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Follow https://skillsmp.com/skill-install/prompt.md to install Agent Skills from https://github.com/vipshop/cache-dit.Loading the repository README…
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High-level guide for integrating a new DiT model into cache-dit: Cache (BlockAdapter/ForwardPattern), Context Parallelism, Tensor Parallelism, Text Encoder Parallelism (TE-P), VAE Parallelism (VAE-P), generate CLI, installation, testing workflow, and detailed…
Use when doing operator migration or kernel migration for CUDA, Triton, or custom ops in cache-dit; porting kernels from nunchaku, deepcompressor, or other repos; designing operator registration and public wrappers; wiring build and packaging for optional…
Use when integrating a new PTQ workflow into cache-dit; designing quantize/load API shape, backend-specific config validation, save/load manifests, benchmark and regression tests, or reviewing a PTQ integration plan. Uses the SVDQ PTQ integration only as a…
Use when writing, debugging, porting, reviewing, or optimizing CUDA C++ or PTX kernels; investigating CUDA Runtime or Driver API behavior; profiling kernels with Nsight Systems or Nsight Compute; or reasoning about Tensor Core instructions, shared memory,…
Use when writing, modifying, porting, or optimizing CuTe DSL GPU kernels in Python; reading CuTe DSL API reference material; integrating a CuTe DSL kernel into a project; or rewriting an existing CUDA or C++ operator into CuTe DSL while preserving correctness…
Use when writing, debugging, porting, reviewing, or optimizing CUTLASS or CuTe C++ kernels and templates; navigating CUTLASS examples, collectives, epilogues, pipelines, GEMM schedules, or CuTe headers; or analyzing template configuration, tiling, memory…
cache-dit 发布全流程（给定一个版本号即可执行完）：full profile 一次构建 PyPI 纯 Python wheel 与带 SVDQuant CUDA 扩展的 +cu13 wheel（cp310–cp314，sm 80/86/89/120a/121a，manylinux 平台标签与 $ORIGIN RPATH 归一）、gh-pages 上的 PEP 503 静态索引发布、GitHub Release 资产上传、PyPI 上传，以及发布后验收与安装语义。Use when cutting a…
Source text: Chinese
Write optimized Triton GPU kernels for deep learning operations. Covers the full spectrum from basic vector ops to Flash Attention, persistent matmul, fused normalization, quantized GEMM, and memory-efficient patterns.
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