + ) diff --git a/kda/_fla/ops/utils/constant.py b/kda/_fla/ops/utils/constant.py new file mode 100644 index 0000000..3a6c837 --- /dev/null +++ b/kda/_fla/ops/utils/constant.py @@ -0,0 +1,10 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +# Approximate value of 1/ln(2), used for log/exp base conversion +# Best FP32 approximation: 1.4426950216 (hex 0x3FB8AA3B) +RCP_LN2 = 1.4426950216 diff --git a/kda/_fla/ops/utils/cumsum.py b/kda/_fla/ops/utils/cumsum.py new file mode 100644 index 0000000..fb31cf5 --- /dev/null +++ b/kda/_fla/ops/utils/cumsum.py @@ -0,0 +1,468 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch +import triton +import triton.language as tl + +from kda._fla.ops.backends import dispatch +from kda._fla.ops.utils.cache import fla_cache_autotune +from kda._fla.ops.utils.index import prepare_chunk_indices +from kda._fla.utils import autotune_cache_kwargs, check_shared_mem, input_guard + +BS_LIST = [32, 64] if check_shared_mem() else [16, 32] + + +@triton.heuristics({ + 'HAS_SCALE': lambda args: args['scale'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({}, num_warps=num_warps) + for num_warps in [1, 2, 4, 8] + ], + key=['B', 'H', 'BT', 'IS_VARLEN', 'REVERSE'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_local_cumsum_scalar_kernel( + s, + o, + scale, + cu_seqlens, + chunk_indices, + T, + B: tl.constexpr, + H: tl.constexpr, + BT: tl.constexpr, + REVERSE: tl.constexpr, + HAS_SCALE: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_t, i_bh = tl.program_id(0).to(tl.int64), tl.program_id(1).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + p_s = s + bos*H + i_h + o_t * H + p_o = o + bos*H + i_h + o_t * H + # [BT] + b_s = tl.load(p_s, mask=m_t, other=0.0).to(tl.float32) + if REVERSE: + b_o = tl.cumsum(b_s, axis=0, reverse=True) + else: + b_o = tl.cumsum(b_s, axis=0) + if HAS_SCALE: + b_o *= scale + tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_t) + + +@triton.heuristics({ + 'HAS_SCALE': lambda args: args['scale'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@fla_cache_autotune( + configs=[ + triton.Config({'BS': BS}, num_warps=num_warps) + for BS in BS_LIST + for num_warps in [2, 4, 8] + ], + key=['B', 'H', 'S', 'BT', 'IS_VARLEN', 'REVERSE'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_local_cumsum_vector_kernel( + s, + o, + scale, + cu_seqlens, + chunk_indices, + T, + B: tl.constexpr, + H: tl.constexpr, + S: tl.constexpr, + BT: tl.constexpr, + BS: tl.constexpr, + REVERSE: tl.constexpr, + HAS_SCALE: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_s, i_t, i_bh = tl.program_id(0), tl.program_id(1).to(tl.int64), tl.program_id(2).to(tl.int64) + i_b, i_h = i_bh // H, i_bh % H + if IS_VARLEN: + i_n, i_t = tl.load(chunk_indices + i_t * 2).to(tl.int32), tl.load(chunk_indices + i_t * 2 + 1).to(tl.int64) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + T = eos - bos + else: + bos, eos = i_b * T, i_b * T + T + + o_t = i_t * BT + tl.arange(0, BT) + o_s = i_s * BS + tl.arange(0, BS) + m_s = (o_t[:, None] < T) & (o_s[None, :] < S) + p_s = s + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] + p_o = o + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] + # [BT, BS] + b_s = tl.load(p_s, mask=m_s, other=0.0).to(tl.float32) + if REVERSE: + b_o = tl.cumsum(b_s, axis=0, reverse=True) + else: + b_o = tl.cumsum(b_s, axis=0) + if HAS_SCALE: + b_o *= scale + tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_s) + + +@triton.heuristics({ + 'HAS_SCALE': lambda args: args['scale'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@triton.autotune( + configs=[ + triton.Config({'BT': BT}, num_warps=num_warps, num_stages=num_stages) + for BT in [32, 64, 128, 256] + for num_warps in [2, 4, 8] + for num_stages in [1, 2, 3, 4] + ], + key=['B', 'H', 'IS_VARLEN', 'REVERSE'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_global_cumsum_scalar_kernel( + s, + o, + scale, + cu_seqlens, + T, + B: tl.constexpr, + H: tl.constexpr, + BT: tl.constexpr, + REVERSE: tl.constexpr, + HAS_SCALE: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_nh = tl.program_id(0).to(tl.int64) + i_n, i_h = i_nh // H, i_nh % H + if IS_VARLEN: + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + else: + bos, eos = i_n * T, i_n * T + T + T = eos - bos + + b_z = tl.zeros([], dtype=tl.float32) + NT = tl.cdiv(T, BT) + for i_c in range(NT): + i_t = NT - 1 - i_c if REVERSE else i_c + o_t = i_t * BT + tl.arange(0, BT) + m_t = o_t < T + p_s = s + bos*H + i_h + o_t * H + p_o = o + bos*H + i_h + o_t * H + b_s = tl.load(p_s, mask=m_t, other=0.0).to(tl.float32) + if REVERSE: + b_o = tl.cumsum(b_s, axis=0, reverse=True) + else: + b_o = tl.cumsum(b_s, axis=0) + b_ss = tl.sum(b_s, 0) + b_o += b_z + if i_c >= 0: + b_z += b_ss + if HAS_SCALE: + b_o *= scale + tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=m_t) + + +@triton.heuristics({ + 'HAS_SCALE': lambda args: args['scale'] is not None, + 'IS_VARLEN': lambda args: args['cu_seqlens'] is not None, +}) +@triton.autotune( + configs=[ + triton.Config({'BT': BT}, num_warps=num_warps, num_stages=num_stages) + for BT in [16, 32, 64, 128] + for num_warps in [2, 4, 8] + for num_stages in [1, 2, 3, 4] + ], + key=['B', 'H', 'S', 'IS_VARLEN', 'REVERSE'], + **autotune_cache_kwargs, +) +@triton.jit(do_not_specialize=['T']) +def chunk_global_cumsum_vector_kernel( + s, + o, + scale, + cu_seqlens, + T, + B: tl.constexpr, + H: tl.constexpr, + S: tl.constexpr, + BT: tl.constexpr, + BS: tl.constexpr, + REVERSE: tl.constexpr, + HAS_SCALE: tl.constexpr, + IS_VARLEN: tl.constexpr, +): + i_s, i_nh = tl.program_id(0), tl.program_id(1).to(tl.int64) + i_n, i_h = i_nh // H, i_nh % H + if IS_VARLEN: + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64) + else: + bos, eos = i_n * T, i_n * T + T + T = eos - bos + + b_z = tl.zeros([BS], dtype=tl.float32) + NT = tl.cdiv(T, BT) + for i_c in range(NT): + i_t = NT - 1 - i_c if REVERSE else i_c + o_t = i_t * BT + tl.arange(0, BT) + o_s = i_s * BS + tl.arange(0, BS) + m_s = (o_t[:, None] < T) & (o_s[None, :] < S) + p_s = s + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] + p_o = o + (bos * H + i_h) * S + o_t[:, None] * (H*S) + o_s[None, :] + # [BT, BS] + b_s = tl.load(p_s, mask=m_s, other=0.0).to(tl.float32) + if REVERSE: + b_c = b_z[None, :] + tl.cumsum(b_s, axis=0, reverse=True) + else: + b_c = b_z[None, :] + tl.cumsum(b_s, axis=0) + if HAS_SCALE: + b_c *= scale + tl.store(p_o, b_c.to(p_o.dtype.element_ty), mask=m_s) + b_z += tl.sum(b_s, 0) + + +def chunk_local_cumsum_scalar( + g: torch.Tensor, + chunk_size: int, + reverse: bool = False, + scale: float = None, + cu_seqlens: torch.Tensor | None = None, + output_dtype: torch.dtype | None = torch.float, + chunk_indices: torch.LongTensor | None = None, + **kwargs, +) -> torch.Tensor: + if 'head_first' in kwargs: + raise DeprecationWarning( + "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", + ) + B, T, H = g.shape + assert chunk_size == 2**(chunk_size.bit_length()-1), "chunk_size must be a power of 2" + BT = chunk_size + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, BT) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype) + grid = (NT, B * H) + chunk_local_cumsum_scalar_kernel[grid]( + s=g_org, + o=g, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + T=T, + B=B, + H=H, + BT=BT, + REVERSE=reverse, + ) + return g + + +def chunk_local_cumsum_vector( + g: torch.Tensor, + chunk_size: int, + reverse: bool = False, + scale: float = None, + cu_seqlens: torch.Tensor | None = None, + output_dtype: torch.dtype | None = torch.float, + chunk_indices: torch.LongTensor | None = None, + **kwargs, +) -> torch.Tensor: + if 'head_first' in kwargs: + raise DeprecationWarning( + "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", + ) + B, T, H, S = g.shape + BT = chunk_size + if chunk_indices is None and cu_seqlens is not None: + chunk_indices = prepare_chunk_indices(cu_seqlens, BT) + NT = triton.cdiv(T, BT) if cu_seqlens is None else len(chunk_indices) + assert chunk_size == 2**(chunk_size.bit_length()-1), "chunk_size must be a power of 2" + + g_org, g = g, torch.empty_like(g, dtype=output_dtype or g.dtype) + def grid(meta): return (triton.cdiv(meta['S'], meta['BS']), NT, B * H) + # keep cummulative normalizer in fp32 + # this kernel is equivalent to + # g = g.view(B, H, NT, BT, -1).cumsum(-2).view(B, H, T, -1) + chunk_local_cumsum_vector_kernel[grid]( + s=g_org, + o=g, + scale=scale, + cu_seqlens=cu_seqlens, + chunk_indices=chunk_indices, + T=T, + B=B, + H=H, + S=S, + BT=BT, + REVERSE=reverse, + ) + return g + + +@input_guard +def chunk_global_cumsum_scalar( + s: torch.Tensor, + reverse: bool = False, + cu_seqlens: torch.Tensor | None = None, + scale: float = None, + output_dtype: torch.dtype | None = torch.float, + **kwargs, +) -> torch.Tensor: + if 'head_first' in kwargs: + raise DeprecationWarning( + "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", + ) + B, T, H = s.shape + N = len(cu_seqlens) - 1 if cu_seqlens is not None else B + + z = torch.empty_like(s, dtype=output_dtype or s.dtype) + grid = (N * H,) + chunk_global_cumsum_scalar_kernel[grid]( + s=s, + o=z, + scale=scale, + cu_seqlens=cu_seqlens, + T=T, + B=B, + H=H, + REVERSE=reverse, + ) + return z + + +@input_guard +def chunk_global_cumsum_vector( + s: torch.Tensor, + reverse: bool = False, + cu_seqlens: torch.Tensor | None = None, + scale: float = None, + output_dtype: torch.dtype | None = torch.float, + **kwargs, +) -> torch.Tensor: + if 'head_first' in kwargs: + raise DeprecationWarning( + "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", + ) + B, T, H, S = s.shape + N = len(cu_seqlens) - 1 if cu_seqlens is not None else B + BS = min(32, triton.next_power_of_2(S)) + + z = torch.empty_like(s, dtype=output_dtype or s.dtype) + grid = (triton.cdiv(S, BS), N * H) + chunk_global_cumsum_vector_kernel[grid]( + s=s, + o=z, + scale=scale, + cu_seqlens=cu_seqlens, + T=T, + B=B, + H=H, + S=S, + BS=BS, + REVERSE=reverse, + ) + return z + + +@input_guard +@dispatch('utils') +def chunk_global_cumsum( + s: torch.Tensor, + reverse: bool = False, + cu_seqlens: torch.Tensor | None = None, + scale: float = None, + output_dtype: torch.dtype | None = torch.float, + **kwargs, +) -> torch.Tensor: + if 'head_first' in kwargs: + raise DeprecationWarning( + "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", + ) + if cu_seqlens is not None: + assert s.shape[0] == 1, "Only batch size 1 is supported when cu_seqlens are provided" + if len(s.shape) == 3: + return chunk_global_cumsum_scalar( + s=s, + reverse=reverse, + cu_seqlens=cu_seqlens, + scale=scale, + output_dtype=output_dtype, + ) + elif len(s.shape) == 4: + return chunk_global_cumsum_vector( + s=s, + reverse=reverse, + cu_seqlens=cu_seqlens, + scale=scale, + output_dtype=output_dtype, + ) + else: + raise ValueError( + f"Unsupported input shape {s.shape}, " + f"which should be [B, T, H] or [B, T, H, D]", + ) + + +@input_guard +@dispatch('utils') +def chunk_local_cumsum( + g: torch.Tensor, + chunk_size: int, + reverse: bool = False, + scale: float = None, + cu_seqlens: torch.Tensor | None = None, + output_dtype: torch.dtype | None = torch.float, + chunk_indices: torch.LongTensor | None = None, + **kwargs, +) -> torch.Tensor: + if 'head_first' in kwargs: + raise DeprecationWarning( + "head_first has been removed. Inputs must be in `[B, T, H, ...]` format.", + ) + if cu_seqlens is not None: + assert g.shape[0] == 1, "Only batch size 1 is supported when cu_seqlens are provided" + if len(g.shape) == 3: + return chunk_local_cumsum_scalar( + g=g, + chunk_size=chunk_size, + reverse=reverse, + scale=scale, + cu_seqlens=cu_seqlens, + output_dtype=output_dtype, + chunk_indices=chunk_indices, + ) + elif len(g.shape) == 4: + return chunk_local_cumsum_vector( + g=g, + chunk_size=chunk_size, + reverse=reverse, + scale=scale, + cu_seqlens=cu_seqlens, + output_dtype=output_dtype, + chunk_indices=chunk_indices, + ) + else: + raise ValueError( + f"Unsupported input shape {g.shape}, " + f"which should be (B, T, H) or (B, T, H, D)", + ) diff --git a/kda/_fla/ops/utils/index.py b/kda/_fla/ops/utils/index.py new file mode 100644 index 0000000..c5bb8f8 --- /dev/null +++ b/kda/_fla/ops/utils/index.py @@ -0,0 +1,183 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import torch +import torch.nn.functional as F +import triton +import triton.language as tl + +from kda._fla.utils import autotune_cache_kwargs, tensor_cache + + +@triton.autotune( + configs=[ + triton.Config({}, num_warps=num_warps) + for num_warps in [4, 8, 16, 32] + ], + key=['B'], + **autotune_cache_kwargs, +) +@triton.jit +def prepare_position_ids_kernel( + y, + cu_seqlens, + B: tl.constexpr, +): + i_n = tl.program_id(0) + bos, eos = tl.load(cu_seqlens + i_n).to(tl.int32), tl.load(cu_seqlens + i_n + 1).to(tl.int32) + T = eos - bos + + o = tl.arange(0, B) + for i in range(0, tl.cdiv(T, B) * B, B): + o_i = o + i + tl.store(y + bos + o_i, o_i, o_i < T) + + +@tensor_cache +def prepare_lens(cu_seqlens: torch.LongTensor) -> torch.LongTensor: + return torch.diff(cu_seqlens) + + +@tensor_cache +def prepare_lens_from_mask(mask: torch.BoolTensor) -> torch.LongTensor: + return mask.sum(dim=-1, dtype=torch.int32) + + +@tensor_cache +def prepare_cu_seqlens_from_lens( + lens: torch.LongTensor, + dtype: torch.dtype | None = torch.int32, +) -> torch.LongTensor: + return F.pad(lens.cumsum(dim=0, dtype=dtype), (1, 0)) + + +@tensor_cache +def prepare_cu_seqlens_from_mask( + mask: torch.BoolTensor, + dtype: torch.dtype | None = torch.int32, +) -> torch.LongTensor: + return prepare_cu_seqlens_from_lens(prepare_lens_from_mask(mask), dtype) + + +@tensor_cache +def prepare_split_cu_seqlens( + batch_size: int | None = None, + seq_len: int | None = None, + split_size: int | None = None, + cu_seqlens: torch.LongTensor | None = None, + dtype: torch.dtype | None = torch.int32, + device: torch.device | None = torch.device('cpu'), +) -> torch.LongTensor: + """Sub-split a (optionally packed) batch along the token axis. + + Two calling modes: + - **Rectangular batch**: pass `batch_size` and `seq_len`, leave + `cu_seqlens=None`. Internally synthesizes `[0, L, 2L, ..., B*L]`. + - **Packed varlen**: pass `cu_seqlens`. `batch_size` and `seq_len` are + ignored (kept as optional kwargs for backward-compat with callers + that used to pass dummies). + + `split_size` is always required. + + The legacy positional signature `(batch_size, seq_len, split_size, ...)` + continues to work — the first two args retain their position but may now + be omitted when `cu_seqlens` is supplied. + """ + if split_size is None: + raise TypeError("prepare_split_cu_seqlens() requires `split_size`") + if cu_seqlens is None: + if batch_size is None or seq_len is None: + raise TypeError( + "prepare_split_cu_seqlens() requires either `cu_seqlens`, " + "or both `batch_size` and `seq_len`" + ) + total_tokens = batch_size * seq_len + cu_seqlens = list(range(0, total_tokens, seq_len)) + [total_tokens] + else: + cu_seqlens = cu_seqlens.tolist() + return torch.tensor( + [ + i + for bos, eos in zip(cu_seqlens[:-1], cu_seqlens[1:], strict=False) + for i in range(bos, eos, split_size) + ] + [cu_seqlens[-1]], + dtype=dtype, + device=device, + ) + + +def _segmented_arange(counts: torch.LongTensor) -> tuple[torch.LongTensor, torch.LongTensor]: + """Expand per-segment counts into flat per-slot index tensors. + + Given segment sizes ``counts = [c0, c1, ...]``, return two 1-D tensors of + length ``counts.sum()`` that together label every slot with its segment and + its position within that segment. + + Example -- ``counts = [2, 3]`` (segment 0 spans 2 slots, segment 1 spans 3):: + + seg_id = [0, 0, 1, 1, 1] # which segment each slot belongs to + intra_idx = [0, 1, 0, 1, 2] # running index within that segment + + With CUDA ``counts``, ``repeat_interleave`` reads ``counts.sum()`` on the + host (one device sync). Pass host-side counts to avoid it. + """ + seg_id = torch.repeat_interleave( + torch.arange(counts.numel(), device=counts.device, dtype=counts.dtype), + counts, + ) + seg_start = F.pad(counts.cumsum(0), (1, 0))[:-1] + intra_idx = torch.arange(seg_id.shape[0], device=counts.device, dtype=counts.dtype) - seg_start[seg_id] + return seg_id, intra_idx + + +@tensor_cache +def prepare_position_ids(cu_seqlens: torch.LongTensor, cu_seqlens_cpu: torch.LongTensor | None = None) -> torch.LongTensor: + src = cu_seqlens_cpu if cu_seqlens_cpu is not None else cu_seqlens + _, position_ids = _segmented_arange(prepare_lens(src)) + return position_ids.to(cu_seqlens) + + +@tensor_cache +def prepare_sequence_ids(cu_seqlens: torch.LongTensor, cu_seqlens_cpu: torch.LongTensor | None = None) -> torch.LongTensor: + return prepare_position_ids(cu_seqlens, cu_seqlens_cpu).eq(0).cumsum(0) - 1 + + +@tensor_cache +def prepare_token_indices(cu_seqlens: torch.LongTensor, cu_seqlens_cpu: torch.LongTensor | None = None) -> torch.LongTensor: + position_ids = prepare_position_ids(cu_seqlens, cu_seqlens_cpu) + return torch.stack([prepare_sequence_ids(cu_seqlens, cu_seqlens_cpu), position_ids], 1).to(cu_seqlens) + + +@tensor_cache +def prepare_chunk_indices( + cu_seqlens: torch.LongTensor, + chunk_size: int, + cu_seqlens_cpu: torch.LongTensor | None = None, +) -> torch.LongTensor: + src = cu_seqlens_cpu if cu_seqlens_cpu is not None else cu_seqlens + chunk_counts = (prepare_lens(src) + (chunk_size - 1)).div(chunk_size, rounding_mode='floor') + seg_id, intra_chunk_idx = _segmented_arange(chunk_counts) + return torch.stack([seg_id, intra_chunk_idx], 1).to(cu_seqlens) + + +@tensor_cache +def prepare_chunk_offsets( + cu_seqlens: torch.LongTensor, + chunk_size: int, +) -> torch.LongTensor: + return F.pad(triton.cdiv(prepare_lens(cu_seqlens), chunk_size), (1, 0), value=0).cumsum(-1) + + +@tensor_cache +def get_max_num_splits( + cu_seqlens: torch.LongTensor, + chunk_size: int, + cu_seqlens_cpu: torch.LongTensor | None = None +) -> int: + if cu_seqlens_cpu is not None: + return triton.cdiv(int(max(prepare_lens(cu_seqlens_cpu))), chunk_size) + return triton.cdiv(int(max(prepare_lens(cu_seqlens))), chunk_size) diff --git a/kda/_fla/ops/utils/op.py b/kda/_fla/ops/utils/op.py new file mode 100644 index 0000000..882b605 --- /dev/null +++ b/kda/_fla/ops/utils/op.py @@ -0,0 +1,101 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import os + +import triton +import triton.language as tl +import triton.language.extra.libdevice as tldevice + +from kda._fla.utils import IS_GATHER_SUPPORTED, IS_NVIDIA_BLACKWELL + +if os.environ.get('FLA_USE_FAST_OPS', '0') == '1': + @triton.jit + def exp(x): return tldevice.fast_expf(x.to(tl.float32)) + @triton.jit + def exp2(x): return tldevice.exp2(x.to(tl.float32)) + @triton.jit + def log(x): return tldevice.fast_logf(x.to(tl.float32)) + @triton.jit + def log2(x): return tldevice.fast_log2f(x.to(tl.float32)) + @triton.jit + def tanh(x): return tldevice.fast_tanhf(x.to(tl.float32)) +else: + @triton.jit + def exp(x): return tl.exp(x.to(tl.float32)) + @triton.jit + def exp2(x): return tl.math.exp2(x.to(tl.float32)) + @triton.jit + def log(x): return tl.log(x.to(tl.float32)) + @triton.jit + def log2(x): return tl.log2(x.to(tl.float32)) + @triton.jit + def tanh(x): return tldevice.tanh(x.to(tl.float32)) + + +if IS_NVIDIA_BLACKWELL: + """ + Compute tl.dot with Blackwell workaround. + + On SM100 datacenter and SM120 consumer Blackwell GPUs, wraps the result in + inline assembly to prevent the TritonGPUHoistTMEMAlloc pass from incorrectly + fusing add and dot operations. + See: https://github.com/fla-org/flash-linear-attention/issues/638 + + TODO: Remove this workaround once the Triton compiler bug is fixed. + Track upstream issue at: https://github.com/triton-lang/triton/issues/8695 + """ + @triton.jit + def safe_dot(a, b, allow_tf32: tl.constexpr = None): + return tl.inline_asm_elementwise( + asm="mov.f32 $0, $1;", + constraints="=r,r", + args=[tl.dot(a, b, allow_tf32=allow_tf32)], + dtype=tl.float32, + is_pure=True, + pack=1, + ) +else: + @triton.jit + def safe_dot(a, b, allow_tf32: tl.constexpr = None): + return tl.dot(a, b, allow_tf32=allow_tf32) + + +if not IS_GATHER_SUPPORTED: + @triton.jit + def gather(src, index, axis, _builder=None): + """ + Gather operation that works when tl.gather is not supported. + This is a fallback implementation that returns None. + Just to make triton compiler happy. + """ + return None +else: + gather = tl.gather + + +if hasattr(triton.language, '_experimental_make_tensor_descriptor'): + # For Triton 3.3.x + make_tensor_descriptor = triton.language._experimental_make_tensor_descriptor +elif hasattr(triton.language, 'make_tensor_descriptor'): + # For Triton 3.4.x and later + make_tensor_descriptor = triton.language.make_tensor_descriptor +else: + """ + Fallback implementation when TMA is not supported. + Returns None to indicate TMA descriptors are unavailable. + Just make triton compiler happy. + """ + @triton.jit + def make_tensor_descriptor( + base, + shape, + strides, + block_shape, + _builder=None, + ): + return None diff --git a/kda/_fla/ops/utils/softplus.py b/kda/_fla/ops/utils/softplus.py new file mode 100644 index 0000000..e6f9d0a --- /dev/null +++ b/kda/_fla/ops/utils/softplus.py @@ -0,0 +1,115 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +# REVISED FROM +# https://github.com/shawntan/stickbreaking-attention/blob/main/stickbreaking_attention/sb_varlen/softplus.py + +import triton +from triton import language as tl + +from kda._fla.utils import IS_NVIDIA + + +def _generate_softplus(num_pack): + template = """ + .reg .pred p; + setp.gt.f32 p, ${in_reg}, 20.; + @p mov.f32 ${out_reg}, ${in_reg}; + @!p mul.f32 ${out_reg}, ${in_reg}, 1.4426950408889634; + @!p ex2.approx.ftz.f32 ${out_reg}, ${out_reg}; + @!p add.f32 ${out_reg}, ${out_reg}, 1.0; + @!p lg2.approx.ftz.f32 ${out_reg}, ${out_reg}; + @!p mul.f32 ${out_reg}, ${out_reg}, 0.6931471805599453; + """ + out_str = "" + + for i in range(num_pack): + inner_str = template.format(out_reg=i, in_reg=i + num_pack) + out_str += "{" + inner_str + "}\n" + # flatten out because torch.compile doesn't like newlines + out_str = " ".join(out_str.split("\n")) + return out_str + + +def _generate_softplus2(num_pack): + template = """ + .reg .pred p; + setp.gt.f32 p, ${in_reg}, 15.; + @p mov.f32 ${out_reg}, ${in_reg}; + @!p ex2.approx.ftz.f32 ${out_reg}, ${in_reg}; + @!p add.f32 ${out_reg}, ${out_reg}, 1.0; + @!p lg2.approx.ftz.f32 ${out_reg}, ${out_reg}; + """ + out_str = "" + + for i in range(num_pack): + inner_str = template.format(out_reg=i, in_reg=i + num_pack) + out_str += "{" + inner_str + "}\n" + # flatten out because torch.compile doesn't like newlines + out_str = " ".join(out_str.split("\n")) + return out_str + + +def _generate_constraints(num_pack): + return ",".join("=r" for i in range(num_pack)) + "," + ",".join("r" for i in range(num_pack)) + + +_NUM_REG = 1 +s_softplus: tl.constexpr = tl.constexpr(_generate_softplus(_NUM_REG)) +s_softplus2: tl.constexpr = tl.constexpr(_generate_softplus2(_NUM_REG)) +s_constraints: tl.constexpr = tl.constexpr(_generate_constraints(_NUM_REG)) +NUM_REG: tl.constexpr = tl.constexpr(_NUM_REG) + + +@triton.jit +def softplus_nv(x): + # equivalent to: + # return tl.where(x < 20.0, tl.math.log(1 + tl.math.exp(x)), x) + return tl.inline_asm_elementwise( + asm=s_softplus, + constraints=s_constraints, + pack=NUM_REG, + args=[ + x, + ], + dtype=tl.float32, + is_pure=True, + ) + + +@triton.jit +def softplus_triton(x): + return tl.where(x < 20.0, tl.math.log(1 + tl.math.exp(x)), x) + + +@triton.jit +def softplus2_nv(x): + # equivalent to: + # return tl.where(x < 15.0, tl.math.log2(1 + tl.math.exp2(x)), x) + return tl.inline_asm_elementwise( + asm=s_softplus2, + constraints=s_constraints, + pack=NUM_REG, + args=[ + x, + ], + dtype=tl.float32, + is_pure=True, + ) + + +@triton.jit +def softplus2_triton(x): + return tl.where(x < 15.0, tl.math.log2(1 + tl.math.exp2(x)), x) + + +if IS_NVIDIA: + softplus = softplus_nv + softplus2 = softplus2_nv +else: + softplus = softplus_triton + softplus2 = softplus2_triton diff --git a/kda/_fla/utils/__init__.py b/kda/_fla/utils/__init__.py new file mode 100644 index 0000000..8272e83 --- /dev/null +++ b/kda/_fla/utils/__init__.py @@ -0,0 +1,92 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import sys + +from ._compat import ( # noqa: F401 + SUPPORTS_AUTOTUNE_CACHE, + TRITON_ABOVE_3_4_0, + TRITON_ABOVE_3_5_1, + TRITON_ABOVE_3_7_1, + autotune_cache_kwargs, + find_spec_cached, + has_usable_nvcc, +) +from ._config import ( # noqa: F401 + FLA_CACHE_RESULTS, + FLA_CI_ENV, + FLA_DISABLE_TENSOR_CACHE, + FLA_TENSOR_CACHE_SIZE, +) +from ._decorators import ( # noqa: F401 + Action, + checkpoint, + contiguous, + deprecate_kwarg, + input_guard, + require_version, + tensor_cache, +) +from ._device import ( # noqa: F401 + IS_AMD, + IS_ARM, + IS_GATHER_SUPPORTED, + IS_INTEL, + IS_INTEL_ALCHEMIST, + IS_NPU, + IS_NVIDIA, + IS_NVIDIA_BLACKWELL, + IS_NVIDIA_HOPPER, + IS_NVIDIA_SM100, + IS_NVIDIA_SM120, + IS_TF32_SUPPORTED, + IS_TMA_SUPPORTED, + Backend, + autocast_custom_bwd, + autocast_custom_fwd, + check_environments, + check_pytorch_version, + check_shared_mem, + custom_device_ctx, + device, + device_name, + device_platform, + device_torch_lib, + get_all_max_shared_mem, + get_available_device, + get_device_capability, + get_device_smem_optin, + get_multiprocessor_count, + map_triton_backend_to_torch_device, +) +from ._testing import assert_close, get_abs_err, get_err_ratio # noqa: F401 + + +def _register_aliases(): + current_module = sys.modules[__name__] + for key in ( + 'IS_AMD', + 'IS_ARM', + 'IS_INTEL', + 'IS_INTEL_ALCHEMIST', + 'IS_NVIDIA', + 'IS_NPU', + 'IS_NVIDIA_BLACKWELL', + 'IS_NVIDIA_HOPPER', + 'IS_NVIDIA_SM100', + 'IS_NVIDIA_SM120', + 'IS_TF32_SUPPORTED', + 'IS_GATHER_SUPPORTED', + 'IS_TMA_SUPPORTED', + ): + if hasattr(current_module, key): + setattr(current_module, key.lower(), getattr(current_module, key)) + + +_register_aliases() + +del _register_aliases diff --git a/kda/_fla/utils/_compat.py b/kda/_fla/utils/_compat.py new file mode 100644 index 0000000..24b463a --- /dev/null +++ b/kda/_fla/utils/_compat.py @@ -0,0 +1,65 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import functools +import importlib.metadata +import inspect +import logging +import os +import shutil +from importlib.util import find_spec +from pathlib import Path + +import triton +from packaging import version as package_version + +from ._config import FLA_CACHE_RESULTS + +logger = logging.getLogger(__name__) + +TRITON_ABOVE_3_4_0 = package_version.parse(triton.__version__) >= package_version.parse("3.4.0") +TRITON_ABOVE_3_5_1 = package_version.parse(triton.__version__) >= package_version.parse("3.5.1") +TRITON_ABOVE_3_7_1 = package_version.parse(triton.__version__) >= package_version.parse("3.7.1") + +SUPPORTS_AUTOTUNE_CACHE = "cache_results" in inspect.signature(triton.autotune).parameters +autotune_cache_kwargs = {"cache_results": FLA_CACHE_RESULTS} if SUPPORTS_AUTOTUNE_CACHE else {} + + +@functools.cache +def find_spec_cached(name): + return find_spec(name) + + +@functools.cache +def has_usable_nvcc() -> bool: + """Whether a usable nvcc compiler is available for TileLang's JIT. + + Mirrors the guesses in ``tilelang.env._find_cuda_home`` (env + CUDA_HOME/CUDA_PATH, nvcc on PATH, the ``nvidia-cuda-nvcc`` wheel, + /usr/local/cuda), but verifies the nvcc binary actually exists — + only ``nvidia-cuda-nvcc`` >= 13.0 ships it, the ``-cu12`` variant + installs just ptxas. + """ + cuda_home = os.environ.get("CUDA_HOME") or os.environ.get("CUDA_PATH") + if cuda_home is not None and (Path(cuda_home) / "bin" / "nvcc").exists(): + return True + if shutil.which("nvcc") is not None: + return True + try: + files = importlib.metadata.files("nvidia-cuda-nvcc") or [] + except importlib.metadata.PackageNotFoundError: + files = [] + if any(f.name in ("nvcc", "nvcc.exe") for f in files): + return True + if (Path("/usr/local/cuda") / "bin" / "nvcc").exists(): + return True + + logger.info( + "[FLA Backend] TileLang is installed but no usable nvcc compiler was found; falling back to Triton. " + "Install a CUDA toolkit or nvidia-cuda-nvcc, or set FLA_TILELANG=0 to disable TileLang explicitly." + ) + return False diff --git a/kda/_fla/utils/_config.py b/kda/_fla/utils/_config.py new file mode 100644 index 0000000..79b6ce8 --- /dev/null +++ b/kda/_fla/utils/_config.py @@ -0,0 +1,17 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import os + +FLA_CI_ENV = os.getenv("FLA_CI_ENV") == "1" +FLA_CACHE_RESULTS = os.getenv('FLA_CACHE_RESULTS', '1') == '1' + +FLA_DISABLE_TENSOR_CACHE = os.getenv('FLA_DISABLE_TENSOR_CACHE', '0') == '1' +try: + FLA_TENSOR_CACHE_SIZE = int(os.getenv('FLA_TENSOR_CACHE_SIZE', "4")) +except ValueError: + FLA_TENSOR_CACHE_SIZE = 4 diff --git a/kda/_fla/utils/_decorators.py b/kda/_fla/utils/_decorators.py new file mode 100644 index 0000000..ca40129 --- /dev/null +++ b/kda/_fla/utils/_decorators.py @@ -0,0 +1,336 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import contextlib +import functools +import inspect +import sys +import warnings +from collections import deque +from collections.abc import Callable +from enum import Enum +from typing import Any + +import torch +from packaging import version as package_version + +from .. import __version__ +from ._config import FLA_DISABLE_TENSOR_CACHE, FLA_TENSOR_CACHE_SIZE +from ._device import custom_device_ctx + + +class Action(Enum): + NONE = "none" + NOTIFY = "notify" + NOTIFY_ALWAYS = "notify_always" + RAISE = "raise" + + +def tensor_cache( + fn: Callable[..., torch.Tensor], +) -> Callable[..., torch.Tensor]: + """ + A decorator that memoizes the most recent results of a function call by argument identity. + + The decorator keeps a bounded queue of up to ``FLA_TENSOR_CACHE_SIZE`` (default 4) + recent ``(args, kwargs, result)`` triples. On each call, every cached entry is checked + in order; an entry is considered a hit when the positional arg count and kwarg key set + match and every argument is the *same object* (``is`` identity) as the cached one. On a + hit the cached result is returned and ``fn`` is skipped; on a miss ``fn`` is invoked and + the new triple is appended (evicting the oldest when the queue is full). + + Caching is fully bypassed when the ``FLA_DISABLE_TENSOR_CACHE`` environment variable is + set to ``'1'``. + + Args: + fn (Callable[..., torch.Tensor]): + The function to be decorated. Intended for functions whose inputs are tensors + (or other objects compared by identity) and whose output is a tensor. + + Returns: + Callable[..., torch.Tensor]: + A wrapped version of ``fn`` backed by an identity-based bounded cache. + """ + cached: deque = deque(maxlen=FLA_TENSOR_CACHE_SIZE) + + def cache_disabled() -> bool: + utils_module = sys.modules.get('kda._fla.utils') + return getattr(utils_module, 'FLA_DISABLE_TENSOR_CACHE', FLA_DISABLE_TENSOR_CACHE) + + @functools.wraps(fn) + def wrapper(*args: Any, **kwargs: Any) -> Any: + if cache_disabled(): + return fn(*args, **kwargs) + + for cached_args, cached_kwargs, cached_result in cached: + if len(args) != len(cached_args) or len(kwargs) != len(cached_kwargs): + continue + if all(a is b for a, b in zip(args, cached_args, strict=False)) and \ + all(k in cached_kwargs and v is cached_kwargs[k] for k, v in kwargs.items()): + return cached_result + + result = fn(*args, **kwargs) + cached.append((args, kwargs, result)) + return result + + return wrapper + + +def _skip_contiguous( + no_guard_contiguous: bool | list[str] | tuple[str, ...] | set[str], + param_name: str, + skip_params: set[str], +) -> bool: + return no_guard_contiguous is True or param_name in skip_params + + +def _contiguous_if_needed(arg: Any, skip: bool) -> Any: + if isinstance(arg, torch.Tensor) and not skip: + return arg.contiguous() + return arg + + +def input_guard( + fn: Callable[..., torch.Tensor] | None = None, + *, + no_guard_contiguous: bool | list[str] | tuple[str, ...] | set[str] = False, +) -> Callable[[Callable[..., torch.Tensor]], Callable[..., torch.Tensor]] | Callable[..., torch.Tensor]: + """ + A decorator to make sure all input tensors are contiguous and set the device based on input tensors. + + Args: + no_guard_contiguous (bool | list[str] | tuple[str, ...] | set[str]): + If True, skip all contiguous checks. If a list/tuple/set of parameter names, skip contiguous check for those parameters. + """ + + def decorator(fn: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]: + # Get function signature for parameter name mapping + sig = inspect.signature(fn) + param_names = list(sig.parameters.keys()) + skip_params = set(no_guard_contiguous) if isinstance(no_guard_contiguous, (list, tuple, set)) else set() + + @functools.wraps(fn) + def wrapper(*args, **kwargs): + # Process args with parameter name mapping + processed_args = [] + for i, arg in enumerate(args): + if i < len(param_names): + param_name = param_names[i] + else: + # For *args beyond signature, use position as name + param_name = f"__arg_{i}" + + processed_args.append(_contiguous_if_needed( + arg, _skip_contiguous(no_guard_contiguous, param_name, skip_params))) + + # Process kwargs + processed_kwargs = {} + for k, v in kwargs.items(): + processed_kwargs[k] = _contiguous_if_needed(v, _skip_contiguous(no_guard_contiguous, k, skip_params)) + + tensor = None + for arg in args: + if isinstance(arg, torch.Tensor): + tensor = arg + break + if tensor is None: + for value in kwargs.values(): + if isinstance(value, torch.Tensor): + tensor = value + break + + if tensor is not None: + ctx = custom_device_ctx(tensor.device.index) + else: + ctx = contextlib.nullcontext() + + with ctx: + return fn(*processed_args, **processed_kwargs) + + return wrapper + + # Handle direct usage without parentheses: @input_guard + if fn is not None: + return decorator(fn) + + return decorator + + +def contiguous(fn: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]: + """Alias for input_guard() without parameters.""" + return input_guard(fn) + + +def require_version(version, hint): + """ + Perform a runtime check of the dependency versions, using the exact same syntax used by pip. + """ + def decorator(fn): + @functools.wraps(fn) + def wrapper(ctx, *args, **kwargs): + from transformers.utils.versions import require_version + require_version(version, hint) + return fn( + ctx, + *(i if not isinstance(i, torch.Tensor) else i.contiguous() for i in args), + **{k: (v if not isinstance(v, torch.Tensor) else v.contiguous()) for k, v in kwargs.items()}, + ) + return wrapper + return decorator + + +def deprecate_kwarg( + old_name: str, + version: str, + new_name: str | None = None, + warn_if_greater_or_equal_version: bool = False, + raise_if_greater_or_equal_version: bool = False, + raise_if_both_names: bool = False, + additional_message: str | None = None, +): + """ + Decorator to notify users about deprecated keyword arguments, replacing them with a new name if specified. + + This decorator allows you to: + - Notify users when a keyword argument is deprecated. + - Automatically replace deprecated keyword arguments with new ones. + - Raise an error if deprecated arguments are used, depending on the specified conditions. + + By default, the decorator notifies the user about the deprecated argument while the `fla.__version__` < specified `version` + in the decorator. To keep notifications with any version `warn_if_greater_or_equal_version=True` can be set. + + Args: + old_name (`str`): + Name of the deprecated keyword argument. + version (`str`): + The version in which the keyword argument was (or will be) deprecated. + new_name (`Optional[str]`, *optional*): + The new name for the deprecated keyword argument. + If specified, the deprecated keyword argument will be replaced with this new name. + warn_if_greater_or_equal_version (`bool`, *optional*, defaults to `False`): + Whether to show warning if current `fla` version is greater or equal to the deprecated version. + raise_if_greater_or_equal_version (`bool`, *optional*, defaults to `False`): + Whether to raise `ValueError` if current `fla` version is greater or equal to the deprecated version. + raise_if_both_names (`bool`, *optional*, defaults to `False`): + Whether to raise `ValueError` if both deprecated and new keyword arguments are set. + additional_message (`Optional[str]`, *optional*): + An additional message to append to the default deprecation message. + + Raises: + ValueError: + If `raise_if_greater_or_equal_version` is `True` and the current version >= the deprecated one, + or if `raise_if_both_names` is `True` and both old and new keyword arguments are provided. + + Returns: + Callable: + A wrapped function that handles the deprecated keyword arguments according to the specified parameters. + + Example usage with renaming argument: + + ```python + @deprecate_kwarg("reduce_labels", new_name="do_reduce_labels", version="6.0.0") + def my_function(do_reduce_labels): + print(do_reduce_labels) + + my_function(reduce_labels=True) # Will show a deprecation warning and use do_reduce_labels=True + ``` + + Example usage without renaming argument: + + ```python + @deprecate_kwarg("max_size", version="6.0.0") + def my_function(max_size): + print(max_size) + + my_function(max_size=1333) # Will show a deprecation warning + ``` + + """ + deprecated_version = package_version.parse(version) + current_version = package_version.parse(__version__) + is_greater_or_equal_version = current_version >= deprecated_version + + if is_greater_or_equal_version: + version_message = f"and removed starting from version {version}" + else: + version_message = f"and will be removed in version {version}" + + def wrapper(func): + # Required for better warning message + sig = inspect.signature(func) + function_named_args = set(sig.parameters.keys()) + is_instance_method = "self" in function_named_args + is_class_method = "cls" in function_named_args + + @functools.wraps(func) + def wrapped_func(*args, **kwargs): + # Get class + function name (just for better warning message) + func_name = func.__name__ + if is_instance_method: + func_name = f"{args[0].__class__.__name__}.{func_name}" + elif is_class_method: + func_name = f"{args[0].__name__}.{func_name}" + + minimum_action = Action.NONE + message = None + + # deprecated kwarg and its new version are set for function call -> replace it with new name + if old_name in kwargs and new_name in kwargs: + minimum_action = Action.RAISE if raise_if_both_names else Action.NOTIFY_ALWAYS + message = ( + f"Both `{old_name}` and `{new_name}` are set for `{func_name}`. " + f"Using `{new_name}={kwargs[new_name]}` and ignoring deprecated `{old_name}={kwargs[old_name]}`." + ) + kwargs.pop(old_name) + + # only deprecated kwarg is set for function call -> replace it with new name + elif old_name in kwargs and new_name is not None and new_name not in kwargs: + minimum_action = Action.NOTIFY + message = ( + f"`{old_name}` is deprecated {version_message} for `{func_name}`. " + f"Use `{new_name}` instead." + ) + kwargs[new_name] = kwargs.pop(old_name) + + # deprecated kwarg is not set for function call and new name is not specified -> just notify + elif old_name in kwargs: + minimum_action = Action.NOTIFY + message = f"`{old_name}` is deprecated {version_message} for `{func_name}`." + + if message is not None and additional_message is not None: + message = f"{message} {additional_message}" + + # update minimum_action if argument is ALREADY deprecated (current version >= deprecated version) + if is_greater_or_equal_version: + # change to (NOTIFY, NOTIFY_ALWAYS) -> RAISE if specified + # in case we want to raise error for already deprecated arguments + if raise_if_greater_or_equal_version and minimum_action != Action.NONE: + minimum_action = Action.RAISE + + # change to NOTIFY -> NONE if specified (NOTIFY_ALWAYS can't be changed to NONE) + elif not warn_if_greater_or_equal_version and minimum_action == Action.NOTIFY: + minimum_action = Action.NONE + + # raise error or notify user + if minimum_action == Action.RAISE: + raise ValueError(message) + elif minimum_action in (Action.NOTIFY, Action.NOTIFY_ALWAYS): + # DeprecationWarning is ignored by default, so we use FutureWarning instead + warnings.warn(message, FutureWarning, stacklevel=2) + + return func(*args, **kwargs) + + return wrapped_func + + return wrapper + + +def checkpoint(fn): + @functools.wraps(fn) + def wrapper(*args, **kwargs): + return torch.utils.checkpoint.checkpoint(fn, *args, **kwargs) + return wrapper diff --git a/kda/_fla/utils/_device.py b/kda/_fla/utils/_device.py new file mode 100644 index 0000000..42d15ad --- /dev/null +++ b/kda/_fla/utils/_device.py @@ -0,0 +1,245 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import contextlib +import functools +import logging +import os +import platform +import sys +import warnings +from enum import Enum +from functools import cache, lru_cache + +import torch +import triton +from packaging import version as package_version + +logger = logging.getLogger(__name__) + + +@lru_cache(maxsize=1) +def check_environments(): + """ + Checks the current operating system, Triton version, and Python version, + issuing warnings if they don't meet recommendations. + This function's body only runs once due to lru_cache. + """ + # Check Operating System + if sys.platform == 'win32': + # Check if triton-windows is installed + try: + from importlib.metadata import PackageNotFoundError, metadata + metadata('triton-windows') + # triton-windows is installed, no warning needed + except PackageNotFoundError: + logger.warning( + "Detected Windows operating system. Consider installing triton-windows " + "(https://github.com/triton-lang/triton-windows) for better compatibility. " + "Without it, some features may not work correctly.", + ) + + triton_version = package_version.parse(triton.__version__) + required_triton_version = package_version.parse("3.3.0") + + if triton_version < required_triton_version: + logger.warning( + f"Current Triton version {triton_version} is below the recommended 3.3.0 version. " + "Errors may occur and these issues will not be fixed. " + "Please consider upgrading Triton.", + ) + + # Check Python version + py_version = package_version.parse(f"{sys.version_info.major}.{sys.version_info.minor}") + required_py_version = package_version.parse("3.11") + + if py_version < required_py_version: + logger.warning( + f"Current Python version {py_version} is below the recommended 3.11 version. " + "It is recommended to upgrade to Python 3.11 or higher for the best experience.", + ) + + return None + + +check_environments() + + +def _cpu_device_warning(): + warnings.warn(('Triton is not supported on current platform, roll back to CPU.'), stacklevel=2) + + +@cache +def check_pytorch_version(version_s: str = '2.4') -> bool: + return package_version.parse(torch.__version__) >= package_version.parse(version_s) + + +@cache +def get_multiprocessor_count(tensor_idx: int = 0, *, use_aicore: bool = False) -> int: + try: + return triton.runtime.driver.active.utils.get_device_properties(tensor_idx)['multiprocessor_count'] + except Exception: + # Maybe we use a NPU device. + try: + if triton.runtime.driver.active.get_current_target().backend == 'npu': + props = triton.runtime.driver.active.utils.get_device_properties(tensor_idx) + return props['num_aicore'] if use_aicore else props['num_vectorcore'] + except Exception: + logger.debug('Failed to get NPU multiprocessor count, falling back to 1.', exc_info=True) + return 1 + + +@cache +def get_device_capability(device_index: int = 0) -> tuple[int, int]: + major, minor = torch.cuda.get_device_capability(device_index) + return int(major), int(minor) + + +@cache +def get_device_smem_optin(device_index: int = 0) -> int: + props = torch.cuda.get_device_properties(device_index) + return int(getattr(props, 'shared_memory_per_block_optin', props.shared_memory_per_block)) + + +@cache +def get_available_device() -> str: + try: + return triton.runtime.driver.active.get_current_target().backend + except Exception: + _cpu_device_warning() + return 'cpu' + + +def map_triton_backend_to_torch_device() -> str: + backend = get_available_device() # 'cuda' | 'hip' | 'xpu' | 'cpu' | ... + return {'cuda': 'cuda', 'hip': 'cuda', 'xpu': 'xpu'}.get(backend, backend) + + +# For AMD GPUs, the triton backend is 'hip', while for Nvidia GPUs, the triton backend is 'cuda'. +# However, the torch backend is 'cuda' for both Nvidia and AMD GPUs. +# Therefore, we need to check the triton backend to determine the actual GPU vendor. +device = get_available_device() if get_available_device() != 'hip' else 'cuda' +device_torch_lib = getattr(torch, device) +device_platform = get_available_device() +device_name = map_triton_backend_to_torch_device() + +IS_AMD = (device_platform == 'hip') + +IS_ARM = platform.machine().lower() in ('aarch64', 'arm64') + +IS_INTEL = (device_platform == 'xpu') +IS_INTEL_ALCHEMIST = (IS_INTEL and 'Intel(R) Arc(TM) A' in torch.xpu.get_device_name(0)) + +IS_NPU = (device_platform == 'npu') + +IS_NVIDIA = (device_platform == 'cuda') +IS_NVIDIA_HOPPER = ( + IS_NVIDIA and ( + 'NVIDIA H' in torch.cuda.get_device_name(0) + or torch.cuda.get_device_capability()[0] == 9 + ) +) +IS_NVIDIA_SM100 = (IS_NVIDIA and torch.cuda.get_device_capability()[0] == 10) +# NOTE: exactly 12.0 — 12.1 (GB10) is a different target that FlashQLA rejects at import time. +IS_NVIDIA_SM120 = (IS_NVIDIA and torch.cuda.get_device_capability() == (12, 0)) +IS_NVIDIA_BLACKWELL = (IS_NVIDIA and torch.cuda.get_device_capability()[0] in (10, 12)) + +# Nvidia Ampere or newer, haven't check AMD and intel yet. +IS_TF32_SUPPORTED = (IS_NVIDIA and torch.cuda.get_device_capability(0)[0] >= 8) +IS_GATHER_SUPPORTED = hasattr(triton.language, 'gather') +IS_TMA_SUPPORTED = ( + IS_NVIDIA + and torch.cuda.get_device_capability(0)[0] >= 9 + and os.environ.get('FLA_USE_TMA', '0') == '1' + and ( + hasattr(triton.language, '_experimental_make_tensor_descriptor') + or hasattr(triton.language, 'make_tensor_descriptor') + ) +) + +if IS_NVIDIA and not IS_TF32_SUPPORTED: + # Make old card happy, since triton will use tf32 by default. + # This is a workaround for old nvidia card. + os.environ['TRITON_F32_DEFAULT'] = 'ieee' + + +def _default_alloc_fn(size: int, alignment: int, stream: int | None): + return torch.empty(size, device=torch.device(device_name, device_torch_lib.current_device()), dtype=torch.int8) + + +if IS_TMA_SUPPORTED: + logger.info('TMA is supported, using TMA by default.') + triton.set_allocator(_default_alloc_fn) +elif IS_NVIDIA_BLACKWELL: + # Blackwell (SM100 datacenter / SM120 consumer): Triton compiler may emit global_scratch for + # autotuned kernels even without TMA. Register a default allocator to + # prevent NullAllocator crashes. See triton-lang/triton#10002. + logger.info('Blackwell detected: registering default global_scratch allocator.') + triton.set_allocator(_default_alloc_fn) + + +def get_all_max_shared_mem(): + try: + return [ + triton.runtime.driver.active.utils.get_device_properties(i)['max_shared_mem'] + for i in range(device_torch_lib.device_count()) + ] + except Exception: + _cpu_device_warning() + return [-1] + + +class Backend(Enum): + ADA = 101376 # RTX 4090 + AMPERE = 166912 # A100 + HOPPER = 232448 # H100 + DEFAULT = 102400 # Default + + @classmethod + def get_shared_memory(cls, arch: str) -> int: + try: + return cls[arch.upper()].value + except KeyError: + return cls.DEFAULT.value + + +@cache +def check_shared_mem(arch: str = "none", tensor_idx: int = 0) -> bool: + try: + device_shared_mem_list = get_all_max_shared_mem() + max_shared_memory = device_shared_mem_list[tensor_idx] + return max_shared_memory >= Backend.get_shared_memory(arch) + except Exception: + return False + + +if check_pytorch_version('2.4'): + if device == 'cpu': + device = 'cuda' + device_torch_lib = getattr(torch, device) + autocast_custom_fwd = functools.partial(torch.amp.custom_fwd, device_type=device) + autocast_custom_bwd = functools.partial(torch.amp.custom_bwd, device_type=device) + + def custom_device_ctx(index: int): + if index is None: + return contextlib.nullcontext() + try: + return device_torch_lib.device(index) + except (AttributeError, AssertionError, RuntimeError): + return contextlib.nullcontext() +else: + assert device == 'cuda', 'Only cuda device is supported for PyTorch version < 2.4.0.' + autocast_custom_fwd = device_torch_lib.amp.custom_fwd + autocast_custom_bwd = device_torch_lib.amp.custom_bwd + + def custom_device_ctx(index: int): + if index is None: + return contextlib.nullcontext() + try: + return torch.cuda.device(index) + except (AttributeError, AssertionError, RuntimeError): + return contextlib.nullcontext() diff --git a/kda/_fla/utils/_testing.py b/kda/_fla/utils/_testing.py new file mode 100644 index 0000000..12064dc --- /dev/null +++ b/kda/_fla/utils/_testing.py @@ -0,0 +1,41 @@ +# Copyright (c) 2023-2026, Songlin Yang, Yu Zhang, Zhiyuan Li +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. +# For a list of all contributors, visit: +# https://github.com/fla-org/flash-linear-attention/graphs/contributors + +import logging +import warnings + +import torch + +from ._config import FLA_CI_ENV + +logger = logging.getLogger(__name__) + + +def get_abs_err(x, y): + return (x.detach() - y.detach()).flatten().abs().max().item() + + +def get_err_ratio(x, y): + err = (x.detach() - y.detach()).flatten().square().mean().sqrt().item() + base = (x.detach()).flatten().square().mean().sqrt().item() + return err / (base + 1e-8) + + +def assert_close(prefix, ref, tri, ratio, warning=False, err_atol=1e-6): + abs_atol = get_abs_err(ref, tri) + error_rate = get_err_ratio(ref, tri) + msg = f"{prefix:>16} diff: {abs_atol:.6f} ratio: {error_rate:.6f}" + logger.info(msg) + if abs_atol <= err_atol: + return + assert not torch.isnan(ref).any(), f"{prefix}: NaN detected in ref" + assert not torch.isnan(tri).any(), f"{prefix}: NaN detected in tri" + if warning or (FLA_CI_ENV and (error_rate < 0.01 or abs_atol <= 0.3)): + if error_rate > ratio: + warnings.warn(msg) + else: + assert error_rate < ratio, msg diff --git a/kda/layers/__init__.py b/kda/layers/__init__.py new file mode 100644 index 0000000..f67d340 --- /dev/null +++ b/kda/layers/__init__.py @@ -0,0 +1,23 @@ +"""Composable mixing layers: attn and ffn both map [B,T,D] -> [B,T,D]. + +Depth mixing (AttnRes) is not a layer_specs kind. CausalLM reads +``config.attnres`` (off | full | block) and wraps DecoderBlock sublayers. +""" + +from .block import DecoderBlock, build_attn, build_ffn +from .kda_attn import KDAAttention +from .latent_moe import LatentMoE +from .mla import GatedMLA +from .rmsnorm import RMSNorm +from .swiglu import SwiGLUMLP + +__all__ = [ + "DecoderBlock", + "GatedMLA", + "KDAAttention", + "LatentMoE", + "RMSNorm", + "SwiGLUMLP", + "build_attn", + "build_ffn", +] diff --git a/kda/layers/attn_res.py b/kda/layers/attn_res.py new file mode 100644 index 0000000..f70d11b --- /dev/null +++ b/kda/layers/attn_res.py @@ -0,0 +1,519 @@ +""" +Attention Residual in one file + +Reference: + Kimi Team, Guangyu Chen, Yu Zhang, Jianlin Su, Weixin Xu, Siyuan Pan, + Yaoyu Wang, Yucheng Wang, Guanduo Chen, et al. + "Attention Residuals." arXiv:2603.15031, 2026. + https://arxiv.org/abs/2603.15031 + +This module is a compact PyTorch reference implementation of: + - Full AttnRes + - Block AttnRes + - two-phase inter/intra-block computation from the paper + +CausalLM wires Full/Block stacks when ``config.attnres`` is ``full`` or +``block``. Standard residual (``x += attn; x += ffn``) is ``attnres="off"``. +""" + +import torch +import torch.nn.functional as F +from einops import rearrange +from torch import Tensor, nn + + +ATTNRES_MODES = ("off", "full", "block") + + +def exists(x): + return x is not None + + +def validate_attnres(mode: str, block_size: int | None) -> None: + if mode not in ATTNRES_MODES: + raise ValueError(f"attnres must be one of {ATTNRES_MODES}, got {mode!r}") + if block_size is not None and block_size < 1: + raise ValueError(f"attnres_block_size must be >= 1, got {block_size}") + + +def atomic_block_size(num_hidden_layers: int, attnres_block_size: int | None) -> int: + """DecoderBlocks per AttnRes block, converted to attn|ffn atomic layers. + + ``None`` targets about 8 blocks: ``max(1, ceil(L / 8))`` DecoderBlocks. + """ + layers_per_block = ( + attnres_block_size + if attnres_block_size is not None + else max(1, (num_hidden_layers + 7) // 8) + ) + if layers_per_block < 1: + raise ValueError(f"attnres_block_size must be >= 1, got {layers_per_block}") + return layers_per_block * 2 + + +class BorrowedSubLayer(nn.Module): + """``fn(norm(x))`` without registering ``norm``/``fn`` (owned by DecoderBlock).""" + + def __init__(self, norm: nn.Module, fn: nn.Module): + super().__init__() + self._borrowed = (norm, fn) + + def forward(self, x: Tensor) -> Tensor: + norm, fn = self._borrowed + return fn(norm(x)) + + +def rms(x: Tensor, eps: float): + return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + eps) + + +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float): + super().__init__() + self.eps = eps + self.weight = nn.Parameter(torch.ones(dim)) + + def forward(self, x: Tensor) -> Tensor: + return rms(x, self.eps) * self.weight + + +class DepthResidual(nn.Module): + """ + h_l = sum_i softmax_i(w_l^T RMSNorm(v_i))*v_i + + Keep query and RMSNorm gain separate + Since q^T (gamma * RMS(v)) == (q * gamma)^T RMS(v), + we can fold gamma into q for scoring. + """ + + def __init__(self, dim: int, eps: float = 1e-8, zero_init: bool = True): + super().__init__() + self.query = nn.Parameter(torch.zeros(dim)) + self.norm = RMSNorm(dim, eps=eps) + + if not zero_init: + nn.init.normal_(self.query, std=0.02) + + def effective_query(self) -> Tensor: + return (self.query * self.norm.weight).float() + + def logits(self, sources: Tensor | list[Tensor] | tuple[Tensor, ...]) -> Tensor: + sources = stack_layers(sources) # [n, b, t, d] + q = self.effective_query() # [d] + k = rms(sources.float(), self.norm.eps) # [n, b, t, d] + return torch.einsum("d, n b t d -> n b t", q, k) + + def forward(self, sources: Tensor | list[Tensor] | tuple[Tensor, ...]) -> Tensor: + sources = stack_layers(sources) + weights = self.logits(sources).softmax(dim=0) + out = torch.einsum("n b t, n b t d -> b t d", weights, sources.float()) + return out.to(sources.dtype) + + +class DepthResidualList(nn.Module): + def __init__(self, dim: int, depth: int, eps: float, zero_init: bool = True): + super().__init__() + # for L layers (depth), create depth residual modules + self.layers = nn.ModuleList( + [DepthResidual(dim, eps=eps, zero_init=zero_init) for _ in range(depth)] + ) + + def __getitem__(self, idx: int) -> DepthResidual: + return self.layers[idx] + + def __iter__(self): + return iter(self.layers) + + def __len__(self): + return len(self.layers) + + +# attnres stacks + + +class FullAttnResStack(nn.Module): + """ + Full AttnRes over atomic layers + eg: f_1,...,f_L + Each entry in `layers` should already be a full atomic layer fxn + x -> f_l(x) + """ + + def __init__( + self, + dim: int, + layers, + *, + eps: float = 1e-8, + zero_init_queries: bool = True, + is_final_aggregate: bool = True, + ): + super().__init__() + self.layers = nn.ModuleList(list(layers)) + self.eps = eps + + depth = len(self.layers) + self.residuals = DepthResidualList(dim, depth, eps, zero_init_queries) + self.final_residual = ( + DepthResidual(dim, eps, zero_init_queries) if is_final_aggregate else None + ) + + def forward_naive(self, x: Tensor) -> Tensor: + sources = [x] + for layer, residual in zip(self.layers, self.residuals): + h = residual(sources) + out = layer(h) + sources.append(out) + + return ( + self.final_residual(sources) if exists(self.final_residual) else sources[-1] + ) + + def forward_two_phase(self, x: Tensor, schedule_block_size: int) -> Tensor: + assert schedule_block_size > 0 + sources = [x] + depth = len(self.layers) + + start = 0 + while start < depth: + end = min(start + schedule_block_size, depth) + queries = torch.stack( + [self.residuals[i].effective_query() for i in range(start, end)], dim=0 + ) + inter_sources = stack_layers(sources) + inter_stats = attn_with_stats(queries, inter_sources, self.eps) + + local_outputs = [] # outputs of intra-block + for local_idx, layer_idx in enumerate(range(start, end)): + stats = inter_stats.select(local_idx) + if len(local_outputs) > 0: + intra_sources = stack_layers(local_outputs) + intra = attn_with_stats( + queries[local_idx : local_idx + 1], intra_sources, self.eps + ).select(0) + stats = merge_attn_stats(stats, intra) + h = stats.normalized() + out = self.layers[layer_idx](h) + local_outputs.append(out) + sources.append(out) + + start = end + + return ( + self.final_residual(sources) if exists(self.final_residual) else sources[-1] + ) + + def forward(self, x: Tensor, schedule_block_size: int | None = None) -> Tensor: + if schedule_block_size is None: + return self.forward_naive(x) + return self.forward_two_phase(x, schedule_block_size) + + +class BlockAttnResStack(nn.Module): + """ + Block AttnRes over atomic layers + + `block_size` is in atomic layers, not Transformer blocks. + Eg: block_size=8 -> 4 transformer blocks when layers alternate attn/MLP + + The default forward path is the two-phase algorithm from the paper: + phase 1: batch inter-block attn from all queries in the block + phase 2: merge the evolving intra-block partial sum with online softmax + """ + + def __init__( + self, + dim: int, + layers, + *, + block_size: int, + eps: float = 1e-8, + zero_init_queries: bool = True, + is_final_aggregate: bool = True, + ): + super().__init__() + self.layers = nn.ModuleList(list(layers)) + assert len(self.layers) > 0 + assert block_size > 0 + self.block_size = block_size + self.eps = eps + + depth = len(self.layers) + self.residuals = DepthResidualList( + dim, depth, eps=eps, zero_init=zero_init_queries + ) + self.final_residual = ( + DepthResidual(dim, eps=eps, zero_init=zero_init_queries) + if is_final_aggregate + else None + ) + + def forward_naive(self, x: Tensor) -> Tensor: + blocks = [x] # b_0=embedding/input representation + partial = None + + for layer_idx, (layer, residual) in enumerate( + zip(self.layers, self.residuals), start=1 + ): + sources = blocks if partial is None else blocks + [partial] + h = residual(sources) + out = layer(h) + partial = out if partial is None else (partial + out) + + if (layer_idx % self.block_size == 0) or (layer_idx == len(self.layers)): + blocks.append(partial) + partial = None + + return ( + self.final_residual(blocks) if exists(self.final_residual) else blocks[-1] + ) + + def _run_block_two_phase( + self, blocks: list[Tensor], start: int, end: int + ) -> Tensor: + queries = torch.stack( + [self.residuals[i].effective_query() for i in range(start, end)], dim=0 + ) + inter_sources = stack_layers(blocks) + inter = attn_with_stats(queries, inter_sources, self.eps) + partial = None + for local_idx, layer_idx in enumerate(range(start, end)): + stats = inter.select(local_idx) + + if partial is not None: + intra = single_source_stats(queries[local_idx], partial, self.eps) + stats = merge_attn_stats(stats, intra) + + h = stats.normalized() + out = self.layers[layer_idx](h) + partial = out if partial is None else (partial + out) + + return partial + + def forward(self, x: Tensor) -> Tensor: + blocks = [x] + depth = len(self.layers) + start = 0 + while start < depth: + end = min(start + self.block_size, depth) + blocks.append(self._run_block_two_phase(blocks, start, end)) + start = end + + return ( + self.final_residual(blocks) if exists(self.final_residual) else blocks[-1] + ) + + +# helpers + + +def stack_layers(sources: Tensor | list[Tensor] | tuple[Tensor, ...]) -> Tensor: + if isinstance(sources, Tensor): + assert sources.ndim == 4, f"expected [n, b, t, d] got {tuple(sources.shape)}" + return sources + assert len(sources) > 0, "needs at least one source" + return torch.stack(tuple(sources), dim=0) + + +class SingleAttnStats: + def __init__(self, numer: Tensor, denom: Tensor, max: Tensor): + self.numer = numer # [b,t,d] + self.max = max # [b,t] + self.denom = denom # [b,t] + + def normalized(self) -> Tensor: + return self.numer / self.denom[..., None] + + +class AttnStats: + # store the numerator => e^{s_{j}-m} * v_j where m is the max score so far + # store the max m = max(s_j) + # store the denominator sum_j e^{s_{j}-m} + def __init__(self, numer: Tensor, denom: Tensor, max: Tensor): + self.numer = numer # [q,b,t,d] + self.max = max # [q,b,t] + self.denom = denom # [q,b,t] + + def select(self, idx: int) -> "SingleAttnStats": + return SingleAttnStats(self.numer[idx], self.denom[idx], self.max[idx]) + + +def attn_with_stats(queries: Tensor, sources: Tensor, eps: float = 1e-8) -> AttnStats: + """ + queries: [q, d] + sources: [n, b, t, d] + + Returns the following for online softmax: + numer = sum_i exp(logit_i - m)*v_i + m = max_i logit_i + denom = sum_i exp(logit_i - m) + """ + normed = rms(sources, eps) + logits = torch.einsum("q d, n b t d -> q n b t", queries, normed) + m = logits.amax(dim=1) + weights = torch.exp(logits - m[:, None]) + numer = torch.einsum("q n b t, n b t d -> q b t d", weights, sources) + denom = weights.sum(dim=1) + return AttnStats(numer, denom, m) + + +def single_source_stats( + query: Tensor, source: Tensor, eps: float = 1e-8 +) -> SingleAttnStats: + score = torch.einsum("d, b t d -> b t", query, rms(source, eps)) + denom = torch.ones_like(score) + return SingleAttnStats(source, denom, score) + + +def merge_attn_stats(a: SingleAttnStats, b: SingleAttnStats) -> SingleAttnStats: + m = torch.maximum(a.max, b.max) + wa = torch.exp(a.max - m) + wb = torch.exp(b.max - m) + numer = wa[..., None] * a.numer + wb[..., None] * b.numer + denom = wa * a.denom + wb * b.denom + return SingleAttnStats(numer, denom, m) + + +# transformer +class PreNorm(nn.Module): + def __init__(self, dim: int, fn: nn.Module, eps: float = 1e-8): + super().__init__() + self.norm = RMSNorm(dim, eps=eps) + self.fn = fn + + def forward(self, x: Tensor) -> Tensor: + return self.fn(self.norm(x)) + + +class CausalAttention(nn.Module): + def __init__( + self, dim: int, heads: int = 8, dim_head: int = 64, dropout: float = 0.0 + ): + super().__init__() + inner_dim = heads * dim_head + self.heads = heads + self.dim_head = dim_head + self.dropout = dropout + + self.to_qkv = nn.Linear(dim, inner_dim * 3, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + + def forward(self, x: Tensor) -> Tensor: + q, k, v = self.to_qkv(x).chunk(3, dim=-1) + + def split_heads(y: Tensor) -> Tensor: + return rearrange(y, "b t (h d) -> b h t d", h=self.heads) + + q, k, v = map(split_heads, (q, k, v)) + + out = F.scaled_dot_product_attention( + q, k, v, is_causal=True, dropout_p=self.dropout if self.training else 0.0 + ) + out = rearrange(out, "b h t d -> b t (h d)") + return self.to_out(out) + + +class SwiGLU(nn.Module): + def __init__(self, dim: int, mult: int = 4, dropout: float = 0.0): + # dropout not needed unless training on a smaller training data + super().__init__() + inner_dim = dim * mult + self.to_hidden = nn.Linear(dim, inner_dim * 2, bias=False) + self.to_out = nn.Linear(inner_dim, dim, bias=False) + self.dropout = nn.Dropout(dropout) + + def forward(self, x: Tensor) -> Tensor: + gate, value = self.to_hidden(x).chunk(2, dim=-1) + x = F.silu(gate) * value + x = self.dropout(x) + return self.to_out(x) + + +class AttnResTransformer(nn.Module): + """ + Small GPT-style reference model using AttnRes + + Using plain PyTorch: tok/pos embedding, alternating + causal attn, SwiGLU MLP layers, final norm, output head. + """ + + def __init__( + self, + *, + num_tokens: int, + dim: int, + depth: int, + max_seq_len: int, + heads: int = 8, + dim_head: int = 64, + ff_mult: int = 4, + attn_dropout: float = 0.0, + ff_dropout: float = 0.0, + attnres: str = "block", # full or block + block_size: int = 8, + zero_init_queries: bool = True, + is_final_aggregate: bool = True, + eps: float = 1e-8, + ): + super().__init__() + assert attnres in {"full", "block"} + self.max_seq_len = max_seq_len + self.attnres = attnres + self.token_emb = nn.Embedding(num_tokens, dim) + self.pos_emb = nn.Embedding(max_seq_len, dim) + + atomic_layers = [] + for _ in range(depth): + atomic_layers.append( + PreNorm(dim, CausalAttention(dim, heads, dim_head, attn_dropout), eps) + ) + atomic_layers.append(PreNorm(dim, SwiGLU(dim, ff_mult, ff_dropout), eps)) + if attnres == "full": + self.backbone = FullAttnResStack( + dim, + atomic_layers, + eps=eps, + zero_init_queries=zero_init_queries, + is_final_aggregate=is_final_aggregate, + ) + else: + self.backbone = BlockAttnResStack( + dim, + atomic_layers, + block_size=block_size, + eps=eps, + zero_init_queries=zero_init_queries, + is_final_aggregate=is_final_aggregate, + ) + + self.final_norm = RMSNorm(dim, eps) + self.to_logits = nn.Linear(dim, num_tokens, bias=False) + + def forward(self, ids: Tensor, schedule_block_size: int | None = None) -> Tensor: + b, t = ids.shape + assert t <= self.max_seq_len + pos = torch.arange(t, device=ids.device) + x = self.token_emb(ids) + self.pos_emb(pos)[None, :, :] + if self.attnres == "full": + x = self.backbone(x, schedule_block_size=schedule_block_size) + else: + x = self.backbone(x) + x = self.final_norm(x) + return self.to_logits(x) + + +__all__ = [ + "ATTNRES_MODES", + "RMSNorm", + "DepthResidual", + "DepthResidualList", + "FullAttnResStack", + "BlockAttnResStack", + "BorrowedSubLayer", + "PreNorm", + "CausalAttention", + "SwiGLU", + "AttnResTransformer", + "atomic_block_size", + "validate_attnres", +] diff --git a/kda/layers/block.py b/kda/layers/block.py new file mode 100644 index 0000000..d5f7ff5 --- /dev/null +++ b/kda/layers/block.py @@ -0,0 +1,51 @@ +"""Decoder block: x += attn(norm(x)); x += ffn(norm(x)). + +attn/ffn are any modules with forward: [B,T,D] -> [B,T,D]. +""" +from __future__ import annotations + +from torch import nn + +from .kda_attn import KDAAttention +from .latent_moe import LatentMoE +from .mla import GatedMLA +from .rmsnorm import RMSNorm +from .swiglu import SwiGLUMLP + + +def build_attn(config, kind: str) -> nn.Module: + if kind == "kda": + return KDAAttention.from_config(config) + if kind == "mla": + return GatedMLA.from_config(config) + raise ValueError(f"unknown attn kind: {kind}") + + +def build_ffn(config, kind: str) -> nn.Module: + if kind == "swiglu": + return SwiGLUMLP.from_config(config) + if kind == "moe": + return LatentMoE.from_config(config) + raise ValueError(f"unknown ffn kind: {kind}") + + +class DecoderBlock(nn.Module): + def __init__(self, hidden_size: int, norm_eps: float, attn: nn.Module, ffn: nn.Module): + super().__init__() + self.attn_norm = RMSNorm(hidden_size, norm_eps) + self.attn = attn + self.ffn_norm = RMSNorm(hidden_size, norm_eps) + self.ffn = ffn + + @classmethod + def from_spec(cls, config, attn_kind: str, ffn_kind: str) -> DecoderBlock: + return cls( + config.hidden_size, + config.norm_eps, + build_attn(config, attn_kind), + build_ffn(config, ffn_kind), + ) + + def forward(self, x): + x = x + self.attn(self.attn_norm(x)) + return x + self.ffn(self.ffn_norm(x)) diff --git a/kda/layers/kda_attn.py b/kda/layers/kda_attn.py new file mode 100644 index 0000000..141aa0c --- /dev/null +++ b/kda/layers/kda_attn.py @@ -0,0 +1,99 @@ +"""KDA attention: project q/k/v/g/beta, run chunk_kda, project back to D.""" +from __future__ import annotations + +import torch +from torch import nn + +from ..ops.api import chunk_kda + + +class KDAAttention(nn.Module): + """Mixing module: x [B,T,D] -> y [B,T,D].""" + + def __init__( + self, + hidden_size: int, + num_heads: int, + num_value_heads: int, + head_dim: int, + *, + chunk_size: int = 16, + initializer_range: float = 0.02, + use_gate_in_kernel: bool = True, + use_qk_l2norm_in_kernel: bool = True, + use_beta_sigmoid_in_kernel: bool = True, + lower_bound: float | None = -5.0, + kda_backend: str = "reference", + ): + super().__init__() + if num_value_heads % num_heads: + raise ValueError("num_value_heads must be divisible by num_heads") + self.hidden_size = hidden_size + self.num_heads = num_heads + self.num_value_heads = num_value_heads + self.head_dim = head_dim + self.chunk_size = chunk_size + self.initializer_range = initializer_range + self.use_gate_in_kernel = use_gate_in_kernel + self.use_qk_l2norm_in_kernel = use_qk_l2norm_in_kernel + self.use_beta_sigmoid_in_kernel = use_beta_sigmoid_in_kernel + self.lower_bound = lower_bound + self.kda_backend = kda_backend + + H, HV, K, V = num_heads, num_value_heads, head_dim, head_dim + self.q_proj = nn.Linear(hidden_size, H * K, bias=False) + self.k_proj = nn.Linear(hidden_size, H * K, bias=False) + self.v_proj = nn.Linear(hidden_size, HV * V, bias=False) + self.g_proj = nn.Linear(hidden_size, HV * K, bias=False) + self.beta_proj = nn.Linear(hidden_size, HV, bias=False) + self.o_proj = nn.Linear(HV * V, hidden_size, bias=False) + self.A_log = nn.Parameter(torch.zeros(HV)) + # With safe_gate=-5, bias=-4 starts at g≈-0.09 (about 91% state retention). + self.dt_bias = nn.Parameter(torch.full((HV, K), -4.0)) + self.apply(self._init_weights) + + @classmethod + def from_config(cls, config) -> KDAAttention: + return cls( + hidden_size=config.hidden_size, + num_heads=config.num_heads, + num_value_heads=getattr(config, "num_value_heads", config.num_heads), + head_dim=config.head_dim, + chunk_size=config.chunk_size, + initializer_range=config.initializer_range, + use_gate_in_kernel=config.use_gate_in_kernel, + use_qk_l2norm_in_kernel=config.use_qk_l2norm_in_kernel, + use_beta_sigmoid_in_kernel=config.use_beta_sigmoid_in_kernel, + lower_bound=config.lower_bound, + kda_backend=config.kda_backend, + ) + + def _init_weights(self, module): + if isinstance(module, nn.Linear): + nn.init.normal_(module.weight, std=self.initializer_range) + + def forward(self, x: torch.Tensor): + B, T, _ = x.shape + H, HV, K, V = self.num_heads, self.num_value_heads, self.head_dim, self.head_dim + q = self.q_proj(x).view(B, T, H, K) + k = self.k_proj(x).view(B, T, H, K) + v = self.v_proj(x).view(B, T, HV, V) + g_raw = self.g_proj(x).view(B, T, HV, K) + beta_raw = self.beta_proj(x).view(B, T, HV) + o, _ = chunk_kda( + q, + k, + v, + g_raw, + beta_raw, + A_log=self.A_log, + dt_bias=self.dt_bias, + use_qk_l2norm_in_kernel=self.use_qk_l2norm_in_kernel, + use_gate_in_kernel=self.use_gate_in_kernel, + use_beta_sigmoid_in_kernel=self.use_beta_sigmoid_in_kernel, + safe_gate=self.lower_bound is not None, + lower_bound=self.lower_bound, + chunk_size=self.chunk_size, + backend=self.kda_backend, + ) + return self.o_proj(o.reshape(B, T, HV * V)) diff --git a/kda/layers/latent_moe.py b/kda/layers/latent_moe.py new file mode 100644 index 0000000..1a719d4 --- /dev/null +++ b/kda/layers/latent_moe.py @@ -0,0 +1,118 @@ +"""Stable LatentMoE (K3): shared 全宽 + routed 半宽专家 + SiTU-GLU + Top-k. + +对照 learning/kimi-k3-notes §Stable LatentMoE: + z = W_down(x) [B, T, ℓ] ℓ = d/2 latent 接口宽 + u = Σ_{i∈Top-k(x)} p_i E_i^rt(z) [B, T, ℓ] routed 专家只在 ℓ 上算 + y = Σ_j E_j^sh(x) + W_up RMSNorm(u) [B, T, d] shared 全宽 + +SiTU-GLU: gate = β1·tanh(W_g x/β1)⊙σ(W_g x); up = β2·tanh(W_u x/β2) + ||SiTU-GLU||_∞ ≤ β1·β2 (=100), 原点附近≈SwiGLU, 远端软饱和防低精度溢出. + E: R^in → R^in (内部中间维 d_ff). + +Router: Top-k logits 基于全宽 x (笔记 Topk(x)); 归一化权重取 softmax(topk). +""" +from __future__ import annotations + +import torch +import torch.nn.functional as F +from torch import nn + +from .rmsnorm import RMSNorm + + +class SiTU(nn.Module): + """SiTU-GLU expert: gate 支软上限 β1, up 支软上限 β2, 输出回到输入维.""" + + def __init__(self, dim_in: int, dim_ff: int, beta1: float = 4.0, beta2: float = 25.0): + super().__init__() + self.beta1, self.beta2 = beta1, beta2 + self.w_g = nn.Linear(dim_in, dim_ff, bias=False) + self.w_u = nn.Linear(dim_in, dim_ff, bias=False) + self.w_o = nn.Linear(dim_ff, dim_in, bias=False) + + def forward(self, x: torch.Tensor): + wg = self.w_g(x) + g = self.beta1 * torch.tanh(wg / self.beta1) * torch.sigmoid(wg) + u = self.beta2 * torch.tanh(self.w_u(x) / self.beta2) + return self.w_o(g * u) + + +class LatentMoE(nn.Module): + def __init__( + self, + hidden_size: int, + latent_size: int, + n_routed: int, + top_k: int, + n_shared: int, + d_ff: int, + beta1: float = 4.0, + beta2: float = 25.0, + ): + super().__init__() + self.latent_size = latent_size + self.n_routed = n_routed + self.top_k = top_k + + self.down = nn.Linear(hidden_size, latent_size, bias=False) # W↓ + self.router = nn.Linear(hidden_size, n_routed, bias=False) # Top-k logits + self.shared = nn.ModuleList( + [SiTU(hidden_size, d_ff, beta1, beta2) for _ in range(n_shared)] + ) + self.experts = nn.ModuleList( + [SiTU(latent_size, d_ff, beta1, beta2) for _ in range(n_routed)] + ) + self.norm = RMSNorm(latent_size) + self.up = nn.Linear(latent_size, hidden_size, bias=False) # W↑ + self.last_route_ids: torch.Tensor | None = None + + @classmethod + def from_config(cls, config) -> LatentMoE: + return cls( + config.hidden_size, + config.moe_latent_size, + config.n_routed, + config.top_k, + config.n_shared, + config.moe_d_ff, + config.situ_beta1, + config.situ_beta2, + ) + + def forward(self, x: torch.Tensor): + B, T, _ = x.shape + z = self.down(x) # [B, T, ℓ] + + logits = self.router(x) # [B, T, n_routed] + topk = torch.topk(logits, self.top_k, dim=-1) + ids = topk.indices # [B, T, k] + self.last_route_ids = ids.detach() + probs = F.softmax(topk.values, dim=-1) # [B, T, k] + + # 向量化 routed: 预计算全部专家输出, 按 token 的 Top-k id 取 + all_out = torch.stack([e(z) for e in self.experts]) # [R, B, T, ℓ] + all_out = all_out.permute(1, 2, 0, 3).reshape(B * T, self.n_routed, self.latent_size) + u = torch.zeros(B, T, self.latent_size, device=x.device, dtype=x.dtype) + for i in range(self.top_k): + idx = ids[:, :, i].reshape(B * T) # [B*T] + sel = all_out[torch.arange(B * T, device=x.device), idx] # [B*T, ℓ] + u += probs[:, :, i : i + 1] * sel.reshape(B, T, self.latent_size) + + shared_out = torch.stack([e(x) for e in self.shared]).sum(0) # [B, T, d] + return shared_out + self.up(self.norm(u)) + + +def moe_route_frac(model: nn.Module) -> torch.Tensor | None: + """Mean expert occupancy over LatentMoE layers from the last forward.""" + hists: list[torch.Tensor] = [] + n_routed: int | None = None + for module in model.modules(): + if not isinstance(module, LatentMoE) or module.last_route_ids is None: + continue + n_routed = module.n_routed + ids = module.last_route_ids.reshape(-1) + hists.append(torch.bincount(ids, minlength=n_routed).float()) + if not hists or n_routed is None: + return None + stacked = torch.stack(hists).sum(0) + return stacked / stacked.sum().clamp_min(1.0) diff --git a/kda/layers/mla.py b/kda/layers/mla.py new file mode 100644 index 0000000..5694d0f --- /dev/null +++ b/kda/layers/mla.py @@ -0,0 +1,97 @@ +"""Gated MLA (K3): NoPE, latent KV compression, matrix absorption, full-rank output gate. + +K3 相对 DeepSeek MLA 的三个改动 (对照 learning/kimi-k3-notes): +1. NoPE — 不显式 RoPE; 位置感交给夹层 KDA 的 decay/gate。 +2. 矩阵吸收 — 训练/推理都不解压 K/V: q 吸收 W_UK 后直接与 latent c 内积, + 输出先在 latent 加权再乘 W_UV 还原 (v2 吸收版)。 +3. Full-rank 输出门 — y = W_o[ σ(W_g x) ⊙ õ ]。 + +形状 (小规模 toy, d 为 hidden): + c = RMSNorm(kv_down(x)) [B, T, r] latent + q = q_up(RMSNorm(q_down(x))) [B, T, H, d_q] d_q = d_nope (NoPE) + W_UK = kv_up[.., :H*d_q].view(H,d_q,r) W_UV = kv_up[.., H*d_q:].view(H,d_v,r) + score = (q @ W_UK^T) @ c^T [B, H, T, T] causal + õ = (softmax(score) @ c) @ W_UV^T [B, T, H, d_v] + y = o_proj( σ(W_g x) ⊙ õ_head ) [B, T, d] +""" +from __future__ import annotations + +import torch +import torch.nn.functional as F +from torch import nn + +from .rmsnorm import RMSNorm + + +class GatedMLA(nn.Module): + def __init__( + self, + hidden_size: int, + num_heads: int, + kv_lora_rank: int, + q_lora_rank: int, + qk_nope_head_dim: int, + v_head_dim: int, + ): + super().__init__() + self.hidden_size = hidden_size + self.num_heads = num_heads + self.qk_nope_head_dim = qk_nope_head_dim + self.v_head_dim = v_head_dim + + # Q 低秩路径 (NoPE, 只有 nope 段) + self.q_down = nn.Linear(hidden_size, q_lora_rank, bias=False) + self.q_norm = RMSNorm(q_lora_rank) + self.q_up = nn.Linear(q_lora_rank, num_heads * qk_nope_head_dim, bias=False) + + # KV latent 压缩 + 解压 (W_UK | W_UV 拼接在同一矩阵里) + self.kv_down = nn.Linear(hidden_size, kv_lora_rank, bias=False) + self.kv_norm = RMSNorm(kv_lora_rank) + self.kv_up = nn.Linear( + kv_lora_rank, num_heads * (qk_nope_head_dim + v_head_dim), bias=False + ) + + # Full-rank 输出门: σ(W_g x) 与 õ (H*d_v 维) 逐元素相乘 + self.gate = nn.Linear(hidden_size, num_heads * v_head_dim, bias=False) + self.o_proj = nn.Linear(num_heads * v_head_dim, hidden_size, bias=False) + + @classmethod + def from_config(cls, config) -> GatedMLA: + return cls( + config.hidden_size, + config.num_heads, + config.kv_lora_rank, + config.q_lora_rank, + config.qk_nope_head_dim, + config.v_head_dim, + ) + + def forward(self, x: torch.Tensor): + B, T, _ = x.shape + H, r = self.num_heads, self.kv_up.in_features + + c = self.kv_norm(self.kv_down(x)) # [B, T, r] + q = self.q_up(self.q_norm(self.q_down(x))) # [B, T, H*d_q] + q = q.view(B, T, H, self.qk_nope_head_dim) # [B, T, H, d_q] + + w = self.kv_up.weight # [H*(d_q+d_v), r] + w_uk = w[: H * self.qk_nope_head_dim].view(H, self.qk_nope_head_dim, r) + w_uv = w[H * self.qk_nope_head_dim :].view(H, self.v_head_dim, r) + + # 吸收 W_UK 进 query: score = (q @ W_UK^T) @ c^T + q_absorb = torch.einsum("bthd,hdj->bthj", q, w_uk) # [B, T, H, r] + scores = torch.einsum("bthj,bsj->bhts", q_absorb, c) # [B, H, T, T] + + mask = torch.triu( + torch.ones(T, T, dtype=torch.bool, device=x.device), diagonal=1 + ) + scores = scores.masked_fill(mask, float("-inf")) + attn = F.softmax(scores, dim=-1) # [B, H, T, T] + + # 先在 latent 加权, 再乘 W_UV^T 还原 v —— 永不解压 + latent_out = torch.einsum("bhts,bsj->bhtj", attn, c) # [B, H, T, r] + o_heads = torch.einsum("bhtj,hvj->bhtv", latent_out, w_uv) # [B, H, T, d_v] + + o_heads = o_heads.transpose(1, 2).reshape(B, T, H * self.v_head_dim) + gate = torch.sigmoid(self.gate(x)) # [B, T, H*d_v] + return self.o_proj(gate * o_heads) # [B, T, d] diff --git a/kda/layers/rmsnorm.py b/kda/layers/rmsnorm.py new file mode 100644 index 0000000..265860d --- /dev/null +++ b/kda/layers/rmsnorm.py @@ -0,0 +1,17 @@ +"""RMSNorm used by attention, FFN, and the final LM stem.""" +from __future__ import annotations + +import torch +from torch import nn + + +class RMSNorm(nn.Module): + def __init__(self, dim: int, eps: float = 1e-6): + super().__init__() + self.weight = nn.Parameter(torch.ones(dim)) + self.eps = eps + + def forward(self, x: torch.Tensor): + dtype = x.dtype + x = x.float() + return (x * torch.rsqrt(x.square().mean(-1, keepdim=True) + self.eps)).to(dtype) * self.weight diff --git a/kda/layers/swiglu.py b/kda/layers/swiglu.py new file mode 100644 index 0000000..3c0c9e8 --- /dev/null +++ b/kda/layers/swiglu.py @@ -0,0 +1,20 @@ +"""SwiGLU FFN: x [B,T,D] -> y [B,T,D].""" +from __future__ import annotations + +import torch.nn.functional as F +from torch import nn + + +class SwiGLUMLP(nn.Module): + def __init__(self, hidden_size: int, intermediate_size: int): + super().__init__() + self.w1 = nn.Linear(hidden_size, intermediate_size, bias=False) + self.w3 = nn.Linear(hidden_size, intermediate_size, bias=False) + self.w2 = nn.Linear(intermediate_size, hidden_size, bias=False) + + @classmethod + def from_config(cls, config) -> SwiGLUMLP: + return cls(config.hidden_size, config.intermediate_size) + + def forward(self, x): + return self.w2(F.silu(self.w1(x)) * self.w3(x)) diff --git a/kda/models/__init__.py b/kda/models/__init__.py new file mode 100644 index 0000000..08150b6 --- /dev/null +++ b/kda/models/__init__.py @@ -0,0 +1,7 @@ +"""Configs and the single CausalLM entry.""" + +from .causal_lm import CausalLM +from .config import KDAConfig +from .k3_config import K3Config + +__all__ = ["CausalLM", "K3Config", "KDAConfig"] diff --git a/kda/models/causal_lm.py b/kda/models/causal_lm.py new file mode 100644 index 0000000..9d1fd45 --- /dev/null +++ b/kda/models/causal_lm.py @@ -0,0 +1,123 @@ +"""Causal LM stem: embed -> DecoderBlock* -> norm -> lm_head. + +KDA-only and K3-like both use this class. Config.layer_specs() chooses +attn/ffn per layer: ("kda"|"mla", "swiglu"|"moe"). + +``config.attnres`` selects the depth mixer: + off — standard residual inside each DecoderBlock (default) + full — Full AttnRes over attn|ffn sublayers + block — Block AttnRes (K3); block size from ``attnres_block_size`` +""" + +from __future__ import annotations + +import torch +import torch.nn.functional as F +from torch import nn +from torch.utils.checkpoint import checkpoint as activation_checkpoint + +from ..layers.attn_res import ( + BlockAttnResStack, + BorrowedSubLayer, + FullAttnResStack, + atomic_block_size, +) +from ..layers.block import DecoderBlock +from ..layers.rmsnorm import RMSNorm + + +def _build_mixer(config, blocks: nn.ModuleList): + mode = getattr(config, "attnres", "off") + if mode == "off": + return None + atomics = [] + for block in blocks: + atomics.append(BorrowedSubLayer(block.attn_norm, block.attn)) + atomics.append(BorrowedSubLayer(block.ffn_norm, block.ffn)) + kwargs = dict( + eps=config.norm_eps, + zero_init_queries=getattr(config, "attnres_zero_init_queries", True), + is_final_aggregate=getattr(config, "attnres_final_aggregate", True), + ) + if mode == "full": + return FullAttnResStack(config.hidden_size, atomics, **kwargs) + if mode == "block": + return BlockAttnResStack( + config.hidden_size, + atomics, + block_size=atomic_block_size( + config.num_hidden_layers, getattr(config, "attnres_block_size", None) + ), + **kwargs, + ) + raise ValueError(f"unknown attnres mode: {mode!r}") + + +class CausalLM(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.attnres = getattr(config, "attnres", "off") + self.embedding = nn.Embedding(config.vocab_size, config.hidden_size) + self.blocks = nn.ModuleList( + [ + DecoderBlock.from_spec(config, attn, ffn) + for attn, ffn in config.layer_specs() + ] + ) + self.mixer = _build_mixer(config, self.blocks) + self.gradient_checkpointing = bool( + getattr(config, "gradient_checkpointing", False) + ) + self.norm = RMSNorm(config.hidden_size, config.norm_eps) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + nn.init.normal_(self.embedding.weight, std=config.initializer_range) + nn.init.normal_(self.lm_head.weight, std=config.initializer_range) + if config.tie_word_embeddings: + self.lm_head.weight = self.embedding.weight + + def forward( + self, + input_ids: torch.Tensor, + labels: torch.Tensor | None = None, + ignore_index: int = -100, + ): + x = self.embedding(input_ids) + if self.mixer is None: + for block in self.blocks: + if self.gradient_checkpointing and self.training: + x = activation_checkpoint(block, x, use_reentrant=False) + else: + x = block(x) + elif self.gradient_checkpointing and self.training: + x = activation_checkpoint(self.mixer, x, use_reentrant=False) + else: + x = self.mixer(x) + logits = self.lm_head(self.norm(x)) + if labels is None: + return logits + return F.cross_entropy( + logits[:, :-1].reshape(-1, logits.size(-1)), + labels[:, 1:].reshape(-1), + ignore_index=ignore_index, + ) + + @torch.inference_mode() + def generate( + self, + input_ids: torch.Tensor, + max_new_tokens: int, + temperature: float = 0.0, + eos_token_id: int | None = None, + ): + for _ in range(max_new_tokens): + logits = self(input_ids)[:, -1] + if temperature > 0: + probs = F.softmax(logits / temperature, dim=-1) + next_token = torch.multinomial(probs, 1) + else: + next_token = logits.argmax(-1, keepdim=True) + input_ids = torch.cat((input_ids, next_token), dim=1) + if eos_token_id is not None and (next_token.squeeze(-1) == eos_token_id).all(): + break + return input_ids diff --git a/kda/models/config.py b/kda/models/config.py new file mode 100644 index 0000000..4dae7c6 --- /dev/null +++ b/kda/models/config.py @@ -0,0 +1,62 @@ +"""KDAConfig — toy Causal LM hyperparameters. + +Defaults match the working reference-backend model: GVA with G=2, +safe gate (lower_bound=-5), q/k L2-norm and beta sigmoid inside the op. +""" +from __future__ import annotations + +from dataclasses import dataclass + + +@dataclass +class KDAConfig: + hidden_size: int = 64 + num_hidden_layers: int = 2 + num_heads: int = 4 + num_value_heads: int = 8 # G = num_value_heads // num_heads + head_dim: int = 16 + chunk_size: int = 16 + vocab_size: int = 256 + intermediate_size: int = 128 + max_position_embeddings: int = 128 + initializer_range: float = 0.02 + norm_eps: float = 1e-6 + use_gate_in_kernel: bool = True + use_qk_l2norm_in_kernel: bool = True + use_beta_sigmoid_in_kernel: bool = True + lower_bound: float | None = -5.0 + tie_word_embeddings: bool = False + kda_backend: str = "reference" # reference | triton | fla + attnres: str = "off" # off | full | block + attnres_block_size: int | None = None # DecoderBlocks / block; None ≈ L/8 + attnres_zero_init_queries: bool = True + attnres_final_aggregate: bool = True + gradient_checkpointing: bool = False + + @property + def H(self) -> int: return self.num_heads + + @property + def G(self) -> int: return self.num_value_heads // self.num_heads + + @property + def HV(self) -> int: return self.num_value_heads + + @property + def K(self) -> int: return self.head_dim + + @property + def V(self) -> int: return self.head_dim + + def __post_init__(self): + from ..layers.attn_res import validate_attnres + + if self.num_value_heads % self.num_heads: + raise ValueError("num_value_heads must be divisible by num_heads") + supported = {"reference", "triton", "fla", "torch", "auto"} + if self.kda_backend not in supported: + raise ValueError(f"kda_backend must be one of {sorted(supported)}") + validate_attnres(self.attnres, self.attnres_block_size) + + def layer_specs(self) -> list[tuple[str, str]]: + return [("kda", "swiglu")] * self.num_hidden_layers diff --git a/kda/models/k3_config.py b/kda/models/k3_config.py new file mode 100644 index 0000000..ce09eea --- /dev/null +++ b/kda/models/k3_config.py @@ -0,0 +1,128 @@ +"""K3Config — Kimi K3 架构的小规模复现配置 (KDA + Gated MLA + Stable LatentMoE). + +对照 learning/kimi-k3-notes §尺寸速查 (真实 K3 → 本 toy 缩比): + hidden 7168 → 256; L 93 → 4; H=HV 96 → 8; K=V 128 → 16; + MLA kv_lora 512 → 32, q_lora 1536 → 64, nope/v 128 → 16; + MoE ℓ=d/2=3584 → 128, 896/16 → 16/2, shared 2, d_ff 3072 → 96. + +Hybrid Attention (K3): 每 4 层 1 次 Gated MLA, 末层强制 MLA. + +Presets: + toy — ~8M, 自训 8k SP, 本地过拟合 + 0.5b — ~482M, Qwen3 词表, 32–40GB bf16；默认 step 是冒烟，翻译前置用 --max-tokens +""" +from __future__ import annotations + +from dataclasses import dataclass + +# Qwen3 config.json; train_k3 overrides with len(tokenizer). +QWEN3_VOCAB_SIZE = 151936 + + +@dataclass +class K3Config: + # 主干 + hidden_size: int = 256 + num_hidden_layers: int = 4 + vocab_size: int = 8192 # toy: data/spm_4k; 0.5b: Qwen3 + initializer_range: float = 0.02 + norm_eps: float = 1e-6 + tie_word_embeddings: bool = False + max_position_embeddings: int = 2048 # NoPE, 仅语义保留 + + # KDA (K3: H = HV = 96, 无 GVA) + num_heads: int = 8 + head_dim: int = 16 + chunk_size: int = 16 + lower_bound: float | None = -5.0 + use_gate_in_kernel: bool = True + use_qk_l2norm_in_kernel: bool = True + use_beta_sigmoid_in_kernel: bool = True + + # Gated MLA (NoPE) + kv_lora_rank: int = 32 + q_lora_rank: int = 64 + qk_nope_head_dim: int = 16 + v_head_dim: int = 16 + + # Stable LatentMoE + moe_latent_size: int = 128 # ℓ = d/2 + n_routed: int = 16 + top_k: int = 2 + n_shared: int = 2 + moe_d_ff: int = 96 + situ_beta1: float = 4.0 + situ_beta2: float = 25.0 + + kda_backend: str = "reference" + + # Depth mixer. off = DecoderBlock residual; block matches K3. + attnres: str = "off" # off | full | block + attnres_block_size: int | None = None # DecoderBlocks / AttnRes block; None ≈ L/8 + attnres_zero_init_queries: bool = True + attnres_final_aggregate: bool = True + gradient_checkpointing: bool = False + + def __post_init__(self): + from ..layers.attn_res import validate_attnres + + validate_attnres(self.attnres, self.attnres_block_size) + + @classmethod + def preset(cls, name: str) -> K3Config: + if name == "toy": + return cls() + if name in {"0.5b", "500m"}: + # H * head_dim == hidden. Routed 16: LatentMoE still runs every expert. + # ~482M with tied Qwen3 embeddings. 6×(3 KDA + 1 MLA). + return cls( + hidden_size=768, + num_hidden_layers=24, + vocab_size=QWEN3_VOCAB_SIZE, + tie_word_embeddings=True, + max_position_embeddings=2048, + num_heads=12, + head_dim=64, + chunk_size=64, + kv_lora_rank=192, + q_lora_rank=512, + qk_nope_head_dim=64, + v_head_dim=64, + moe_latent_size=384, + n_routed=16, + top_k=2, + n_shared=2, + moe_d_ff=512, + # The pure-PyTorch reference is far too slow at this size. + kda_backend="triton", + gradient_checkpointing=True, + ) + raise ValueError(f"unknown preset: {name}") + + @property + def H(self) -> int: + return self.num_heads + + @property + def HV(self) -> int: + return self.num_heads + + @property + def K(self) -> int: + return self.head_dim + + @property + def V(self) -> int: + return self.head_dim + + def layer_types(self) -> list[str]: + """Hybrid pattern: 每 4 层 1 次 MLA (0-based 层 3,7,...), 末层强制 MLA.""" + types = ["kda"] * self.num_hidden_layers + for i in range(self.num_hidden_layers): + if i % 4 == 3: + types[i] = "mla" + types[-1] = "mla" + return types + + def layer_specs(self) -> list[tuple[str, str]]: + return [(kind, "moe") for kind in self.layer_types()] diff --git a/kda/ops/__init__.py b/kda/ops/__init__.py new file mode 100644 index 0000000..6095684 --- /dev/null +++ b/kda/ops/__init__.py @@ -0,0 +1,5 @@ +"""KDA operator API and implementation backends.""" + +from .api import chunk_kda + +__all__ = ["chunk_kda"] diff --git a/kda/ops/api.py b/kda/ops/api.py new file mode 100644 index 0000000..3608b33 --- /dev/null +++ b/kda/ops/api.py @@ -0,0 +1,167 @@ +"""Training-facing KDA operator with the same boundary as FLA's ``chunk_kda``.""" + +from __future__ import annotations + +import warnings +from functools import lru_cache + +import torch +import torch.nn.functional as F + +from .reference.chunkwise import DECAY_BLOCK, _EXP_LIMIT, naive_chunk_kda + + +@lru_cache(maxsize=1) +def _fla_chunk_kda(): + try: + from fla.ops.kda import chunk_kda + except ImportError: + return None + return chunk_kda + + +def _reference_chunk_size(T: int, requested: int) -> int: + size = min(T, requested) + while T % size: + size -= 1 + return size + + +def chunk_kda( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + *, + A_log: torch.Tensor | None = None, + dt_bias: torch.Tensor | None = None, + scale: float | None = None, + initial_state: torch.Tensor | None = None, + output_final_state: bool = False, + use_qk_l2norm_in_kernel: bool = False, + use_gate_in_kernel: bool = False, + use_beta_sigmoid_in_kernel: bool = False, + safe_gate: bool = False, + lower_bound: float | None = None, + chunk_size: int = 64, + backend: str = "reference", +): + """Run an explicitly selected KDA implementation. + + ``reference`` and its legacy alias ``torch`` use this repository's + differentiable PyTorch implementation. ``triton`` uses the vendored + FLA NVIDIA Triton kernels in ``kda._fla`` (chunk_size 32 or 64, + CUDA). ``fla`` is reserved for explicit upstream parity runs. + """ + supported = {"reference", "triton", "fla", "torch", "auto"} + if backend not in supported: + raise ValueError(f"backend must be one of {sorted(supported)}") + if not use_qk_l2norm_in_kernel: + # Backend-independent: this is a property of the recurrence, not of any + # one implementation. + warnings.warn( + "use_qk_l2norm_in_kernel=False: KDA's chunkwise form assumes " + "||k||=1 so that I + tril(A_kk*beta) has a convergent Neumann " + "series. Unnormalised k makes the exact output grow like " + "||k||^chunk_size and can reach inf on any backend.", + RuntimeWarning, + stacklevel=2, + ) + if backend == "auto": + warnings.warn( + "backend='auto' is deprecated and now selects the local reference backend; " + "use backend='fla' explicitly for upstream FLA", + DeprecationWarning, + stacklevel=2, + ) + backend = "reference" + if backend == "torch": + backend = "reference" + if backend == "triton": + from .triton.chunk import chunk_kda as triton_chunk_kda + + fla_chunk = 32 if chunk_size <= 32 else 64 + return triton_chunk_kda( + q, + k, + v, + g, + beta, + scale=scale, + initial_state=initial_state, + output_final_state=output_final_state, + chunk_size=fla_chunk, + use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel, + use_gate_in_kernel=use_gate_in_kernel, + use_beta_sigmoid_in_kernel=use_beta_sigmoid_in_kernel, + A_log=A_log, + dt_bias=dt_bias, + safe_gate=safe_gate, + lower_bound=lower_bound, + ) + if backend == "fla": + fused_op = _fla_chunk_kda() + if fused_op is None: + raise RuntimeError( + "backend='fla' requires a complete flash-linear-attention installation" + ) + return fused_op( + q, + k, + v, + g, + beta, + A_log=A_log, + dt_bias=dt_bias, + scale=scale, + initial_state=initial_state, + output_final_state=output_final_state, + use_qk_l2norm_in_kernel=use_qk_l2norm_in_kernel, + use_gate_in_kernel=use_gate_in_kernel, + use_beta_sigmoid_in_kernel=use_beta_sigmoid_in_kernel, + safe_gate=safe_gate, + lower_bound=lower_bound, + chunk_size=32 if chunk_size <= 32 else 64, + ) + + if safe_gate and lower_bound is not None: + # _decayed_dot exponentiates at most DECAY_BLOCK steps of gate decay, + # and safe_gate bounds each step by |lower_bound|. + budget = DECAY_BLOCK * abs(lower_bound) + if budget > _EXP_LIMIT: + raise ValueError( + f"lower_bound={lower_bound} allows a gate span of {budget:.1f} " + f"per {DECAY_BLOCK}-row block, which overflows exp() " + f"(limit {_EXP_LIMIT:.1f}) and yields NaN. Use " + f"|lower_bound| < {_EXP_LIMIT / DECAY_BLOCK:.2f} or " + "backend='triton'." + ) + if use_qk_l2norm_in_kernel: + q, k = F.normalize(q, dim=-1), F.normalize(k, dim=-1) + if use_beta_sigmoid_in_kernel: + beta = beta.sigmoid() + if use_gate_in_kernel: + if A_log is None: + raise ValueError("A_log is required when use_gate_in_kernel=True") + bias = 0 if dt_bias is None else dt_bias.view(g.shape[-2:]) + gate_input = g + bias + rate = A_log.exp().view(1, 1, -1, 1) + if safe_gate: + if lower_bound is None: + raise ValueError("lower_bound is required when safe_gate=True") + g = lower_bound * torch.sigmoid(rate * gate_input) + else: + g = -rate * F.softplus(gate_input) + + return naive_chunk_kda( + q, + k, + v, + g, + beta, + scale=scale, + initial_state=initial_state, + output_final_state=output_final_state, + chunk_size=_reference_chunk_size(q.shape[1], chunk_size), + ) diff --git a/kda/ops/recurrent/__init__.py b/kda/ops/recurrent/__init__.py new file mode 100644 index 0000000..fc1c45f --- /dev/null +++ b/kda/ops/recurrent/__init__.py @@ -0,0 +1,5 @@ +"""Incremental recurrent KDA implementations and state containers.""" + +from .fused import KDAState, fused_recurrent_kda, fused_recurrent_kda_step + +__all__ = ["KDAState", "fused_recurrent_kda", "fused_recurrent_kda_step"] diff --git a/kda/ops/recurrent/fused.py b/kda/ops/recurrent/fused.py new file mode 100644 index 0000000..f92c13e --- /dev/null +++ b/kda/ops/recurrent/fused.py @@ -0,0 +1,73 @@ +"""L6: FLA fused recurrent KDA decode with optional step cache.""" + +from __future__ import annotations + +from dataclasses import dataclass + +import torch + +from kda._fla.ops.kda.fused_recurrent import fused_recurrent_kda as _fused_recurrent_kda + + +@dataclass +class KDAState: + """Mutable recurrent state cache: ``S`` is ``[B, HV, K, V]``.""" + + S: torch.Tensor + pos: int = 0 + + def reset(self): + self.S.zero_() + self.pos = 0 + + +def fused_recurrent_kda_step( + state: KDAState, + q_t: torch.Tensor, + k_t: torch.Tensor, + v_t: torch.Tensor, + g_t: torch.Tensor, + beta_t: torch.Tensor, + scale: float | None = None, +): + """Single-token step. Inputs are ``[B, H|HV, ...]`` (no time dim).""" + o, ht = _fused_recurrent_kda( + q_t.unsqueeze(1), + k_t.unsqueeze(1), + v_t.unsqueeze(1), + g_t.unsqueeze(1), + beta_t.unsqueeze(1), + scale=scale, + initial_state=state.S, + output_final_state=True, + ) + state.S = ht + state.pos += 1 + return o.squeeze(1) + + +def fused_recurrent_kda( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + scale: float | None = None, + initial_state: torch.Tensor | None = None, + output_final_state: bool = False, + **kwargs, +): + return _fused_recurrent_kda( + q, + k, + v, + g, + beta, + scale=scale, + initial_state=initial_state, + output_final_state=output_final_state, + **kwargs, + ) + + +__all__ = ["KDAState", "fused_recurrent_kda", "fused_recurrent_kda_step"] diff --git a/kda/ops/reference/__init__.py b/kda/ops/reference/__init__.py new file mode 100644 index 0000000..b2d5d82 --- /dev/null +++ b/kda/ops/reference/__init__.py @@ -0,0 +1,13 @@ +"""Readable PyTorch implementations used as correctness references.""" + +from .chunkwise import naive_chunk_kda +from .gate import kda_gate_naive, kda_gate_reference +from .recurrent import naive_kda, naive_kda_fwd + +__all__ = [ + "kda_gate_naive", + "kda_gate_reference", + "naive_chunk_kda", + "naive_kda", + "naive_kda_fwd", +] diff --git a/kda/ops/reference/chunkwise.py b/kda/ops/reference/chunkwise.py new file mode 100644 index 0000000..7577fb9 --- /dev/null +++ b/kda/ops/reference/chunkwise.py @@ -0,0 +1,155 @@ +"""Pure-PyTorch chunked reference implementation of KDA.""" +from __future__ import annotations + +import math +import warnings + +import torch +from einops import rearrange + +#: ``exp`` overflows past this exponent in fp32 and bf16 (both top out at 3.4e38). +_EXP_LIMIT = math.log(torch.finfo(torch.float32).max) + + +#: Row-block size for :func:`_decayed_dot`. +#: +#: The g_ref GEMM exponentiates the gate span between the reference row and the +#: rows/columns it covers, so the block size caps that exponent at +#: ``DECAY_BLOCK * max|g|``. With the default ``lower_bound=-5`` gate that is +#: ``16 * 5 = 80 < ln(3.4e38) = 88.7``, i.e. fp32/bf16-safe for any chunk size. +#: Referencing a whole 64-row chunk instead would allow ``64 * 5 = 320`` and +#: overflow to NaN once the gate saturates. +DECAY_BLOCK = 16 + + +def _decayed_dot(x: torch.Tensor, k: torch.Tensor, g: torch.Tensor) -> torch.Tensor: + """Return ``A[..., i, j] = `` (FLA g_ref GEMM). + + Only the causal part (``j <= i``) is exact; callers mask the rest, which is + left at zero. Rows are processed in blocks of :data:`DECAY_BLOCK` against + the block's own first row, which is what bounds the exponent: for a row + block starting at ``r``, ``exp(g_i - g_ref)`` spans at most ``DECAY_BLOCK`` + steps, and ``exp(g_ref - g_j)`` is ``<= 1`` for ``j < r`` and likewise spans + at most ``DECAY_BLOCK`` steps for ``j >= r``. + """ + C = g.shape[-2] + out = g.new_zeros(*g.shape[:-1], C) + for r in range(0, C, DECAY_BLOCK): + end = min(r + DECAY_BLOCK, C) + g_ref = g[..., r : r + 1, :] + rows = x[..., r:end, :] * (g[..., r:end, :] - g_ref).exp() + cols = k[..., :end, :] * (g_ref - g[..., :end, :]).exp() + out[..., r:end, :end] = rows @ cols.transpose(-1, -2) + return out + + +#: Whether :func:`naive_chunk_kda` checks the gate span against the ``exp`` +#: budget. The check costs one device sync per call; set it to ``False`` if that +#: matters more than diagnosing a NaN. +CHECK_DECAY_SPAN = True + + +def _max_decay_span(g_cumsum: torch.Tensor) -> torch.Tensor: + """Largest ``|g_ref - g_j|`` any row block will exponentiate.""" + C = g_cumsum.shape[-2] + if C % DECAY_BLOCK == 0: + blocks = g_cumsum.unflatten(-2, (C // DECAY_BLOCK, DECAY_BLOCK)) + return (blocks[..., :1, :] - blocks).abs().amax() + return torch.stack( + [ + (g_cumsum[..., r : r + 1, :] - g_cumsum[..., r : r + DECAY_BLOCK, :]) + .abs() + .amax() + for r in range(0, C, DECAY_BLOCK) + ] + ).amax() + + +def _warn_if_decay_span_overflows(g_cumsum: torch.Tensor) -> None: + """Warn when a row block's gate span is about to overflow ``exp``. + + ``DECAY_BLOCK`` bounds this for the default ``safe_gate`` path, but an + unbounded gate (``-A.exp() * softplus(x)``) can still exceed it. + """ + span = _max_decay_span(g_cumsum).item() + if span > _EXP_LIMIT: + warnings.warn( + f"gate span within a {DECAY_BLOCK}-row block is {span:.1f} > " + f"{_EXP_LIMIT:.1f}; exp() will overflow to inf and the output will " + "be NaN. Reduce the gate magnitude (e.g. safe_gate with a smaller " + "|lower_bound|) or use backend='triton'.", + RuntimeWarning, + stacklevel=3, + ) + + +def naive_chunk_kda( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + scale: float | None = None, + initial_state: torch.Tensor | None = None, + output_final_state: bool = False, + chunk_size: int = 64, +): + """Chunk-parallel, inter-chunk recurrent KDA reference. + + Shapes are ``q/k: [B,T,H,K]``, ``v: [B,T,HV,V]``, + ``g: [B,T,HV,K]`` and ``beta: [B,T,HV]``. + """ + dtype = v.dtype + B, T, H, K = q.shape + HV, V = v.shape[2], v.shape[-1] + C = chunk_size + assert HV % H == 0, f"HV={HV} must be divisible by H={H}" + assert T % C == 0, f"T={T} must be divisible by chunk_size={C}" + scale = K**-0.5 if scale is None else scale + + q, k = [ + rearrange(x, "b (n c) h d -> b h n c d", c=C) + .repeat_interleave(HV // H, dim=1) + for x in (q, k) + ] + v, g = [rearrange(x, "b (n c) h d -> b h n c d", c=C) for x in (v, g)] + beta = rearrange(beta, "b (n c) h -> b h n c", c=C) + q = q * scale + g = g.cumsum(dim=-2) + if CHECK_DECAY_SPAN: + _warn_if_decay_span_overflows(g) + + # r_i + sum_{j r_j + # = v_i - . + mask_upper = torch.triu(torch.ones(C, C, dtype=torch.bool, device=q.device)) + mask_strict_upper = torch.triu(mask_upper, diagonal=1) + eye = torch.eye(C, dtype=q.dtype, device=q.device) + A_kk = _decayed_dot(k, k, g) + M = eye + (A_kk * beta[..., None, :]).masked_fill(mask_upper, 0) + W = torch.linalg.solve_triangular(M, g.exp() * k, upper=False) + U = torch.linalg.solve_triangular(M, v, upper=False) + + # Output includes the current token, hence the diagonal is retained. + A_qk = (_decayed_dot(q, k, g) * beta[..., None, :]).masked_fill(mask_strict_upper, 0) + + S = q.new_zeros(B, HV, K, V) + if initial_state is not None: + S = S + initial_state + o = v.new_empty(B, HV, T // C, C, V) + + for n in range(T // C): + q_n, k_n, g_n = q[:, :, n], k[:, :, n], g[:, :, n] + r = U[:, :, n] - W[:, :, n] @ S + o[:, :, n] = (q_n * g_n.exp()) @ S + A_qk[:, :, n] @ r + + decay = (g_n[:, :, -1:, :] - g_n).exp() + S = S * g_n[:, :, -1, :, None].exp() + S = S + (decay * k_n).transpose(-1, -2) @ (r * beta[:, :, n, :, None]) + + if not output_final_state: + S = None + return rearrange(o, "b h n c d -> b (n c) h d").to(dtype), S + + +# Backward-compatible name used by earlier notes/scripts. +naive_chunk_kda_fwd = naive_chunk_kda diff --git a/kda/ops/reference/gate.py b/kda/ops/reference/gate.py new file mode 100644 index 0000000..46bb926 --- /dev/null +++ b/kda/ops/reference/gate.py @@ -0,0 +1,47 @@ +"""PyTorch references for the two KDA gate activations.""" +from __future__ import annotations + +import torch +import torch.nn.functional as F + + +def kda_gate_reference( + g: torch.Tensor, + A_log: torch.Tensor, + dt_bias: torch.Tensor | None = None, + *, + safe_gate: bool = False, + lower_bound: float | None = None, +) -> torch.Tensor: + """Compute the official KDA gate semantics in PyTorch. + + ``A_log`` is head-wise with shape ``[HV]`` and ``dt_bias`` is + per-dimension with shape ``[HV, K]`` (or flattened to ``[HV*K]``). + """ + HV, K = g.shape[-2:] + gate_input = g if dt_bias is None else g + dt_bias.view(HV, K) + rate = A_log.view(HV, 1).exp() + if safe_gate: + if lower_bound is None: + raise ValueError("lower_bound is required when safe_gate=True") + return lower_bound * torch.sigmoid(rate * gate_input) + return -rate * F.softplus(gate_input) + + +def kda_gate_naive( + g: torch.Tensor, + A_log: torch.Tensor, + dt_bias: torch.Tensor | None = None, + lower_bound: float | None = None, +) -> torch.Tensor: + """Compatibility name matching FLA's reference gate convention.""" + return kda_gate_reference( + g, + A_log, + dt_bias, + safe_gate=lower_bound is not None, + lower_bound=lower_bound, + ) + + +__all__ = ["kda_gate_naive", "kda_gate_reference"] diff --git a/kda/ops/reference/recurrent.py b/kda/ops/reference/recurrent.py new file mode 100644 index 0000000..301c1ff --- /dev/null +++ b/kda/ops/reference/recurrent.py @@ -0,0 +1,298 @@ +"""L1: Naive recurrent KDA fwd+bwd (torch only). + +公式 (per timestep t, log-space gate; q/k 入口 H 维, 内部 repeat_interleave 到 HV): + S_t = exp(g_t) * S_{t-1} + (beta_t * k_t) outer (v_t - k_t . (exp(g_t) * S_{t-1})) + o_t = (q_t * scale) . S_t + +backward (BPTT, T -> 0): + 设 dS_t 为进入 t 步累积的反传梯度 (含 o_t 反传). + 1. o_t = q_t . S_t -> dS_t += q_t outer do_t (i.e. dS = dS + q_t·do_t) + dq_t = do_t . S_t^T -> einsum('bhv,bhkv->bhk') + 2. S_t = S_decay + a_t outer r_t, a_t = b_t k_t, r_t = v_t - k_t . S_decay + 其中 S_decay = exp(g_t) * S_{t-1} + dS_{t-1} = exp(g_t) * (dS_t - r_t outer da_t - a_t outer dr_t) via residual 反传 + 更具体: + dS_decay = dS_t - (a_t outer dr_t) - (da_t outer r_t) + dS_{t-1} += exp(g_t) * dS_decay + 其中 dr_t = -dv_t + dS_t . a_t^T (因为 r_t = v - k·S_dec, dr 来自 -dv - k·dS_decay) + da_t = -r_t outer dS_t? 让我直接推导下面. + 推导 (设 G1 = S_t, 走 a = r 反向链 通过 autograd): + o_t = q_t . G1 + dq_t = do_t . G1^T -> [B,HV,K] + dG1 = q_t outer do_t -> [B,HV,K,V] = dS_t (上游) + G1 = Sdec + a outer r -> Sdec = G1[...] (跳过) + dSdec = dG1 + da_t = r_t outer dG1 -> [B,HV,K] (因为 a outer r 是 K-V, d(a outer r) = r outer d[...,V]) + 但在 einsum 表示: dA_t.grad = einsum('bhkv,bhv->bhk', dS_t, r_t) + dr_t = a_t outer dG1 -> [B,HV,V] = einsum('bhkv,bhk->bhv', dS_t, a_t) + plus: S_t = Sdec + a outer r -> a outer r - outer product 形状是 [B,HV,K,V] = einsum('bhk,bhv->bhkv') + d(a outer r) 的雅可比: let G1_m = a_t ⊗ r_t (rank-1 matrix per (b,h)) + dG1_m[i,j] = da_t[i] * r_t[j] + a_t[i] * dr_t[j] + 在外积形式, 即 dG1_m = a_outer r 的张量积正交分解: + da_t = sum_j r_t[j] dG1_m[i,j] = einsum('bhkv,bhv->bhk', dG1_m, r_t) + dr_t = sum_i a_t[i] dG1_m[i,j] = einsum('bhkv,bhk->bhv', dG1_m, a_t) + 因为 a_t = b_t k_t -> da_t = db_t k_t + b_t dk_t (b_t 是 ...) + db_t = einsum('bhk,bhk->bh', da_t, k_t) + dk_t_a = b_t * da_t (来自 a_t 路径, 还有来自 r_t 路径和 S_dec 路径) + 因为 r_t = v_t - k_t . S_dec -> 注 rk_t grad via dg,S_dec 和 dv_t + dv_t = -dr_t (实际 dr 的负梯度) 即 dv_t = -dr_t + 这里 r_t = v_t - k_t · S_dec, 写作矩阵乘 r = v - einsum('bhk,bhkv->bhv', k, S_dec) + dr = -dv - einsum('bhk,bhkv->bhv', dk_from_r, S_dec) + eigengrad via S_dec + 更精确的反向: r_t = v_t - k_t . S_dec + dv_t += -dr_t -> dv_t = -dr_t + dk_t_r_path = -S_dec outer dr_t (即 -dS_dec 传递来自 k_t 的部分) + 具体: d(k·S) = dk·S + k·dS -> dS_dec 这层, dk 的贡献: -S_dec outer dr_t + 即 dk_t_r = einsum('bhv,bhkv->bhk', -dr_t, S_dec) + dS_dec_r = -k_t outer dr_t = -einsum('bhv,bhk->bhkv', dr_t, k_t) + 合并: dS_dec 合总 = dG1 + (-k_t outer dr_t) + = dS_t - k_t outer dr_t + (相加过的 dv, dk_r, dS_dec_r 都上面项) + Sdec = exp(g_t) * S_{t-1}: + dS_{t-1} = exp(g_t) ⊙ dS_dec (因为 Sdec = exp_g * S_prev, 微分后 exp_g 直接相乘) + dg_t = exp(g_t) * S_prev * dS_dec (微分时对 g_t (log-space) 求偏导数) + 即 dg_t = exp(g_t) * (S_{t-1} ⊙ dS_dec) -> 沿 K 维求和 + in einsum: dg_t = sum over v of (exp(g_t) * S_{t-1}) ⊙ dS_dec ...\n + = einsum('bhk, bhk, bhkv -> bhk', exp_g, S_prev, dS_dec) + 更简洁: Sdec = exp_g * S_prev (per-(b,h,k)/v), 故 dSdec/dg_t = S_prev * exp_g + 所以 dg_t = sum_v S_prev_sub_k_dim * exp_g * dS_dec -> [B, HV, K] + einsum: dg_t = einsum('bhkv,bhkv->bhk', Sdec, dS_dec) + (因为 Sdec = S_prev * exp_g, sum_v Sdec[:, :, :, v] * dS_dec[:, :, :, v] = sum_v Sdec_eachK * dSdec_eachK) + einsum上是 einsum('bhkv,bhkv->bhk', Sdec, dSdec) + dS_{t-1} = exp_g ⊙ dSdec (per (b,h,k,v) entrywise multiply exp_g with dSdec) + +GVA 反归约: + q,k 入口 [B, T, H, K] --repeat_interleave(G, dim=2)--> [B, T, HV, K] + 内部计算后, dq/dk 在 HV 维上 -> dV 拿 shape [B,T,HV,K] + bwd 通过 sum 回 H: dq_H = dq_HV.view(B,T,H,G,K).sum(dim=3) -> [B,T,H,K] + (因为 repeat_interleave 是复制, 反传是 sum 路径相同意义) + +记号对照: + a_t = b_t * k_t (a = beta * k) [B, HV, K] + r_t = v_t - k_t . S_dec (residual) [B, HV, V] + S_dec = exp(g_t) * S_{t-1} [B, HV, K, V] + S_t = S_dec + a_t outer r_t [B, HV, K, V] + o_t = q_t . S_t = (q_t_eff * scale) . S_t [B, HV, V] +""" +from __future__ import annotations + +import math + +import torch + + +def naive_kda_fwd( + q: torch.Tensor, # [B, T, H, K] + k: torch.Tensor, # [B, T, H, K] + v: torch.Tensor, # [B, T, HV, V] + g: torch.Tensor, # [B, T, HV, K] + beta: torch.Tensor, # [B, T, HV] + scale: float | None = None, + initial_state: torch.Tensor | None = None, # [B, HV, K, V] + output_final_state: bool = False, + *, + force_float32: bool = False, +): + """纯 forward, 不带 autograd. 与上游 naive_recurrent_kda 数值等价. + + force_float32=True 时强制 fp32 计算 (与上游对拍时用); + 默认保持输入 dtype (gradcheck 用 fp64). + """ + dtype = v.dtype + B, T, H, K = q.shape + HV, V = v.shape[2], v.shape[-1] + G = HV // H + if scale is None: + scale = 1.0 / math.sqrt(K) + + # 上游强制 fp32; 本实现默认保留输入 dtype 以便 gradcheck 适用 fp64 + # force_float32=True 时与上游逐位对齐 + work_dtype = torch.float if force_float32 else q.dtype + q = q.to(work_dtype) + k = k.to(work_dtype) + v = v.to(work_dtype) + g = g.to(work_dtype) + beta = beta.to(work_dtype) + + # GVA: expand q/k from H to HV + qe = q.repeat_interleave(G, dim=2) * scale # [B, T, HV, K] + ke = k.repeat_interleave(G, dim=2) # [B, T, HV, K] + + S = torch.zeros(B, HV, K, V, dtype=work_dtype, device=q.device) + if initial_state is not None: + S = S + initial_state.to(work_dtype) + + o = torch.empty(B, T, HV, V, dtype=work_dtype, device=q.device) + for t in range(T): + q_t = qe[:, t] # [B, HV, K] + k_t = ke[:, t] # [B, HV, K] + v_t = v[:, t] # [B, HV, V] + g_t = g[:, t] # [B, HV, K] + b_t = beta[:, t] # [B, HV] + + S_dec = S * g_t.exp().unsqueeze(-1) # [B, HV, K, V] + p_t = torch.einsum('b h k, b h k v -> b h v', k_t, S_dec) # [B, HV, V] + r_t = v_t - p_t # [B, HV, V] + a_t = b_t.unsqueeze(-1) * k_t # [B, HV, K] + S = S_dec + torch.einsum('b h k, b h v -> b h k v', a_t, r_t) + o[:, t] = torch.einsum('b h k, b h k v -> b h v', q_t, S) + + if not output_final_state: + S = None + return o.to(dtype), S + + +class KDAFunction(torch.autograd.Function): + """autograd Function (forward + backward). + + forward 入参顺序 (q, k, v, g, beta, scale, initial_state, output_final_state) + backward 必须返回一致: (dq, dk, dv, dg, dbeta, None, dinit_state, None) + """ + + @staticmethod + def forward(ctx, q, k, v, g, beta, scale, initial_state, output_final_state): + dtype = v.dtype + B, T, H, K = q.shape + HV, V = v.shape[2], v.shape[-1] + G = HV // H + if scale is None: + scale = 1.0 / math.sqrt(K) + + work_dtype = q.dtype + qf = q.to(work_dtype).contiguous() + kf = k.to(work_dtype).contiguous() + vf = v.to(work_dtype).contiguous() + gf = g.to(work_dtype).contiguous() + bf = beta.to(work_dtype).contiguous() + + # GVA: expand q/k from H to HV + qe = qf.repeat_interleave(G, dim=2) * scale # [B, T, HV, K] + ke = kf.repeat_interleave(G, dim=2) # [B, T, HV, K] + + S = torch.zeros(B, HV, K, V, dtype=work_dtype, device=q.device) + if initial_state is not None: + S = S + initial_state.to(work_dtype) + + o = torch.empty(B, T, HV, V, dtype=work_dtype, device=q.device) + q_ts, k_ts, b_ts, S_decs, r_ts, a_ts, exp_g_ts = [], [], [], [], [], [], [] + + for t in range(T): + q_t = qe[:, t] + k_t = ke[:, t] + v_t = vf[:, t] + g_t = gf[:, t] + b_t = bf[:, t] + exp_g_t = g_t.exp() + S_dec = S * exp_g_t.unsqueeze(-1) + p_t = torch.einsum('b h k, b h k v -> b h v', k_t, S_dec) + r_t = v_t - p_t + a_t = b_t.unsqueeze(-1) * k_t + S = S_dec + torch.einsum('b h k, b h v -> b h k v', a_t, r_t) + o[:, t] = torch.einsum('b h k, b h k v -> b h v', q_t, S) + + q_ts.append(q_t) + k_ts.append(k_t) + b_ts.append(b_t) + S_decs.append(S_dec) + r_ts.append(r_t) + a_ts.append(a_t) + exp_g_ts.append(exp_g_t) + + ctx.save_for_backward( + torch.stack(q_ts, dim=1), + torch.stack(k_ts, dim=1), + torch.stack(b_ts, dim=1), + torch.stack(S_decs, dim=1), + torch.stack(r_ts, dim=1), + torch.stack(a_ts, dim=1), + torch.stack(exp_g_ts, dim=1), + ) + ctx.G = G + ctx.H = H + ctx.HV = HV + ctx.K = K + ctx.V = V + ctx.T = T + ctx.B = B + ctx.scale = scale + ctx.dtype = dtype + ctx.has_initial_state = initial_state is not None + ctx.output_final_state = output_final_state + + final_S = S if output_final_state else None + return o.to(dtype), final_S + + @staticmethod + def backward(ctx, do, dS): + q_ts, k_ts, b_ts, S_decs, r_ts, a_ts, exp_g_ts = ctx.saved_tensors + B, T, H, HV, K, V, G = ctx.B, ctx.T, ctx.H, ctx.HV, ctx.K, ctx.V, ctx.G + + work_dtype = q_ts.dtype + device = q_ts.device + + dq_e = torch.zeros(B, T, HV, K, dtype=work_dtype, device=device) + dk_e = torch.zeros(B, T, HV, K, dtype=work_dtype, device=device) + dv = torch.zeros(B, T, HV, V, dtype=work_dtype, device=device) + dg = torch.zeros(B, T, HV, K, dtype=work_dtype, device=device) + dbeta= torch.zeros(B, T, HV, dtype=work_dtype, device=device) + + if dS is None: + dS_acc = torch.zeros(B, HV, K, V, dtype=work_dtype, device=device) + else: + dS_acc = dS.to(work_dtype).clone() + + for t in range(T - 1, -1, -1): + q_t = q_ts[:, t] + k_t = k_ts[:, t] + b_t = b_ts[:, t] + S_dec = S_decs[:, t] + r_t = r_ts[:, t] + a_t = a_ts[:, t] + exp_g_t = exp_g_ts[:, t] + do_t = do[:, t].to(work_dtype) + + S_t = S_dec + torch.einsum('b h k, b h v -> b h k v', a_t, r_t) + dS_acc = dS_acc + torch.einsum('b h k, b h v -> b h k v', q_t, do_t) + dq_e[:, t] = torch.einsum('b h v, b h k v -> b h k', do_t, S_t) + + da_t = torch.einsum('b h v, b h k v -> b h k', r_t, dS_acc) + dr_t = torch.einsum('b h k, b h k v -> b h v', a_t, dS_acc) + + dbeta[:, t] = torch.einsum('b h k, b h k -> b h', k_t, da_t) + dk_t_a = b_t.unsqueeze(-1) * da_t + + dv[:, t] = dr_t + dS_dec_from_r = -torch.einsum('b h v, b h k -> b h k v', dr_t, k_t) + dk_t_r = -torch.einsum('b h v, b h k v -> b h k', dr_t, S_dec) + + dS_dec_total = dS_acc + dS_dec_from_r + dk_e[:, t] = dk_t_a + dk_t_r + + dg[:, t] = torch.einsum('b h k v, b h k v -> b h k', S_dec, dS_dec_total) + dS_acc = exp_g_t.unsqueeze(-1) * dS_dec_total + + if HV > H: + dq_H = dq_e.view(B, T, H, G, K).sum(dim=3) + dk_H = dk_e.view(B, T, H, G, K).sum(dim=3) + else: + dq_H = dq_e + dk_H = dk_e + + # q 在 forward 内被乘过 scale (qe = q * scale), chain rule: dq_orig = dq_e * scale + dq_H = dq_H * ctx.scale + + return (dq_H.to(ctx.dtype), dk_H.to(ctx.dtype), dv.to(ctx.dtype), + dg.to(ctx.dtype), dbeta.to(ctx.dtype), None, None, None) + + +def naive_kda( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + scale: float | None = None, + initial_state: torch.Tensor | None = None, + output_final_state: bool = False, +): + """对外入口: 调 KDAFunction.apply.""" + return KDAFunction.apply(q, k, v, g, beta, scale, initial_state, output_final_state) diff --git a/kda/ops/triton/__init__.py b/kda/ops/triton/__init__.py new file mode 100644 index 0000000..7af7d52 --- /dev/null +++ b/kda/ops/triton/__init__.py @@ -0,0 +1,7 @@ +"""Local Triton KDA kernels vendored from FLA chunk_{fwd,intra,bwd,wy,gate}.""" + +from .chunk import ChunkKDAFunction, chunk_kda +from .chunk_fwd import chunk_kda_fwd +from .gate import kda_gate_fwd + +__all__ = ["ChunkKDAFunction", "chunk_kda", "chunk_kda_fwd", "kda_gate_fwd"] diff --git a/kda/ops/triton/chunk.py b/kda/ops/triton/chunk.py new file mode 100644 index 0000000..c14409e --- /dev/null +++ b/kda/ops/triton/chunk.py @@ -0,0 +1,5 @@ +"""FLA ``chunk_kda`` surface used by ``ops.api`` backend='triton'.""" + +from kda._fla.ops.kda.chunk import ChunkKDAFunction, chunk_kda + +__all__ = ["ChunkKDAFunction", "chunk_kda"] diff --git a/kda/ops/triton/chunk_bwd.py b/kda/ops/triton/chunk_bwd.py new file mode 100644 index 0000000..9d195d7 --- /dev/null +++ b/kda/ops/triton/chunk_bwd.py @@ -0,0 +1,5 @@ +"""Vendored FLA chunk KDA backward.""" + +from kda._fla.ops.kda.chunk_bwd import chunk_kda_bwd + +__all__ = ["chunk_kda_bwd"] diff --git a/kda/ops/triton/chunk_fwd.py b/kda/ops/triton/chunk_fwd.py new file mode 100644 index 0000000..d60a29e --- /dev/null +++ b/kda/ops/triton/chunk_fwd.py @@ -0,0 +1,37 @@ +"""Vendored FLA chunk KDA forward, returning ``(o, ht)`` like the public op.""" + +from __future__ import annotations + +import torch + +from kda._fla.ops.kda.chunk import chunk_kda +from kda._fla.ops.kda.chunk_fwd import chunk_kda_fwd as fla_chunk_kda_fwd + +__all__ = ["chunk_kda_fwd", "fla_chunk_kda_fwd"] + + +def chunk_kda_fwd( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g: torch.Tensor, + beta: torch.Tensor, + scale: float | None = None, + initial_state: torch.Tensor | None = None, + output_final_state: bool = False, + chunk_size: int = 64, + **kwargs, +): + """Chunked KDA forward with FLA kernels. Returns ``(o, ht)``.""" + return chunk_kda( + q, + k, + v, + g, + beta, + scale=scale, + initial_state=initial_state, + output_final_state=output_final_state, + chunk_size=chunk_size, + **kwargs, + ) diff --git a/kda/ops/triton/gate.py b/kda/ops/triton/gate.py new file mode 100644 index 0000000..91d5b83 --- /dev/null +++ b/kda/ops/triton/gate.py @@ -0,0 +1,36 @@ +"""Vendored FLA KDA gate fusion (standard + safe gate + chunk cumsum).""" + +from __future__ import annotations + +import torch + +from kda._fla.ops.kda.gate import ( + kda_gate_bwd, + kda_gate_chunk_cumsum, + kda_gate_fwd as _kda_gate_fwd, +) + +DEFAULT_LOWER_BOUND = -5.0 + + +def kda_gate_fwd( + g: torch.Tensor, + A_log: torch.Tensor, + dt_bias: torch.Tensor | None = None, + lower_bound: float | None = DEFAULT_LOWER_BOUND, +): + return _kda_gate_fwd( + g, + A_log=A_log, + dt_bias=dt_bias, + lower_bound=lower_bound, + output_dtype=g.dtype, + ) + + +__all__ = [ + "DEFAULT_LOWER_BOUND", + "kda_gate_bwd", + "kda_gate_chunk_cumsum", + "kda_gate_fwd", +] diff --git a/kda/ops/triton/wy_fast.py b/kda/ops/triton/wy_fast.py new file mode 100644 index 0000000..607949e --- /dev/null +++ b/kda/ops/triton/wy_fast.py @@ -0,0 +1,5 @@ +"""Vendored FLA WY recompute used by the chunk KDA backward.""" + +from kda._fla.ops.kda.wy_fast import recompute_w_u_fwd + +__all__ = ["recompute_w_u_fwd"] diff --git a/kda/training/__init__.py b/kda/training/__init__.py new file mode 100644 index 0000000..64c6bde --- /dev/null +++ b/kda/training/__init__.py @@ -0,0 +1,5 @@ +"""Training and checkpoint helpers.""" + +from .toy import load_ckpt, make_toy_data, save_ckpt, train_one_batch + +__all__ = ["load_ckpt", "make_toy_data", "save_ckpt", "train_one_batch"] diff --git a/kda/training/data.py b/kda/training/data.py new file mode 100644 index 0000000..cedbf69 --- /dev/null +++ b/kda/training/data.py @@ -0,0 +1,355 @@ +"""Pretrain / SFT sample construction. + +Pretrain: Wikipedia parquet → tokenize → pack (B, T). Languages mix 1:1 by +token via seq_len-sized blocks so each training chunk is monolingual. + +SFT: instruction-parallel rows → prompt-masked labels. Template lives in +``prompts.instruction_prompt`` (same string as eval_mt). +""" +from __future__ import annotations + +import json +import os +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable, Protocol + +import torch + +from .prompts import instruction_prompt + +WIKI_SHARD_TOTAL = {"zh": 6, "en": 41} +WIKI_BASE = ( + "https://huggingface.co/datasets/wikimedia/wikipedia/resolve/main/20231101.{lang}" +) +IGNORE_INDEX = -100 + + +class Tokenizer(Protocol): + vocab_size: int + + def encode(self, text: str) -> list[int]: ... + + def decode(self, ids: list[int]) -> str: ... + + +@dataclass +class SentencePieceTokenizer: + _sp: object + + @property + def vocab_size(self) -> int: + return int(self._sp.vocab_size()) + + def encode(self, text: str) -> list[int]: + return list(self._sp.encode(text, out_type=int)) + + def decode(self, ids: list[int]) -> str: + return str(self._sp.decode(ids)) + + +@dataclass +class HuggingFaceTokenizer: + _tok: object + + @property + def vocab_size(self) -> int: + return int(len(self._tok)) + + def encode(self, text: str) -> list[int]: + return list(self._tok.encode(text, add_special_tokens=False)) + + def decode(self, ids: list[int]) -> str: + return str(self._tok.decode(ids, skip_special_tokens=True)) + + +def load_tokenizer(source: str) -> Tokenizer: + """`.model` 走 SentencePiece, 其它当作 HuggingFace 名或本地目录.""" + if source.endswith(".model"): + from sentencepiece import SentencePieceProcessor + + return SentencePieceTokenizer(SentencePieceProcessor(model_file=source)) + from transformers import AutoTokenizer + + tok = AutoTokenizer.from_pretrained(source, trust_remote_code=True) + return HuggingFaceTokenizer(tok) + + +def pretrain_dir() -> Path: + for candidate in ( + os.environ.get("KDA_PRETRAIN_DIR"), + "/data/pretrain", + "data/pretrain", + ): + if candidate and Path(candidate).is_dir(): + return Path(candidate) + return Path("data/pretrain") + + +def _wiki_files(lang: str, n_shards: int) -> list[str]: + if lang not in WIKI_SHARD_TOTAL: + raise ValueError(f"unsupported wiki lang {lang!r}; expected zh or en") + total = WIKI_SHARD_TOTAL[lang] + n = min(max(n_shards, 1), total) + base = WIKI_BASE.format(lang=lang) + return [f"{base}/train-{i:05d}-of-{total:05d}.parquet" for i in range(n)] + + +def _cache_path(cache_dir: Path, lang: str, n_shards: int, limit: int) -> Path: + return cache_dir / f"wiki-{lang}-n{n_shards}-limit{limit}.jsonl" + + +def fetch_wiki_texts( + limit: int, + lang: str = "zh", + n_shards: int = 2, + cache_dir: str | Path | None = None, +) -> list[str]: + """Load up to ``limit`` article bodies, caching jsonl under pretrain_dir.""" + cache = Path(cache_dir) if cache_dir is not None else pretrain_dir() + cache.mkdir(parents=True, exist_ok=True) + path = _cache_path(cache, lang, n_shards, limit) + if path.exists(): + texts: list[str] = [] + with path.open(encoding="utf-8") as fh: + for line in fh: + line = line.strip() + if not line: + continue + texts.append(json.loads(line)["text"]) + if len(texts) >= limit: + break + if texts: + return texts + + from datasets import load_dataset + + files = _wiki_files(lang, n_shards) + ds = load_dataset("parquet", data_files=files, split="train", streaming=True) + texts = [] + for i, row in enumerate(ds): + if i >= limit: + break + texts.append(row["text"]) + tmp = path.with_suffix(path.suffix + ".tmp") + with tmp.open("w", encoding="utf-8") as fh: + for text in texts: + fh.write(json.dumps({"text": text}, ensure_ascii=False) + "\n") + tmp.replace(path) + return texts + + +def tokenize_corpus(texts: list[str], tok: Tokenizer) -> list[int]: + ids: list[int] = [] + for text in texts: + ids.extend(tok.encode(text)) + return ids + + +def interleave_balanced(ids_a: list[int], ids_b: list[int], block: int) -> list[int]: + """1:1 by token: seq_len-sized monolingual blocks, drop the longer tail.""" + if block < 1: + raise ValueError(f"block must be >= 1, got {block}") + n = min(len(ids_a), len(ids_b)) + n = (n // block) * block + out: list[int] = [] + a, b = ids_a, ids_b + for i in range(0, n, block): + out.extend(a[i : i + block]) + out.extend(b[i : i + block]) + return out + + +def chunk_ids(ids: list[int], batch: int, seq_len: int) -> torch.Tensor: + """切成 (num_chunks, B, T); 末尾不足部分丢弃.""" + n = (len(ids) // (batch * seq_len)) * (batch * seq_len) + t = torch.tensor(ids[:n], dtype=torch.long) + if n == 0: + return t.view(0, batch, seq_len) + return t.view(batch, -1, seq_len).transpose(0, 1) + + +def split_heldout( + chunks: torch.Tensor, + frac: float = 0.01, + min_heldout: int = 1, +) -> tuple[torch.Tensor, torch.Tensor]: + """Last ``frac`` of packed chunks for CE only. Empty held-out if too few.""" + n = int(chunks.size(0)) + if n <= 1 or frac <= 0: + return chunks, chunks[:0] + h = max(min_heldout, int(n * frac)) + h = min(h, n - 1) + return chunks[:-h], chunks[-h:] + + +def iter_chunks(chunks: torch.Tensor): + """逐块产出 (input_ids, labels), labels 右移 (模型内 CE shift).""" + for chunk in chunks: + yield chunk, chunk.clone() + + +def iter_indexed(chunks: torch.Tensor, start: int = 0): + """Infinite cycle with a global index (for --resume).""" + n = int(chunks.size(0)) + if n == 0: + raise ValueError("no training chunks") + i = start + while True: + x = chunks[i % n] + yield i, x, x.clone() + i += 1 + + +def load_pretrain_chunks( + tok: Tokenizer, + *, + langs: Iterable[str], + limit: int, + batch: int, + seq_len: int, + heldout_frac: float = 0.01, + n_shards: int = 2, + cache_dir: str | Path | None = None, +) -> tuple[torch.Tensor, torch.Tensor, int]: + """Fetch / cache / tokenize / pack. Returns train chunks, held-out, token count.""" + lang_list = [lang.strip() for lang in langs if lang.strip()] + if not lang_list: + raise ValueError("langs must contain at least one of zh, en") + streams: list[list[int]] = [] + for lang in lang_list: + print(f"loading {limit} wiki articles ({lang}) ...") + texts = fetch_wiki_texts(limit, lang=lang, n_shards=n_shards, cache_dir=cache_dir) + streams.append(tokenize_corpus(texts, tok)) + print(f" {lang}: {len(streams[-1]):,} tokens from {len(texts)} articles") + if len(streams) == 1: + ids = streams[0] + else: + ids = streams[0] + for extra in streams[1:]: + ids = interleave_balanced(ids, extra, seq_len) + chunks = chunk_ids(ids, batch, seq_len) + train, held = split_heldout(chunks, heldout_frac) + return train, held, len(ids) + + +def pad_id(tok: Tokenizer) -> int: + inner = getattr(tok, "_tok", None) + if inner is not None: + pid = getattr(inner, "pad_token_id", None) + if pid is not None: + return int(pid) + eid = getattr(inner, "eos_token_id", None) + if eid is not None: + return int(eid) + return 0 + + +def eos_id(tok: Tokenizer) -> int | None: + inner = getattr(tok, "_tok", None) + if inner is not None: + eid = getattr(inner, "eos_token_id", None) + if eid is not None: + return int(eid) + convert = getattr(inner, "convert_tokens_to_ids", None) + if convert is not None: + tid = convert("<|im_end|>") + if isinstance(tid, int) and tid >= 0: + return tid + return None + + +def encode_sft_row( + tok: Tokenizer, + src: str, + tgt: str, + target_lang: str, + max_len: int, + eos: int | None = None, +) -> tuple[list[int], list[int]]: + prompt_ids = tok.encode(instruction_prompt(src, target_lang)) + tgt_ids = tok.encode(tgt) + if eos is not None: + tgt_ids = tgt_ids + [eos] + ids = prompt_ids + tgt_ids + labels = [IGNORE_INDEX] * len(prompt_ids) + list(tgt_ids) + if len(ids) > max_len: + overflow = len(ids) - max_len + cut = min(overflow, max(len(prompt_ids) - 1, 0)) + ids = ids[cut:] + labels = labels[cut:] + if len(ids) > max_len: + ids = ids[:max_len] + labels = labels[:max_len] + return ids, labels + + +def load_sft_rows(path: str | Path) -> list[dict]: + """jsonl ``{src,tgt,target_lang}`` or TSV ``src\\ttgt\\ttarget_lang``.""" + p = Path(path) + rows: list[dict] = [] + text = p.read_text(encoding="utf-8") + if p.suffix == ".jsonl" or p.suffix == ".json": + for line in text.splitlines(): + line = line.strip() + if not line: + continue + obj = json.loads(line) + rows.append( + { + "src": obj["src"], + "tgt": obj["tgt"], + "target_lang": obj.get("target_lang", "en"), + } + ) + return rows + for line in text.splitlines(): + line = line.strip() + if not line or line.startswith("#"): + continue + parts = line.split("\t") + if len(parts) < 2: + raise ValueError(f"SFT TSV needs src, tgt [, target_lang]: {line[:80]!r}") + lang = parts[2] if len(parts) > 2 else "en" + rows.append({"src": parts[0], "tgt": parts[1], "target_lang": lang}) + return rows + + +def collate_sft( + rows: list[dict], + tok: Tokenizer, + max_len: int, +) -> tuple[torch.Tensor, torch.Tensor]: + pad = pad_id(tok) + eos = eos_id(tok) + encoded = [ + encode_sft_row(tok, r["src"], r["tgt"], r["target_lang"], max_len, eos) + for r in rows + ] + width = min(max(len(ids) for ids, _ in encoded), max_len) + width = max(width, 2) + bsz = len(encoded) + input_ids = torch.full((bsz, width), pad, dtype=torch.long) + labels = torch.full((bsz, width), IGNORE_INDEX, dtype=torch.long) + for i, (ids, lab) in enumerate(encoded): + n = min(len(ids), width) + input_ids[i, :n] = torch.tensor(ids[:n], dtype=torch.long) + labels[i, :n] = torch.tensor(lab[:n], dtype=torch.long) + return input_ids, labels + + +def iter_sft_batches( + rows: list[dict], + tok: Tokenizer, + batch: int, + max_len: int, + start: int = 0, +): + n = len(rows) + if n == 0: + raise ValueError("no SFT rows") + i = start + while True: + sl = [rows[j % n] for j in range(i, i + batch)] + yield i, *collate_sft(sl, tok, max_len) + i += batch diff --git a/kda/training/eval_mt.py b/kda/training/eval_mt.py new file mode 100644 index 0000000..2f7f22e --- /dev/null +++ b/kda/training/eval_mt.py @@ -0,0 +1,139 @@ +"""Greedy translation eval on line-aligned src/ref files. + + python -m kda.training.eval_mt \\ + --ckpt ckpts/k3_wiki.pt --src /data/eval/zh2en.src.txt \\ + --ref /data/eval/zh2en.ref.txt --target-lang en +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import torch + +from kda.training.data import eos_id, load_tokenizer +from kda.training.prompts import instruction_prompt +from kda.training.success import _chrf, _detect_lang, translation_success +from kda.training.toy import load_ckpt + + +def _read_lines(path: str) -> list[str]: + return [ln.strip() for ln in Path(path).read_text(encoding="utf-8").splitlines() if ln.strip()] + + +def _instruction(src: str, target_lang: str) -> str: + return instruction_prompt(src, target_lang) + + +@torch.inference_mode() +def decode_one(model, tok, prompt: str, device: str, max_new: int) -> str: + ids = tok.encode(prompt) + if not ids: + return "" + inp = torch.tensor([ids], dtype=torch.long, device=device) + out = model.generate(inp, max_new, eos_token_id=eos_id(tok)) + gen = out[0, inp.size(1) :].tolist() + return tok.decode(gen).strip() + + +def evaluate_pairs( + model, + tok, + srcs: list[str], + refs: list[str], + *, + target_lang: str, + device: str, + max_new: int, + limit: int | None, +) -> dict: + n = len(srcs) + if limit is not None: + n = min(n, limit) + hyps: list[str] = [] + wins = 0 + copies = 0 + lang_ok = 0 + chrf_sum = 0.0 + for i in range(n): + src, ref = srcs[i], refs[i] + hyp = decode_one(model, tok, _instruction(src, target_lang), device, max_new) + hyps.append(hyp) + ok = translation_success(src, hyp, ref, target_lang=target_lang) + wins += int(ok) + copies += int(_chrf(hyp, src) >= 80.0 or hyp == src) + want = "zh" if target_lang.startswith("zh") else "en" + lang_ok += int(_detect_lang(hyp) == want) + chrf_sum += _chrf(hyp, ref) + corpus = {} + try: + from sacrebleu.metrics import BLEU, CHRF + + corpus["chrf"] = float(CHRF(word_order=2).corpus_score(hyps, [refs[:n]]).score) + corpus["bleu"] = float(BLEU().corpus_score(hyps, [refs[:n]]).score) + except Exception: + corpus["chrf"] = chrf_sum / max(n, 1) + corpus["bleu"] = None + return { + "n": n, + "success_rate": wins / max(n, 1), + "copy_rate": copies / max(n, 1), + "lang_ok": lang_ok / max(n, 1), + "chrf": corpus["chrf"], + "bleu": corpus["bleu"], + "hyps": hyps, + } + + +def main() -> None: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--ckpt", required=True) + p.add_argument("--tokenizer", default=None, help="override ckpt tokenizer field") + p.add_argument("--src", default=None, help="one source sentence per line") + p.add_argument("--ref", default=None, help="one reference sentence per line") + p.add_argument("--target-lang", default="en", choices=["en", "zh"]) + p.add_argument("--max-new", type=int, default=64) + p.add_argument("--limit", type=int, default=None) + p.add_argument("--prefix", default=None, help="single-prompt smoke decode") + p.add_argument("--device", default="auto") + args = p.parse_args() + + device = args.device + if device == "auto": + device = "cuda" if torch.cuda.is_available() else "cpu" + + model, _config = load_ckpt(args.ckpt) + model.to(device).eval() + payload = torch.load(args.ckpt, map_location="cpu", weights_only=False) + tok_src = args.tokenizer or payload.get("tokenizer") + if not tok_src: + raise SystemExit("need --tokenizer or a 'tokenizer' field in the checkpoint") + tok = load_tokenizer(tok_src) + + if args.prefix: + print(decode_one(model, tok, args.prefix, device, args.max_new)) + + if args.src and args.ref: + srcs, refs = _read_lines(args.src), _read_lines(args.ref) + if len(srcs) != len(refs): + raise SystemExit(f"src/ref length mismatch: {len(srcs)} vs {len(refs)}") + out = evaluate_pairs( + model, + tok, + srcs, + refs, + target_lang=args.target_lang, + device=device, + max_new=args.max_new, + limit=args.limit, + ) + printable = {k: v for k, v in out.items() if k != "hyps"} + print(json.dumps(printable, ensure_ascii=False, indent=2)) + elif not args.prefix: + raise SystemExit("pass --prefix and/or --src + --ref") + + +if __name__ == "__main__": + main() diff --git a/kda/training/prompts.py b/kda/training/prompts.py new file mode 100644 index 0000000..c7cd59f --- /dev/null +++ b/kda/training/prompts.py @@ -0,0 +1,7 @@ +"""Instruction strings shared by SFT and eval. Do not drift.""" + + +def instruction_prompt(src: str, target_lang: str) -> str: + if target_lang.startswith("zh"): + return f"Translate to Chinese:\n{src}" + return f"Translate to English:\n{src}" diff --git a/kda/training/schedule.py b/kda/training/schedule.py new file mode 100644 index 0000000..f56e3c5 --- /dev/null +++ b/kda/training/schedule.py @@ -0,0 +1,47 @@ +"""LR scale and token-horizon helpers for train_k3 / train_sft.""" +from __future__ import annotations + +import math + + +def lr_scale( + opt_step: int, + warmup: int, + total_opt: int, + min_ratio: float = 0.1, +) -> float: + """Linear warmup (optimizer steps) then cosine down to ``min_ratio``. + + ``opt_step`` is 0-indexed at the optimizer update that is about to run. + """ + if warmup > 0 and opt_step < warmup: + return (opt_step + 1) / warmup + denom = max(total_opt - warmup - 1, 1) + progress = min(max(opt_step - warmup, 0) / denom, 1.0) + cosine = 0.5 * (1.0 + math.cos(math.pi * progress)) + return min_ratio + (1.0 - min_ratio) * cosine + + +def tokens_per_micro(batch: int, seq_len: int) -> int: + return batch * seq_len + + +def total_opt_steps( + *, + max_tokens: int | None, + max_micro: int | None, + batch: int, + seq_len: int, + grad_acc: int, +) -> int: + """Optimizer-step horizon used by cosine. At least 1.""" + acc = max(grad_acc, 1) + candidates: list[int] = [] + if max_tokens is not None and max_tokens > 0: + tpm = max(tokens_per_micro(batch, seq_len), 1) + candidates.append(math.ceil(max_tokens / (tpm * acc))) + if max_micro is not None and max_micro > 0: + candidates.append(math.ceil(max_micro / acc)) + if not candidates: + return 1 + return max(min(candidates), 1) diff --git a/kda/training/success.py b/kda/training/success.py new file mode 100644 index 0000000..4422687 --- /dev/null +++ b/kda/training/success.py @@ -0,0 +1,86 @@ +"""Frozen translation success() — SFT eval and RL reward must call this.""" + +from __future__ import annotations + +import re + +CHRF_MIN = 40.0 +COPY_CHRF_MAX = 80.0 +_CJK = re.compile(r"[\u4e00-\u9fff]") + + +def _detect_lang(text: str) -> str | None: + sample = text.strip() + if not sample: + return None + try: + from langdetect import detect + + tag = detect(sample) + except Exception: + if _CJK.search(sample): + return "zh" + if any(c.isascii() and c.isalpha() for c in sample): + return "en" + return None + if tag.startswith("zh"): + return "zh" + return tag[:2] + + +def _chrf(hyp: str, ref: str) -> float: + """chrF++ in 0–100. Falls back to char unigram F if sacrebleu is missing.""" + if not hyp or not ref: + return 0.0 + try: + from sacrebleu.metrics import CHRF + + return float(CHRF(word_order=2).sentence_score(hyp, [ref]).score) + except Exception: + hyp_c, ref_c = list(hyp), list(ref) + if not hyp_c: + return 0.0 + ref_set = set(ref_c) + overlap = sum(1 for c in hyp_c if c in ref_set) + prec = overlap / len(hyp_c) + rec = overlap / max(len(ref_c), 1) + if prec + rec == 0: + return 0.0 + return 100.0 * 2 * prec * rec / (prec + rec) + + +def translation_success( + src: str, + hyp: str, + ref: str | None = None, + *, + target_lang: str, + chrf_min: float = CHRF_MIN, + copy_chrf_max: float = COPY_CHRF_MAX, +) -> bool: + """Binary task success for zh↔en instruction translation. + + 1. non-empty hyp, no instruction leak prefix + 2. langid(hyp) matches target_lang (zh / en) + 3. hyp is not a copy of src + 4. if ref is given, chrF(hyp, ref) >= chrf_min + """ + hyp = hyp.strip() + src = src.strip() + if not hyp: + return False + leak = ("翻译如下", "translate to", "translation:", "译文：") + head = hyp[:40].lower() + if any(p in head or p in hyp[:20] for p in leak): + return False + want = "zh" if target_lang.startswith("zh") else "en" + got = _detect_lang(hyp) + if got != want: + return False + if src and _chrf(hyp, src) >= copy_chrf_max: + return False + if hyp == src: + return False + if ref is not None and _chrf(hyp, ref.strip()) < chrf_min: + return False + return True diff --git a/kda/training/toy.py b/kda/training/toy.py new file mode 100644 index 0000000..f1b785f --- /dev/null +++ b/kda/training/toy.py @@ -0,0 +1,110 @@ +"""L7: toy training loop — overfit 起步. + +target: + 端到端验证模型 + 数据流 + optimizer + ckpt + generate. + +toy data: + 建一份 256-token vocab 的小数据集: e.g. 1000 个长度 32 随机 token 序列 + 起步只取 batch=4, 看能否在 ~320 steps 内把 loss 压到 < 0.1 (overfit 单 batch). + +step: + optimizer = AdamW(lr=1e-3, wd=0.01) + loss.backward(); optimizer.step(); optimizer.zero_grad() + every N steps: 打印 loss + end: 保存 ckpt to ckpts/kda_toy.pt + +ckpt: + save: + torch.save({"model_state": model.state_dict(), "config": asdict(config)}, path) + load: + torch.load -> model.load_state_dict +""" +from __future__ import annotations + +import os +from dataclasses import asdict, fields + +import torch + +from ..models.causal_lm import CausalLM +from ..models.config import KDAConfig +from ..models.k3_config import K3Config + + +def make_toy_data(batch: int = 4, seq_len: int = 32, vocab: int = 256, seed: int = 42): + """单 batch overfit 数据: 同一组序列循环.""" + torch.manual_seed(seed) + seq = torch.randint(0, vocab, (batch, seq_len), dtype=torch.long) + return seq # 用作 input_ids 和 labels (shift one inside forward) + + +def train_one_batch(model, optimizer, input_ids, labels): + optimizer.zero_grad(set_to_none=True) + loss = model(input_ids, labels=labels) + loss.backward() + optimizer.step() + return loss.detach() + + +def save_ckpt(model, config, path: str): + os.makedirs(os.path.dirname(path) or ".", exist_ok=True) + torch.save({"model_state": model.state_dict(), "config": asdict(config)}, path) + + +#: The feed-forward submodule was named after its contents (``mlp`` in the +#: dense config, ``moe`` in K3) before both were unified under ``ffn``. +#: Checkpoints saved before that rename still carry the old prefixes. +_LEGACY_PREFIXES = { + ".mlp.": ".ffn.", + ".mlp_norm.": ".ffn_norm.", + ".moe.": ".ffn.", + ".moe_norm.": ".ffn_norm.", +} + + +def _rename_legacy_keys(state: dict) -> dict: + def fix(key: str) -> str: + for old, new in _LEGACY_PREFIXES.items(): + if old in key: + return key.replace(old, new) + return key + + return {fix(k): v for k, v in state.items()} + + +def _config_from(payload_config: dict) -> K3Config | KDAConfig: + """Pick the config class the checkpoint was written with. + + ``moe_latent_size`` is a K3-only field, so its presence identifies the + hybrid K3 architecture; anything else is the dense KDA config. + """ + cls = K3Config if "moe_latent_size" in payload_config else KDAConfig + known = {item.name for item in fields(cls)} + return cls(**{k: v for k, v in payload_config.items() if k in known}) + + +def load_ckpt(path: str, model: CausalLM | None = None) -> tuple[CausalLM, K3Config | KDAConfig]: + payload = torch.load(path, map_location="cpu", weights_only=False) + config = _config_from(payload["config"]) + if model is None: + model = CausalLM(config) + model.load_state_dict(_rename_legacy_keys(payload["model_state"])) + return model, config + + +def main(): + """主入口: overfit 起步. 320 steps 期望 loss < 0.1.""" + device = "cuda" if torch.cuda.is_available() else "cpu" + config = KDAConfig() + model = CausalLM(config).to(device) + tokens = make_toy_data(seq_len=32, vocab=config.vocab_size).to(device) + optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=0.01) + for step in range(320): + loss = train_one_batch(model, optimizer, tokens, tokens) + if step % 64 == 0 or step == 319: + print(f"step {step:3d} loss {loss.item():.4f}") + save_ckpt(model, config, "ckpts/kda_toy.pt") + + +if __name__ == "__main__": + main() diff --git a/kda/training/train_tokenizer.py b/kda/training/train_tokenizer.py new file mode 100644 index 0000000..33121ca --- /dev/null +++ b/kda/training/train_tokenizer.py @@ -0,0 +1,51 @@ +"""Train a SentencePiece tokenizer on a Chinese Wikipedia subset. + +用法: + uv run python kda/training/train_tokenizer.py \ + --out data/spm_4k --vocab-size 4096 --limit 20000 + +产出: + data/spm_4k.model / data/spm_4k.vocab (BPE/unigram, 中文小语料) +""" +from __future__ import annotations + +import argparse + +import sentencepiece as spm + +from .data import fetch_wiki_texts + + +def main() -> None: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--out", default="data/spm_4k", help="输出前缀 (model/vocab 文件)") + p.add_argument("--vocab-size", type=int, default=8192) + p.add_argument("--limit", type=int, default=20000, help="用于训练的 wiki 文章数") + p.add_argument("--model-type", default="unigram", choices=["unigram", "bpe"]) + p.add_argument("--character-coverage", type=float, default=0.9995) + args = p.parse_args() + + texts = fetch_wiki_texts(args.limit) + corpus = "".join(texts) + tmp = args.out + ".corpus.txt" + with open(tmp, "w", encoding="utf-8") as f: + f.write(corpus) + print(f"corpus: {len(corpus):,} chars from {len(texts)} articles") + + spm.SentencePieceTrainer.train( + input=tmp, + model_prefix=args.out, + vocab_size=args.vocab_size, + model_type=args.model_type, + character_coverage=args.character_coverage, + unk_id=0, + pad_id=1, + bos_id=-1, + eos_id=-1, + num_threads=4, + ) + print(f"tokenizer saved: {args.out}.model / {args.out}.vocab") + + +if __name__ == "__main__": + main() diff --git a/notes/ledger.yaml b/notes/ledger.yaml new file mode 100644 index 0000000..a2e213b --- /dev/null +++ b/notes/ledger.yaml @@ -0,0 +1,187 @@ +schema: superpaper.ledger/v1 +retired_ids: [] +paper: + id: "kda-project" + title: "KDA 训练→推理 手写实现 — 完整笔记" + authors: ["dela"] + notes_language: zh + source: + kind: markdown + +coverage: + mode: full + sections_in: + - "KDA 递归核心" + - "Gate 激活" + - "分块并行计算" + - "GVA 分组值注意力" + - "KDAAttention 层" + - "Gated MLA 矩阵吸收版" + - "SiTU-GLU 与 Stable LatentMoE" + - "K3 混合架构" + - "Attention Residual 深度残差" + - "反向传播推导" + sections_skipped: + - "Triton kernel 细节" + - "Docker 部署" + - "AttnRes 论文的 kernel 级调度与 pipeline 重叠" + +questions: + - id: Q1 + text: "KDA 的状态更新如何避免 softmax、实现线性复杂度？" + - id: Q2 + text: "safe gate 与 standard gate 的区别是什么？" + - id: Q3 + text: "分块并行如何在保持递归等价的同时利用 GPU 并行？" + - id: Q4 + text: "GVA 的 repeat_interleave + sum 反向是怎么回事？" + - id: Q5 + text: "MLA 矩阵吸收如何避免解压 K/V？" + - id: Q6 + text: "SiTU-GLU 为什么比 SwiGLU 更稳定？" + - id: Q7 + text: "AttnRes 如何把残差流从等权累加换成按内容选择？" + - id: Q8 + text: "Block AttnRes 的两阶段算法为什么和 naive 逐层实现数值等价？" + - id: Q9 + text: "深度残差接入 CausalLM 时怎样避免参数被重复注册？" + +claims: + - id: C1 + text: "KDA 用 delta rule 更新 KV 状态矩阵，不需要 softmax，复杂度 O(T·K·V)" + kind: methodological + status: core + - id: C2 + text: "safe gate = lower_bound · σ(rate · input)，保证 gate 值在 [lower_bound, 0] 范围内" + kind: methodological + status: core + - id: C3 + text: "分块计算：chunk 内用下三角解，chunk 间用状态递推，数值等价于 naive recurrent" + kind: methodological + status: core + - id: C4 + text: "MLA 矩阵吸收：q 吸收 W_UK 后直接与 latent c 内积，永不解压 K/V" + kind: methodological + status: core + - id: C5 + text: "LatentMoE 通过 latent 接口把 routed 专家限制在半宽空间 ℓ=d/2" + kind: methodological + status: core + - id: C6 + text: "AttnRes 用逐 token 的深度维 softmax 代替等权残差累加：打分在 RMS 归一化后做，加权和在原始张量上做" + kind: methodological + status: core + - id: C7 + text: "Block AttnRes 块内退化为普通求和、只让块输出进入源列表，源数从 O(N) 降到 O(N/S)" + kind: methodological + status: core + - id: C8 + text: "两阶段算法 = inter 块间批量 einsum + intra online-softmax 增量合并，与 naive 逐层实现数值等价 (atol 1e-5)" + kind: methodological + status: core + - id: C9 + text: "BorrowedSubLayer 用普通 tuple 持有 norm/fn，不注册为子模块，保证参数与 state_dict 键不重复" + kind: methodological + status: supporting + +symbols: + - {name: B, latex: "B", meaning: "batch size", kind: "shape parameter"} + - {name: T, latex: "T", meaning: "序列长度", kind: "shape parameter"} + - {name: H, latex: "H", meaning: "query/key 头数", kind: "shape parameter"} + - {name: HV, latex: "H_V", meaning: "value 头数 (GVA)", kind: "shape parameter"} + - {name: G, latex: "G", meaning: "GVA 组数 = HV/H", kind: "shape parameter"} + - {name: K, latex: "K", meaning: "key/query 头维度", kind: "shape parameter"} + - {name: V, latex: "V", meaning: "value 头维度 (= K)", kind: "shape parameter"} + - {name: D, latex: "D", meaning: "hidden_size", kind: "shape parameter"} + - {name: C, latex: "C", meaning: "chunk_size", kind: "shape parameter"} + - {name: r, latex: "r", meaning: "KV latent rank (kv_lora_rank)", kind: "shape parameter"} + - {name: ell, latex: "\\ell", meaning: "MoE latent 宽度 = d/2", kind: "shape parameter"} + - {name: S, latex: "S", meaning: "KV 状态矩阵", domain: "[B, HV, K, V]", kind: value} + - {name: q, latex: "q", meaning: "query", domain: "[B, T, H, K]", kind: value} + - {name: k, latex: "k", meaning: "key", domain: "[B, T, H, K]", kind: value} + - {name: v, latex: "v", meaning: "value", domain: "[B, T, HV, V]", kind: value} + - {name: g, latex: "g", meaning: "gate (log-space decay)", domain: "[B, T, HV, K]", kind: value} + - {name: beta, latex: "\\beta", meaning: "学习率/写入强度", domain: "[B, T, HV]", kind: value} + - {name: A_log, latex: "A_{\\log}", meaning: "head-wise 衰减参数 (log-space)", domain: "[HV]", kind: value} + - {name: dt_bias, latex: "\\Delta_b", meaning: "per-dim gate bias", domain: "[HV, K]", kind: value} + - {name: c, latex: "c", meaning: "KV latent 向量", domain: "[B, T, r]", kind: value} + - {name: W_UK, latex: "W_{UK}", meaning: "Key 解压矩阵 (MLA)", domain: "[H, d_q, r]", kind: value} + - {name: W_UV, latex: "W_{UV}", meaning: "Value 解压矩阵 (MLA)", domain: "[H, d_v, r]", kind: value} + - {name: N, latex: "N", meaning: "AttnRes 原子层数 = 2L", kind: "shape parameter"} + - {name: S, latex: "S", meaning: "AttnRes 块大小（原子层）", kind: "shape parameter"} + - {name: v_i, latex: "v_i", meaning: "AttnRes 第 i 个源（v_0 = embedding 输出）", domain: "[B, T, D]", kind: value} + - {name: w_l, latex: "w_l", meaning: "第 l 层 depth query（零初始化）", domain: "[D]", kind: value} + - {name: alpha, latex: "\\alpha_{l,i}", meaning: "深度维 softmax 权重", domain: "[n, B, T]", kind: value} + - {name: h_l, latex: "h_l", meaning: "深度注意力聚合出的层输入", domain: "[B, T, D]", kind: value} + - {name: b_j, latex: "b_j", meaning: "Block AttnRes 第 j 块的输出", domain: "[B, T, D]", kind: value} + - {name: p, latex: "p", meaning: "块内 running partial", domain: "[B, T, D]", kind: value} + +terms: + - {canonical: "KDA", aliases: ["Key-Decayed Attention", "键衰减注意力"]} + - {canonical: "GVA", aliases: ["Grouped Value Attention", "分组值注意力"]} + - {canonical: "MLA", aliases: ["Multi-head Latent Attention", "多头隐变量注意力"]} + - {canonical: "MoE", aliases: ["Mixture of Experts", "混合专家"]} + - {canonical: "SiTU-GLU", aliases: ["Sigmoid Tanh Unit GLU"]} + - {canonical: "delta rule", aliases: ["δ 规则"]} + - {canonical: "safe gate", aliases: ["安全门控"]} + - {canonical: "matrix absorption", aliases: ["矩阵吸收"]} + - {canonical: "AttnRes", aliases: ["Attention Residual", "注意力残差", "深度残差"]} + - {canonical: "depth residual", aliases: ["DepthResidual", "深度维残差"]} + - {canonical: "online softmax", aliases: ["在线 softmax", "增量 softmax"]} + - {canonical: "atomic layer", aliases: ["原子层", "atomic sublayer"]} + +derivations: + - id: DER1 + claim: C1 + title: "KDA 递归状态更新推导" + expand: true + figure: null + steps: + - {id: "1", from: "S_{t-1}", to: "S_{\\mathrm{dec}} = \\exp(g_t) \\odot S_{t-1}", rule: scale} + - {id: "2", from: "S_{\\mathrm{dec}}", to: "r_t = v_t - k_t \\cdot S_{\\mathrm{dec}}", rule: definition} + - {id: "3", from: "r_t", to: "S_t = S_{\\mathrm{dec}} + (\\beta_t k_t) \\otimes r_t", rule: definition} + - {id: "4", from: "S_t", to: "o_t = (q_t \\cdot \\text{scale}) \\cdot S_t", rule: definition} + - id: DER2 + claim: C4 + title: "MLA 矩阵吸收推导" + expand: true + figure: null + steps: + - {id: "1", from: "q \\in [B,T,H,d_q]", to: "q_{\\mathrm{abs}} = q \\cdot W_{UK} \\in [B,T,H,r]", rule: substitute} + - {id: "2", from: "q_{\\mathrm{abs}}, c", to: "\\text{score} = q_{\\mathrm{abs}} \\cdot c^T \\in [B,H,T,T]", rule: definition} + - {id: "3", from: "\\text{attn}, c", to: "\\tilde{o}_{\\mathrm{lat}} = \\text{attn} \\cdot c \\in [B,H,T,r]", rule: definition} + - {id: "4", from: "\\tilde{o}_{\\mathrm{lat}}", to: "\\tilde{o} = \\tilde{o}_{\\mathrm{lat}} \\cdot W_{UV}^T \\in [B,H,T,d_v]", rule: substitute} + - id: DER3 + claim: C8 + title: "AttnRes 两阶段 online softmax 合并推导" + expand: true + figure: null + steps: + - {id: "1", from: "s_{l,i} = \\tilde{w}_l^T \\mathrm{RMS}(v_i)", to: "(m, n, d) = (\\max_i s_i, \\sum_i e^{s_i - m} v_i, \\sum_i e^{s_i - m})", rule: definition} + - {id: "2", from: "inter sources b_0..b_{j-1} 固定", to: "一次批量 einsum 'q d, n b t d -> q n b t' 得块内全部 query 的 (m,n,d)", rule: substitute} + - {id: "3", from: "单源 partial p", to: "(m, n, d) = (s_p, p, 1)，因为 e^{s_p - m} = 1", rule: definition} + - {id: "4", from: "(m_a,n_a,d_a), (m_b,n_b,d_b)", to: "m = \\max(m_a,m_b);\\ n = e^{m_a-m} n_a + e^{m_b-m} n_b;\\ d = e^{m_a-m} d_a + e^{m_b-m} d_b", rule: scale} + - {id: "5", from: "(m, n, d)", to: "h_l = n / d，与 forward_naive 逐位一致", rule: definition} + +figures: + - id: F1 + claim: C1 + title: "KDA 递归状态更新张量图" + grammar: tensor-face + toolkit: supertensor + signals: [shape, contraction, broadcast] + status: planned + - id: F2 + claim: C4 + title: "MLA 矩阵吸收计算流" + grammar: tensor-face + toolkit: supertensor + signals: [shape, contraction, transpose] + status: planned + - id: F3 + claim: C7 + title: "Full vs Block AttnRes 的源列表增长" + grammar: tensor-face + toolkit: supertensor + signals: [shape, contraction] + status: planned diff --git a/notes/notes-macros.tex b/notes/notes-macros.tex new file mode 100644 index 0000000..36a23c3 --- /dev/null +++ b/notes/notes-macros.tex @@ -0,0 +1,94 @@ +% KDA 笔记 preamble — 基于 superpaper/assets/notes-macros.tex +\usepackage[fontset=fandol]{ctex} +\usepackage{amsmath,amssymb} +\usepackage{graphicx} +\usepackage[margin=2.2cm]{geometry} +\usepackage[most]{tcolorbox} +\usepackage{etoolbox} +\usepackage{listings} +\usepackage{booktabs} +\usepackage{subcaption} +\usepackage{float} +\usepackage{tikz} +\usepackage{hyperref} +\usepackage{xcolor} +\usepackage{multicol} +\usepackage{tabularx} +\usepackage{array} +\usepackage{enumitem} + +% ---------- 颜色 ---------- +\definecolor{codebg}{HTML}{F7F7F7} +\definecolor{codeframe}{HTML}{CCCCCC} +\definecolor{shapecolor}{HTML}{2E86C1} +\definecolor{einsumcolor}{HTML}{884EA0} +\definecolor{notegreen}{HTML}{27AE60} +\definecolor{warnorange}{HTML}{E67E22} + +% ---------- 代码样式 ---------- +\lstdefinestyle{pycode}{ + language=Python, + backgroundcolor=\color{codebg}, + frame=single, + rulecolor=\color{codeframe}, + basicstyle=\ttfamily\small, + keywordstyle=\color{blue!70!black}\bfseries, + commentstyle=\color{gray}, + stringstyle=\color{red!60!black}, + showstringspaces=false, + breaklines=true, + tabsize=4, + columns=flexible, + xleftmargin=4pt, + xrightmargin=4pt, + aboveskip=6pt, + belowskip=6pt, +} +\lstset{style=pycode} + +% ---------- 形状标注命令 ---------- +\newcommand{\shape}[1]{{\color{shapecolor}\ensuremath{[#1]}}} +\newcommand{\einsum}[1]{{\color{einsumcolor}\texttt{einsum(#1)}}} +\newcommand{\note}[1]{{\color{notegreen}\textit{#1}}} + +% ---------- 盒子 ---------- +\newtcolorbox{knowledgebox}[1]{ + enhanced, colback=blue!5!white, colframe=blue!75!black, colbacktitle=blue!75!black, + coltitle=white, fonttitle=\bfseries, title=#1, + attach boxed title to top left={yshift=-2mm, xshift=2mm}, + boxrule=1pt, sharp corners +} +\newtcolorbox{importantbox}[1]{ + enhanced, colback=yellow!10!white, colframe=yellow!80!black, colbacktitle=yellow!80!black, + coltitle=black, fonttitle=\bfseries, title=#1, sharp corners +} +\newtcolorbox{warningbox}[1]{ + enhanced, colback=red!5!white, colframe=red!75!black, colbacktitle=red!75!black, + coltitle=white, fonttitle=\bfseries, title=#1, sharp corners +} +\newtcolorbox{tensorbox}[1]{ + enhanced, colback=blue!3!white, colframe=blue!40!black, colbacktitle=blue!50!black, + coltitle=white, fonttitle=\bfseries, title=#1, sharp corners, + boxrule=0.8pt +} + +% 代码-公式并行盒子 +\newtcolorbox{codemathtop}[1]{ + enhanced, colback=codebg, colframe=codeframe, + fonttitle=\bfseries\ttfamily\small, title=#1, + sharp corners, boxrule=0.6pt, + left=4pt, right=4pt, top=2pt, bottom=2pt, +} +\newtcolorbox{codemathbot}{ + enhanced, colback=white, colframe=blue!30!black, + sharp corners, boxrule=0.6pt, + left=4pt, right=4pt, top=4pt, bottom=4pt, +} + +% ---------- 文档元信息 ---------- +\newcommand{\notetitle}{KDA 训练→推理 完整笔记} +\newcommand{\notesubtitle}{递归 · 分块 · Gate · MLA · MoE · AttnRes · K3 架构} +\newcommand{\notedate}{\today} + +\newcommand{\splabel}[1]{\hypertarget{sp:#1}{}\label{sp:#1}} +\newcommand{\spref}[1]{\hyperlink{sp:#1}{\texttt{#1}}} diff --git a/notes/notes.pdf b/notes/notes.pdf new file 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+\input{notes-macros} + +\begin{document} + +% ---------- 封面 ---------- +\begin{titlepage} +\centering +\vspace{2cm} +{\huge\bfseries \notetitle\par} +\vspace{0.6cm} +{\Large \notesubtitle\par} +\vspace{0.8cm} +{\large \notedate\par} +\vspace{1.5cm} +\begin{tcolorbox}[width=0.88\textwidth, colback=black!2!white, colframe=black!60, sharp corners] +\textbf{项目}：\texttt{projects/kda/} — KDA 手写实现（naive recurrent → chunked → Triton）\\ +\textbf{架构}：KDA + Gated MLA + Stable LatentMoE + AttnRes 深度残差 (K3-like)\\ +\textbf{参考}：KDA arXiv:2510.26692; AttnRes arXiv:2603.15031; Kimi K3 architecture notes\\ +\textbf{代码}：\texttt{kda/ops/}, \texttt{kda/layers/}, \texttt{kda/models/} +\end{tcolorbox} +\end{titlepage} + +\tableofcontents +\newpage + +\input{sections/sec-01} % KDA 递归核心 +\input{sections/sec-02} % Gate 激活 +\input{sections/sec-03} % 分块并行计算 +\input{sections/sec-04} % GVA 分组值注意力 +\input{sections/sec-05} % KDAAttention 层 +\input{sections/sec-06} % Gated MLA 矩阵吸收版 +\input{sections/sec-07} % SiTU-GLU 与 Stable LatentMoE +\input{sections/sec-08} % K3 混合架构 +\input{sections/sec-09} % Attention Residual 深度残差 +\input{sections/sec-10} % 反向传播推导 +\input{sections/sec-11} % 符号表 + +\end{document} diff --git a/notes/sections/sec-01.tex b/notes/sections/sec-01.tex new file mode 100644 index 0000000..a8f16e0 --- /dev/null +++ b/notes/sections/sec-01.tex @@ -0,0 +1,127 @@ +% teach: +% gap: 读者知道 softmax attention 但不知道线性注意力怎么维护状态矩阵 +% takeaway: KDA 用 delta rule 逐步更新 [K,V] 状态矩阵, 写入=擦旧写新, 每步 O(KV) +% jump: 为什么 r_t = v - k·S 而不是直接用 v？delta rule 的"先擦再写" +% omit: KDA 论文的 related work、实验细节 + +\section{KDA 递归核心} +\splabel{C1} + +\subsection{动机：从 softmax 到状态矩阵} + +标准 attention 每个 token 都要回看所有历史，复杂度 $O(T^2)$。 +线性注意力换掉 softmax，把 $\sum_j v_j k_j^T$ 压成一个 $K \times V$ 的状态矩阵 $S$， +每步只做 $o_t = q_t \cdot S$，复杂度降到 $O(T \cdot K \cdot V)$。 + +但裸线性注意力的问题是：$S$ 只能加，不能改。写进去的信息永远在那里。 +KDA 的核心想法是给 $S$ 加两个操作：\textbf{衰减}（逐渐忘记旧信息）和 +\textbf{delta rule}（先擦旧的，再写新的）。 + +\begin{importantbox}{如果你只记一件事} +KDA 的状态更新 = 衰减旧状态 + $\beta_t k_t$ 写入 $(v_t - k_t \cdot S_{\mathrm{dec}})$。 +减去 $k_t \cdot S_{\mathrm{dec}}$ 就是"先把 $k_t$ 方向的旧预测擦掉"。 +\end{importantbox} + +\subsection{逐步公式} + +\noindent\textbf{输入张量：} + +\begin{center} +\begin{tabular}{lll} +\toprule +符号 & 形状 & 含义 \\ +\midrule +$q_t$ & \shape{B, HV, K} & query（已经 repeat\_interleave 到 HV） \\ +$k_t$ & \shape{B, HV, K} & key（同上） \\ +$v_t$ & \shape{B, HV, V} & value \\ +$g_t$ & \shape{B, HV, K} & gate（log-space 衰减率，逐维） \\ +$\beta_t$ & \shape{B, HV} & 写入强度标量 \\ +$S_{t-1}$ & \shape{B, HV, K, V} & 上一步的 KV 状态 \\ +\bottomrule +\end{tabular} +\end{center} + +\noindent\textbf{四步更新：} + +\begin{enumerate}[leftmargin=2em] +\item \textbf{衰减旧状态}（逐元素，$g_t$ 是 log-space 所以取 exp）： +\[ + S_{\mathrm{dec}} = \exp(g_t) \odot S_{t-1} + \qquad \shape{B, HV, K, V} +\] + +\item \textbf{计算残差}（先用 $k_t$ 查旧状态，得到"旧预测"，再减掉）： +\[ + p_t = \sum_k k_{t,k} \cdot S_{\mathrm{dec},k,\cdot} + = \texttt{einsum('bhk, bhkv -> bhv')} + \qquad \shape{B, HV, V} +\] +\[ + r_t = v_t - p_t \qquad \shape{B, HV, V} +\] + +\item \textbf{写入状态}（外积 rank-1 更新）： +\[ + a_t = \beta_t \cdot k_t \qquad \shape{B, HV, K} +\] +\[ + S_t = S_{\mathrm{dec}} + a_t \otimes r_t + = S_{\mathrm{dec}} + \texttt{einsum('bhk, bhv -> bhkv')} + \qquad \shape{B, HV, K, V} +\] + +\item \textbf{读出}： +\[ + o_t = \frac{1}{\sqrt{K}} \cdot q_t \cdot S_t + = \texttt{einsum('bhk, bhkv -> bhv')} + \qquad \shape{B, HV, V} +\] +\end{enumerate} + +\subsection{代码对照} + +\begin{codemathtop}{ops/reference/recurrent.py — naive\_kda\_fwd (核心循环)} +\begin{lstlisting} +for t in range(T): + q_t = qe[:, t] # [B, HV, K] + k_t = ke[:, t] # [B, HV, K] + v_t = v[:, t] # [B, HV, V] + g_t = g[:, t] # [B, HV, K] + b_t = beta[:, t] # [B, HV] + + # Step 1: decay + S_dec = S * g_t.exp().unsqueeze(-1) # [B,HV,K,V] + + # Step 2: residual (delta rule) + p_t = einsum('bhk, bhkv -> bhv', k_t, S_dec) + r_t = v_t - p_t # [B,HV,V] + + # Step 3: write (rank-1 update) + a_t = b_t.unsqueeze(-1) * k_t # [B,HV,K] + S = S_dec + einsum('bhk, bhv -> bhkv', a_t, r_t) + + # Step 4: read + o[:, t] = einsum('bhk, bhkv -> bhv', q_t, S) +\end{lstlisting} +\end{codemathtop} + +\begin{warningbox}{为什么 exp(g\_t) 要 unsqueeze(-1)？} +$g_t$ 的形状是 \shape{B, HV, K}，而 $S$ 是 \shape{B, HV, K, V}。 +衰减是在 $K$ 维上逐元素（同一 $k$ 索引的所有 $v$ 维度共享同一个衰减率）， +所以 \texttt{exp(g\_t).unsqueeze(-1)} 把 K 维 broadcast 到 $K \times V$。 +\end{warningbox} + +\subsection{Delta rule 的直觉} + +\begin{knowledgebox}{为什么减去 $k_t \cdot S_{\mathrm{dec}}$？} +把 $S$ 想象成一个 $K \to V$ 的线性映射。用 $k_t$ 去查它，得到的 $p_t = k_t^T S$ +就是``旧状态对 $k_t$ 方向的预测''。如果 $p_t$ 已经很接近 $v_t$，说明这个方向的信息 +已经写好了，不需要再写。$r_t = v_t - p_t$ 就是``需要修正的量''。 + +这就是 Widrow-Hoff delta rule：不是盲目地加，而是只修正误差。 +\end{knowledgebox} + +\subsection{本章小结} + +KDA 的状态更新 = 衰减旧状态 + $\beta_t k_t$ 写入残差 $(v_t - k_t \cdot S_{\mathrm{dec}})$。 +每步复杂度 $O(K \cdot V)$（两次矩阵-向量乘 + 一次外积），不需要 softmax。 diff --git a/notes/sections/sec-02.tex b/notes/sections/sec-02.tex new file mode 100644 index 0000000..dd1356c --- /dev/null +++ b/notes/sections/sec-02.tex @@ -0,0 +1,111 @@ +% teach: +% gap: 读者知道 g_t 是 gate 但不知道它怎么从 raw projection 变成一个负的 log-space 衰减 +% takeaway: safe gate 用 sigmoid 把值夹在 [lower_bound, 0], standard gate 用 -softplus 保证负 +% jump: 论文没解释为什么需要 A_log 和 dt_bias 两层 +% omit: Triton gate kernel 的 fused 实现细节 + +\section{Gate 激活} +\splabel{C2} + +\subsection{Gate 的角色} + +回顾 §1：$S_{\mathrm{dec}} = \exp(g_t) \odot S_{t-1}$。$g_t$ 必须 $\leq 0$ +才是衰减（$\exp(g_t) \leq 1$），否则状态会指数增长爆炸。 + +\texttt{g\_raw} 是从 \texttt{g\_proj(x)} 出来的 raw 值，没有约束。 +Gate 激活函数的任务是：把 raw 值映射到一个保证 $\leq 0$ 的范围。 + +\subsection{两种 Gate} + +\begin{center} +\begin{tabular}{p{3cm}p{5.5cm}p{5cm}} +\toprule +& \textbf{Standard gate} & \textbf{Safe gate} \\ +\midrule +公式 & +$g = -\mathrm{rate} \cdot \mathrm{softplus}(\mathrm{input})$ & +$g = L \cdot \sigma(\mathrm{rate} \cdot \mathrm{input})$ \\ +值域 & +$(-\infty, 0]$ & +$[L, 0]$（$L$ 是 lower\_bound，如 $-5$） \\ +衰减范围 & +$\exp(g) \in (0, 1]$ & +$\exp(g) \in [\exp(L), 1]$ \\ +稳定性 & +衰减可以任意快 & +衰减有下限，不会瞬间清零 \\ +\bottomrule +\end{tabular} +\end{center} + +\noindent 其中： +\begin{itemize}[nosep] +\item $\mathrm{input} = g_{\mathrm{raw}} + \Delta_b$ \quad（$\Delta_b$ + 是 \texttt{dt\_bias} \shape{HV, K}） +\item $\mathrm{rate} = \exp(A_{\log})$ \quad（$A_{\log}$ 是 + \texttt{A\_log} \shape{HV}，head-wise 可学习） +\end{itemize} + +\begin{importantbox}{如果你只记一件事} +Safe gate = $L \cdot \sigma(\mathrm{rate} \cdot \mathrm{input})$， +$L=-5$ 时 $\exp(g) \geq \exp(-5) \approx 0.0067$， +状态永远不会被``一次性清零''。 +\end{importantbox} + +\subsection{代码对照} + +\begin{codemathtop}{ops/reference/gate.py — kda\_gate\_reference} +\begin{lstlisting} +def kda_gate_reference(g, A_log, dt_bias=None, *, + safe_gate=False, lower_bound=None): + HV, K = g.shape[-2:] + gate_input = g if dt_bias is None else g + dt_bias.view(HV, K) + rate = A_log.view(HV, 1).exp() + + if safe_gate: + # safe: g in [lower_bound, 0] + return lower_bound * torch.sigmoid(rate * gate_input) + # standard: g in (-inf, 0] + return -rate * F.softplus(gate_input) +\end{lstlisting} +\end{codemathtop} + +\subsection{初始化与默认值} + +\begin{center} +\begin{tabular}{llp{7cm}} +\toprule +参数 & 初始值 & 效果 \\ +\midrule +\texttt{A\_log} & $\mathbf{0}$ \shape{HV} & $\mathrm{rate} = \exp(0) = 1$，不缩放 \\ +\texttt{dt\_bias} & $-4.0$ \shape{HV, K} & 初始时 $\mathrm{input} \approx g_{\mathrm{raw}} - 4$， + 配合 safe gate ($L=-5$) 得到 $g \approx -5 \cdot \sigma(-4) \approx -0.09$， + 即 $\exp(g) \approx 0.91$（约 91\% 状态保留） \\ +\texttt{lower\_bound} & $-5.0$ & safe gate 的下限 \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{Gate 在 API 中的位置} + +Gate 激活在 \texttt{ops/api.py} 的 \texttt{chunk\_kda} 中调用， +在进入 chunkwise 或 recurrent 核心之前完成。 +当 \texttt{use\_gate\_in\_kernel=True} 时，\texttt{g\_raw} 进入 API， +API 内部完成 gate 激活；否则调用方自己完成。 + +\begin{lstlisting} +# ops/api.py (simplified) +if use_gate_in_kernel: + gate_input = g + dt_bias.view(g.shape[-2:]) + rate = A_log.exp().view(1, 1, -1, 1) + if safe_gate: + g = lower_bound * torch.sigmoid(rate * gate_input) + else: + g = -rate * F.softplus(gate_input) +\end{lstlisting} + +\subsection{本章小结} + +Gate 把 raw projection 映射到 $\leq 0$ 的 log-space 衰减率。 +Safe gate 用 sigmoid 限制在 $[L, 0]$，防止瞬间清零； +standard gate 用 softplus 不限制下限。默认配置下初始状态保留约 91\%。 diff --git a/notes/sections/sec-03.tex b/notes/sections/sec-03.tex new file mode 100644 index 0000000..b2b302c --- /dev/null +++ b/notes/sections/sec-03.tex @@ -0,0 +1,148 @@ +% teach: +% gap: 读者知道递归形式但不知道怎么在 GPU 上并行, 以为只能一步步算 +% takeaway: chunk 内用下三角线性系统并行求解, chunk 间递推状态, 数值等价于 naive recurrent +% jump: 论文直接写了 triangular solve 但没解释为什么要 solve 而不是直接矩阵乘 +% omit: Triton 实现细节 + +\section{分块并行计算（Chunkwise）} +\splabel{C3} + +\subsection{为什么需要分块？} + +Naive recurrent 一步一步算，$T$ 步串行，GPU 利用率低。 +分块的想法是把序列切成 $T/C$ 个长度为 $C$ 的 chunk： +\begin{itemize}[nosep] +\item \textbf{chunk 内}：$C$ 个 token 之间的依赖可以用矩阵运算并行处理 +\item \textbf{chunk 间}：状态 $S$ 从上一个 chunk 传到下一个，仍然是递推 +\end{itemize} + +\begin{importantbox}{如果你只记一件事} +Chunkwise = chunk 内并行 + chunk 间递推。数值结果与 naive recurrent 逐位一致。 +\end{importantbox} + +\subsection{chunk 内的 cumsum 与下三角解} + +在每个 chunk 内，先对 $g$ 做 cumsum（前缀和），这样衰减就变成了相对距离的函数： +\[ + g_{\mathrm{cum},i} = \sum_{j=0}^{i} g_j, \qquad + \text{token } i \text{ 对 token } j \text{ 的衰减} = \exp(g_{\mathrm{cum},i} - g_{\mathrm{cum},j}) +\] + +定义 \textbf{decayed dot} 矩阵（chunk 内 $C \times C$）： +\[ + A_{ij} = \langle x_i, \exp(g_{\mathrm{cum},i} - g_{\mathrm{cum},j}) \cdot k_j \rangle + \qquad \shape{..., C, C} +\] + +这个矩阵的构造是 chunk 内计算的核心。用它可以构造一个下三角线性系统： +\[ + M = I + \mathrm{tril}(A_{kk} \cdot \beta, \text{diagonal}=-1) + \qquad \shape{..., C, C} +\] +\[ + M \cdot W = \exp(g_{\mathrm{cum}}) \cdot k \qquad \Rightarrow \qquad + W = M^{-1} (\exp(g_{\mathrm{cum}}) \cdot k) +\] +\[ + M \cdot U = v \qquad \Rightarrow \qquad U = M^{-1} v +\] + +\subsection{代码对照} + +\begin{codemathtop}{ops/reference/chunkwise.py — naive\_chunk\_kda (核心)} +\begin{lstlisting} +# Rearrange: [B,T,H,K] -> [B,H,N,C,K] where N=T/C +q, k = [rearrange(x, 'b (n c) h d -> b h n c d', c=C) + .repeat_interleave(HV//H, dim=1) for x in (q, k)] +v, g = [rearrange(x, 'b (n c) h d -> b h n c d', c=C) + for x in (v, g)] +beta = rearrange(beta, 'b (n c) h -> b h n c', c=C) +q = q * scale +g = g.cumsum(dim=-2) # chunk 内 cumsum + +# Construct triangular system +A_kk = _decayed_dot(k, k, g) # [B,HV,N,C,C] +M = eye + (A_kk * beta[...,None,:]).masked_fill(mask_upper, 0) +W = solve_triangular(M, g.exp() * k, upper=False) +U = solve_triangular(M, v, upper=False) + +# A_qk: query 对 key 的 decayed dot (含对角线) +A_qk = (_decayed_dot(q, k, g) * beta[...,None,:]) + .masked_fill(mask_strict_upper, 0) +\end{lstlisting} +\end{codemathtop} + +\subsection{chunk 间递推} + +每个 chunk 内算完后，用 $W$ 和 $U$ 来处理跨 chunk 的状态： + +\begin{codemathtop}{ops/reference/chunkwise.py — chunk 间循环} +\begin{lstlisting} +S = zeros(B, HV, K, V) # inter-chunk state +for n in range(T // C): + # r = "local residual, adjusted by cross-chunk state" + r = U[:,:,n] - W[:,:,n] @ S # [B,HV,C,V] + + # output: cross-chunk part + intra-chunk part + o[:,:,n] = (q_n * g_n.exp()) @ S + A_qk[:,:,n] @ r + + # update cross-chunk state + decay = (g_n[:,:,-1:,:] - g_n).exp() # decay to chunk end + S = S * g_n[:,:,-1,:,None].exp() # decay old state + S = S + (decay * k_n).T @ (r * beta_n) # write new +\end{lstlisting} +\end{codemathtop} + +\subsection{形状流水线} + +\begin{center} +\begin{tabular}{lll} +\toprule +变量 & 形状 & 说明 \\ +\midrule +\texttt{q, k} (chunked) & \shape{B, HV, N, C, K} & $N = T/C$ 个 chunk \\ +\texttt{v, g} (chunked) & \shape{B, HV, N, C, V/K} & \\ +\texttt{beta} (chunked) & \shape{B, HV, N, C} & \\ +\texttt{A\_kk} & \shape{B, HV, N, C, C} & key-key decayed dot \\ +\texttt{M} & \shape{B, HV, N, C, C} & 下三角系统 \\ +\texttt{W} & \shape{B, HV, N, C, K} & $M^{-1}(\exp(g) \cdot k)$ \\ +\texttt{U} & \shape{B, HV, N, C, V} & $M^{-1} v$ \\ +\texttt{A\_qk} & \shape{B, HV, N, C, C} & query-key decayed dot \\ +\texttt{S} & \shape{B, HV, K, V} & 跨 chunk 状态 \\ +\texttt{r} & \shape{B, HV, C, V} & 调整后的残差 \\ +\texttt{o (chunk n)} & \shape{B, HV, C, V} & 本 chunk 输出 \\ +\bottomrule +\end{tabular} +\end{center} + +\begin{warningbox}{为什么用 triangular solve 而不是直接矩阵乘？} +Delta rule 的"先擦再写"引入了 chunk 内 token 之间的递归依赖： +token $i$ 的写入依赖 token $j < i$ 的写入结果。 +这个依赖关系恰好形成一个下三角线性系统 $M \cdot x = b$， +用 \texttt{solve\_triangular} 可以在 $O(C^2)$ 内并行求解， +而展开递归需要 $O(C)$ 步串行。 +\end{warningbox} + +\subsection{Decayed dot 函数} + +\begin{codemathtop}{ops/reference/chunkwise.py — \_decayed\_dot} +\begin{lstlisting} +def _decayed_dot(x, k, g): + """A[..., i, j] = """ + C = x.shape[-2] + A = empty(*x.shape[:-2], C, C) + for i in range(C): + decay = (g[..., i:i+1, :] - g).exp() # [.., 1, K] - [.., C, K] + A[..., i, :] = einsum('...jk,...jk->...j', + x[..., i, None, :] * decay, k) + return A +\end{lstlisting} +\end{codemathtop} + +\noindent 这是一个 $C \times C$ 的矩阵，每个元素 $(i,j)$ 是 +$x_i$ 和 $\exp(g_i - g_j) \cdot k_j$ 的内积。Triton 实现会把这个双循环融合成一个 kernel。 + +\subsection{本章小结} + +分块把 $T$ 步串行拆成 $T/C$ 个 chunk，chunk 内用下三角 solve 并行处理 delta rule 依赖， +chunk 间递推状态 $S$。最终输出与 naive recurrent 逐位相同。 diff --git a/notes/sections/sec-04.tex b/notes/sections/sec-04.tex new file mode 100644 index 0000000..97ec7c2 --- /dev/null +++ b/notes/sections/sec-04.tex @@ -0,0 +1,114 @@ +% teach: +% gap: 读者不知道 q/k 和 v 为什么可以有不同的头数, 以及 repeat_interleave 的反传怎么做 +% takeaway: GVA 让 G 组 value heads 共享一组 q/k, forward repeat_interleave, backward sum +% jump: 论文没解释为什么反传是 sum 而不是 mean +% omit: GQA 的历史 + +\section{GVA（分组值注意力）} +\splabel{GVA} + +\subsection{为什么头数不一样？} + +标准 MHA 里 $H_q = H_k = H_v$。GQA（Grouped Query Attention）让多组 q/k 共享同一组 v/k， +减少 KV cache。KDA 反过来做：$H$ 组 q/k 对应 $H_V = G \cdot H$ 组 value heads。 + +直觉：value 维度决定表达能力，多一点 value head 增加容量； +q/k 主要负责路由（``看哪里''），可以共享。 + +\begin{center} +\begin{tabular}{lll} +\toprule +& 标准头数 & GVA \\ +\midrule +$q, k$ & \shape{B, T, H, K} & \shape{B, T, H, K}（不变）\\ +$v$ & \shape{B, T, H, V} & \shape{B, T, HV, V}（$H_V = G \cdot H$） \\ +$g, \beta$ & \shape{B, T, H, K/1} & \shape{B, T, HV, K/1} \\ +$S$ & \shape{B, H, K, V} & \shape{B, HV, K, V} \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{Forward: repeat\_interleave} + +进入 KDA 核心前，$q$ 和 $k$ 从 $H$ 维复制到 $H_V$ 维： + +\begin{lstlisting} +G = HV // H +qe = q.repeat_interleave(G, dim=2) * scale # [B,T,H,K] -> [B,T,HV,K] +ke = k.repeat_interleave(G, dim=2) # [B,T,H,K] -> [B,T,HV,K] +\end{lstlisting} + +\noindent 例如 $H=4, G=2, H_V=8$：head 0 的 q/k 复制到 value head 0 和 1， +head 1 复制到 value head 2 和 3，依此类推。 + +\subsection{Backward: view + sum} + +反传时，$dq_e$ 和 $dk_e$ 的形状是 \shape{B, T, HV, K}（在 $H_V$ 维上计算的梯度）。 +因为 forward 是复制，反传就是求和： + +\begin{lstlisting} +# Backward: HV -> H +dq_H = dq_e.view(B, T, H, G, K).sum(dim=3) # [B,T,HV,K] -> [B,T,H,K] +dk_H = dk_e.view(B, T, H, G, K).sum(dim=3) +\end{lstlisting} + +\begin{warningbox}{为什么是 sum 不是 mean？} +\texttt{repeat\_interleave} 是\textbf{复制}：$y_0 = x_0, y_1 = x_0, y_2 = x_1, \ldots$ + +对 $x_0$ 的梯度 = $\frac{\partial L}{\partial y_0} + \frac{\partial L}{\partial y_1}$ += \textbf{sum}（不是 mean）。 + +这和 \texttt{.expand()} 的反传一样：复制的反传是求和。 +\end{warningbox} + +\subsection{scale 的处理} + +$q$ 在 repeat\_interleave 之后乘了 \texttt{scale = $1/\sqrt{K}$}。 +反传时 chain rule 要求 $dq_{\mathrm{orig}} = dq_e \cdot \texttt{scale}$： + +\begin{lstlisting} +# q 在 forward 内被乘过 scale, chain rule: +dq_H = dq_H * scale +\end{lstlisting} + +\subsection{KDAAttention 层中的投影} + +\begin{codemathtop}{layers/kda\_attn.py — forward} +\begin{lstlisting} +def forward(self, x): # x: [B, T, D] + B, T, _ = x.shape + H, HV, K, V = self.num_heads, self.num_value_heads, ... + + q = self.q_proj(x).view(B, T, H, K) # [B,T,D] -> [B,T,H*K] -> [B,T,H,K] + k = self.k_proj(x).view(B, T, H, K) # 同上 + v = self.v_proj(x).view(B, T, HV, V) # [B,T,D] -> [B,T,HV*V] -> [B,T,HV,V] + g_raw = self.g_proj(x).view(B, T, HV, K) + beta_raw = self.beta_proj(x).view(B, T, HV) + + o, _ = chunk_kda(q, k, v, g_raw, beta_raw, ...) + return self.o_proj(o.reshape(B, T, HV * V)) # [B,T,HV,V] -> [B,T,D] +\end{lstlisting} +\end{codemathtop} + +\subsection{投影矩阵形状总览} + +\begin{center} +\begin{tabular}{llll} +\toprule +投影 & 权重形状 & 输入 & 输出 \\ +\midrule +\texttt{q\_proj} & \shape{H \cdot K, D} & \shape{B,T,D} & \shape{B,T,H,K} \\ +\texttt{k\_proj} & \shape{H \cdot K, D} & \shape{B,T,D} & \shape{B,T,H,K} \\ +\texttt{v\_proj} & \shape{HV \cdot V, D} & \shape{B,T,D} & \shape{B,T,HV,V} \\ +\texttt{g\_proj} & \shape{HV \cdot K, D} & \shape{B,T,D} & \shape{B,T,HV,K} \\ +\texttt{beta\_proj} & \shape{HV, D} & \shape{B,T,D} & \shape{B,T,HV} \\ +\texttt{o\_proj} & \shape{D, HV \cdot V} & \shape{B,T,HV \cdot V} & \shape{B,T,D} \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{本章小结} + +GVA 让 $H_V = G \cdot H$ 组 value heads 共享 $H$ 组 q/k。 +Forward 用 \texttt{repeat\_interleave} 复制，backward 用 \texttt{view+sum} 归约。 +$v, g, \beta$ 直接在 $H_V$ 维投影，q/k 在 $H$ 维投影。 diff --git a/notes/sections/sec-05.tex b/notes/sections/sec-05.tex new file mode 100644 index 0000000..49fc36d --- /dev/null +++ b/notes/sections/sec-05.tex @@ -0,0 +1,75 @@ +% teach: +% gap: 读者已知各组件, 但不清楚它们怎么黏在一起成为一个层 +% takeaway: KDAAttention = 投影 → gate+norm → chunk_kda → output 投影, 整个层就是 x → y [B,T,D] +% jump: none +% omit: from_config 工厂方法细节 + +\section{KDAAttention 层} + +\subsection{完整数据流} + +\texttt{KDAAttention} 把投影、gate 激活、KDA 核心计算和输出投影封装成一个 +\texttt{[B,T,D] $\to$ [B,T,D]} 的模块。 + +\begin{center} +\begin{tabular}{rlll} +\toprule +步骤 & 操作 & 输入形状 & 输出形状 \\ +\midrule +1 & \texttt{q\_proj(x)} & \shape{B,T,D} & \shape{B,T,H,K} \\ +2 & \texttt{k\_proj(x)} & \shape{B,T,D} & \shape{B,T,H,K} \\ +3 & \texttt{v\_proj(x)} & \shape{B,T,D} & \shape{B,T,HV,V} \\ +4 & \texttt{g\_proj(x)} & \shape{B,T,D} & \shape{B,T,HV,K} \\ +5 & \texttt{beta\_proj(x)} & \shape{B,T,D} & \shape{B,T,HV} \\ +6 & \texttt{chunk\_kda(...)} & 上述 5 项 + 参数 & \shape{B,T,HV,V} \\ +7 & \texttt{o.reshape(...)} & \shape{B,T,HV,V} & \shape{B,T,HV \cdot V} \\ +8 & \texttt{o\_proj(...)} & \shape{B,T,HV \cdot V} & \shape{B,T,D} \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{chunk\_kda 内部做了什么} + +\texttt{chunk\_kda}（\texttt{ops/api.py}）是统一入口，按 \texttt{backend} 分发： + +\begin{enumerate}[nosep] +\item 如果 \texttt{use\_qk\_l2norm\_in\_kernel}：$q, k \leftarrow \text{L2-normalize}(q), \text{L2-normalize}(k)$ +\item 如果 \texttt{use\_beta\_sigmoid\_in\_kernel}：$\beta \leftarrow \sigma(\beta_{\mathrm{raw}})$ +\item 如果 \texttt{use\_gate\_in\_kernel}：应用 gate 激活（§2） +\item 调用 \texttt{naive\_chunk\_kda}（或 triton/fla 版本） +\end{enumerate} + +\begin{knowledgebox}{三个 ``in\_kernel'' 开关} +\begin{itemize}[nosep] +\item \texttt{use\_qk\_l2norm}：L2-norm 让 $\langle q, k \rangle$ 变成余弦相似度， + 稳定训练 +\item \texttt{use\_beta\_sigmoid}：sigmoid 把 $\beta$ 限制在 $(0,1)$， + 控制写入强度 +\item \texttt{use\_gate\_in\_kernel}：gate 激活在 API 内部完成（vs 调用方自己做） +\end{itemize} +默认三个都是 \texttt{True}。 +\end{knowledgebox} + +\subsection{可学习参数清单} + +\begin{center} +\begin{tabular}{lll} +\toprule +参数 & 形状 & 说明 \\ +\midrule +\texttt{q\_proj.weight} & \shape{H \cdot K, D} & query 投影 \\ +\texttt{k\_proj.weight} & \shape{H \cdot K, D} & key 投影 \\ +\texttt{v\_proj.weight} & \shape{HV \cdot V, D} & value 投影 \\ +\texttt{g\_proj.weight} & \shape{HV \cdot K, D} & gate 投影 \\ +\texttt{beta\_proj.weight} & \shape{HV, D} & beta 投影 \\ +\texttt{o\_proj.weight} & \shape{D, HV \cdot V} & 输出投影 \\ +\texttt{A\_log} & \shape{HV} & head-wise 衰减率（log-space）\\ +\texttt{dt\_bias} & \shape{HV, K} & per-dim gate bias \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{本章小结} + +KDAAttention 是一个完整的 mixing 模块：5 个线性投影 + gate 激活 + KDA 核心 + 输出投影。 +三个 ``in\_kernel'' 开关控制 L2-norm、sigmoid、gate 是否在 API 内部完成。 diff --git a/notes/sections/sec-06.tex b/notes/sections/sec-06.tex new file mode 100644 index 0000000..c7a8be5 --- /dev/null +++ b/notes/sections/sec-06.tex @@ -0,0 +1,173 @@ +% teach: +% gap: 读者知道标准 MHA 但不知道 MLA 怎么压缩 KV、矩阵吸收怎么避免解压 +% takeaway: MLA 把 KV 压成低秩 latent c, 通过吸收 W_UK 进 q 直接在 latent 空间算 attention +% jump: 为什么可以先在 latent 加权再乘 W_UV？因为矩阵乘和加权求和可交换 +% omit: RoPE (K3 用 NoPE) + +\section{Gated MLA（矩阵吸收版）} +\splabel{C4} + +\subsection{标准 MHA 的 KV cache 问题} + +标准 MHA 推理时需要缓存所有历史 token 的 $K, V$，cache 大小 $\propto T \cdot H \cdot d$。 +MLA 的想法：把 $K, V$ 压缩成一个低秩 latent $c$，cache 大小 $\propto T \cdot r$， +其中 $r \ll H \cdot d$。 + +\subsection{低秩压缩} + +\[ + c = \mathrm{RMSNorm}(W_{\downarrow} \cdot x) \qquad \shape{B, T, r} +\] + +推理时只缓存 $c$，不缓存解压后的 $K, V$。 +解压矩阵 $W_{\mathrm{KV}\uparrow}$ 包含两部分： +\[ + W_{\mathrm{KV}\uparrow} = \begin{bmatrix} W_{UK} \\ W_{UV} \end{bmatrix} + \qquad \shape{H \cdot (d_q + d_v), r} +\] +拆开：$W_{UK} \in \mathbb{R}^{H \times d_q \times r}$（key 解压）， +$W_{UV} \in \mathbb{R}^{H \times d_v \times r}$（value 解压）。 + +\subsection{矩阵吸收的核心思路} + +\textbf{不解压} $K$ 和 $V$。标准做法会先解压再算 attention： + +\begin{center} +\textit{标准}：$k_h = c \cdot W_{UK,h}^T$ \shape{B,T,d_q}， +$\mathrm{score} = q_h \cdot k_h^T$ +\end{center} + +矩阵吸收反过来：把 $W_{UK}$ 吸收进 $q$： + +\begin{center} +\textit{吸收}：$q_{\mathrm{abs},h} = q_h \cdot W_{UK,h}$ \shape{B,T,r}， +$\mathrm{score} = q_{\mathrm{abs},h} \cdot c^T$ +\end{center} + +\begin{importantbox}{如果你只记一件事} +$(q \cdot W_{UK}^T) \cdot c^T = q \cdot (W_{UK}^T \cdot c^T) = q_{\mathrm{abs}} \cdot c^T$ + +吸收后，attention 直接在 latent 空间 $r$ 维上算，永不解压到 $H \cdot d_q$ 维。 +\end{importantbox} + +\subsection{完整计算流（四步）} + +\begin{enumerate}[leftmargin=2em] +\item \textbf{Q 低秩路径}（NoPE，只有 nope 段）： +\[ + q = W_{q\uparrow} \cdot \mathrm{RMSNorm}(W_{q\downarrow} \cdot x) + \qquad \shape{B, T, H, d_q} +\] + +\item \textbf{吸收 $W_{UK}$ + 打分}： +\[ + q_{\mathrm{abs}} = q \cdot W_{UK} \quad + \xrightarrow{\texttt{einsum('bthd,hdj->bthj')}} \quad \shape{B, T, H, r} +\] +\[ + \mathrm{score} = q_{\mathrm{abs}} \cdot c^T \quad + \xrightarrow{\texttt{einsum('bthj,bsj->bhts')}} \quad \shape{B, H, T, T} +\] +\[ + \mathrm{attn} = \mathrm{softmax}(\mathrm{causal\_mask}(\mathrm{score})) + \qquad \shape{B, H, T, T} +\] + +\item \textbf{先在 latent 加权，再乘 $W_{UV}^T$}： +\[ + \tilde{o}_{\mathrm{lat}} = \mathrm{attn} \cdot c \quad + \xrightarrow{\texttt{einsum('bhts,bsj->bhtj')}} \quad \shape{B, H, T, r} +\] +\[ + \tilde{o} = \tilde{o}_{\mathrm{lat}} \cdot W_{UV}^T \quad + \xrightarrow{\texttt{einsum('bhtj,hvj->bhtv')}} \quad \shape{B, H, T, d_v} +\] + +\item \textbf{输出门 + 投影}： +\[ + y = W_o \big[ \sigma(W_g \cdot x) \odot \tilde{o}_{\mathrm{flat}} \big] + \qquad \shape{B, T, D} +\] +\end{enumerate} + +\subsection{代码对照} + +\begin{codemathtop}{layers/mla.py — GatedMLA.forward} +\begin{lstlisting} +def forward(self, x): # x: [B, T, D] + B, T, _ = x.shape + H, r = self.num_heads, self.kv_up.in_features + + # Step 1: latent + query + c = self.kv_norm(self.kv_down(x)) # [B, T, r] + q = self.q_up(self.q_norm(self.q_down(x))) # [B, T, H*d_q] + q = q.view(B, T, H, self.qk_nope_head_dim) # [B, T, H, d_q] + + # Split W_UK, W_UV from kv_up.weight + w = self.kv_up.weight # [H*(d_q+d_v), r] + w_uk = w[:H*d_q].view(H, d_q, r) # [H, d_q, r] + w_uv = w[H*d_q:].view(H, d_v, r) # [H, d_v, r] + + # Step 2: absorb W_UK, score + q_absorb = einsum('bthd,hdj->bthj', q, w_uk) # [B,T,H,r] + scores = einsum('bthj,bsj->bhts', q_absorb, c) # [B,H,T,T] + scores = scores.masked_fill(causal_mask, -inf) + attn = softmax(scores, dim=-1) # [B,H,T,T] + + # Step 3: latent-space weighted sum, then W_UV + latent_out = einsum('bhts,bsj->bhtj', attn, c) # [B,H,T,r] + o_heads = einsum('bhtj,hvj->bhtv', latent_out, w_uv) # [B,H,T,d_v] + + # Step 4: output gate + o_heads = o_heads.transpose(1,2).reshape(B,T, H*d_v) + gate = sigmoid(self.gate(x)) # [B,T,H*d_v] + return self.o_proj(gate * o_heads) # [B,T,D] +\end{lstlisting} +\end{codemathtop} + +\begin{warningbox}{为什么可以先加权再乘 $W_{UV}$？} +标准做法：$o = \mathrm{attn} \cdot V = \mathrm{attn} \cdot (c \cdot W_{UV}^T)$ + +交换顺序：$o = (\mathrm{attn} \cdot c) \cdot W_{UV}^T$ + +这能成立是因为矩阵乘法的结合律：$A(BC) = (AB)C$。 +$\mathrm{attn} \cdot c$ 先在 latent 空间 $r$ 维上加权求和， +得到的 \shape{B,H,T,r} 再乘 $W_{UV}^T$ 还原到 $d_v$ 维。 +全程不需要显式构造 $H \cdot T$ 大小的 $V$ 矩阵。 +\end{warningbox} + +\subsection{形状与参数对比} + +\begin{center} +\begin{tabular}{lll} +\toprule +参数 & 形状 & 说明 \\ +\midrule +\texttt{kv\_down.weight} & \shape{r, D} & KV latent 压缩 \\ +\texttt{kv\_up.weight} & \shape{H \cdot (d_q+d_v), r} & 包含 $W_{UK}$ 和 $W_{UV}$ \\ +\texttt{q\_down.weight} & \shape{r_q, D} & Q 低秩 \\ +\texttt{q\_up.weight} & \shape{H \cdot d_q, r_q} & Q 解压 \\ +\texttt{gate.weight} & \shape{H \cdot d_v, D} & 输出门 \\ +\texttt{o\_proj.weight} & \shape{D, H \cdot d_v} & 输出投影 \\ +\bottomrule +\end{tabular} +\end{center} + +\noindent KV cache 大小对比（推理时）： + +\begin{center} +\begin{tabular}{ll} +\toprule +方法 & Cache 大小 per token \\ +\midrule +标准 MHA & $2 \times H \times d = 2 H d$ \\ +MLA (latent) & $r$（只存 $c$） \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{本章小结} + +Gated MLA 把 KV 压缩到低秩 latent $c$ \shape{B,T,r}，通过矩阵吸收 +（$q_{\mathrm{abs}} = q \cdot W_{UK}$）直接在 latent 空间打分和加权， +永不解压 K/V。输出通过 sigmoid 门控。NoPE：不使用 RoPE，位置感交给夹层 KDA 的 decay/gate。 diff --git a/notes/sections/sec-07.tex b/notes/sections/sec-07.tex new file mode 100644 index 0000000..169ad74 --- /dev/null +++ b/notes/sections/sec-07.tex @@ -0,0 +1,158 @@ +% teach: +% gap: 读者知道 MoE 的 top-k 路由但不知道 LatentMoE 的 latent 接口和 SiTU-GLU +% takeaway: LatentMoE 通过 latent 接口把 routed 专家限制在 ℓ=d/2 上算, SiTU-GLU 用软上限防溢出 +% jump: 为什么 routed 专家在 latent 空间而 shared 在全宽？省参数 +% omit: load balancing loss + +\section{SiTU-GLU 与 Stable LatentMoE} +\splabel{C5} + +\subsection{SiTU-GLU：带软上限的激活} + +SwiGLU 在低精度（fp16/bf16）训练时可能溢出：$\mathrm{silu}(x) \cdot x$ 没有上限。 +SiTU-GLU 用 $\tanh$ 给门控和上投影加软上限： + +\[ + \mathrm{SiTU}(x) = W_o \big[\underbrace{\beta_1 \tanh\!\left(\frac{W_g x}{\beta_1}\right) \cdot \sigma(W_g x)}_{\text{gate}} \;\cdot\; \underbrace{\beta_2 \tanh\!\left(\frac{W_u x}{\beta_2}\right)}_{\text{up}}\big] +\] + +\begin{center} +\begin{tabular}{lp{8cm}} +\toprule +性质 & 说明 \\ +\midrule +输出上限 & $\|\mathrm{SiTU}\|_\infty \leq \beta_1 \cdot \beta_2 = 4 \times 25 = 100$ \\ +原点附近 & $\tanh(x/\beta) \approx x/\beta$，所以 $\beta \cdot \tanh(x/\beta) \approx x$，退化为 SwiGLU \\ +远端 & 软饱和，防 fp16 溢出 \\ +\bottomrule +\end{tabular} +\end{center} + +\begin{codemathtop}{layers/latent\_moe.py — SiTU} +\begin{lstlisting} +class SiTU(nn.Module): + def __init__(self, dim_in, dim_ff, beta1=4.0, beta2=25.0): + self.w_g = nn.Linear(dim_in, dim_ff, bias=False) + self.w_u = nn.Linear(dim_in, dim_ff, bias=False) + self.w_o = nn.Linear(dim_ff, dim_in, bias=False) + + def forward(self, x): # [*, dim_in] + wg = self.w_g(x) + g = self.beta1 * tanh(wg / self.beta1) * sigmoid(wg) # gate + u = self.beta2 * tanh(self.w_u(x) / self.beta2) # up + return self.w_o(g * u) # [*, dim_in] +\end{lstlisting} +\end{codemathtop} + +\subsection{LatentMoE 架构} + +\begin{center} +\begin{tabular}{rl} +\toprule +组件 & 说明 \\ +\midrule +\textbf{Shared 专家} & $n_{\mathrm{shared}}$ 个 SiTU，全宽 $d \to d$，所有 token 都经过 \\ +\textbf{Routed 专家} & $n_{\mathrm{routed}}$ 个 SiTU，半宽 $\ell \to \ell$（$\ell = d/2$） \\ +\textbf{Latent 接口} & $W_\downarrow: d \to \ell$, $W_\uparrow: \ell \to d$（压缩/还原） \\ +\textbf{Router} & $W_r: d \to n_{\mathrm{routed}}$，Top-k 选择 + softmax 归一化 \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{计算流（五步）} + +\begin{enumerate}[leftmargin=2em] +\item \textbf{Latent 压缩}： +\[ + z = W_\downarrow \cdot x \qquad \shape{B, T, \ell} +\] + +\item \textbf{Routing}： +\[ + \mathrm{logits} = W_r \cdot x \qquad \shape{B, T, n_{\mathrm{routed}}} +\] +\[ + \mathrm{ids}, \mathrm{probs} = \mathrm{TopK}(\mathrm{logits}, k) + \qquad \mathrm{ids}: \shape{B, T, k}, \;\; \mathrm{probs}: \shape{B, T, k} +\] + +\item \textbf{Routed 专家}（在 latent 空间 $\ell$ 上）： +\[ + u = \sum_{i \in \mathrm{Top\text{-}k}} p_i \cdot E_i^{\mathrm{rt}}(z) + \qquad \shape{B, T, \ell} +\] + +\item \textbf{Shared 专家}（全宽 $d$）： +\[ + s = \sum_j E_j^{\mathrm{sh}}(x) \qquad \shape{B, T, d} +\] + +\item \textbf{合并}： +\[ + y = s + W_\uparrow \cdot \mathrm{RMSNorm}(u) \qquad \shape{B, T, d} +\] +\end{enumerate} + +\begin{importantbox}{如果你只记一件事} +Routed 专家只在 $\ell = d/2$ 的 latent 空间操作， +参数量是全宽专家的 $1/4$（$\ell^2$ vs $d^2$）。 +Shared 专家保持全宽 $d$，提供基础表达能力。 +\end{importantbox} + +\subsection{代码对照} + +\begin{codemathtop}{layers/latent\_moe.py — LatentMoE.forward} +\begin{lstlisting} +def forward(self, x): # [B, T, d] + z = self.down(x) # [B, T, ell] + + logits = self.router(x) # [B, T, n_routed] + topk = torch.topk(logits, self.top_k, dim=-1) + ids = topk.indices # [B, T, k] + probs = F.softmax(topk.values, dim=-1) # [B, T, k] + + # All expert outputs (vectorized) + all_out = stack([e(z) for e in self.experts]) # [R, B, T, ell] + # Gather top-k and weighted sum + u = zeros(B, T, ell) + for i in range(self.top_k): + idx = ids[:,:,i].reshape(B*T) + sel = all_out[arange, idx] + u += probs[:,:,i:i+1] * sel.reshape(B, T, ell) + + shared_out = stack([e(x) for e in self.shared]).sum(0) # [B, T, d] + return shared_out + self.up(self.norm(u)) # [B, T, d] +\end{lstlisting} +\end{codemathtop} + +\begin{warningbox}{为什么 router 用 $x$（全宽）而不是 $z$（latent）？} +路由需要看到 token 的完整表示才能做好选择。 +如果用 $z$ 路由，压缩过程可能丢失路由需要的信息。 +K3 论文里也是用全宽 $x$ 做 Top-k，然后在 $\ell$ 空间计算。 +\end{warningbox} + +\subsection{形状总览} + +\begin{center} +\begin{tabular}{llll} +\toprule +变量 & 形状 & 说明 \\ +\midrule +$x$ & \shape{B, T, d} & 输入 \\ +$z$ & \shape{B, T, \ell} & latent（$\ell = d/2$）\\ +logits & \shape{B, T, n_r} & router 输出 \\ +ids & \shape{B, T, k} & Top-k 专家索引 \\ +probs & \shape{B, T, k} & Top-k softmax 权重 \\ +\texttt{all\_out} & \shape{n_r, B, T, \ell} & 所有 routed 专家输出 \\ +$u$ & \shape{B, T, \ell} & 加权求和后的 routed 输出 \\ +\texttt{shared\_out} & \shape{B, T, d} & shared 专家求和 \\ +$y$ & \shape{B, T, d} & 最终输出 \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{本章小结} + +LatentMoE 把 routed 专家限制在 $\ell = d/2$ 的 latent 空间，省参数。 +SiTU-GLU 给 gate 和 up 加 $\tanh$ 软上限（$\beta_1=4, \beta_2=25$）， +防止低精度溢出。Shared 专家全宽，提供基础能力；routed 专家通过 Top-k 路由提供专业化能力。 diff --git a/notes/sections/sec-08.tex b/notes/sections/sec-08.tex new file mode 100644 index 0000000..e1ef771 --- /dev/null +++ b/notes/sections/sec-08.tex @@ -0,0 +1,162 @@ +% teach: +% gap: 读者已知各组件但不知道怎么组装成完整模型 +% takeaway: K3 = Hybrid(3 KDA + 1 MLA) × DecoderBlock(attn + MoE), 末层强制 MLA +% jump: 为什么每 4 层才放一次 MLA？位置感知只需要周期性提供 +% omit: 0.5b preset 的训练超参 + +\section{K3 混合架构} + +\subsection{整体结构} + +\begin{center} +\texttt{Embedding} $\to$ \texttt{DecoderBlock} $\times L$ $\to$ \texttt{RMSNorm} $\to$ \texttt{LM Head} +\end{center} + +每个 \texttt{DecoderBlock} 是 Pre-Norm 残差： + +\begin{lstlisting} +def forward(self, x): + x = x + self.attn(self.attn_norm(x)) # mixing + return x + self.ffn(self.ffn_norm(x)) # channel +\end{lstlisting} + +\subsection{Hybrid Attention Pattern} + +K3 用两种 attention 层交替： + +\begin{center} +\begin{tabular}{ccccccccc} +\toprule +层 & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 \\ +\midrule +Attn & KDA & KDA & KDA & \textbf{MLA} & KDA & KDA & KDA & \textbf{MLA} \\ +FFN & MoE & MoE & MoE & MoE & MoE & MoE & MoE & MoE \\ +\bottomrule +\end{tabular} +\end{center} + +\noindent 规则：每 4 层放 1 次 MLA（0-based 层 3, 7, 11, ...），\textbf{末层强制 MLA}。 + +\begin{codemathtop}{models/k3\_config.py — layer\_types} +\begin{lstlisting} +def layer_types(self) -> list[str]: + """Hybrid: 3 KDA + 1 MLA per group, last always MLA.""" + types = ["kda"] * self.num_hidden_layers + for i in range(self.num_hidden_layers): + if i % 4 == 3: + types[i] = "mla" + types[-1] = "mla" # last layer forced + return types + +def layer_specs(self): + return [(kind, "moe") for kind in self.layer_types()] +\end{lstlisting} +\end{codemathtop} + +\begin{knowledgebox}{为什么 KDA 不需要 RoPE？} +KDA 的 gate/decay 机制天然提供位置感知： +远的 token 衰减更多，近的保留更多。 +但 softmax attention（MLA）没有这个机制，所以真实 K3 用 NoPE +（本复现的 MLA 也是 NoPE）。 +位置感知从 KDA 层``渗透''到 MLA 层——3:1 的比例足够了。 +\end{knowledgebox} + +\subsection{CausalLM 完整数据流} + +\begin{codemathtop}{models/causal\_lm.py — CausalLM} +\begin{lstlisting} +class CausalLM(nn.Module): + def __init__(self, config): + self.embedding = nn.Embedding(vocab_size, D) # [V, D] + self.blocks = ModuleList([ + DecoderBlock.from_spec(config, attn, ffn) + for attn, ffn in config.layer_specs() + ]) + self.norm = RMSNorm(D) + self.lm_head = nn.Linear(D, vocab_size) # [V, D] + if config.tie_word_embeddings: + self.lm_head.weight = self.embedding.weight + + def forward(self, input_ids, labels=None): + x = self.embedding(input_ids) # [B,T] -> [B,T,D] + if self.mixer is None: # attnres="off" + for block in self.blocks: + x = block(x) # [B,T,D] -> [B,T,D] + else: + x = self.mixer(x) # AttnRes 深度残差, 见 §9 + logits = self.lm_head(self.norm(x)) # [B,T,D] -> [B,T,V] + if labels is None: + return logits + # Shifted CE: predict next token + return cross_entropy(logits[:,:-1], labels[:,1:]) +\end{lstlisting} +\end{codemathtop} + +\begin{knowledgebox}{残差流是可替换的} +上面的 \texttt{DecoderBlock} 逐层堆叠（\texttt{x = x + sublayer(norm(x))}） +是 \texttt{config.attnres="off"} 时的默认路径。 +置为 \texttt{"full"} / \texttt{"block"} 时，\texttt{CausalLM} 会把每个 block +拆成 attn / ffn 两个原子子层交给 \texttt{mixer}，用\textbf{深度维注意力} +代替等权残差加法——见 \S9。真实 K3 用的是 \texttt{block} 模式。 +\end{knowledgebox} + +\subsection{两种配置} + +\begin{center} +\begin{tabular}{lll} +\toprule +& \textbf{KDAConfig}（纯 KDA） & \textbf{K3Config}（混合） \\ +\midrule +Attn & KDA only & 3 KDA + 1 MLA \\ +FFN & SwiGLU & LatentMoE \\ +典型规模 & \textasciitilde8M (toy) & \textasciitilde8M (toy) / \textasciitilde500M (0.5b) \\ +\texttt{layer\_specs()} & \texttt{[("kda","swiglu")] * L} & \texttt{[(kind,"moe") for kind in ...]} \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{K3 toy 尺寸} + +\begin{center} +\begin{tabular}{llll} +\toprule +参数 & 真实 K3 & toy 复现 & 缩比 \\ +\midrule +$D$ & 7168 & 256 & 28$\times$ \\ +$L$ & 93 & 4 & 23$\times$ \\ +$H = H_V$ & 96 & 8 & 12$\times$ \\ +$K = V$ & 128 & 16 & 8$\times$ \\ +kv\_lora\_rank & 512 & 32 & 16$\times$ \\ +q\_lora\_rank & 1536 & 64 & 24$\times$ \\ +$\ell$ (MoE latent) & 3584 & 128 & 28$\times$ \\ +$n_{\mathrm{routed}}$ / Top-$k$ & 896/16 & 16/2 & 56$\times$ / 8$\times$ \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{DecoderBlock 构建} + +\begin{codemathtop}{layers/block.py — build\_attn / build\_ffn} +\begin{lstlisting} +def build_attn(config, kind: str) -> nn.Module: + if kind == "kda": return KDAAttention.from_config(config) + if kind == "mla": return GatedMLA.from_config(config) + +def build_ffn(config, kind: str) -> nn.Module: + if kind == "swiglu": return SwiGLUMLP.from_config(config) + if kind == "moe": return LatentMoE.from_config(config) + +class DecoderBlock(nn.Module): + def forward(self, x): + x = x + self.attn(self.attn_norm(x)) + return x + self.ffn(self.ffn_norm(x)) +\end{lstlisting} +\end{codemathtop} + +\subsection{本章小结} + +K3 架构 = Hybrid Attention（3 KDA + 1 MLA，末层强制 MLA）+ LatentMoE。 +KDA 层提供线性复杂度的序列混合和位置感知（通过 decay）， +MLA 层提供全局 softmax attention（NoPE，利用 KDA 渗透的位置信息）。 +每层默认是 Pre-Norm 残差 DecoderBlock；\texttt{config.attnres} 可以把这条 +等权残差流换成 AttnRes 深度注意力（\S9）。 diff --git a/notes/sections/sec-09.tex b/notes/sections/sec-09.tex new file mode 100644 index 0000000..65837a1 --- /dev/null +++ b/notes/sections/sec-09.tex @@ -0,0 +1,379 @@ +% teach: +% gap: 读者知道 Pre-Norm 残差是"无条件等权累加", 但不知道怎么把它换成"按内容选择读哪一层" +% takeaway: AttnRes = 深度维 softmax 注意力残差; Block 版把 O(N^2) 源数压到 O(N/S); 两阶段 = inter 批量 + intra online-softmax 合并 +% jump: 为什么打分用 RMSNorm 后的 v, 加权和却用原始 v +% omit: 论文里的 kernel 级调度与 pipeline 重叠 + +\section{Attention Residual 深度残差} + +\subsection{从"等权累加"到"按内容选择"} + +标准 Pre-Norm 残差把每层输出\textbf{无条件加}进残差流： + +\[ + x_l = x_{l-1} + f_l(x_{l-1}), + \qquad + x_N = x_0 + \sum_{l=1}^{N} f_l(x_{l-1}) +\] + +\noindent 展开后每一项权重恒为 1：第 3 层的输出和第 80 层的输出对最终表示的 +"名义"贡献一样大，深层无法表达"我这一步应该主要读第 12 层的结果"。 + +AttnRes（\texttt{arXiv:2603.15031}）把这个加法换成\textbf{深度维上的 softmax 注意力}： +第 $l$ 层持有一个可学习 query 向量 $w_l \in \mathbb{R}^D$， +把此前所有层的输出当成"可读的记忆"： + +\begin{align} + s_{l,i} &= w_l^{\top}\,\mathrm{RMS}(v_i), + & i = 0,1,\dots,l-1 \tag{A1} \\ + \alpha_{l,i} &= \frac{\exp(s_{l,i})}{\sum_{j} \exp(s_{l,j})} + & \shape{n, B, T} \tag{A2} \\ + h_l &= \sum_{i} \alpha_{l,i}\, v_i + & \shape{B, T, D} \tag{A3} \\ + v_l &= f_l(h_l) \tag{A4} +\end{align} + +\noindent 其中 $v_0 = x$（embedding 输出），$f_l$ 是已经含 Pre-Norm 的原子子层， +$\mathrm{RMS}(\cdot)$ 是不带 gain 的 RMS 归一化。 +最后（\texttt{is\_final\_aggregate=True}）再用一个独立 query 聚合所有源得到 $y$。 + +\begin{importantbox}{注意力权重是逐 token 的} +$s_{l,i}$ 的形状是 \shape{n, B, T}——每个 batch、每个位置 $t$ 都有自己的一套深度权重。 +所以同一个位置在不同深度可以读不同的层，但\textbf{不跨时间混合}， +因果性完全不受影响（\texttt{test\_attnres\_is\_still\_causal}）。 +\end{importantbox} + +\subsection{DepthResidual：三个实现细节} + +\begin{codemathtop}{layers/attn\_res.py — DepthResidual} +\begin{lstlisting} +class DepthResidual(nn.Module): + def __init__(self, dim, eps=1e-8, zero_init=True): + self.query = nn.Parameter(torch.zeros(dim)) # [D] + self.norm = RMSNorm(dim, eps=eps) # gain gamma + + def effective_query(self): + return (self.query * self.norm.weight).float() # 折叠 gain + + def forward(self, sources): + sources = stack_layers(sources) # [n,B,T,D] + q = self.effective_query() # [D] + k = rms(sources.float(), self.norm.eps) # 只用于打分 + logits = einsum('d, n b t d -> n b t', q, k) + w = logits.softmax(dim=0) # 在深度维 softmax + out = einsum('n b t, n b t d -> b t d', w, sources.float()) + return out.to(sources.dtype) +\end{lstlisting} +\end{codemathtop} + +\paragraph{(1) gain 折叠} +RMSNorm 的可学习 gain $\gamma$ 本该作用在 key 上，但 +$w^{\top}(\gamma \odot \mathrm{RMS}(v)) = (w \odot \gamma)^{\top}\mathrm{RMS}(v)$， +所以直接把 $\gamma$ 折进 query：$\tilde{w}_l = w_l \odot \gamma_l$。 +少一次 \shape{n,B,T,D} 的逐元素乘法，两阶段算法里也只需要传一个向量。 + +\paragraph{(2) 打分用归一化的 $v$，加权和用原始 $v$} +注意 \texttt{logits} 用 \texttt{k = rms(sources)}，而 \texttt{out} 用的是 +\texttt{sources} 本身。 + +\begin{knowledgebox}{为什么这样不对称？} +打分要的是\textbf{方向}：$\mathrm{RMS}$ 之后 $s_{l,i}$ 与 $\|v_i\|$ 无关， +一层输出幅度大不会自动抢到高权重，softmax 只按"内容像不像我要读的东西"分配。\\ +加权和要的是\textbf{原始信息}：如果对归一化后的 $v$ 求和，每层输出的模长 +（承载着"这层贡献多大"的信息）就被抹掉了，深层的小幅修正会被放大到和主干同量级。 +\end{knowledgebox} + +\paragraph{(3) zero-init query} +\texttt{query} 默认初始化为 $0$ $\Rightarrow$ 所有 logits 为 $0$ +$\Rightarrow$ softmax 均匀 $\Rightarrow$ +\[ + h_l = \frac{1}{l}\sum_{i=0}^{l-1} v_i +\] +训练起步就是\textbf{等权深度平均}（已实测：零初始化时 \texttt{forward} 输出与 +\texttt{sources.mean(0)} 逐位相同），行为接近标准残差但自带 $1/l$ 缩放， +之后由梯度慢慢学出偏好。设 \texttt{zero\_init\_queries=False} 则用 $\mathcal{N}(0, 0.02^2)$。 + +\subsection{Full 与 Block：源数量的差别} + +两种堆叠方式的区别只在\textbf{谁有资格进入源列表}： + +\begin{itemize}[nosep, leftmargin=2em] +\item \texttt{FullAttnResStack}\\ + 保留\textbf{每一个原子层}的输出作为源，第 $l$ 层在 $l+1$ 个源上做注意力。 +\item \texttt{BlockAttnResStack}\\ + 把 $N$ 个原子层切成大小为 $S$ 的块，\textbf{块内退化成普通求和} + （running partial $p \leftarrow p + v$）， + 只有\textbf{块的输出} $b_j$ 才进入源列表。 +\end{itemize} + +\begin{center} +\begin{tabular}{lccc} +\toprule + & \textbf{Full} & \textbf{Block ($S$)} & 标准残差 \\ +\midrule +注意力源数 & 最多 $N+1$ & 最多 $N/S + 2$ & 1 \\ +需保留的 \shape{B,T,D} 激活 & $O(N)$ & $O(N/S)$ & $O(1)$ \\ +深度注意力 FLOPs & $O(N^2 BTD)$ & $O(N^2 BTD / S)$ & 0 \\ +新增参数 & $2(N{+}1)D$ & $2(N{+}1)D$ & 0 \\ +\bottomrule +\end{tabular} +\end{center} + +\noindent 参数量不变（每个原子层都有自己的 query），变的是\textbf{显存与带宽}。 +真实 K3（$L=93$，$N=186$）取 $S=24$ 个原子层（12 个 DecoderBlock）， +源数从 187 降到 $\le 10$。 + +\begin{codemathtop}{layers/attn\_res.py — BlockAttnResStack.forward\_naive（语义参考实现）} +\begin{lstlisting} +blocks = [x] # b_0 = embedding +partial = None +for layer_idx, (layer, residual) in enumerate(zip(self.layers, self.residuals), 1): + sources = blocks if partial is None else blocks + [partial] + h = residual(sources) # 深度注意力 + out = layer(h) + partial = out if partial is None else (partial + out) # 块内: 普通累加 + if (layer_idx % self.block_size == 0) or (layer_idx == len(self.layers)): + blocks.append(partial) # 块边界: 定型成一个新源 + partial = None +return self.final_residual(blocks) +\end{lstlisting} +\end{codemathtop} + +\subsection{两阶段算法（inter / intra）} + +块内逐层跑上面的 naive 版本有个浪费：块内每一层的 query 面对的 +\textbf{块间源 $b_0 \dots b_{j-1}$ 是完全相同且固定的}， +唯一在变的只有 running partial $p$。于是拆成两个阶段： + +\begin{enumerate}[nosep] +\item \textbf{inter（批量）}：把块内 $S$ 个 query 堆成 \shape{S, D}， + 对固定源做\textbf{一次}批量 einsum，拿到每个 query 的 online-softmax 三元组 + $(m,\ \text{numer},\ \text{denom})$； +\item \textbf{intra（串行）}：逐层把新出现的 $p$ 作为\textbf{单个源}合并进去， + 用 online softmax 的 merge 规则更新三元组，再 \texttt{normalized()} 出 $h$。 +\end{enumerate} + +\noindent online softmax 的三元组定义与合并规则（和 FlashAttention 同构， +只是"序列维"换成了"深度维"）： + +\begin{align} + m = \max_i s_i, + \quad + n = \sum_i e^{s_i - m} v_i, + \quad + d = \sum_i e^{s_i - m}, + \quad + h = n / d + \tag{OS1} +\end{align} + +\noindent 合并两组统计量 $(m_a, n_a, d_a)$ 与 $(m_b, n_b, d_b)$， +令 $m = \max(m_a, m_b)$、$w_a = e^{m_a - m}$、$w_b = e^{m_b - m}$： + +\begin{align} + n = w_a\, n_a + w_b\, n_b, + \quad + d = w_a\, d_a + w_b\, d_b + \tag{OS2} +\end{align} + +\noindent 单个源 $p$ 的三元组是 $(\,m = s_p,\ \text{numer} = p,\ \text{denom} = 1\,)$ +——因为 $e^{s_p - m} = 1$，不需要真的算指数（\texttt{single\_source\_stats}）。 + +\begin{codemathtop}{layers/attn\_res.py — \_run\_block\_two\_phase} +\begin{lstlisting} +queries = torch.stack([self.residuals[i].effective_query() + for i in range(start, end)], dim=0) # [S, D] +inter = attn_with_stats(queries, stack_layers(blocks), self.eps) # phase 1: 一次算完 + +partial = None +for local_idx, layer_idx in enumerate(range(start, end)): # phase 2: 串行 + stats = inter.select(local_idx) + if partial is not None: + intra = single_source_stats(queries[local_idx], partial, self.eps) + stats = merge_attn_stats(stats, intra) # online softmax merge + h = stats.normalized() + out = self.layers[layer_idx](h) + partial = out if partial is None else (partial + out) +return partial +\end{lstlisting} +\end{codemathtop} + +\begin{importantbox}{等价性是被测出来的，不是假设的} +\texttt{test\_block\_two\_phase\_matches\_naive} 直接对拍 +\texttt{mixer.forward\_naive(emb)} 与 \texttt{mixer(emb)}， +\texttt{atol=rtol=1e-5} 通过。Full 版同理：不传 \texttt{schedule\_block\_size} +走 naive，传了走两阶段，两者一致。 +\end{importantbox} + +\subsection{接入 CausalLM} + +\subsubsection*{原子层 = 半个 DecoderBlock} + +深度注意力的粒度是\textbf{原子层}而不是 DecoderBlock： +每个 block 拆成"norm + attn"和"norm + ffn"两个 Pre-Norm 原子层， +所以原子层数 $N = 2L$。 + +\begin{codemathtop}{models/causal\_lm.py — \_build\_mixer} +\begin{lstlisting} +atomics = [] +for block in blocks: + atomics.append(BorrowedSubLayer(block.attn_norm, block.attn)) + atomics.append(BorrowedSubLayer(block.ffn_norm, block.ffn)) + +if mode == "full": + return FullAttnResStack(D, atomics, eps=..., zero_init_queries=..., ...) +if mode == "block": + return BlockAttnResStack(D, atomics, + block_size=atomic_block_size(config.num_hidden_layers, + config.attnres_block_size), ...) +\end{lstlisting} +\end{codemathtop} + +\subsubsection*{BorrowedSubLayer：借用而不注册} + +\begin{codemathtop}{layers/attn\_res.py — BorrowedSubLayer} +\begin{lstlisting} +class BorrowedSubLayer(nn.Module): + def __init__(self, norm, fn): + self._borrowed = (norm, fn) # 普通 tuple, 不是 self.norm = norm + + def forward(self, x): + norm, fn = self._borrowed + return fn(norm(x)) +\end{lstlisting} +\end{codemathtop} + +\begin{warningbox}{为什么必须用 tuple 藏起来？} +如果写成 \texttt{self.norm = norm}，\texttt{nn.Module} 会把它\textbf{注册成子模块}， +于是同一份权重同时挂在 \texttt{blocks.0.attn.*} 和 \texttt{mixer.layers.0.fn.*} 下： +\begin{itemize}[nosep] +\item \texttt{model.parameters()} 出现重复 $\Rightarrow$ 优化器对同一参数更新两次 +\item \texttt{state\_dict()} 多出一份镜像键 $\Rightarrow$ 旧 checkpoint 加载不上 +\end{itemize} +放进普通 tuple 后 \texttt{blocks.*} 仍是唯一属主， +\texttt{mixer} 下只多出 depth query 与 gain（\texttt{test\_no\_duplicate\_parameter\_ids} 守这条）。 +\end{warningbox} + +\subsubsection*{forward：mixer 接管整条残差流} + +\begin{codemathtop}{models/causal\_lm.py — CausalLM.forward} +\begin{lstlisting} +x = self.embedding(input_ids) +if self.mixer is None: + for block in self.blocks: # attnres="off": 老路径 + x = block(x) +else: + x = self.mixer(x) # full / block: DecoderBlock.forward 被完全绕过 +logits = self.lm_head(self.norm(x)) +\end{lstlisting} +\end{codemathtop} + +\noindent 注意 \texttt{mixer} 打开后 \texttt{DecoderBlock.forward} +（\S8 里的 \texttt{x = x + attn(...)}）\textbf{一次都不会被调用}—— +残差加法整个交给深度注意力，DecoderBlock 退化成"两个子层的容器"。 + +\subsubsection*{新增参数量：可忽略} + +每个 DepthResidual 只有 query \shape{D} 和 gain \shape{D}，共 $N+1$ 个： + +\begin{center} +\begin{tabular}{lrrr} +\toprule +配置 & $D$ / $L$ & 原子层 $N$ & 新增参数 \\ +\midrule +toy (K3Config) & 256 / 4 & 8 & 4{,}608 \\ +0.5b preset & 768 / 24 & 48 & 75{,}264 \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{配置与命令行} + +\begin{center} +\begin{tabular}{lll} +\toprule +字段 & 默认 & 含义 \\ +\midrule +\texttt{attnres} & \texttt{"off"} & \texttt{off} / \texttt{full} / \texttt{block} \\ +\texttt{attnres\_block\_size} & \texttt{None} & 每块几个 \textbf{DecoderBlock}；\texttt{None} $\to \lceil L/8 \rceil$ \\ +\texttt{attnres\_zero\_init\_queries} & \texttt{True} & query 零初始化（等权起步）\\ +\texttt{attnres\_final\_aggregate} & \texttt{True} & 末尾再做一次全源聚合 \\ +\bottomrule +\end{tabular} +\end{center} + +\begin{codemathtop}{layers/attn\_res.py — atomic\_block\_size} +\begin{lstlisting} +def atomic_block_size(num_hidden_layers, attnres_block_size): + """DecoderBlock 数 -> 原子层数。None 时目标约 8 块。""" + layers_per_block = (attnres_block_size if attnres_block_size is not None + else max(1, (num_hidden_layers + 7) // 8)) + return layers_per_block * 2 # 每个 DecoderBlock = attn|ffn 两个原子层 +\end{lstlisting} +\end{codemathtop} + +\noindent 单位换算是最容易踩的一处： +配置字段的单位是 \textbf{DecoderBlock 数}， +而堆叠类收到的 \texttt{block\_size} 是\textbf{原子层数}（$\times 2$）。 +例如 $L = 24$、块大小留 \texttt{None}： +\[ + \lceil 24/8 \rceil = 3 \text{ 个 DecoderBlock} + \;\to\; S = 6 \text{ 个原子层} + \;\to\; N/S = 48/6 = 8 \text{ 块} +\] + +\begin{lstlisting} +uv run python train_k3.py --preset toy --attnres block --attnres-block-size 2 +uv run python train_k3.py --preset 0.5b --attnres block # 块大小自动 ~L/8 +\end{lstlisting} + +\noindent \texttt{KDAConfig} 与 \texttt{K3Config} 都在 +\texttt{\_\_post\_init\_\_} 里调 \texttt{validate\_attnres}， +非法模式 / 块大小在构造时就报错。 +旧 checkpoint 的 config 里没有这几个字段，加载时回落到 +\texttt{off}（见 \S 9.7 验证清单最后两行）。 + +\subsection{验证清单} + +\texttt{tests/integration/test\_attn\_res.py}，14 项全过： + +\begin{center} +\begin{tabular}{ll} +\toprule +测试 & 守住的性质 \\ +\midrule +\texttt{default\_attnres\_is\_off} & 默认不改变任何既有行为，\texttt{mixer is None} \\ +\texttt{invalid\_attnres\_rejected} & 非法 mode / \texttt{block\_size=0} 构造期报错 \\ +\texttt{mixer\_kind\_and\_atomic\_count} & 原子层数 $= 2L$，块大小 $\times 2$ 换算 \\ +\texttt{auto\_block\_size\_targets\_eight\_blocks} & $L=93 \to 24$（K3 $S=12$ 个 block）\\ +\texttt{no\_duplicate\_parameter\_ids} & 借用不注册，参数 id / 名字均无重复 \\ +\texttt{off\_and\_block\_differ\_at\_same\_seed} & 同种子下确实换了计算图 \\ +\texttt{block\_two\_phase\_matches\_naive} & 两阶段 $\equiv$ naive，\texttt{atol 1e-5} \\ +\texttt{attnres\_is\_still\_causal} & 改末位 token 不影响前缀 logits \\ +\texttt{kda\_config\_block\_runs} & 纯 KDA 配置也能开 \\ +\texttt{attnres\_ckpt\_roundtrip} & 存取后逐位一致，config 字段保真 \\ +\texttt{old\_ckpt\_without\_attnres\_stays\_off} & 向后兼容 \\ +\texttt{attnres\_block\_overfits\_single\_batch} & 200 步 loss $< 0.5$，能训 \\ +\bottomrule +\end{tabular} +\end{center} + +\begin{warningbox}{混合精度} +\texttt{DepthResidual.forward}（naive 路径）显式 \texttt{.float()} 后再算 softmax +与加权和，最后 cast 回原 dtype；两阶段路径的 query 是 fp32、源保持原 dtype， +靠 einsum 的类型提升处理。bf16 autocast 下前向实测正常。 +和 \S1 的结论一致：\textbf{指数/累和一律不要放进 fp16}。 +\end{warningbox} + +\subsection{本章小结} + +AttnRes 把残差流从"等权累加"升级成"深度维 softmax 注意力"： +每层用自己的 query 决定读此前哪些层的输出，打分在 RMS 归一化后做（方向）、 +加权和在原始张量上做（保留模长），query 零初始化让训练从等权平均起步。 +Full 版源数随深度线性增长，Block 版把块内退化成普通求和、只让块输出进入源列表， +把源数压到 $O(N/S)$；两阶段算法进一步把块间注意力批量化， +块内用 online softmax 增量合并，与 naive 实现数值等价。 +接入 \texttt{CausalLM} 时每个 DecoderBlock 拆成 attn / ffn 两个原子层， +\texttt{BorrowedSubLayer} 用普通 tuple 借用权重以免重复注册， +\texttt{attnres="off"} 保持旧路径不变。 diff --git a/notes/sections/sec-10.tex b/notes/sections/sec-10.tex new file mode 100644 index 0000000..db200dc --- /dev/null +++ b/notes/sections/sec-10.tex @@ -0,0 +1,157 @@ +% teach: +% gap: 读者会用 autograd 但不知道手写 KDA backward 的具体展开 +% takeaway: backward = 逆序遍历时间步, 每步求 dq/dk/dv/dg/dbeta + 累积 dS; GVA 反传 = view+sum +% jump: 为什么 dS_dec 要加 dS_acc 和 -k⊗dr 两项 +% omit: Triton backward 优化 + +\section{反向传播推导} + +\subsection{Forward 回顾} + +逐步写下 forward（省略 batch/head 下标）： + +\begin{align} + S_{\mathrm{dec}} &= \exp(g_t) \odot S_{t-1} \tag{F1} \\ + p_t &= k_t^T S_{\mathrm{dec}} \tag{F2} \\ + r_t &= v_t - p_t \tag{F3} \\ + a_t &= \beta_t \cdot k_t \tag{F4} \\ + S_t &= S_{\mathrm{dec}} + a_t \otimes r_t \tag{F5} \\ + o_t &= q_t^T S_t \tag{F6} +\end{align} + +\subsection{反传公式（BPTT，$T \to 0$）} + +设 $dS_{\mathrm{acc}}$ 是从时间步 $t$ 开始累积到 $S_t$ 上的梯度。逆序遍历： + +\paragraph{Step 1: $o_t = q_t^T S_t$} + +\begin{align} + dS_{\mathrm{acc}} &\mathrel{+}= q_t \otimes do_t + & \xrightarrow{\texttt{einsum('bhk,bhv->bhkv')}} + & \quad \shape{B, HV, K, V} \\ + dq_t &= do_t^T S_t + & \xrightarrow{\texttt{einsum('bhv,bhkv->bhk')}} + & \quad \shape{B, HV, K} +\end{align} + +\paragraph{Step 2: $S_t = S_{\mathrm{dec}} + a_t \otimes r_t$} + +外积的反传：$d(a \otimes r) = (\cdot)$，分解为： + +\begin{align} + da_t &= \sum_v r_{t,v} \cdot dS_{\mathrm{acc},\cdot,v} + & \xrightarrow{\texttt{einsum('bhv,bhkv->bhk')}} + & \quad \shape{B, HV, K} \\ + dr_t &= \sum_k a_{t,k} \cdot dS_{\mathrm{acc},k,\cdot} + & \xrightarrow{\texttt{einsum('bhk,bhkv->bhv')}} + & \quad \shape{B, HV, V} +\end{align} + +\paragraph{Step 3: $a_t = \beta_t \cdot k_t$} + +\begin{align} + d\beta_t &= \sum_k k_{t,k} \cdot da_{t,k} + & \xrightarrow{\texttt{einsum('bhk,bhk->bh')}} + & \quad \shape{B, HV} \\ + dk_t^{(a)} &= \beta_t \cdot da_t + & & \quad \shape{B, HV, K} +\end{align} + +\paragraph{Step 4: $r_t = v_t - p_t = v_t - k_t^T S_{\mathrm{dec}}$} + +\begin{align} + dv_t &= dr_t & & \shape{B, HV, V} \\ + dk_t^{(r)} &= -S_{\mathrm{dec}}^T \cdot dr_t + & \xrightarrow{\texttt{einsum('bhv,bhkv->bhk')}} + & \quad \shape{B, HV, K} \\ + dS_{\mathrm{dec}}^{(r)} &= -k_t \otimes dr_t + & \xrightarrow{\texttt{einsum('bhv,bhk->bhkv')}} + & \quad \shape{B, HV, K, V} +\end{align} + +\paragraph{Step 5: 合并 $dS_{\mathrm{dec}}$ 并传递 $dg_t$, $dS_{t-1}$} + +\[ + dS_{\mathrm{dec}}^{\mathrm{total}} = dS_{\mathrm{acc}} + dS_{\mathrm{dec}}^{(r)} + = dS_{\mathrm{acc}} - k_t \otimes dr_t +\] + +因为 $S_{\mathrm{dec}} = \exp(g_t) \odot S_{t-1}$： + +\begin{align} + dg_t &= S_{\mathrm{dec}} \odot dS_{\mathrm{dec}}^{\mathrm{total}} + & \xrightarrow{\texttt{einsum('bhkv,bhkv->bhk')}} + & \quad \shape{B, HV, K} \\ + dS_{t-1} &= \exp(g_t) \odot dS_{\mathrm{dec}}^{\mathrm{total}} + & & \quad \shape{B, HV, K, V} +\end{align} + +\paragraph{Step 6: 合并 $dk_t$ 和 GVA 归约} + +\[ + dk_t = dk_t^{(a)} + dk_t^{(r)} + = \beta_t \cdot da_t - S_{\mathrm{dec}}^T \cdot dr_t +\] + +GVA 反传（$H_V \to H$）： +\[ + dq_H = dq_{H_V}.\texttt{view}(B, T, H, G, K).\texttt{sum}(\text{dim}=3) \cdot \mathrm{scale} +\] +\[ + dk_H = dk_{H_V}.\texttt{view}(B, T, H, G, K).\texttt{sum}(\text{dim}=3) +\] + +\subsection{代码对照} + +\begin{codemathtop}{ops/reference/recurrent.py — KDAFunction.backward} +\begin{lstlisting} +for t in range(T - 1, -1, -1): + q_t, k_t, b_t = q_ts[:,t], k_ts[:,t], b_ts[:,t] + S_dec, r_t, a_t = S_decs[:,t], r_ts[:,t], a_ts[:,t] + exp_g_t, do_t = exp_g_ts[:,t], do[:,t] + + # Step 1: o_t = q_t . S_t + S_t = S_dec + einsum('bhk,bhv->bhkv', a_t, r_t) + dS_acc += einsum('bhk,bhv->bhkv', q_t, do_t) + dq_e[:,t] = einsum('bhv,bhkv->bhk', do_t, S_t) + + # Step 2: outer product grads + da_t = einsum('bhv,bhkv->bhk', r_t, dS_acc) + dr_t = einsum('bhk,bhkv->bhv', a_t, dS_acc) + + # Step 3: a_t = beta_t * k_t + dbeta[:,t] = einsum('bhk,bhk->bh', k_t, da_t) + dk_t_a = b_t.unsqueeze(-1) * da_t + + # Step 4: r_t = v_t - k_t . S_dec + dv[:,t] = dr_t + dS_dec_from_r = -einsum('bhv,bhk->bhkv', dr_t, k_t) + dk_t_r = -einsum('bhv,bhkv->bhk', dr_t, S_dec) + + # Step 5: S_dec = exp(g) * S_{t-1} + dS_dec_total = dS_acc + dS_dec_from_r + dk_e[:,t] = dk_t_a + dk_t_r + dg[:,t] = einsum('bhkv,bhkv->bhk', S_dec, dS_dec_total) + dS_acc = exp_g_t.unsqueeze(-1) * dS_dec_total + +# Step 6: GVA reduce +dq_H = dq_e.view(B,T,H,G,K).sum(dim=3) * scale +dk_H = dk_e.view(B,T,H,G,K).sum(dim=3) +\end{lstlisting} +\end{codemathtop} + +\begin{warningbox}{$dS_{\mathrm{dec}}^{\mathrm{total}}$ 为什么包含两项？} +$S_t = S_{\mathrm{dec}} + a_t \otimes r_t$，$S_{\mathrm{dec}}$ 同时参与了： +\begin{enumerate}[nosep] +\item 直接传递到 $dS_{\mathrm{acc}}$（作为 $S_t$ 的一部分被读出） +\item 通过 $r_t = v_t - k_t \cdot S_{\mathrm{dec}}$ 参与 delta rule +\end{enumerate} +所以 $dS_{\mathrm{dec}}^{\mathrm{total}} = dS_{\mathrm{acc}} + dS_{\mathrm{dec}}^{(r)}$， +两条路径的梯度要\textbf{加}起来（chain rule 分叉处求和）。 +\end{warningbox} + +\subsection{本章小结} + +KDA backward 是 BPTT 展开：逆序遍历时间步，每步 6 个 einsum + 一次 $dS$ 累积更新。 +GVA 反传在最后做 \texttt{view+sum}。手写 backward 的关键是正确处理 +$dS_{\mathrm{dec}}$ 的两条梯度路径（直接传递 + 通过 $r_t$ 的 delta rule 路径）。 diff --git a/notes/sections/sec-11.tex b/notes/sections/sec-11.tex new file mode 100644 index 0000000..452233e --- /dev/null +++ b/notes/sections/sec-11.tex @@ -0,0 +1,183 @@ +% teach: +% gap: none — this is a reference appendix +% takeaway: 一表查所有符号 +% jump: none +% omit: none + +\section{符号表} + +\subsection{形状参数} + +\begin{center} +\begin{tabular}{lll} +\toprule +符号 & 含义 & 典型值 (toy) \\ +\midrule +$B$ & batch size & 2--4 \\ +$T$ & 序列长度 & 128--2048 \\ +$D$ & hidden\_size & 64 / 256 \\ +$H$ & query/key 头数 & 4 / 8 \\ +$H_V$ & value 头数（GVA） & $G \cdot H$ \\ +$G$ & GVA 组数 & $H_V / H$ \\ +$K$ & key/query 头维度 & 16 \\ +$V$ & value 头维度（$= K$） & 16 \\ +$C$ & chunk\_size & 16 / 64 \\ +$r$ & KV latent rank (MLA) & 32 \\ +$d_q$ & MLA query head dim & 16 \\ +$d_v$ & MLA value head dim & 16 \\ +$\ell$ & MoE latent width ($= D/2$) & 128 \\ +$n_r$ & routed 专家数 & 16 \\ +$k$ & Top-$k$ & 2 \\ +$n_s$ & shared 专家数 & 2 \\ +$d_{\mathrm{ff}}$ & 专家中间维度 & 96 \\ +$N$ & AttnRes 原子层数 ($= 2L$) & 8 \\ +$S$ & AttnRes 块大小（原子层） & 2--24 \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{KDA 变量} + +\begin{center} +\begin{tabular}{llp{7cm}} +\toprule +符号 & 形状 & 含义 \\ +\midrule +$q_t$ & \shape{B, HV, K} & query（已 GVA 展开 + scale） \\ +$k_t$ & \shape{B, HV, K} & key（已 GVA 展开） \\ +$v_t$ & \shape{B, HV, V} & value \\ +$g_t$ & \shape{B, HV, K} & gate（log-space 衰减，逐维逐头） \\ +$\beta_t$ & \shape{B, HV} & 写入强度 \\ +$S_t$ & \shape{B, HV, K, V} & KV 状态矩阵 \\ +$S_{\mathrm{dec}}$ & \shape{B, HV, K, V} & 衰减后的状态 \\ +$p_t$ & \shape{B, HV, V} & 旧状态对 $k_t$ 的预测 \\ +$r_t$ & \shape{B, HV, V} & delta rule 残差 = $v_t - p_t$ \\ +$a_t$ & \shape{B, HV, K} & 写入向量 = $\beta_t \cdot k_t$ \\ +$o_t$ & \shape{B, HV, V} & 读出 = $q_t \cdot S_t$ \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{Gate 变量} + +\begin{center} +\begin{tabular}{llp{6cm}} +\toprule +符号 & 形状 & 含义 \\ +\midrule +$g_{\mathrm{raw}}$ & \shape{B, T, HV, K} & gate 投影原始输出 \\ +$A_{\log}$ & \shape{HV} & head-wise 衰减参数 (log-space) \\ +$\Delta_b$ & \shape{HV, K} & per-dim gate bias \\ +$\mathrm{rate}$ & \shape{HV, 1} & $\exp(A_{\log})$ \\ +$\mathrm{input}$ & \shape{B, T, HV, K} & $g_{\mathrm{raw}} + \Delta_b$ \\ +$L$ & 标量 & lower\_bound ($-5.0$) \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{MLA 变量} + +\begin{center} +\begin{tabular}{llp{6cm}} +\toprule +符号 & 形状 & 含义 \\ +\midrule +$c$ & \shape{B, T, r} & KV latent（推理时缓存这个） \\ +$q$ & \shape{B, T, H, d_q} & query（低秩路径输出） \\ +$W_{UK}$ & \shape{H, d_q, r} & key 解压矩阵（吸收进 $q$） \\ +$W_{UV}$ & \shape{H, d_v, r} & value 解压矩阵 \\ +$q_{\mathrm{abs}}$ & \shape{B, T, H, r} & 吸收后的 query \\ +score & \shape{B, H, T, T} & $q_{\mathrm{abs}} \cdot c^T$ \\ +attn & \shape{B, H, T, T} & causal softmax \\ +$\tilde{o}_{\mathrm{lat}}$ & \shape{B, H, T, r} & latent 加权输出 \\ +$\tilde{o}$ & \shape{B, H, T, d_v} & 解压后的输出 \\ +gate & \shape{B, T, H \cdot d_v} & $\sigma(W_g x)$ \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{LatentMoE 变量} + +\begin{center} +\begin{tabular}{llp{6cm}} +\toprule +符号 & 形状 & 含义 \\ +\midrule +$x$ & \shape{B, T, D} & 输入 \\ +$z$ & \shape{B, T, \ell} & latent ($\ell = D/2$) \\ +logits & \shape{B, T, n_r} & router logits \\ +ids & \shape{B, T, k} & Top-$k$ 专家索引 \\ +probs & \shape{B, T, k} & softmax 权重 \\ +$u$ & \shape{B, T, \ell} & routed 加权输出 \\ +$s$ & \shape{B, T, D} & shared 专家求和 \\ +$y$ & \shape{B, T, D} & $s + W_\uparrow \mathrm{RMSNorm}(u)$ \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{AttnRes 变量} + +\begin{center} +\begin{tabular}{llp{6.4cm}} +\toprule +符号 & 形状 & 含义 \\ +\midrule +$v_i$ & \shape{B, T, D} & 第 $i$ 个源（$v_0 =$ embedding 输出） \\ +$w_l$ & \shape{D} & 第 $l$ 层的 depth query（零初始化） \\ +$\gamma_l$ & \shape{D} & DepthResidual 的 RMSNorm gain \\ +$\tilde{w}_l$ & \shape{D} & 折叠后的 query $= w_l \odot \gamma_l$ \\ +$s_{l,i}$ & \shape{n, B, T} & 深度打分 $= \tilde{w}_l^{\top}\mathrm{RMS}(v_i)$ \\ +$\alpha_{l,i}$ & \shape{n, B, T} & 深度维 softmax 权重 \\ +$h_l$ & \shape{B, T, D} & 第 $l$ 层的输入 $= \sum_i \alpha_{l,i} v_i$ \\ +$b_j$ & \shape{B, T, D} & 第 $j$ 个块的输出（Block 版的源） \\ +$p$ & \shape{B, T, D} & 块内 running partial \\ +$m, n, d$ & \shape{B, T} / \shape{B,T,D} / \shape{B,T} & online softmax 三元组 \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{Einsum 速查} + +\begin{center} +\small +\begin{tabular}{p{6cm}lp{3.5cm}} +\toprule +操作 & einsum & 结果形状 \\ +\midrule +key 查状态 & \texttt{'bhk,bhkv->bhv'} & $p_t$ \shape{B,HV,V} \\ +外积写入 & \texttt{'bhk,bhv->bhkv'} & $a_t \otimes r_t$ \shape{B,HV,K,V} \\ +读出 & \texttt{'bhk,bhkv->bhv'} & $o_t$ \shape{B,HV,V} \\ +MLA 吸收 $W_{UK}$ & \texttt{'bthd,hdj->bthj'} & $q_{\mathrm{abs}}$ \shape{B,T,H,r} \\ +MLA 打分 & \texttt{'bthj,bsj->bhts'} & score \shape{B,H,T,T} \\ +MLA latent 加权 & \texttt{'bhts,bsj->bhtj'} & $\tilde{o}_{\mathrm{lat}}$ \shape{B,H,T,r} \\ +MLA 解压 & \texttt{'bhtj,hvj->bhtv'} & $\tilde{o}$ \shape{B,H,T,d_v} \\ +AttnRes 深度打分 & \texttt{'d,nbtd->nbt'} & $s_{l,i}$ \shape{n,B,T} \\ +AttnRes 深度加权和 & \texttt{'nbt,nbtd->btd'} & $h_l$ \shape{B,T,D} \\ +AttnRes 批量打分（inter） & \texttt{'qd,nbtd->qnbt'} & logits \shape{S,n,B,T} \\ +\bottomrule +\end{tabular} +\end{center} + +\subsection{总结与延伸} + +\subsubsection*{核心要点回顾} + +\begin{enumerate}[nosep] +\item \textbf{KDA} = delta rule 状态更新 + gate 衰减，线性复杂度 +\item \textbf{分块} = chunk 内下三角解 + chunk 间状态递推，等价于 naive recurrent +\item \textbf{GVA} = $H_V = G \cdot H$，forward repeat\_interleave / backward view+sum +\item \textbf{MLA} = 低秩 latent + 矩阵吸收，KV cache 从 $2Hd$ 降到 $r$ +\item \textbf{LatentMoE} = shared 全宽 + routed 半宽 latent + SiTU-GLU 防溢出 +\item \textbf{K3 Hybrid} = 3 KDA + 1 MLA，KDA 提供位置感知 +\item \textbf{AttnRes} = 深度维 softmax 残差，Block 版把源数压到 $O(N/S)$， + 两阶段 = inter 批量 + intra online-softmax 合并 +\end{enumerate} + +\subsubsection*{未完成项} + +\begin{itemize}[nosep] +\item L5 — 项目内自研 fused gate Triton kernel +\item L6 — recurrent decode cache（推理加速） +\item AttnRes 与 recurrent decode 的组合（增量解码时的深度源缓存） +\item AttnRes 开 / 关的收敛质量对比实验（目前只验证了等价性与可训练性） +\end{itemize} diff --git a/notes/verify_gref_overflow.py b/notes/verify_gref_overflow.py new file mode 100644 index 0000000..59949d6 --- /dev/null +++ b/notes/verify_gref_overflow.py @@ -0,0 +1,106 @@ +"""验证 reference `_decayed_dot` 的 g_ref 因子在真实训练门控量级下是否溢出。 + +_decayed_dot 把 exp(g_i-g_j) 拆成 (x*exp(g_i-g_ref)) @ (k*exp(g_ref-g_j))， +g_ref = g_cumsum[..., 0, :]。因 g<0，最大中间因子为 + exp(g_ref - g_{C-1}) = exp(sum_{t=1}^{C-1} |g_t|) +溢出阈值: sum|g| > ln(3.39e38)=88.7 (fp32/bf16) ; > ln(65504)=11.09 (fp16) + +门控: safe_gate, g = lower_bound * sigmoid(exp(A_log) * (g_raw + dt_bias)) + lower_bound=-5 => |g| ∈ (0, 5) 逐步硬上界 +""" +import math +import sys + +import torch + +import kda.ops.api as kapi +from kda.models.causal_lm import CausalLM +from kda.models.config import KDAConfig +from kda.models.k3_config import K3Config + +dev = "cuda" +LIM = {"fp32/bf16": math.log(3.39e38), "fp16": math.log(65504.0)} + +# ---------- 1) 解析上界: 每个 chunk_size 需要的平均 |g| ---------- +print("=== 解析: 溢出所需的 chunk 内平均 |g|/step (硬上界 |g|<5) ===") +print(f"{'C':>4} | {'fp32/bf16 阈值':>15} | {'可达?':>6} | {'fp16 阈值':>10} | {'可达?':>6}") +for C in (16, 32, 64, 128): + a, b = LIM["fp32/bf16"] / (C - 1), LIM["fp16"] / (C - 1) + print(f"{C:4d} | {a:15.2f} | {'YES' if a < 5 else 'no':>6} | " + f"{b:10.2f} | {'YES' if b < 5 else 'no':>6}") + +# ---------- 2) 实测: 真实 checkpoint 上的 chunk 内 sum|g| ---------- +_orig = kapi.naive_chunk_kda # api.py 在 import 时绑定, 必须 patch 这里 +stats = [] + + +def max_sum_abs_g(g, C): + """chunk 内最大 sum_{t=1..C-1}|g_t| (= max exp(g_ref-g_j) 的指数)。""" + T = g.shape[1] + gg = g[:, : T - T % C].reshape(g.shape[0], -1, C, *g.shape[2:]) + return gg[:, :, 1:].abs().sum(dim=2).max().item() + + +def patched(q, k, v, g, beta, **kw): + stats.append(g.detach().float()) + return _orig(q, k, v, g, beta, **kw) + + +kapi.naive_chunk_kda = patched + + +def build(cfg_dict): + """checkpoint 的 config 是 dict, 按字段集合判断是 K3 还是纯 KDA。""" + cls = K3Config if "moe_latent_size" in cfg_dict else KDAConfig + return cls(**{k: v for k, v in cfg_dict.items() + if k in cls.__dataclass_fields__}) + + +for path in ("ckpts/k3_wiki.pt", "ckpts/kda_toy.pt"): + ck = torch.load(path, map_location="cpu", weights_only=False) + cfg = build(ck["config"]) + model = CausalLM(cfg).to(dev).eval() + # 旧 checkpoint 用 moe/moe_norm 命名, 现已重命名为 ffn/ffn_norm + sd = {k.replace(".moe_norm.", ".ffn_norm.").replace(".moe.", ".ffn.") + .replace(".mlp_norm.", ".ffn_norm.").replace(".mlp.", ".ffn."): v + for k, v in ck["model_state"].items()} + missing, unexpected = model.load_state_dict(sd, strict=False) + assert not missing and not unexpected, (path, missing[:5], unexpected[:5]) + + stats.clear() + ids = torch.randint(0, cfg.vocab_size, (2, 512), device=dev) + with torch.no_grad(): + model(ids) + if not stats: + print(f"\n{path}: 未走 reference 路径 (backend={cfg.kda_backend})") + continue + gs = list(stats) + print(f"\n=== {path} (训练用 C={cfg.chunk_size}, KDA 层数 {len(gs)}) ===") + print(f" |g| mean/step: {sum(g.abs().mean().item() for g in gs)/len(gs):.4f} " + f"|g| max/step: {max(g.abs().max().item() for g in gs):.4f} " + f"(硬上界 {abs(cfg.lower_bound)})") + print(f" {'C':>4} | {'max chunk sum|g|':>16} | {'max exp 因子':>12} | " + f"{'fp32/bf16':>18} | {'fp16':>12}") + for C in (16, 32, 64, 128): + mg = max(max_sum_abs_g(g, C) for g in gs) + fac = math.exp(mg) if mg < 709 else float("inf") + f32 = f"{LIM['fp32/bf16']/mg:.2f}x OK" if mg < LIM["fp32/bf16"] else "OVERFLOW" + f16 = f"{LIM['fp16']/mg:.2f}x OK" if mg < LIM["fp16"] else "OVERFLOW" + mark = " <- 训练配置" if C == cfg.chunk_size else "" + print(f" {C:4d} | {mg:16.2f} | {fac:12.3e} | {f32:>18} | {f16:>12}{mark}") + +# ---------- 3) 随机初始化模型 (未训练) 同样测一遍 ---------- +for preset in ("toy", "0.5b"): + cfg = K3Config.preset(preset) + cfg.vocab_size = 2048 + cfg.kda_backend = "reference" + model = CausalLM(cfg).to(dev).eval() + stats.clear() + with torch.no_grad(): + model(torch.randint(0, cfg.vocab_size, (2, 512), device=dev)) + if stats: + gs = list(stats) + mg = max(max_sum_abs_g(g, cfg.chunk_size) for g in gs) + print(f"\n=== init preset={preset} (C={cfg.chunk_size}) ===") + print(f" |g| mean/step {sum(g.abs().mean().item() for g in gs)/len(gs):.4f} " + f"max chunk sum|g| {mg:.3f} fp32 余量 {LIM['fp32/bf16']/max(mg,1e-9):.0f}x") diff --git a/notes/verify_l2norm_stability.py b/notes/verify_l2norm_stability.py new file mode 100644 index 0000000..38ba085 --- /dev/null +++ b/notes/verify_l2norm_stability.py @@ -0,0 +1,83 @@ +"""验证: 不做 L2-norm 时 KDA 的爆炸是"数学上真实"还是"浮点误差"。 + +判据: 逐步递推 (naive_kda_fwd, 无三角求解) 在 float64 下的输出。 + - 若 fp64 递推也 ~1e32 => 爆炸是 KDA 递推本身的数学性质 + - 若 fp64 递推 O(1) 而 chunkwise 爆炸 => 是 solve 的浮点失效 +""" +import torch +import torch.nn.functional as F + +from kda.ops.reference.chunkwise import naive_chunk_kda +from kda.ops.reference.recurrent import naive_kda_fwd + +torch.manual_seed(0) +dev = "cuda" +B, T, H, K, V = 1, 64, 1, 16, 32 +C = 64 + + +def make(norm: bool, dtype): + g0 = torch.Generator(device=dev).manual_seed(0) + q = torch.randn(B, T, H, K, device=dev, dtype=dtype, generator=g0) + k = torch.randn(B, T, H, K, device=dev, dtype=dtype, generator=g0) + v = torch.randn(B, T, H, V, device=dev, dtype=dtype, generator=g0) + # 训练里 g = -A.exp()*softplus(...) < 0，量级温和 + g = -F.softplus(torch.randn(B, T, H, K, device=dev, dtype=dtype, generator=g0)) * 0.1 + beta = torch.rand(B, T, H, device=dev, dtype=dtype, generator=g0) + if norm: + q, k = F.normalize(q, dim=-1), F.normalize(k, dim=-1) + return q, k, v, g, beta + + +def amax(x): + return x.abs().max().item() + + +print(f"{'norm':>5} | {'‖k‖':>6} | {'rec fp64':>10} | {'rec fp32':>10} | " + f"{'chunk fp64':>10} | {'chunk fp32':>10} | {'rel(chunk64,rec64)':>18}") +print("-" * 100) + +for norm in (True, False): + row = {} + for dt in (torch.float64, torch.float32): + q, k, v, g, beta = make(norm, dt) + o_rec, _ = naive_kda_fwd(q, k, v, g, beta) + o_chk, _ = naive_chunk_kda(q, k, v, g, beta, chunk_size=C) + row[dt] = (amax(o_rec), amax(o_chk), o_chk, o_rec) + knorm = make(norm, torch.float64)[1].norm(dim=-1).mean().item() + r64, c64, oc64, or64 = row[torch.float64] + r32, c32, oc32, _ = row[torch.float32] + rel = ((oc64 - or64).abs().max() / (or64.abs().max() + 1e-30)).item() + print(f"{str(norm):>5} | {knorm:6.2f} | {r64:10.3e} | {r32:10.3e} | " + f"{c64:10.3e} | {c32:10.3e} | {rel:18.3e}") + +# ---- M 的结构诊断 ---- +print("\n=== M = I + tril(A_kk*beta, -1) 诊断 (float64) ===") +print(f"{'norm':>5} | {'max|N|':>9} | {'‖M⁻¹‖∞':>10} | {'cond2(M)':>10} | {'|S_final|max':>12}") +for norm in (True, False): + q, k, v, g, beta = make(norm, torch.float64) + gc = g.cumsum(dim=1)[0, :, 0] # [T,K] + kk = k[0, :, 0] # [T,K] + gref = gc[:1] + A = (kk * (gc - gref).exp()) @ (kk * (gref - gc).exp()).T + N = (A * beta[0, :, 0][None, :]).tril(-1) + M = torch.eye(T, dtype=torch.float64, device=dev) + N + Minv = torch.linalg.inv(M) + _, Sf = naive_kda_fwd(q, k, v, g, beta, output_final_state=True) + print(f"{str(norm):>5} | {amax(N):9.3e} | {Minv.abs().sum(1).max():10.3e} | " + f"{torch.linalg.cond(M).item():10.3e} | {amax(Sf):12.3e}") + +# ---- ‖M⁻¹‖ 随 chunk 长度的增长 ---- +print("\n=== ‖M⁻¹‖∞ vs chunk 长度 C (float64) ===") +for norm in (True, False): + q, k, v, g, beta = make(norm, torch.float64) + gc = g.cumsum(dim=1)[0, :, 0] + kk = k[0, :, 0] + out = [] + for C_ in (4, 8, 16, 32, 64): + gs, ks, bs = gc[:C_], kk[:C_], beta[0, :C_, 0] + gref = gs[:1] + A = (ks * (gs - gref).exp()) @ (ks * (gref - gs).exp()).T + M = torch.eye(C_, dtype=torch.float64, device=dev) + (A * bs[None, :]).tril(-1) + out.append(f"C={C_:2d}:{torch.linalg.inv(M).abs().sum(1).max():.2e}") + print(f" norm={str(norm):>5} " + " ".join(out)) diff --git a/notes/verify_severity_sweep.py b/notes/verify_severity_sweep.py new file mode 100644 index 0000000..0013d0b --- /dev/null +++ b/notes/verify_severity_sweep.py @@ -0,0 +1,72 @@ +"""严重度扫描: 把"数学爆炸"和"两条路径分道扬镳"分开测。 + +对每个 k 缩放系数 s: + rec64 = 逐步递推 float64 (无三角求解) -> 数学真值 + chk64 = chunkwise float64 (全局 solve) + chk32 = chunkwise float32 + tri32 = FLA/vendored triton 16x16 分块路径 (float32 in/out) +""" +import torch +import torch.nn.functional as F + +from kda.ops.reference.chunkwise import naive_chunk_kda +from kda.ops.reference.recurrent import naive_kda_fwd + +dev = "cuda" +B, T, H, K, V = 1, 64, 1, 16, 32 + +try: + from kda.ops.triton.chunk import chunk_kda as triton_chunk_kda +except Exception as e: # pragma: no cover + triton_chunk_kda = None + print("triton backend unavailable:", e) + + +def make(scale_k, dtype): + gen = torch.Generator(device=dev).manual_seed(0) + q = torch.randn(B, T, H, K, device=dev, dtype=dtype, generator=gen) + k = torch.randn(B, T, H, K, device=dev, dtype=dtype, generator=gen) + v = torch.randn(B, T, H, V, device=dev, dtype=dtype, generator=gen) + g = -F.softplus(torch.randn(B, T, H, K, device=dev, dtype=dtype, generator=gen)) * 0.1 + beta = torch.rand(B, T, H, device=dev, dtype=dtype, generator=gen) + return q, F.normalize(k, dim=-1) * scale_k, v, g, beta + + +def amax(x): + return x.abs().max().item() + + +hdr = (f"{'‖k‖':>6} | {'max|N|':>9} | {'‖M⁻¹‖∞':>10} | {'rec fp64':>10} | " + f"{'chk fp64':>10} | {'chk fp32':>10} | {'tri fp32':>10} | " + f"{'rel(chk64/rec64)':>16} | {'rel(chk32/chk64)':>16} | {'rel(tri32/chk32)':>16}") +print(hdr) +print("-" * len(hdr)) + +for s in (1.0, 1.5, 2.0, 2.5, 3.0, 4.0, 5.0): + q, k, v, g, beta = make(s, torch.float64) + o_rec, _ = naive_kda_fwd(q, k, v, g, beta) + o_c64, _ = naive_chunk_kda(q, k, v, g, beta, chunk_size=64) + + q3, k3, v3, g3, b3 = [x.float() for x in (q, k, v, g, beta)] + o_c32, _ = naive_chunk_kda(q3, k3, v3, g3, b3, chunk_size=64) + if triton_chunk_kda is not None: + o_t32, _ = triton_chunk_kda(q3, k3, v3, g3, b3, chunk_size=64) + o_t32 = o_t32.double() + else: + o_t32 = torch.full_like(o_c64, float("nan")) + + gc = g.cumsum(dim=1)[0, :, 0] + kk = k[0, :, 0] + gref = gc[:1] + A = (kk * (gc - gref).exp()) @ (kk * (gref - gc).exp()).T + M = torch.eye(T, dtype=torch.float64, device=dev) + (A * beta[0, :, 0][None, :]).tril(-1) + minv = torch.linalg.inv(M).abs().sum(1).max().item() + + def rel(a, b): + return ((a - b).abs().max() / (b.abs().max() + 1e-300)).item() + + print(f"{k.norm(dim=-1).mean():6.2f} | {amax((A * beta[0, :, 0][None, :]).tril(-1)):9.2e} | " + f"{minv:10.2e} | {amax(o_rec):10.2e} | {amax(o_c64):10.2e} | " + f"{amax(o_c32):10.2e} | {amax(o_t32):10.2e} | " + f"{rel(o_c64, o_rec):16.2e} | {rel(o_c32.double(), o_c64):16.2e} | " + f"{rel(o_t32, o_c32.double()):16.2e}") diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..afe1d05 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,43 @@ +[project] +name = "kda" +version = "0.0.1" +description = "Hand-written KDA implementation from naive recurrent to fused Triton + training + inference" +requires-python = ">=3.10" + +# kda 是独立 uv 项目 (自带 uv.lock + .venv), 自声明运行依赖, 不依赖仓库根环境 +dependencies = [ + "torch>=2.9.0", # uv 解析最新满足版 (含 CUDA 构建); 与根环境 2.9.0+cu128 仅下限一致 + "einops>=0.7.0", # kda/ops/reference/chunkwise.py 的 rearrange + "packaging>=23.0", # vendored FLA utils version checks + "sentencepiece>=0.2.0", # toy SentencePiece + "datasets>=3.0.0", # 中文 wiki 语料加载 + "transformers>=4.51.0", # Qwen3 tokenizer for 0.5b preset + "swanlab>=0.9.7", +] + +# 训练机 / Docker 镜像: uv sync --extra train +[project.optional-dependencies] +train = [ + "swanlab>=0.6.0", + "sacrebleu>=2.4.0", + "langdetect>=1.0.9", +] + +# uv run / uv sync 默认安装 dev group +[dependency-groups] +dev = [ + "pytest>=7.0", + "torchlens>=2.34", # 计算图展开集成测试; 未装时测试模块自动 skip + "tensorlens>=0.0.3", # Flask viewer 集成测试; 未装时测试模块自动 skip +] + +[tool.uv] +# 公共入口: `from kda import CausalLM, KDAConfig, K3Config, chunk_kda` +package = true + +[build-system] +requires = ["hatchling"] +build-backend = "hatchling.build" + +[tool.hatch.build.targets.wheel] +packages = ["kda"] diff --git a/pyrightconfig.json b/pyrightconfig.json new file mode 100644 index 0000000..6b6ad4e --- /dev/null +++ b/pyrightconfig.json @@ -0,0 +1,17 @@ +{ + "include": ["kda", "tests", "train.py", "train_k3.py", "train_sft.py"], + "exclude": ["**/__pycache__", ".venv", "ckpts", ".pytest_cache"], + "venvPath": ".", + "venv": ".venv", + "pythonVersion": "3.12", + "pythonPlatform": "Linux", + "typeCheckingMode": "standard", + "reportUnknownMemberType": false, + "reportUnknownArgumentType": false, + "reportUnknownVariableType": false, + "reportUnknownParameterType": false, + "reportUnknownLambdaType": false, + "reportMissingTypeStubs": false, + "reportMissingModuleSource": "none", + "reportAny": false +} diff --git a/scripts/container_help.py b/scripts/container_help.py new file mode 100644 index 0000000..6e04ecf --- /dev/null +++ b/scripts/container_help.py @@ -0,0 +1,43 @@ +"""Default container command: print usage and refuse to start a silent toy run.""" + +from __future__ import annotations + +HELP = """kda image: one GPU runtime for train / monitor / eval. + +Do not docker run without a command — toy overfit is not the default. + +Volumes (host → container): + ./ckpts → /workspace/kda/ckpts + pretrain corpus → /data/pretrain (ro) + SFT bitext → /data/sft (ro) + frozen eval → /data/eval (ro) + HF tokenizer cache → /cache/huggingface + SWANLAB_API_KEY via -e (never COPY into the image) + +Examples: + docker run --rm --gpus all kda: python train.py + docker run --rm --gpus all -e SWANLAB_API_KEY \\ + -v "$PWD/ckpts:/workspace/kda/ckpts" \\ + kda: python train_k3.py --preset toy --attnres off + docker run --rm --gpus all kda: swanlab ping + docker run --rm --gpus all \\ + -v "$PWD/ckpts:/workspace/kda/ckpts" -v "$PWD/data/eval:/data/eval:ro" \\ + kda: python -m kda.training.eval_mt \\ + --ckpt /workspace/kda/ckpts/k3_wiki.pt \\ + --src /data/eval/zh2en.src.txt --ref /data/eval/zh2en.ref.txt \\ + --target-lang en + docker run --rm --gpus all \\ + -v "$PWD/ckpts:/workspace/kda/ckpts" -v "$PWD/data/sft:/data/sft:ro" \\ + kda: python train_sft.py --ckpt /workspace/kda/ckpts/k3_wiki.pt \\ + --data /data/sft/toy.jsonl + docker run --rm --gpus all kda: python -m pytest -q +""" + + +def main() -> None: + print(HELP) + raise SystemExit(2) + + +if __name__ == "__main__": + main() diff --git a/scripts/export_flores.py b/scripts/export_flores.py new file mode 100644 index 0000000..376a2d6 --- /dev/null +++ b/scripts/export_flores.py @@ -0,0 +1,61 @@ +"""Export FLORES-200 zh↔en into line-aligned src/ref files for eval_mt. + +Corpus is not committed. Typical: + + uv run python scripts/export_flores.py --out data/eval +""" + +from __future__ import annotations + +import argparse +from pathlib import Path + + +def _write(path: Path, lines: list[str]) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text("\n".join(lines) + "\n", encoding="utf-8") + + +def main() -> None: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--out", default="data/eval") + p.add_argument("--split", default="devtest", choices=["dev", "devtest"]) + args = p.parse_args() + + from datasets import load_dataset + + ds = None + err: Exception | None = None + for config in ("eng_Latn-zho_Hans", "default"): + try: + ds = load_dataset("facebook/flores", config, split=args.split) + break + except Exception as exc: # noqa: BLE001 — try the next config name + err = exc + if ds is None: + raise SystemExit(f"could not load facebook/flores ({err})") + + cols = set(ds.column_names) + en_key = next( + (c for c in ("sentence_eng_Latn", "eng_Latn", "sentence_en") if c in cols), + None, + ) + zh_key = next( + (c for c in ("sentence_zho_Hans", "zho_Hans", "sentence_zh") if c in cols), + None, + ) + if en_key is None or zh_key is None: + raise SystemExit(f"FLORES columns not found: {sorted(cols)}") + + en = [row[en_key].strip() for row in ds] + zh = [row[zh_key].strip() for row in ds] + out = Path(args.out) + _write(out / "flores.zh2en.src.txt", zh) + _write(out / "flores.zh2en.ref.txt", en) + _write(out / "flores.en2zh.src.txt", en) + _write(out / "flores.en2zh.ref.txt", zh) + print(f"wrote {len(zh)} pairs under {out}/flores.*.txt") + + +if __name__ == "__main__": + main() diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..c5bab3c --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +# allow running tests from package root diff --git a/tests/correctness/__init__.py b/tests/correctness/__init__.py new file mode 100644 index 0000000..b6827a0 --- /dev/null +++ b/tests/correctness/__init__.py @@ -0,0 +1 @@ +"""Reference correctness tests.""" diff --git a/tests/correctness/test_api.py b/tests/correctness/test_api.py new file mode 100644 index 0000000..431c77d --- /dev/null +++ b/tests/correctness/test_api.py @@ -0,0 +1,107 @@ +"""Public backend selection must be explicit and reproducible.""" +import pytest +import torch + +from kda.models.config import KDAConfig +from kda.ops.api import chunk_kda + + +def _inputs(): + torch.manual_seed(41) + B, T, H, HV, K, V = 1, 4, 1, 2, 2, 2 + return ( + torch.randn(B, T, H, K), + torch.randn(B, T, H, K), + torch.randn(B, T, HV, V), + -torch.rand(B, T, HV, K), + torch.rand(B, T, HV), + ) + + +def test_default_config_enables_kernel_side_transforms(): + config = KDAConfig() + assert config.use_gate_in_kernel + assert config.use_qk_l2norm_in_kernel + assert config.use_beta_sigmoid_in_kernel + assert config.lower_bound == -5.0 + + +def test_default_backend_is_local_reference(): + inputs = _inputs() + default_output, _ = chunk_kda(*inputs, chunk_size=4, use_qk_l2norm_in_kernel=True) + reference_output, _ = chunk_kda(*inputs, chunk_size=4, backend="reference", use_qk_l2norm_in_kernel=True) + torch.testing.assert_close(default_output, reference_output) + assert KDAConfig().kda_backend == "reference" + + +def test_auto_is_deprecated_reference_alias(): + inputs = _inputs() + with pytest.warns(DeprecationWarning, match="selects the local reference backend"): + auto_output, _ = chunk_kda(*inputs, chunk_size=4, backend="auto", use_qk_l2norm_in_kernel=True) + reference_output, _ = chunk_kda(*inputs, chunk_size=4, backend="reference", use_qk_l2norm_in_kernel=True) + torch.testing.assert_close(auto_output, reference_output) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +def test_triton_backend_matches_reference(): + torch.manual_seed(41) + B, T, H, HV, K, V = 2, 64, 4, 8, 32, 32 + device = "cuda" + q = torch.randn(B, T, H, K, device=device) + k = torch.randn(B, T, H, K, device=device) + v = torch.randn(B, T, HV, V, device=device) + g = -torch.rand(B, T, HV, K, device=device) + beta = torch.rand(B, T, HV, device=device) + triton_out, _ = chunk_kda( + q, k, v, g, beta, chunk_size=64, backend="triton", use_qk_l2norm_in_kernel=True + ) + reference_out, _ = chunk_kda( + q, k, v, g, beta, chunk_size=64, backend="reference", use_qk_l2norm_in_kernel=True + ) + torch.testing.assert_close( + triton_out.float(), reference_out.float(), atol=2e-3, rtol=2e-3 + ) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +def test_unnormalized_parity_is_not_meaningful(): + """对拍必须用与训练一致的 q/k L2-norm (守卫测试). + + M = I + tril(A_kk*beta, -1) 是单位下三角, 行列式恒为 1、特征值全为 1, + 不存在"失去对角占优"一说. 真正的机制是 M = I + N 中 N 严格下三角 (幂零), + 故 M^-1 = sum_{m1, 精确解本身就以 ‖k‖^chunk_size 增长 (实测输出 1e31 量级 + 乃至 inf). 这不是某个后端的缺陷: fp64 逐步递推 (完全不含三角求解) 与分块 + 形式吻合到 8e-15, 说明爆炸就是该递推的真值. 两个后端的相对误差其实很接近 + (1.3e-3 → 2.6e-2), 但绝对差随输出量级一起走, 任何一致性容差都必然失败. + """ + torch.manual_seed(41) + B, T, H, HV, K, V = 2, 64, 4, 8, 32, 32 + device = "cuda" + q = torch.randn(B, T, H, K, device=device) # 故意不归一化 + k = torch.randn(B, T, H, K, device=device) + v = torch.randn(B, T, HV, V, device=device) + g = -torch.rand(B, T, HV, K, device=device) + beta = torch.rand(B, T, HV, device=device) + + with pytest.warns(RuntimeWarning, match="use_qk_l2norm_in_kernel=False"): + triton_out, _ = chunk_kda(q, k, v, g, beta, chunk_size=64, backend="triton") + reference_out, _ = chunk_kda(q, k, v, g, beta, chunk_size=64, backend="reference") + + diff = (triton_out.float() - reference_out.float()).abs().max() + assert not torch.isfinite(diff).item() or diff.item() > 1e-2, ( + f"unnormalised parity diff {diff.item():.3e} should be pathological" + ) + + +def test_triton_backend_does_not_import_upstream_fla(): + import sys + + for name in list(sys.modules): + if name == "fla" or name.startswith("fla."): + sys.modules.pop(name) + from kda.ops.triton.chunk import chunk_kda as _triton_chunk_kda # noqa: F401 + + assert not any(n == "fla" or n.startswith("fla.") for n in sys.modules) diff --git a/tests/correctness/test_chunkwise.py b/tests/correctness/test_chunkwise.py new file mode 100644 index 0000000..4fe64a3 --- /dev/null +++ b/tests/correctness/test_chunkwise.py @@ -0,0 +1,47 @@ +"""L2: chunked naive vs L1 naive (fwd) + gradcheck.""" +import torch + +from kda.ops.reference.chunkwise import naive_chunk_kda +from kda.ops.reference.recurrent import naive_kda + + +def test_fwd_matches_naive(): + """chunked vs naive fwd, atol=1e-4.""" + B, T, H, HV, K, V = 2, 32, 2, 4, 8, 8 + torch.manual_seed(1) + q = torch.randn(B, T, H, K, dtype=torch.float64) + k = torch.randn(B, T, H, K, dtype=torch.float64) + v = torch.randn(B, T, HV, V, dtype=torch.float64) + g = torch.randn(B, T, HV, K, dtype=torch.float64) * 0.1 + beta = torch.rand(B, T, HV, dtype=torch.float64) + + o_ref, _ = naive_kda(q, k, v, g, beta, output_final_state=True) + o_chk, _ = naive_chunk_kda(q, k, v, g, beta, + output_final_state=True, chunk_size=8) + + diff = (o_ref - o_chk).abs().max().item() + assert diff < 1e-4, f"chunk vs naive fwd max diff {diff:.2e} > 1e-4" + print(f"L2 fwd-vs-naive: PASSED (max diff {diff:.2e})") + + +def test_gradcheck(): + """L2 chunked gradcheck atol=1e-4 (allowing rtol 1e-3 for triangular path).""" + B, T, H, HV, K, V = 2, 16, 2, 4, 4, 4 + torch.manual_seed(2) + q = torch.randn(B, T, H, K, dtype=torch.float64, requires_grad=True) + k = torch.randn(B, T, H, K, dtype=torch.float64, requires_grad=True) + v = torch.randn(B, T, HV, V, dtype=torch.float64, requires_grad=True) + g = torch.randn(B, T, HV, K, dtype=torch.float64, requires_grad=True) * 0.1 + beta = torch.rand(B, T, HV, dtype=torch.float64, requires_grad=True) + + assert torch.autograd.gradcheck( + lambda q, k, v, g, b: naive_chunk_kda(q, k, v, g, b, chunk_size=4)[0], + (q, k, v, g, beta), + eps=1e-6, atol=1e-4, rtol=1e-3, + ), "L2 gradcheck 失败" + print("L2 gradcheck: PASSED") + + +if __name__ == "__main__": + test_fwd_matches_naive() + test_gradcheck() diff --git a/tests/correctness/test_decay_span.py b/tests/correctness/test_decay_span.py new file mode 100644 index 0000000..2136164 --- /dev/null +++ b/tests/correctness/test_decay_span.py @@ -0,0 +1,99 @@ +"""Saturated gates must not overflow the reference ``exp``. + +The existing kernel tests draw ``g = -rand(...)``, which keeps the per-chunk +gate span near 1 per step and never exercises the exponent budget. A trained +``safe_gate`` model pins whole channels at ``|g| = |lower_bound|`` for a full +chunk, which used to overflow ``_decayed_dot`` and produce NaN for any +``chunk_size > 16``. +""" +import pytest +import torch + +from kda.ops.api import chunk_kda +from kda.ops.reference.chunkwise import DECAY_BLOCK, _EXP_LIMIT + +LOWER_BOUND = -5.0 + + +def _saturated_inputs(T, device="cpu", seed=0): + """Gates pinned at ``lower_bound`` on one channel, mild elsewhere.""" + torch.manual_seed(seed) + B, H, K, V = 1, 2, 8, 8 + q = torch.randn(B, T, H, K, device=device) + k = torch.randn(B, T, H, K, device=device) + v = torch.randn(B, T, H, V, device=device) + g = -torch.rand(B, T, H, K, device=device) * 0.1 + g[..., 0] = LOWER_BOUND # fully saturated channel, the worst case + beta = torch.rand(B, T, H, device=device) + return q, k, v, g, beta + + +def _run(inputs, **kwargs): + kwargs.setdefault("use_qk_l2norm_in_kernel", True) + return chunk_kda(*inputs, **kwargs)[0] + + +@pytest.mark.parametrize("chunk_size", [16, 32, 64]) +def test_saturated_gate_does_not_overflow(chunk_size): + out = _run(_saturated_inputs(128), chunk_size=chunk_size) + assert torch.isfinite(out).all(), f"NaN/inf at chunk_size={chunk_size}" + + +def test_chunk_size_does_not_change_the_result(): + """Chunking is exact algebra, so every chunk_size must agree.""" + inputs = _saturated_inputs(128) + base = _run(inputs, chunk_size=16) + for chunk_size in (32, 64): + other = _run(inputs, chunk_size=chunk_size) + torch.testing.assert_close(other, base, atol=1e-5, rtol=1e-5) + + +def test_gradients_flow_through_the_row_blocks(): + """_decayed_dot writes its row blocks into a preallocated buffer.""" + q, k, v, g, beta = _saturated_inputs(64) + for t in (q, k, v, g, beta): + t.requires_grad_(True) + _run((q, k, v, g, beta), chunk_size=64).sum().backward() + for name, t in zip("qkvgb", (q, k, v, g, beta)): + assert t.grad is not None and torch.isfinite(t.grad).all(), name + + +def test_decay_block_fits_the_exp_budget(): + assert DECAY_BLOCK * abs(LOWER_BOUND) < _EXP_LIMIT + + +def test_lower_bound_beyond_the_budget_is_rejected(): + too_deep = -(_EXP_LIMIT / DECAY_BLOCK) - 1.0 + with pytest.raises(ValueError, match="overflows exp"): + _run( + _saturated_inputs(32), + chunk_size=32, + safe_gate=True, + lower_bound=too_deep, + use_gate_in_kernel=True, + A_log=torch.zeros(2), + ) + + +@pytest.mark.parametrize("chunk_size", [32, 50, 64]) +def test_unbounded_gate_past_the_budget_warns(chunk_size): + """safe_gate is bounded, but ``-A.exp() * softplus(x)`` is not.""" + q, k, v, g, beta = _saturated_inputs(100) + g[...] = -(_EXP_LIMIT / DECAY_BLOCK) - 0.5 # just over the per-block budget + with pytest.warns(RuntimeWarning, match="gate span within a"): + _run((q, k, v, g, beta), chunk_size=chunk_size) + + +def test_unnormalised_qk_warns(): + with pytest.warns(RuntimeWarning, match="Neumann series"): + _run(_saturated_inputs(32), chunk_size=32, use_qk_l2norm_in_kernel=False) + + +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") +def test_saturated_gate_matches_triton(): + inputs = _saturated_inputs(128, device="cuda") + reference = _run(inputs, chunk_size=64, backend="reference") + triton = _run(inputs, chunk_size=64, backend="triton") + torch.testing.assert_close( + triton.float(), reference.float(), atol=2e-3, rtol=2e-3 + ) diff --git a/tests/correctness/test_gate.py b/tests/correctness/test_gate.py new file mode 100644 index 0000000..d359abe --- /dev/null +++ b/tests/correctness/test_gate.py @@ -0,0 +1,52 @@ +"""KDA gate reference semantics and autograd coverage.""" +import torch +import torch.nn.functional as F + +from kda.ops.reference.gate import kda_gate_reference + + +def test_standard_gate_matches_formula(): + B, T, HV, K = 1, 32, 4, 8 + torch.manual_seed(10) + g = torch.randn(B, T, HV, K, dtype=torch.float64) + A_log = torch.randn(HV, dtype=torch.float64) * 0.5 + dt_bias = torch.randn(HV, K, dtype=torch.float64) * 0.1 + expected = -A_log[:, None].exp() * F.softplus(g + dt_bias) + actual = kda_gate_reference(g, A_log, dt_bias) + torch.testing.assert_close(actual, expected) + + +def test_safe_gate_matches_formula(): + B, T, HV, K = 1, 8, 2, 4 + torch.manual_seed(12) + g = torch.randn(B, T, HV, K, dtype=torch.float64) + A_log = torch.randn(HV, dtype=torch.float64) + dt_bias = torch.randn(HV, K, dtype=torch.float64) + lower_bound = -5.0 + expected = lower_bound * torch.sigmoid(A_log[:, None].exp() * (g + dt_bias)) + actual = kda_gate_reference( + g, A_log, dt_bias, safe_gate=True, lower_bound=lower_bound + ) + torch.testing.assert_close(actual, expected) + + +def test_bwd_gradcheck(): + B, T, HV, K = 1, 8, 2, 4 + torch.manual_seed(11) + g = torch.randn(B, T, HV, K, dtype=torch.float64, requires_grad=True) + A_log = torch.randn(HV, dtype=torch.float64, requires_grad=True) * 0.5 + dt_bias = torch.randn(HV, K, dtype=torch.float64, requires_grad=True) * 0.1 + A_log.requires_grad_(True) + dt_bias.requires_grad_(True) + + def fn(g, A_log, dt_bias): + return kda_gate_reference(g, A_log, dt_bias).sum() + + assert torch.autograd.gradcheck(fn, (g, A_log, dt_bias), eps=1e-6, atol=1e-4) + print("L5 bwd-gradcheck: PASSED") + + +if __name__ == "__main__": + test_standard_gate_matches_formula() + test_safe_gate_matches_formula() + test_bwd_gradcheck() diff --git a/tests/correctness/test_recurrent.py b/tests/correctness/test_recurrent.py new file mode 100644 index 0000000..e1de6dd --- /dev/null +++ b/tests/correctness/test_recurrent.py @@ -0,0 +1,34 @@ +"""L1: gradcheck for naive_recurrent_kda. + +验证策略: torch.autograd.gradcheck 走 forward+backward 五个梯度. +强制 dtype=float64; eps=1e-6, atol=1e-4. + +shape (small): + B=2, T=8, H=2, HV=4, K=4, V=4 +""" +import torch + +from kda.ops.reference.recurrent import naive_kda + + +def test_gradcheck(): + B, T, H, HV, K, V = 2, 8, 2, 4, 4, 4 + torch.manual_seed(0) + + # 所有输入都需要 requires_grad=True + q = torch.randn(B, T, H, K, dtype=torch.float64, requires_grad=True) + k = torch.randn(B, T, H, K, dtype=torch.float64, requires_grad=True) + v = torch.randn(B, T, HV, V, dtype=torch.float64, requires_grad=True) + g = torch.randn(B, T, HV, K, dtype=torch.float64, requires_grad=True) * 0.1 + beta = torch.rand(B, T, HV, dtype=torch.float64, requires_grad=True) + + assert torch.autograd.gradcheck( + lambda q, k, v, g, b: naive_kda(q, k, v, g, b, output_final_state=True), + (q, k, v, g, beta), + eps=1e-6, atol=1e-4, rtol=1e-3, + ), "L1 gradcheck 失败" + print("L1 gradcheck: PASSED") + + +if __name__ == "__main__": + test_gradcheck() diff --git a/tests/inference/__init__.py b/tests/inference/__init__.py new file mode 100644 index 0000000..7645a90 --- /dev/null +++ b/tests/inference/__init__.py @@ -0,0 +1 @@ +"""Incremental inference tests.""" diff --git a/tests/inference/test_recurrent_decode.py b/tests/inference/test_recurrent_decode.py new file mode 100644 index 0000000..9e2f068 --- /dev/null +++ b/tests/inference/test_recurrent_decode.py @@ -0,0 +1,30 @@ +"""L6: fused_recurrent decode matches naive recurrent.""" +import pytest +import torch + +from kda.ops.recurrent.fused import fused_recurrent_kda +from kda.ops.reference.recurrent import naive_kda + +pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") + + +def test_step_matches_naive(): + B, T, H, HV, K, V = 2, 32, 2, 4, 8, 8 + torch.manual_seed(20) + device = "cuda" + q = torch.randn(B, T, H, K, device=device) + k = torch.randn(B, T, H, K, device=device) + v = torch.randn(B, T, HV, V, device=device) + g = -torch.rand(B, T, HV, K, device=device) * 2 + beta = torch.rand(B, T, HV, device=device) + + o_naive, _ = naive_kda( + q.double(), k.double(), v.double(), g.double(), + beta.double(), output_final_state=False, + ) + o_step, _ = fused_recurrent_kda(q, k, v, g, beta, output_final_state=False) + torch.testing.assert_close(o_step.float(), o_naive.float(), rtol=2e-3, atol=2e-3) + + +if __name__ == "__main__": + test_step_matches_naive() diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py new file mode 100644 index 0000000..c12baa2 --- /dev/null +++ b/tests/integration/__init__.py @@ -0,0 +1 @@ +"""Model and training integration tests.""" diff --git a/tests/integration/test_attn_res.py b/tests/integration/test_attn_res.py new file mode 100644 index 0000000..4ca9271 --- /dev/null +++ b/tests/integration/test_attn_res.py @@ -0,0 +1,176 @@ +"""AttnRes depth mixer: switch, no double-register, causality, two-phase match.""" +from dataclasses import asdict + +import pytest +import torch + +from kda.layers.attn_res import BlockAttnResStack, FullAttnResStack, atomic_block_size +from kda.layers.kda_attn import KDAAttention +from kda.models.causal_lm import CausalLM +from kda.models.config import KDAConfig +from kda.models.k3_config import K3Config +from kda.training.toy import load_ckpt, save_ckpt + + +def _tiny_k3(**kwargs): + defaults = dict( + hidden_size=32, + num_hidden_layers=4, + num_heads=4, + head_dim=8, + chunk_size=4, + vocab_size=64, + moe_latent_size=16, + moe_d_ff=16, + n_routed=4, + top_k=2, + n_shared=1, + kv_lora_rank=8, + q_lora_rank=16, + qk_nope_head_dim=8, + v_head_dim=8, + ) + defaults.update(kwargs) + return K3Config(**defaults) + + +def test_default_attnres_is_off(): + cfg = K3Config(num_hidden_layers=2) + assert cfg.attnres == "off" + model = CausalLM(cfg) + assert model.mixer is None + assert isinstance(model.blocks[0].attn, KDAAttention) + + +def test_invalid_attnres_rejected(): + with pytest.raises(ValueError, match="attnres"): + K3Config(attnres="yes") + with pytest.raises(ValueError, match="attnres_block_size"): + K3Config(attnres="block", attnres_block_size=0) + + +@pytest.mark.parametrize("mode, stack_cls", [("block", BlockAttnResStack), ("full", FullAttnResStack)]) +def test_mixer_kind_and_atomic_count(mode, stack_cls): + cfg = _tiny_k3(attnres=mode, attnres_block_size=2) + model = CausalLM(cfg) + assert model.attnres == mode + assert isinstance(model.mixer, stack_cls) + assert len(model.mixer.layers) == 2 * cfg.num_hidden_layers + if mode == "block": + assert model.mixer.block_size == 4 # 2 DecoderBlocks × attn|ffn + + +def test_auto_block_size_targets_eight_blocks(): + assert atomic_block_size(24, None) == 6 # 3 DecoderBlocks × 2 + assert atomic_block_size(4, None) == 2 + assert atomic_block_size(93, None) == 24 # 12 DecoderBlocks × 2, K3 S=12 + + +def test_no_duplicate_parameter_ids(): + model = CausalLM(_tiny_k3(attnres="block")) + ids = [id(p) for p in model.parameters()] + assert len(ids) == len(set(ids)) + names = [n for n, _ in model.named_parameters()] + assert len(names) == len(set(names)) + residual_names = [n for n in names if "residuals" in n or "final_residual" in n] + assert residual_names + block_names = [n for n in names if n.startswith("blocks.")] + mixer_weight_names = [ + n for n in names if n.startswith("mixer.layers.") and "query" not in n and "norm" not in n + ] + assert block_names + assert mixer_weight_names == [] + + +def test_off_and_block_differ_at_same_seed(): + torch.manual_seed(0) + off = CausalLM(_tiny_k3(attnres="off")) + torch.manual_seed(0) + on = CausalLM(_tiny_k3(attnres="block")) + x = torch.tensor([[1, 2, 3, 4]]) + with torch.no_grad(): + assert not torch.allclose(off(x), on(x)) + + +def test_block_two_phase_matches_naive(): + torch.manual_seed(4) + model = CausalLM(_tiny_k3(attnres="block", attnres_block_size=2)).eval() + x = torch.randint(0, 64, (2, 8)) + with torch.no_grad(): + emb = model.embedding(x) + naive = model.mixer.forward_naive(emb) + two_phase = model.mixer(emb) + torch.testing.assert_close(naive, two_phase, atol=1e-5, rtol=1e-5) + + +@pytest.mark.parametrize("mode", ["block", "full"]) +def test_attnres_is_still_causal(mode): + torch.manual_seed(51) + model = CausalLM(_tiny_k3(attnres=mode, num_hidden_layers=2)).eval() + with torch.no_grad(): + a = model(torch.tensor([[1, 2, 3, 4]])) + b = model(torch.tensor([[1, 2, 3, 9]])) + torch.testing.assert_close(a[:, :3], b[:, :3], atol=1e-5, rtol=1e-5) + + +def test_kda_config_block_runs(): + cfg = KDAConfig( + hidden_size=16, + num_hidden_layers=2, + num_heads=2, + num_value_heads=2, + head_dim=4, + chunk_size=4, + vocab_size=32, + intermediate_size=32, + attnres="block", + attnres_block_size=1, + ) + model = CausalLM(cfg) + logits = model(torch.tensor([[1, 2, 3, 4]])) + assert logits.shape == (1, 4, 32) + + +def test_attnres_ckpt_roundtrip(tmp_path): + cfg = _tiny_k3(attnres="block", attnres_block_size=2) + model = CausalLM(cfg) + path = str(tmp_path / "attnres.pt") + save_ckpt(model, cfg, path) + loaded, loaded_cfg = load_ckpt(path) + assert loaded_cfg.attnres == "block" + assert loaded_cfg.attnres_block_size == 2 + torch.manual_seed(1) + x = torch.randint(0, cfg.vocab_size, (2, 8)) + with torch.no_grad(): + torch.testing.assert_close(model(x), loaded(x), atol=1e-5, rtol=1e-5) + + +def test_old_ckpt_without_attnres_stays_off(tmp_path): + cfg = _tiny_k3() + payload = asdict(cfg) + payload.pop("attnres") + payload.pop("attnres_block_size") + payload.pop("attnres_zero_init_queries") + payload.pop("attnres_final_aggregate") + model = CausalLM(cfg) + path = str(tmp_path / "legacy.pt") + torch.save({"model_state": model.state_dict(), "config": payload}, path) + _, loaded_cfg = load_ckpt(path) + assert loaded_cfg.attnres == "off" + assert loaded_cfg.attnres_block_size is None + + +def test_attnres_block_overfits_single_batch(): + torch.manual_seed(30) + cfg = _tiny_k3(attnres="block", num_hidden_layers=2, attnres_block_size=1) + model = CausalLM(cfg) + x = torch.randint(0, cfg.vocab_size, (2, 8)) + optim = torch.optim.AdamW(model.parameters(), lr=3e-3) + final = None + for _ in range(200): + optim.zero_grad() + loss = model(x, labels=x) + loss.backward() + optim.step() + final = loss.item() + assert final < 0.5, f"final loss {final:.4f} >= 0.5" diff --git a/tests/integration/test_causal_checkpoint.py b/tests/integration/test_causal_checkpoint.py new file mode 100644 index 0000000..78c8527 --- /dev/null +++ b/tests/integration/test_causal_checkpoint.py @@ -0,0 +1,64 @@ +import torch +import torch.nn.functional as F + +from kda.models.causal_lm import CausalLM +from kda.models.config import KDAConfig +from kda.models.k3_config import K3Config + + +def _tiny(): + torch.manual_seed(4) + cfg = KDAConfig( + hidden_size=16, + num_hidden_layers=2, + num_heads=2, + num_value_heads=2, + head_dim=4, + chunk_size=4, + vocab_size=32, + intermediate_size=32, + kda_backend="reference", + ) + return CausalLM(cfg), cfg + + +def test_ignore_index_skips_masked_positions(): + model, _ = _tiny() + tokens = torch.tensor([[1, 2, 3, 4]]) + labels = tokens.clone() + labels[:, 1:3] = -100 + with torch.no_grad(): + logits = model(tokens) + actual = model(tokens, labels=labels) + expected = F.cross_entropy( + logits[:, :-1].reshape(-1, logits.size(-1)), + labels[:, 1:].reshape(-1), + ignore_index=-100, + ) + torch.testing.assert_close(actual, expected) + + +def test_gradient_checkpointing_matches_eager_grad(): + torch.manual_seed(8) + tokens = torch.randint(0, 32, (2, 8)) + m1, cfg = _tiny() + m2 = CausalLM(cfg) + m2.load_state_dict(m1.state_dict()) + m2.gradient_checkpointing = True + m1.train() + m2.train() + l1 = m1(tokens, labels=tokens) + l2 = m2(tokens, labels=tokens) + torch.testing.assert_close(l1, l2, atol=1e-5, rtol=1e-5) + l1.backward() + l2.backward() + for p1, p2 in zip(m1.parameters(), m2.parameters()): + if p1.grad is None: + assert p2.grad is None + continue + torch.testing.assert_close(p1.grad, p2.grad, atol=1e-4, rtol=1e-4) + + +def test_0_5b_preset_enables_checkpointing(): + assert K3Config.preset("0.5b").gradient_checkpointing is True + assert K3Config.preset("toy").gradient_checkpointing is False diff --git a/tests/integration/test_causal_lm.py b/tests/integration/test_causal_lm.py new file mode 100644 index 0000000..2c5ecf4 --- /dev/null +++ b/tests/integration/test_causal_lm.py @@ -0,0 +1,78 @@ +"""Causal-language-model behavior independent of toy memorization.""" +import torch +import torch.nn.functional as F + +from kda.layers.kda_attn import KDAAttention +from kda.layers.swiglu import SwiGLUMLP +from kda.models.causal_lm import CausalLM +from kda.models.config import KDAConfig + + +def _model(): + torch.manual_seed(51) + config = KDAConfig( + hidden_size=16, + num_hidden_layers=1, + num_heads=2, + num_value_heads=2, + head_dim=4, + chunk_size=4, + vocab_size=32, + intermediate_size=32, + kda_backend="reference", + ) + return CausalLM(config).eval() + + +def test_kda_schedule_and_unified_stem(): + config = KDAConfig(num_hidden_layers=2) + assert config.layer_specs() == [("kda", "swiglu"), ("kda", "swiglu")] + model = CausalLM(config) + assert isinstance(model.blocks[0].attn, KDAAttention) + assert isinstance(model.blocks[0].ffn, SwiGLUMLP) + + +def test_future_token_does_not_change_past_logits(): + model = _model() + first = torch.tensor([[1, 2, 3, 4]]) + second = torch.tensor([[1, 2, 3, 9]]) + with torch.no_grad(): + first_logits = model(first) + second_logits = model(second) + torch.testing.assert_close(first_logits[:, :3], second_logits[:, :3]) + + +def test_attention_reads_operator_flags_from_config(): + torch.manual_seed(52) + config = KDAConfig( + hidden_size=16, + num_hidden_layers=1, + num_heads=2, + num_value_heads=2, + head_dim=4, + chunk_size=4, + vocab_size=32, + intermediate_size=32, + use_gate_in_kernel=False, + use_qk_l2norm_in_kernel=False, + use_beta_sigmoid_in_kernel=False, + lower_bound=None, + kda_backend="reference", + ) + model = CausalLM(config).eval() + with torch.no_grad(): + logits = model(torch.tensor([[1, 2, 3, 4]])) + assert logits.shape == (1, 4, 32) + + +def test_loss_is_shifted_next_token_cross_entropy(): + model = _model() + tokens = torch.tensor([[1, 2, 3, 4]]) + with torch.no_grad(): + logits = model(tokens) + actual = model(tokens, labels=tokens) + expected = F.cross_entropy( + logits[:, :-1].reshape(-1, logits.size(-1)), + tokens[:, 1:].reshape(-1), + ) + torch.testing.assert_close(actual, expected) diff --git a/tests/integration/test_ckpt_compat.py b/tests/integration/test_ckpt_compat.py new file mode 100644 index 0000000..bfe16d4 --- /dev/null +++ b/tests/integration/test_ckpt_compat.py @@ -0,0 +1,56 @@ +"""load_ckpt must read checkpoints written before the ffn/config renames.""" +from dataclasses import asdict + +import pytest +import torch + +from kda.models.config import KDAConfig +from kda.models.k3_config import K3Config +from kda.training.toy import load_ckpt, save_ckpt + + +def _legacy(state, new, old): + """Undo the ffn rename, reproducing a pre-rename checkpoint.""" + renamed = { + k.replace(f".{new}.", f".{old}.").replace(f".{new}_norm.", f".{old}_norm."): v + for k, v in state.items() + } + assert any(f".{old}." in k for k in renamed), "fixture renamed nothing" + return renamed + + +@pytest.mark.parametrize( + ("config", "old"), + [ + (KDAConfig(num_hidden_layers=2), "mlp"), # dense: was named .mlp + (K3Config(num_hidden_layers=2), "moe"), # K3: was named .moe + ], + ids=["kda-mlp", "k3-moe"], +) +def test_legacy_ffn_names_still_load(tmp_path, config, old): + from kda.models.causal_lm import CausalLM + + model = CausalLM(config) + path = str(tmp_path / "legacy.pt") + torch.save( + { + "model_state": _legacy(model.state_dict(), "ffn", old), + "config": asdict(config), + }, + path, + ) + + loaded, loaded_config = load_ckpt(path) + assert type(loaded_config) is type(config) + for name, want in model.state_dict().items(): + torch.testing.assert_close(loaded.state_dict()[name], want) + + +@pytest.mark.parametrize("config", [KDAConfig(num_hidden_layers=2), K3Config(num_hidden_layers=2)]) +def test_roundtrip_picks_the_right_config_class(tmp_path, config): + from kda.models.causal_lm import CausalLM + + path = str(tmp_path / "ckpt.pt") + save_ckpt(CausalLM(config), config, path) + _, loaded_config = load_ckpt(path) + assert loaded_config == config diff --git a/tests/integration/test_eval_files.py b/tests/integration/test_eval_files.py new file mode 100644 index 0000000..d752989 --- /dev/null +++ b/tests/integration/test_eval_files.py @@ -0,0 +1,23 @@ +from pathlib import Path + +from kda.training.eval_mt import _instruction +from kda.training.prompts import instruction_prompt + +_ROOT = Path(__file__).resolve().parents[2] / "data" / "eval" + + +def _lines(name: str) -> list[str]: + return [ln.strip() for ln in (_ROOT / name).read_text(encoding="utf-8").splitlines() if ln.strip()] + + +def test_frozen_eval_files_are_aligned(): + zh_src, zh_ref = _lines("zh2en.src.txt"), _lines("zh2en.ref.txt") + en_src, en_ref = _lines("en2zh.src.txt"), _lines("en2zh.ref.txt") + assert len(zh_src) == len(zh_ref) >= 16 + assert len(en_src) == len(en_ref) >= 16 + assert all("\t" not in s for s in zh_src + en_src) + + +def test_eval_instruction_is_the_sft_template(): + assert _instruction("q", "en") == instruction_prompt("q", "en") + assert _instruction("q", "zh") == instruction_prompt("q", "zh") diff --git a/tests/integration/test_eval_mt.py b/tests/integration/test_eval_mt.py new file mode 100644 index 0000000..1c2a795 --- /dev/null +++ b/tests/integration/test_eval_mt.py @@ -0,0 +1,44 @@ +"""translation_success and eval helpers (no GPU, no FLORES download).""" +from kda.training.success import translation_success + + +def test_empty_and_copy_fail(): + src = "人工智能的发展改变了世界。" + assert translation_success(src, "", ref="The development of AI changed the world.", target_lang="en") is False + assert translation_success(src, src, ref="The development of AI changed the world.", target_lang="en") is False + + +def test_wrong_language_fails(): + src = "The cat sat on the mat." + hyp = "The cat sat on the mat and smiled." + ref = "猫坐在垫子上。" + assert translation_success(src, hyp, ref, target_lang="zh") is False + + +def test_instruction_leak_fails(): + src = "Hello" + hyp = "翻译如下：你好" + assert translation_success(src, hyp, ref="你好", target_lang="zh") is False + + +def test_good_zh2en_passes(): + src = "今天天气很好。" + hyp = "The weather is very nice today." + ref = "The weather is very nice today." + assert translation_success(src, hyp, ref, target_lang="en") is True + + +def test_container_help_exits_2(): + import importlib.util + from pathlib import Path + + import pytest + + path = Path(__file__).resolve().parents[2] / "scripts" / "container_help.py" + spec = importlib.util.spec_from_file_location("container_help", path) + mod = importlib.util.module_from_spec(spec) + assert spec.loader is not None + spec.loader.exec_module(mod) + with pytest.raises(SystemExit) as ei: + mod.main() + assert ei.value.code == 2 diff --git a/tests/integration/test_k3_arch.py b/tests/integration/test_k3_arch.py new file mode 100644 index 0000000..d80f895 --- /dev/null +++ b/tests/integration/test_k3_arch.py @@ -0,0 +1,145 @@ +"""K3 架构复现测试: MLA 吸收等价, LatentMoE 路由, hybrid pattern, 因果性, overfit.""" +import torch +import torch.nn.functional as F +import pytest + +from kda.layers.kda_attn import KDAAttention +from kda.layers.latent_moe import LatentMoE +from kda.layers.mla import GatedMLA +from kda.models.causal_lm import CausalLM +from kda.models.k3_config import K3Config + + +def _mla(d=64, H=4, r=16, q_r=32, d_q=16, d_v=16): + torch.manual_seed(7) + return GatedMLA(d, H, r, q_r, d_q, d_v) + + +def _naive_mla(x, module: GatedMLA): + """解压版参考: 标准 attention (吸收版数学上应与它逐位一致).""" + B, T, _ = x.shape + H, r = module.num_heads, module.kv_up.in_features + c = module.kv_norm(module.kv_down(x)) # [B,T,r] + q = module.q_up(module.q_norm(module.q_down(x))).view(B, T, H, module.qk_nope_head_dim) + w = module.kv_up.weight + w_uk = w[: H * module.qk_nope_head_dim].view(H, module.qk_nope_head_dim, r) + w_uv = w[H * module.qk_nope_head_dim :].view(H, module.v_head_dim, r) + k = torch.einsum("btj,hvj->bthv", c, w_uk) # 解压 K + v = torch.einsum("btj,hvj->bthv", c, w_uv) # 解压 V + scores = torch.einsum("bthv,bshv->bhts", q, k) # [B,H,T,T] + mask = torch.triu(torch.ones(T, T, dtype=torch.bool), diagonal=1) + scores = scores.masked_fill(mask, float("-inf")) + attn = F.softmax(scores, dim=-1) + o = torch.einsum("bhts,bshv->bthv", attn, v) # [B,T,H,d_v] + o = o.reshape(B, T, H * module.v_head_dim) + gate = torch.sigmoid(module.gate(x)) + return module.o_proj(gate * o) + + +def test_mla_absorption_matches_unrolled(): + m = _mla().eval() + x = torch.randn(3, 12, 64) + with torch.no_grad(): + absorbed = m(x) + unrolled = _naive_mla(x, m) + torch.testing.assert_close(absorbed, unrolled, atol=1e-5, rtol=1e-5) + + +def test_mla_absorption_matches_unrolled_grad(): + """吸收版与解压版的梯度也应一致 (fwd+bwd 双重验证).""" + m1, m2 = _mla(), _mla() + m2.load_state_dict(m1.state_dict()) + x = torch.randn(2, 8, 64) + l1 = m1(x).square().mean() + l2 = _naive_mla(x, m2).square().mean() + l1.backward() + l2.backward() + for (n1, p1), (n2, p2) in zip(m1.named_parameters(), m2.named_parameters()): + torch.testing.assert_close(p1.grad, p2.grad, atol=1e-5, rtol=1e-5) + + +def test_hybrid_layer_pattern(): + cfg = K3Config(num_hidden_layers=4) + assert cfg.layer_types() == ["kda", "kda", "kda", "mla"] + cfg8 = K3Config(num_hidden_layers=8) + assert cfg8.layer_types() == ["kda", "kda", "kda", "mla"] * 2 + # 末层强制 MLA: L=5 → 层 3 MLA + 层 4 (末层) MLA + cfg5 = K3Config(num_hidden_layers=5) + assert cfg5.layer_types() == ["kda", "kda", "kda", "mla", "mla"] + assert cfg.layer_specs() == [("kda", "moe"), ("kda", "moe"), ("kda", "moe"), ("mla", "moe")] + model = CausalLM(K3Config(num_hidden_layers=4, hidden_size=32, moe_d_ff=16, moe_latent_size=16)) + assert isinstance(model.blocks[0].attn, KDAAttention) + assert isinstance(model.blocks[3].attn, GatedMLA) + assert isinstance(model.blocks[0].ffn, LatentMoE) + + +def test_preset_0_5b_schedule(): + cfg = K3Config.preset("0.5b") + assert cfg.hidden_size == 768 + assert cfg.num_heads * cfg.head_dim == cfg.hidden_size + assert cfg.num_hidden_layers == 24 + assert cfg.tie_word_embeddings + assert cfg.chunk_size == 64 + assert cfg.gradient_checkpointing is True + assert cfg.moe_latent_size == cfg.hidden_size // 2 + types = cfg.layer_types() + assert types.count("mla") == 6 + assert types[-1] == "mla" + assert cfg.layer_specs()[3] == ("mla", "moe") + + +def test_moe_router_activates_topk_only(): + from kda.layers.latent_moe import LatentMoE + torch.manual_seed(3) + moe = LatentMoE(hidden_size=32, latent_size=16, n_routed=8, top_k=2, n_shared=1, d_ff=24) + x = torch.randn(2, 6, 32) + with torch.no_grad(): + y = moe(x) + logits = moe.router(x) + topk = torch.topk(logits, moe.top_k, dim=-1) + z = moe.down(x) + # 手算: 只有 top-k 专家输出被加权, 再经 shared + up(norm(u)) + expected_u = torch.zeros(2, 6, moe.latent_size) + all_out = torch.stack([e(z) for e in moe.experts]) # [R,B,T,ℓ] + probs = F.softmax(topk.values, dim=-1) + for i in range(moe.top_k): + idx = topk.indices[:, :, i] + for b in range(2): + for t in range(6): + expected_u[b, t] += probs[b, t, i] * all_out[idx[b, t], b, t] + shared = torch.stack([e(x) for e in moe.shared]).sum(0) + expected_y = shared + moe.up(moe.norm(expected_u)) + torch.testing.assert_close(y, expected_y, atol=1e-5, rtol=1e-5) + assert moe.last_route_ids is not None + assert moe.last_route_ids.shape[-1] == moe.top_k + + +def test_k3_causal_future_does_not_change_past_logits(): + torch.manual_seed(51) + cfg = K3Config(hidden_size=64, num_hidden_layers=4, num_heads=4, head_dim=8, + chunk_size=4, vocab_size=64, moe_latent_size=32, moe_d_ff=24, + n_routed=8, kv_lora_rank=16, q_lora_rank=32, qk_nope_head_dim=8, v_head_dim=8) + m = CausalLM(cfg).eval() + with torch.no_grad(): + a = m(torch.tensor([[1, 2, 3, 4]])) + b = m(torch.tensor([[1, 2, 3, 9]])) + torch.testing.assert_close(a[:, :3], b[:, :3], atol=1e-6, rtol=0) + + +def test_k3_small_model_overfits_single_batch(): + """K3 混合架构单 batch overfit 冒烟: loss < 0.5 (收敛即架构可训).""" + torch.manual_seed(30) + cfg = K3Config(hidden_size=64, num_hidden_layers=2, num_heads=4, head_dim=8, + chunk_size=4, vocab_size=64, moe_latent_size=32, moe_d_ff=24, + n_routed=8, kv_lora_rank=16, q_lora_rank=32, qk_nope_head_dim=8, v_head_dim=8) + m = CausalLM(cfg) + x = torch.randint(0, cfg.vocab_size, (2, 16)) + optim = torch.optim.AdamW(m.parameters(), lr=3e-3) + final = None + for step in range(200): + optim.zero_grad() + loss = m(x, labels=x) + loss.backward() + optim.step() + final = loss.item() + assert final < 0.5, f"final loss {final:.4f} >= 0.5" diff --git a/tests/integration/test_pretrain_data.py b/tests/integration/test_pretrain_data.py new file mode 100644 index 0000000..8c05198 --- /dev/null +++ b/tests/integration/test_pretrain_data.py @@ -0,0 +1,53 @@ +import json +import sys + +import torch + +from kda.training.data import ( + chunk_ids, + fetch_wiki_texts, + interleave_balanced, + split_heldout, +) + + +def test_interleave_is_one_to_one_monolingual_blocks(): + a = list(range(10)) + b = list(range(100, 112)) + out = interleave_balanced(a, b, block=4) + assert out == [0, 1, 2, 3, 100, 101, 102, 103, 4, 5, 6, 7, 104, 105, 106, 107] + + +def test_split_heldout_keeps_at_least_one_train(): + chunks = torch.arange(10).view(10, 1, 1) + train, held = split_heldout(chunks, frac=0.01, min_heldout=1) + assert train.size(0) == 9 + assert held.size(0) == 1 + empty_train, empty_held = split_heldout(chunks[:1], frac=0.5) + assert empty_train.size(0) == 1 + assert empty_held.size(0) == 0 + + +def test_chunk_ids_drops_tail(): + ids = list(range(10)) + chunks = chunk_ids(ids, batch=2, seq_len=4) + assert chunks.shape == (1, 2, 4) + + +def test_wiki_cache_roundtrip(tmp_path, monkeypatch): + cache = tmp_path / "pretrain" + cache.mkdir() + path = cache / "wiki-zh-n2-limit3.jsonl" + path.write_text( + "\n".join(json.dumps({"text": f"article {i}"}) for i in range(3)) + "\n", + encoding="utf-8", + ) + + def _boom(*_a, **_k): + raise AssertionError("must not hit the network") + + fake = type(sys)("datasets") + fake.load_dataset = _boom + monkeypatch.setitem(sys.modules, "datasets", fake) + texts = fetch_wiki_texts(3, lang="zh", cache_dir=cache) + assert texts == ["article 0", "article 1", "article 2"] diff --git a/tests/integration/test_schedule.py b/tests/integration/test_schedule.py new file mode 100644 index 0000000..fe52ba9 --- /dev/null +++ b/tests/integration/test_schedule.py @@ -0,0 +1,21 @@ +from kda.training.schedule import lr_scale, total_opt_steps + + +def test_warmup_then_cosine_floor(): + assert abs(lr_scale(0, warmup=10, total_opt=100) - 0.1) < 1e-9 + assert abs(lr_scale(9, warmup=10, total_opt=100) - 1.0) < 1e-9 + assert abs(lr_scale(10, warmup=10, total_opt=100) - 1.0) < 1e-6 + end = lr_scale(99, warmup=10, total_opt=100) + assert abs(end - 0.1) < 1e-6 + + +def test_horizon_prefers_the_earlier_stop(): + # 8.2M tokens @ batch 2 seq 2048 acc 8 -> 250 opt + opt_from_tokens = total_opt_steps( + max_tokens=8_192_000, max_micro=10_000, batch=2, seq_len=2048, grad_acc=8 + ) + assert opt_from_tokens == 250 + opt_from_micro = total_opt_steps( + max_tokens=10**12, max_micro=2000, batch=2, seq_len=2048, grad_acc=8 + ) + assert opt_from_micro == 250 diff --git a/tests/integration/test_sft_data.py b/tests/integration/test_sft_data.py new file mode 100644 index 0000000..c7b1897 --- /dev/null +++ b/tests/integration/test_sft_data.py @@ -0,0 +1,52 @@ +from kda.training.data import IGNORE_INDEX, collate_sft, encode_sft_row, load_sft_rows +from kda.training.prompts import instruction_prompt + + +class _Tok: + vocab_size = 32 + + def encode(self, text: str) -> list[int]: + return [min((ord(c) % 30) + 1, 31) for c in text[:12]] or [1] + + def decode(self, ids: list[int]) -> str: + return "x" * len(ids) + + +def test_instruction_matches_eval_template(): + assert instruction_prompt("你好", "en") == "Translate to English:\n你好" + assert instruction_prompt("Hello", "zh") == "Translate to Chinese:\nHello" + + +def test_prompt_tokens_are_ignored(): + tok = _Tok() + src, tgt = "ab", "cd" + ids, labels = encode_sft_row(tok, src, tgt, "en", max_len=64) + prompt_n = len(tok.encode(instruction_prompt(src, "en"))) + assert labels[:prompt_n] == [IGNORE_INDEX] * prompt_n + assert all(v != IGNORE_INDEX for v in labels[prompt_n:]) + assert ids[prompt_n:] == tok.encode(tgt) + + +def test_collate_and_jsonl(tmp_path): + path = tmp_path / "tiny.jsonl" + path.write_text( + '{"src": "a", "tgt": "b", "target_lang": "en"}\n' + '{"src": "c", "tgt": "d", "target_lang": "zh"}\n', + encoding="utf-8", + ) + rows = load_sft_rows(path) + assert len(rows) == 2 + x, y = collate_sft(rows, _Tok(), max_len=32) + assert x.shape == y.shape + assert x.size(0) == 2 + assert (y == IGNORE_INDEX).any() + + +def test_toy_sft_file_parses(): + from pathlib import Path + + path = Path(__file__).resolve().parents[2] / "data" / "sft" / "toy.jsonl" + rows = load_sft_rows(path) + assert len(rows) >= 20 + langs = {r["target_lang"] for r in rows} + assert langs == {"en", "zh"} diff --git a/tests/integration/test_tensorlens.py b/tests/integration/test_tensorlens.py new file mode 100644 index 0000000..450128b --- /dev/null +++ b/tests/integration/test_tensorlens.py @@ -0,0 +1,193 @@ +"""TensorLens 集成测试: KDA 模型张量 -> trace -> 全局 store -> Flask 端点. + +依赖: tensorlens (未安装时整个模块 skip, 用 `pip install tensorlens` 启用). + +测三层: +1. trace — KDA 前向的真实 1D/2D/3D 张量 (embed/block 输出/logits/权重) + 规范化成 int8 存入 tensorlens 全局 store +2. normalize — 四种策略 (clip/minmax/zscore/none) 的边界行为 +3. HTTP — 用 Flask test_client 验证 /api/list_tensors 与 /api/get_tensor, + 不启动阻塞的 gunicorn server (viewer() 为交互式入口, 不做自动化) +""" +import numpy as np +import pytest +import torch + +pytest.importorskip("tensorlens") + +from tensorlens.core import global_store +from tensorlens.tensorlens import normalize_to_int8, trace +from tensorlens.web.server import app + +from kda.models.causal_lm import CausalLM +from kda.models.config import KDAConfig + + +def _model(): + torch.manual_seed(51) + config = KDAConfig( + hidden_size=16, + num_hidden_layers=1, + num_heads=2, + num_value_heads=2, + head_dim=4, + chunk_size=4, + vocab_size=32, + intermediate_size=32, + kda_backend="reference", + ) + return CausalLM(config).eval() + + +@pytest.fixture(autouse=True) +def _clean_store(): + """global_store 是模块级单例, 每个测试前后清空, 避免互相污染.""" + global_store.INMEMORY_TENSORS.clear() + yield + global_store.INMEMORY_TENSORS.clear() + + +def _trace_kda_tensors(model): + """前向一次, 把 KDA 的 1D/2D/3D 张量全部 trace 进 store, 返回 logits numpy.""" + x = torch.tensor([[1, 2, 3, 4]]) + hidden = {} + + def hook_fn(name): + def hook(module, inp, out): + hidden[name] = out.detach() + + return hook + + model.embedding.register_forward_hook(hook_fn("embed")) + model.blocks[0].register_forward_hook(hook_fn("block0")) + + with torch.no_grad(): + logits = model(x) + + logits_np = logits.detach().numpy() # [1, T, vocab] 3D + trace("lm_head.weight", model.lm_head.weight.detach().numpy()) # [vocab, hidden] 2D + trace("embed", hidden["embed"].numpy()) # [1, T, hidden] 3D + trace("block0.out", hidden["block0"].numpy()) # [1, T, hidden] 3D + trace("logits", logits_np) # [1, T, vocab] 3D + trace("logits.row0", logits_np[0, 0]) # [vocab] 1D + return logits_np + + +# --------------------------------------------------------------------------- +# Layer 1: trace — KDA 张量进 store +# --------------------------------------------------------------------------- + + +def test_trace_stores_int8_with_expected_shape(): + model = _model() + logits_np = _trace_kda_tensors(model) + store = global_store.INMEMORY_TENSORS + + assert set(store) == {"lm_head.weight", "embed", "block0.out", "logits", "logits.row0"} + assert store["logits"].dtype == np.int8 + assert store["logits"].shape == logits_np.shape + assert store["lm_head.weight"].shape == (32, 16) + assert store["logits.row0"].ndim == 1 + assert store["logits"].min() >= -128 and store["logits"].max() <= 127 + + +# --------------------------------------------------------------------------- +# Layer 2: normalize_to_int8 — 四种策略边界行为 +# --------------------------------------------------------------------------- + + +def test_clip_normalization_bounds(): + t = np.array([[-100.0, 0.0, 100.0]]) + out = normalize_to_int8(t, (-1.0, 1.0), "clip") + assert out.dtype == np.int8 + assert out[0, 0] == -127 and out[0, 2] == 127 + assert out[0, 1] == 0 + + +def test_minmax_constant_tensor_returns_zeros(): + t = np.full((2, 3), 0.5) + out = normalize_to_int8(t, (-1.0, 1.0), "minmax") + assert (out == 0).all() + + +def test_zscore_zero_std_returns_zeros(): + t = np.ones((4, 4)) + out = normalize_to_int8(t, (-1.0, 1.0), "zscore") + assert (out == 0).all() + + +def test_none_strategy_scales_by_127(): + t = np.array([[0.5, -0.5]]) + out = normalize_to_int8(t, (-1.0, 1.0), "none") + assert out[0, 0] == 63 and out[0, 1] == -63 # int8 cast 向零截断 + + +def test_unsupported_normalization_raises(): + with pytest.raises(ValueError): + normalize_to_int8(np.zeros(3), (-1.0, 1.0), "bogus") + + +# --------------------------------------------------------------------------- +# Layer 2.5: trace 输入校验 +# --------------------------------------------------------------------------- + + +def test_trace_rejects_non_ndarray(): + with pytest.raises(TypeError): + trace("bad", torch.zeros(3)) + + +def test_trace_rejects_empty_key(): + with pytest.raises(ValueError): + trace("", np.zeros(3)) + + +# --------------------------------------------------------------------------- +# Layer 3: Flask 端点 (test_client, 不起真实 server) +# --------------------------------------------------------------------------- + + +def test_list_tensors_endpoint_reports_kda_tensors(): + model = _model() + _trace_kda_tensors(model) + resp = app.test_client().get("/api/list_tensors") + assert resp.status_code == 200 + body = resp.get_json() + assert body["count"] == 5 + keys = [t["key"] for t in body["available_tensors"]] + assert "logits" in keys and "lm_head.weight" in keys + + +def test_get_tensor_endpoint_returns_data(): + model = _model() + logits_np = _trace_kda_tensors(model) + resp = app.test_client().get("/api/get_tensor?tensor_key=logits") + assert resp.status_code == 200 + body = resp.get_json() + assert body["shape"] == list(logits_np.shape) + assert len(body["data"]) == logits_np.shape[0] + + +def test_get_tensor_missing_key_returns_400(): + model = _model() + _trace_kda_tensors(model) + resp = app.test_client().get("/api/get_tensor") + assert resp.status_code == 400 + assert "tensor_key" in resp.get_json()["error"] + + +def test_get_tensor_unknown_key_returns_404(): + resp = app.test_client().get("/api/get_tensor?tensor_key=nope") + assert resp.status_code == 404 + + +def test_config_endpoint(): + resp = app.test_client().get("/api/config") + assert resp.status_code == 200 + assert resp.get_json()["status"] == "ok" + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/tests/integration/test_torchlens.py b/tests/integration/test_torchlens.py new file mode 100644 index 0000000..7582a25 --- /dev/null +++ b/tests/integration/test_torchlens.py @@ -0,0 +1,83 @@ +"""TorchLens 集成测试: 计算图展开 KDA 模型并提取逐层激活. + +依赖: torchlens (未安装时整个模块 skip, 用 `pip install torchlens` 启用). + +验证三点: +1. trace 能展开 KDA 模型 —— 关键子模块 (embedding / attention 各投影 / + block 输出 / norm / lm_head 输出) 的激活被捕获且形状正确 +2. 展开不改变模型行为 —— trace 记录的输出与直接 forward 严格一致 +3. extract 便捷接口 —— 按模块名批量取激活 + +torchlens 2.34 的 trace[key] 返回 Op 对象, 取原始 tensor 用 `.tensor`. +""" +import pytest +import torch + +pytest.importorskip("torchlens") + +import torchlens as tl + +from kda.models.causal_lm import CausalLM +from kda.models.config import KDAConfig + + +def _model(): + torch.manual_seed(51) + config = KDAConfig( + hidden_size=16, + num_hidden_layers=1, + num_heads=2, + num_value_heads=2, + head_dim=4, + chunk_size=4, + vocab_size=32, + intermediate_size=32, + kda_backend="reference", + ) + return CausalLM(config).eval() + + +def test_trace_captures_kda_submodule_activations(): + model = _model() + x = torch.tensor([[1, 2, 3, 4]]) + with torch.no_grad(): + trace = tl.trace(model, x, capture=tl.options.CaptureOptions(verbose=False)) + + # 模块激活被捕获, 形状正确 + assert tuple(trace["embedding"].tensor.shape) == (1, 4, 16) + assert tuple(trace["blocks.0.attn.q_proj"].tensor.shape) == (1, 4, 8) # H*K = 2*4 + assert tuple(trace["blocks.0.attn.v_proj"].tensor.shape) == (1, 4, 8) # HV*V = 2*4 + assert tuple(trace["blocks.0.attn"].tensor.shape) == (1, 4, 16) + assert tuple(trace["blocks.0.ffn"].tensor.shape) == (1, 4, 16) + assert tuple(trace["norm"].tensor.shape) == (1, 4, 16) + assert tuple(trace["output"].tensor.shape) == (1, 4, 32) # vocab_size + + +def test_trace_does_not_change_model_behavior(): + model = _model() + x = torch.tensor([[1, 2, 3, 4]]) + with torch.no_grad(): + trace = tl.trace(model, x, capture=tl.options.CaptureOptions(verbose=False)) + traced_logits = trace["output"].tensor + direct_logits = model(x) + torch.testing.assert_close(traced_logits, direct_logits) + + +def test_extract_returns_activations_by_module_name(): + model = _model() + x = torch.tensor([[1, 2, 3, 4]]) + with torch.no_grad(): + acts = tl.extract( + model, x, ["embedding", "blocks.0.attn.q_proj", "blocks.0", "output"] + ) + assert set(acts) == {"embedding", "blocks.0.attn.q_proj", "blocks.0", "output"} + assert tuple(acts["embedding"].shape) == (1, 4, 16) + assert tuple(acts["blocks.0.attn.q_proj"].shape) == (1, 4, 8) + assert tuple(acts["blocks.0"].shape) == (1, 4, 16) + assert tuple(acts["output"].shape) == (1, 4, 32) + + +if __name__ == "__main__": + import sys + + sys.exit(pytest.main([__file__, "-v"])) diff --git a/tests/integration/test_train_overfit.py b/tests/integration/test_train_overfit.py new file mode 100644 index 0000000..5a03a03 --- /dev/null +++ b/tests/integration/test_train_overfit.py @@ -0,0 +1,32 @@ +"""L7: toy overfit smoke test. 320 steps loss < 0.1.""" +import torch + +from kda.models.causal_lm import CausalLM +from kda.models.config import KDAConfig + + +def test_overfit(): + cfg = KDAConfig() # 起步默认 toy 配置 + torch.manual_seed(30) + model = CausalLM(cfg).cuda() + + x = torch.randint(0, cfg.vocab_size, (4, 16), device="cuda") + labels = x.clone() + # A single repeated batch is an optimizer/dataflow smoke test, so converge it quickly. + optim = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=0.01) + + for step in range(320): + optim.zero_grad() + loss = model(x, labels=labels) + loss.backward() + optim.step() + if step % 64 == 0 or step == 319: + print(f" step {step:3d} loss {loss.item():.4f}") + + final = loss.item() + assert final < 0.1, f"final loss {final:.4f} > 0.1" + print(f"L7 overfit: PASSED (final loss {final:.4f})") + + +if __name__ == "__main__": + test_overfit() diff --git a/tests/kernels/__init__.py b/tests/kernels/__init__.py new file mode 100644 index 0000000..6913d67 --- /dev/null +++ b/tests/kernels/__init__.py @@ -0,0 +1 @@ +"""Local kernel implementation tests.""" diff --git a/tests/kernels/test_gate.py b/tests/kernels/test_gate.py new file mode 100644 index 0000000..92b7868 --- /dev/null +++ b/tests/kernels/test_gate.py @@ -0,0 +1,20 @@ +"""The vendored FLA fused gate must match the PyTorch reference.""" +import pytest +import torch + +from kda.ops.reference.gate import kda_gate_reference +from kda.ops.triton.gate import kda_gate_fwd + +pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") + + +def test_triton_gate_matches_reference(): + B, T, HV, K = 1, 32, 4, 8 + torch.manual_seed(10) + device = "cuda" + g = torch.randn(B, T, HV, K, device=device, dtype=torch.float32) + A_log = torch.randn(HV, device=device, dtype=torch.float32) * 0.5 + dt_bias = torch.randn(HV, K, device=device, dtype=torch.float32) * 0.1 + expected = kda_gate_reference(g, A_log, dt_bias) + actual = kda_gate_fwd(g, A_log, dt_bias, lower_bound=None) + torch.testing.assert_close(actual, expected, rtol=1e-4, atol=1e-4) diff --git a/tests/kernels/test_triton_bwd.py b/tests/kernels/test_triton_bwd.py new file mode 100644 index 0000000..73839b4 --- /dev/null +++ b/tests/kernels/test_triton_bwd.py @@ -0,0 +1,67 @@ +"""L4: vendored FLA Triton bwd vs naive chunked. + +Triton kernels run in fp32, so this checks VJP vs L2 rather than fp64 gradcheck. +Inputs L2-normalize q/k like the trained KDA path. +""" +import pytest +import torch +import torch.nn.functional as F + +from kda.ops.reference.chunkwise import naive_chunk_kda +from kda.ops.triton.chunk_fwd import chunk_kda_fwd + +pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") + + +def test_bwd_matches_naive(): + B, T, H, HV, K, V = 2, 64, 4, 8, 32, 32 + torch.manual_seed(5) + dev = "cuda" + q = F.normalize(torch.randn(B, T, H, K, device=dev), dim=-1).requires_grad_() + k = F.normalize(torch.randn(B, T, H, K, device=dev), dim=-1).requires_grad_() + v = torch.randn(B, T, HV, V, device=dev, requires_grad=True) + g = (-torch.rand(B, T, HV, K, device=dev) * 2).requires_grad_() + beta = torch.rand(B, T, HV, device=dev, requires_grad=True) + + o_na, _ = naive_chunk_kda(q, k, v, g, beta, chunk_size=64) + o_na.sum().backward() + dq_na, dk_na = q.grad.clone(), k.grad.clone() + dv_na, dg_na, db_na = v.grad.clone(), g.grad.clone(), beta.grad.clone() + + q.grad = k.grad = v.grad = g.grad = beta.grad = None + o_tr, _ = chunk_kda_fwd(q, k, v, g, beta, chunk_size=64) + o_tr.sum().backward() + dq_tr, dk_tr = q.grad.clone(), k.grad.clone() + dv_tr, dg_tr, db_tr = v.grad.clone(), g.grad.clone(), beta.grad.clone() + + torch.testing.assert_close(dq_tr, dq_na, rtol=2e-2, atol=2e-3) + torch.testing.assert_close(dk_tr, dk_na, rtol=2e-2, atol=2e-3) + torch.testing.assert_close(dv_tr, dv_na, rtol=2e-2, atol=2e-3) + torch.testing.assert_close(dg_tr, dg_na, rtol=2e-2, atol=2e-3) + torch.testing.assert_close(db_tr, db_na, rtol=2e-2, atol=2e-3) + + +def test_triton_kda_attention_backward_dt_bias_rank2(): + """Layer stores dt_bias as [HV, K]; Triton bwd used to return a flat [HV*K].""" + from kda.layers.kda_attn import KDAAttention + + torch.manual_seed(0) + model = KDAAttention( + hidden_size=64, + num_heads=4, + num_value_heads=8, + head_dim=16, + chunk_size=64, + kda_backend="triton", + ).cuda() + x = torch.randn(2, 64, 64, device="cuda") + model(x).sum().backward() + assert model.dt_bias.grad is not None + assert model.dt_bias.grad.shape == model.dt_bias.shape + assert model.A_log.grad is not None + assert torch.isfinite(model.dt_bias.grad).all() + + +if __name__ == "__main__": + test_bwd_matches_naive() + test_triton_kda_attention_backward_dt_bias_rank2() diff --git a/tests/kernels/test_triton_fwd.py b/tests/kernels/test_triton_fwd.py new file mode 100644 index 0000000..0c562b4 --- /dev/null +++ b/tests/kernels/test_triton_fwd.py @@ -0,0 +1,29 @@ +"""L3: vendored FLA Triton fwd vs naive chunked.""" +import pytest +import torch +import torch.nn.functional as F + +from kda.ops.reference.chunkwise import naive_chunk_kda +from kda.ops.triton.chunk_fwd import chunk_kda_fwd + +pytestmark = pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required") + + +def test_triton_fwd_matches_naive(): + B, T, H, HV, K, V = 2, 64, 4, 8, 32, 32 + torch.manual_seed(3) + device = "cuda" + q = F.normalize(torch.randn(B, T, H, K, device=device), dim=-1) + k = F.normalize(torch.randn(B, T, H, K, device=device), dim=-1) + v = torch.randn(B, T, HV, V, device=device) + g = -torch.rand(B, T, HV, K, device=device) * 2 + beta = torch.rand(B, T, HV, device=device) + + o_ref, S_ref = naive_chunk_kda( + q, k, v, g, beta, output_final_state=True, chunk_size=64 + ) + o_tr, S_tr = chunk_kda_fwd( + q, k, v, g, beta, output_final_state=True, chunk_size=64 + ) + torch.testing.assert_close(o_tr.float(), o_ref.float(), rtol=2e-3, atol=2e-3) + torch.testing.assert_close(S_tr.float(), S_ref.float(), rtol=2e-3, atol=2e-3) diff --git a/train.py b/train.py new file mode 100644 index 0000000..a5fe747 --- /dev/null +++ b/train.py @@ -0,0 +1,6 @@ +"""Toy causal-language-model training loop.""" + +from kda.training.toy import main + +if __name__ == "__main__": + main() diff --git a/train_k3.py b/train_k3.py new file mode 100644 index 0000000..5b8a266 --- /dev/null +++ b/train_k3.py @@ -0,0 +1,461 @@ +"""Train a Kimi-K3-like model (KDA + Gated MLA + LatentMoE) on bilingual wiki. + +用法: + uv run python train_k3.py # 8M toy, zh+en wiki + uv run python train_k3.py --preset 0.5b # ~0.5B smoke (8M tokens) + # 32–40GB Ampere, 1B-token 翻译前置预训练: + uv run python train_k3.py --preset 0.5b --attnres block \\ + --max-tokens 1000000000 --warmup 2000 +""" + +from __future__ import annotations + +import argparse +import os +import time +from dataclasses import asdict + +import torch + +from kda.layers.latent_moe import moe_route_frac +from kda.models.causal_lm import CausalLM +from kda.models.k3_config import K3Config +from kda.training.data import iter_indexed, load_pretrain_chunks, load_tokenizer +from kda.training.schedule import lr_scale, tokens_per_micro, total_opt_steps +from kda.training.toy import load_ckpt + +_TOY_TRAIN = { + "tokenizer": "data/spm_4k.model", + "out": "ckpts/k3_wiki.pt", + "limit": 8000, + "batch": 4, + "seq_len": 256, + "steps": 500, + "lr": 2e-3, + "warmup": 50, + "grad_acc": 1, + "eval_every": 100, +} +_B500M_TRAIN = { + "tokenizer": "Qwen/Qwen3-8B", + "out": "ckpts/k3_0.5b.pt", + "limit": 20000, + "batch": 2, + "seq_len": 2048, + "steps": 2000, + "lr": 3e-4, + "warmup": 64, + "grad_acc": 8, + "eval_every": 100, +} + + +def _sibling(path: str, suffix: str) -> str: + root, ext = os.path.splitext(path) + return f"{root}{suffix}{ext or '.pt'}" + + +def _set_lr(optim: torch.optim.Optimizer, lr: float) -> None: + for group in optim.param_groups: + group["lr"] = lr + + +def _init_swanlab(cfg: K3Config, args: argparse.Namespace): + """Cloud monitor if SWANLAB_API_KEY is set; otherwise no-op.""" + key = os.environ.get("SWANLAB_API_KEY") + if not key: + return None + try: + import swanlab + except ImportError: + print("SWANLAB_API_KEY set but swanlab is not installed") + return None + try: + swanlab.login(api_key=key, save=False) + # swanlab 0.9 Settings.project is nested; a string SWANLAB_PROJECT env crashes init. + project = os.environ.pop("SWANLAB_PROJECT", None) or "kda" + return swanlab.init( + project=project, + name=f"{args.preset}-{cfg.attnres}", + config={ + "preset": args.preset, + "attnres": cfg.attnres, + "attnres_block_size": cfg.attnres_block_size, + "lr": args.lr, + "batch": args.batch, + "seq_len": args.seq_len, + "steps": args.steps, + "max_tokens": args.max_tokens, + "grad_acc": args.grad_acc, + "warmup": args.warmup, + "langs": args.langs, + "kda_backend": cfg.kda_backend, + "gradient_checkpointing": cfg.gradient_checkpointing, + }, + ) + except Exception as exc: + print(f"swanlab init failed ({exc}); continuing without cloud monitor") + return None + + +def _payload( + cfg: K3Config, + model: CausalLM, + optim: torch.optim.Optimizer, + args: argparse.Namespace, + *, + micro_step: int, + opt_step: int, + tokens: int, + chunk_index: int, + best_heldout: float, +): + return { + "config": asdict(cfg), + "model_state": model.state_dict(), + "optimizer_state": optim.state_dict(), + "tokenizer": args.tokenizer, + "preset": args.preset, + "micro_step": micro_step, + "opt_step": opt_step, + "tokens": tokens, + "chunk_index": chunk_index, + "best_heldout": best_heldout, + } + + +def _save(path: str, payload: dict) -> None: + os.makedirs(os.path.dirname(path) or ".", exist_ok=True) + tmp = path + ".tmp" + torch.save(payload, tmp) + os.replace(tmp, path) + + +@torch.no_grad() +def _heldout_loss( + model, chunks, device, use_bf16, max_batches: int = 4 +) -> float | None: + if chunks is None or chunks.numel() == 0: + return None + model.eval() + losses = [] + for x, y in zip(chunks[:max_batches], chunks[:max_batches]): + x = x.to(device) + y = y.to(device) + with torch.autocast("cuda", dtype=torch.bfloat16, enabled=use_bf16): + losses.append(float(model(x, labels=y).item())) + model.train() + return sum(losses) / max(len(losses), 1) + + +def _moe_log(model) -> dict: + frac = moe_route_frac(model) + if frac is None: + return {} + n = frac.numel() + dead = float((frac < 1.0 / (2 * n)).sum().item()) + return { + "moe/max_frac": float(frac.max()), + "moe/min_frac": float(frac.min()), + "moe/n_dead": dead, + } + + +def main() -> None: + pre = argparse.ArgumentParser(add_help=False) + pre.add_argument("--preset", default="toy", choices=["toy", "0.5b"]) + pre_args, _ = pre.parse_known_args() + train_defaults = _B500M_TRAIN if pre_args.preset == "0.5b" else _TOY_TRAIN + + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--preset", default="toy", choices=["toy", "0.5b"]) + p.add_argument( + "--tokenizer", "--sp", dest="tokenizer", default=train_defaults["tokenizer"] + ) + p.add_argument("--out", default=train_defaults["out"]) + p.add_argument("--limit", type=int, default=train_defaults["limit"]) + p.add_argument("--batch", type=int, default=train_defaults["batch"]) + p.add_argument("--seq-len", type=int, default=train_defaults["seq_len"]) + p.add_argument("--steps", type=int, default=train_defaults["steps"]) + p.add_argument( + "--max-tokens", + type=int, + default=None, + help="stop after this many tokens (primary budget). --steps is a micro-step cap", + ) + p.add_argument("--lr", type=float, default=train_defaults["lr"]) + p.add_argument( + "--warmup", + type=int, + default=train_defaults["warmup"], + help="linear warmup in optimizer steps, then cosine to 0.1× lr", + ) + p.add_argument("--grad-acc", type=int, default=train_defaults["grad_acc"]) + p.add_argument("--eval-every", type=int, default=train_defaults["eval_every"]) + p.add_argument( + "--langs", + default="zh,en", + help="comma-separated wiki languages mixed 1:1 by token (default zh,en)", + ) + p.add_argument("--heldout-frac", type=float, default=0.01) + p.add_argument("--resume", default=None, help="checkpoint to continue from") + p.add_argument("--gen-prefix", action="append", default=None) + p.add_argument("--device", default="auto") + p.add_argument( + "--attnres", + default="off", + choices=["off", "full", "block"], + help="depth mixer: off=standard residual, block=K3 AttnRes, full=per-layer AttnRes", + ) + p.add_argument( + "--attnres-block-size", + type=int, + default=None, + help="DecoderBlocks per AttnRes block (block mode). Default ≈ L/8", + ) + p.add_argument( + "--grad-checkpoint", + dest="grad_checkpoint", + action="store_true", + default=None, + help="activation checkpointing (0.5b preset default on)", + ) + p.add_argument( + "--no-grad-checkpoint", + dest="grad_checkpoint", + action="store_false", + ) + args = p.parse_args() + if args.gen_prefix is None: + args.gen_prefix = ["人工智能的发展", "The history of computing"] + + if args.seq_len % K3Config.preset(args.preset).chunk_size: + raise SystemExit( + f"seq-len {args.seq_len} must be divisible by chunk_size " + f"{K3Config.preset(args.preset).chunk_size}" + ) + + device = args.device + if device == "auto": + device = "cuda" if torch.cuda.is_available() else "cpu" + use_bf16 = device == "cuda" and torch.cuda.is_bf16_supported() + if device == "cuda" and not use_bf16: + raise SystemExit( + "KDA training needs bf16; this GPU does not support it (avoid V100 fp16)" + ) + + print(f"loading tokenizer {args.tokenizer} ...") + tok = load_tokenizer(args.tokenizer) + cfg = K3Config.preset(args.preset) + cfg.vocab_size = tok.vocab_size + cfg.attnres = args.attnres + cfg.attnres_block_size = args.attnres_block_size + if args.grad_checkpoint is not None: + cfg.gradient_checkpointing = args.grad_checkpoint + + langs = [part.strip() for part in args.langs.split(",") if part.strip()] + tpm = tokens_per_micro(args.batch, args.seq_len) + horizon = total_opt_steps( + max_tokens=args.max_tokens, + max_micro=args.steps, + batch=args.batch, + seq_len=args.seq_len, + grad_acc=args.grad_acc, + ) + + micro_step = 0 + opt_step = 0 + tokens = 0 + chunk_index = 0 + best_heldout = float("inf") + best_train = float("inf") + + if args.resume: + print(f"resume {args.resume}") + model, loaded_cfg = load_ckpt(args.resume) + if not isinstance(loaded_cfg, K3Config): + raise SystemExit( + f"train_k3.py requires a K3 checkpoint; {args.resume} has " + f"{type(loaded_cfg).__name__}" + ) + cfg = loaded_cfg + cfg.attnres = args.attnres + cfg.attnres_block_size = args.attnres_block_size + if args.grad_checkpoint is not None: + cfg.gradient_checkpointing = args.grad_checkpoint + model.gradient_checkpointing = cfg.gradient_checkpointing + model.to(device) + payload = torch.load(args.resume, map_location="cpu", weights_only=False) + if payload.get("tokenizer") and payload["tokenizer"] != args.tokenizer: + print(f"warning: ckpt tokenizer {payload['tokenizer']} != {args.tokenizer}") + micro_step = int(payload.get("micro_step", 0)) + opt_step = int(payload.get("opt_step", 0)) + tokens = int(payload.get("tokens", 0)) + chunk_index = int(payload.get("chunk_index", 0)) + best_heldout = float(payload.get("best_heldout", best_heldout)) + else: + model = CausalLM(cfg).to(device) + + tracker = _init_swanlab(cfg, args) + n = sum(p.numel() for p in model.parameters()) + print( + f"preset={args.preset} model={n:,} params ({n / 1e6:.1f}M) on {device} " + f"bf16={use_bf16} checkpoint={cfg.gradient_checkpointing}" + ) + print( + f"vocab={cfg.vocab_size} tied={cfg.tie_word_embeddings} " + f"layers={cfg.layer_types()} attnres={cfg.attnres} langs={langs}" + ) + if args.max_tokens is None: + print( + f"token budget: --steps {args.steps} micro " + f"({args.steps * tpm:,} tokens); pass --max-tokens for a real run" + ) + else: + print(f"token budget: {args.max_tokens:,} cosine horizon {horizon} opt steps") + + train_chunks, held_chunks, n_ids = load_pretrain_chunks( + tok, + langs=langs, + limit=args.limit, + batch=args.batch, + seq_len=args.seq_len, + heldout_frac=args.heldout_frac, + ) + print( + f"packed tokens {n_ids:,} -> {train_chunks.size(0)} train / " + f"{held_chunks.size(0)} held-out chunks of [{args.batch}, {args.seq_len}]" + ) + if train_chunks.size(0) == 0: + raise SystemExit("no training chunks; raise --limit or lower --batch/--seq-len") + + optim = torch.optim.AdamW( + model.parameters(), + lr=args.lr, + weight_decay=0.1 if args.preset == "0.5b" else 0.01, + ) + if args.resume: + payload = torch.load(args.resume, map_location="cpu", weights_only=False) + if payload.get("optimizer_state"): + optim.load_state_dict(payload["optimizer_state"]) + + def gen_sample(prefix: str, max_new: int = 24) -> str: + ids_ = tok.encode(prefix) + if not ids_: + return "" + inp = torch.tensor([ids_], dtype=torch.long, device=device) + was_training = model.training + model.eval() + with torch.inference_mode(): + out = model.generate(inp, max_new) + if was_training: + model.train() + return tok.decode(out[0].tolist()) + + model.train() + t0 = time.perf_counter() + tokens_at_t0 = tokens + for chunk_index, x, y in iter_indexed(train_chunks, start=chunk_index): + if args.max_tokens is not None and tokens >= args.max_tokens: + break + if micro_step >= args.steps: + break + x, y = x.to(device), y.to(device) + scale = lr_scale(opt_step, args.warmup, horizon) + _set_lr(optim, args.lr * scale) + with torch.autocast("cuda", dtype=torch.bfloat16, enabled=use_bf16): + loss = model(x, labels=y) / args.grad_acc + loss.backward() + do_step = (micro_step + 1) % args.grad_acc == 0 + grad_norm = None + if do_step: + grad_norm = float(torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)) + optim.step() + optim.zero_grad(set_to_none=True) + opt_step += 1 + + raw_loss = loss.item() * args.grad_acc + tokens += tpm + micro_step += 1 + lr_now = optim.param_groups[0]["lr"] + if raw_loss < best_train: + best_train = raw_loss + + metrics = {"train/loss": raw_loss, "train/lr": lr_now, "train/tokens": tokens} + if grad_norm is not None: + metrics["train/grad_norm"] = grad_norm + elapsed = time.perf_counter() - t0 + if elapsed > 0: + metrics["train/tok_s"] = (tokens - tokens_at_t0) / elapsed + + log_now = ( + micro_step % args.eval_every == 0 + or micro_step == 1 + or (args.max_tokens is not None and tokens >= args.max_tokens) + or micro_step >= args.steps + ) + if log_now: + held = _heldout_loss(model, held_chunks, device, use_bf16) + if held is not None: + metrics["heldout/loss"] = held + metrics.update(_moe_log(model)) + print( + f"micro {micro_step:6d} opt {opt_step:6d} tok {tokens:,} " + f"loss {raw_loss:.4f} lr {lr_now:.2e}" + + (f" held {held:.4f}" if held is not None else "") + ) + if micro_step % (args.eval_every * 2) == 0 or micro_step <= args.eval_every: + for prefix in args.gen_prefix: + sample = gen_sample(prefix) + print(f" gen[{prefix[:16]}]: {sample}") + if tracker is not None: + import swanlab + + tracker.log( + {f"gen/{prefix[:24]}": swanlab.Text(sample)}, + step=micro_step, + ) + payload = _payload( + cfg, + model, + optim, + args, + micro_step=micro_step, + opt_step=opt_step, + tokens=tokens, + chunk_index=chunk_index + 1, + best_heldout=best_heldout, + ) + _save(_sibling(args.out, "_last"), payload) + if held is not None and held < best_heldout: + best_heldout = held + payload["best_heldout"] = best_heldout + _save(_sibling(args.out, "_best"), payload) + print( + f" best held-out {best_heldout:.4f} -> {_sibling(args.out, '_best')}" + ) + if tracker is not None: + tracker.log(metrics, step=micro_step) + + payload = _payload( + cfg, + model, + optim, + args, + micro_step=micro_step, + opt_step=opt_step, + tokens=tokens, + chunk_index=chunk_index + 1, + best_heldout=best_heldout, + ) + _save(args.out, payload) + print( + f"best train {best_train:.4f} best held-out {best_heldout:.4f}; " + f"tokens {tokens:,} -> {args.out}" + ) + if tracker is not None: + tracker.finish() + + +if __name__ == "__main__": + main() diff --git a/train_sft.py b/train_sft.py new file mode 100644 index 0000000..a6da519 --- /dev/null +++ b/train_sft.py @@ -0,0 +1,196 @@ +"""Instruction SFT for zh↔en translation. Prompt template matches eval_mt. + +用法: + uv run python train_sft.py --ckpt ckpts/k3_wiki.pt --data data/sft/train.jsonl + uv run python train_sft.py --ckpt ckpts/k3_0.5b_best.pt --data data/sft/opus.jsonl \\ + --seq-len 512 --batch 4 --lr 5e-5 --epochs 2 +""" + +from __future__ import annotations + +import argparse +import os +from dataclasses import asdict + +import torch + +from kda.training.data import ( + IGNORE_INDEX, + iter_sft_batches, + load_sft_rows, + load_tokenizer, +) +from kda.training.eval_mt import evaluate_pairs +from kda.training.schedule import lr_scale, total_opt_steps +from kda.training.toy import load_ckpt + + +def _set_lr(optim: torch.optim.Optimizer, lr: float) -> None: + for group in optim.param_groups: + group["lr"] = lr + + +def _init_swanlab(args: argparse.Namespace): + key = os.environ.get("SWANLAB_API_KEY") + if not key: + return None + try: + import swanlab + except ImportError: + return None + try: + swanlab.login(api_key=key, save=False) + project = os.environ.pop("SWANLAB_PROJECT", None) or "kda" + return swanlab.init( + project=project, + name=f"sft-{os.path.basename(args.ckpt)}", + config={ + "ckpt": args.ckpt, + "data": args.data, + "lr": args.lr, + "batch": args.batch, + "seq_len": args.seq_len, + "epochs": args.epochs, + }, + ) + except Exception as exc: + print(f"swanlab init failed ({exc}); continuing without cloud monitor") + return None + + +def _read_lines(path: str) -> list[str]: + from pathlib import Path + + return [ + ln.strip() + for ln in Path(path).read_text(encoding="utf-8").splitlines() + if ln.strip() + ] + + +def main() -> None: + p = argparse.ArgumentParser(description=__doc__) + p.add_argument("--ckpt", required=True) + p.add_argument("--data", required=True, help="jsonl {src,tgt,target_lang} or TSV") + p.add_argument("--out", default="ckpts/k3_sft.pt") + p.add_argument("--tokenizer", default=None) + p.add_argument("--batch", type=int, default=4) + p.add_argument("--seq-len", type=int, default=256) + p.add_argument("--lr", type=float, default=1e-3) + p.add_argument("--warmup", type=int, default=20) + p.add_argument("--epochs", type=int, default=2) + p.add_argument("--max-steps", type=int, default=None) + p.add_argument("--grad-acc", type=int, default=1) + p.add_argument("--eval-every", type=int, default=50) + p.add_argument("--src", default=None, help="frozen eval src (not used as train)") + p.add_argument("--ref", default=None) + p.add_argument("--target-lang", default="en", choices=["en", "zh"]) + p.add_argument("--device", default="auto") + args = p.parse_args() + + device = args.device + if device == "auto": + device = "cuda" if torch.cuda.is_available() else "cpu" + use_bf16 = device == "cuda" and torch.cuda.is_bf16_supported() + if device == "cuda" and not use_bf16: + raise SystemExit("KDA training needs bf16") + + model, cfg = load_ckpt(args.ckpt) + model.to(device) + payload = torch.load(args.ckpt, map_location="cpu", weights_only=False) + tok_src = args.tokenizer or payload.get("tokenizer") + if not tok_src: + raise SystemExit("need --tokenizer or a tokenizer field in the checkpoint") + tok = load_tokenizer(tok_src) + rows = load_sft_rows(args.data) + if not rows: + raise SystemExit(f"no SFT rows in {args.data}") + print(f"SFT {len(rows)} rows from {args.data}; model {cfg.__class__.__name__}") + + steps_per_epoch = max((len(rows) + args.batch - 1) // args.batch, 1) + max_micro = args.max_steps + if max_micro is None: + max_micro = steps_per_epoch * args.epochs + horizon = total_opt_steps( + max_tokens=None, + max_micro=max_micro, + batch=args.batch, + seq_len=args.seq_len, + grad_acc=args.grad_acc, + ) + optim = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=0.01) + tracker = _init_swanlab(args) + model.train() + step = 0 + opt_step = 0 + best = float("inf") + + for _, x, y in iter_sft_batches(rows, tok, args.batch, args.seq_len): + if step >= max_micro: + break + x, y = x.to(device), y.to(device) + _set_lr(optim, args.lr * lr_scale(opt_step, args.warmup, horizon)) + with torch.autocast("cuda", dtype=torch.bfloat16, enabled=use_bf16): + loss = model(x, labels=y, ignore_index=IGNORE_INDEX) / args.grad_acc + loss.backward() + if (step + 1) % args.grad_acc == 0: + torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) + optim.step() + optim.zero_grad(set_to_none=True) + opt_step += 1 + raw = loss.item() * args.grad_acc + if raw < best: + best = raw + if tracker is not None: + tracker.log( + {"sft/loss": raw, "sft/lr": optim.param_groups[0]["lr"]}, + step=step, + ) + if step % args.eval_every == 0 or step == max_micro - 1: + print(f"step {step:4d} sft loss {raw:.4f} lr {optim.param_groups[0]['lr']:.2e}") + if args.src and args.ref: + model.eval() + srcs, refs = _read_lines(args.src), _read_lines(args.ref) + out = evaluate_pairs( + model, + tok, + srcs, + refs, + target_lang=args.target_lang, + device=device, + max_new=64, + limit=None, + ) + printable = {k: v for k, v in out.items() if k != "hyps"} + print(printable) + if tracker is not None: + tracker.log( + { + "eval/success_rate": printable["success_rate"], + "eval/chrf": printable["chrf"], + "eval/copy_rate": printable["copy_rate"], + }, + step=step, + ) + model.train() + step += 1 + + os.makedirs(os.path.dirname(args.out) or ".", exist_ok=True) + torch.save( + { + "config": asdict(cfg), + "model_state": model.state_dict(), + "optimizer_state": optim.state_dict(), + "tokenizer": tok_src, + "sft_data": args.data, + "pretrained_ckpt": args.ckpt, + }, + args.out, + ) + print(f"best sft loss {best:.4f}; checkpoint -> {args.out}") + if tracker is not None: + tracker.finish() + + +if __name__ == "__main__": + main() diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..2a26f52 --- /dev/null +++ b/uv.lock @@ -0,0 +1,4133 @@ +version = 1 +revision = 3 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 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