dela
584f7e9e73
Standalone tree split from LLMRL/projects/kda. Includes Triton dt_bias backward fix, train_k3 --preset 0.5b, SFT, Docker runtime, and tests.
44 lines
1.4 KiB
TOML
44 lines
1.4 KiB
TOML
[project]
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name = "kda"
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version = "0.0.1"
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description = "Hand-written KDA implementation from naive recurrent to fused Triton + training + inference"
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requires-python = ">=3.10"
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# 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
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dependencies = [
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"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
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"einops>=0.7.0", # kda/ops/reference/chunkwise.py 的 rearrange
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"packaging>=23.0", # vendored FLA utils version checks
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"sentencepiece>=0.2.0", # toy SentencePiece
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"datasets>=3.0.0", # 中文 wiki 语料加载
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"transformers>=4.51.0", # Qwen3 tokenizer for 0.5b preset
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"swanlab>=0.9.7",
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]
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# 训练机 / Docker 镜像: uv sync --extra train
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[project.optional-dependencies]
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train = [
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"swanlab>=0.6.0",
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"sacrebleu>=2.4.0",
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"langdetect>=1.0.9",
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]
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# uv run / uv sync 默认安装 dev group
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[dependency-groups]
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dev = [
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"pytest>=7.0",
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"torchlens>=2.34", # 计算图展开集成测试; 未装时测试模块自动 skip
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"tensorlens>=0.0.3", # Flask viewer 集成测试; 未装时测试模块自动 skip
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]
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[tool.uv]
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# 公共入口: `from kda import CausalLM, KDAConfig, K3Config, chunk_kda`
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package = true
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["kda"]
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