[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"]