Loading...
Loading...
Found 28 Skills
Capable of completing the installation and deployment of Ascend NPU drivers and firmware, featuring regular expression-based installation package extraction, on-demand addition of executable permissions, dual package verification via Python+Shell, pre-check and installation of system dependencies, and compatibility with CentOS/RHEL/Ubuntu/Debian systems. It is suitable for the installation and deployment of Ascend NPU drivers and firmware.
Migrate GPU/CUDA Triton operators to Triton-Ascend, or rewrite Python/PyTorch operators into Triton-Ascend implementations that can run on Ascend NPU. When clear optimization opportunities are identified, directly output the optimized code, minimal validation script, and troubleshooting instructions. This skill should be prioritized when users mention 昇腾 (Ascend), Ascend, NPU, triton-ascend, Triton operator migration, PyTorch operator rewriting, coreDim, UB overflow, 1D grid, physical core binding, block_ptr, stride, memory access alignment, mask performance, dtype degradation, operator optimization, or directly ask questions like "How to use this skill", "How to run it in the command line", "How to perform migration/validation in a container", even if users do not explicitly say "write a skill" or "perform migration".
AI for Science 场景下的昇腾 NPU Profiling 采集与性能分析 Skill，用于在华为 Ascend NPU 上使用 torch_npu.profiler 采集 L0、L1、L2 级性能数据，分析训练或推理中的算子耗时、调用栈、内存与瓶颈，并指导后续调优。
This skill provides comprehensive guidance for adapting Wan-series video generation models (Wan2.1/Wan2.2) from NVIDIA CUDA to Huawei Ascend NPU. It should be used when performing NPU migration of DiT-based video diffusion models, including device layer adaptation, operator replacement, distributed parallelism refactoring, attention optimization, VAE parallelization, and model quantization. This skill covers 9 major adaptation domains derived from real-world Wan2.2 CUDA-to-Ascend porting experience.
Evaluate the performance of Triton operators on Ascend NPU. It is used when users need to analyze operator performance bottlenecks, collect and compare operator performance using msprof/msprof op, diagnose Memory-Bound/Compute-Bound bottlenecks, measure hardware utilization metrics, and generate performance evaluation reports.
HCCL (Huawei Collective Communication Library) performance testing for Ascend NPU clusters. Use for testing distributed communication bandwidth, verifying HCCL functionality, and benchmarking collective operations like AllReduce, AllGather. Covers MPI installation, multi-node pre-flight checks (SSH/CANN version/NPU health), and production testing workflows.
Migrate vision/detection/segmentation small models to Ascend NPU, covering the full workflow: model structure analysis, migration verification, performance profiling, and optimization. Based on torch_npu and msprof Use this skill when the user wants to: (1) migrate encoder-only models like ResNet, YOLO, UNet to Ascend NPU, (2) analyze model structure for migration feasibility, (3) verify model inference on NPU, (4) identify performance bottlenecks and get optimization suggestions Trigger: user mentions "migrate", "migration", "Ascend", "NPU", "YOLO", "ResNet", "encoder-only", "detection", "segmentation", "adaptation", "adapt", "迁移", "昇腾迁移", "小模型", "适配", "昇腾适配", "GPU迁移", "NPU适配", "适配NPU", "适配昇腾"
Provides installation guidance for CANN on Ascend NPU. Call this skill when users need to install CANN, configure the Ascend environment, or resolve installation issues.
将简单Vector类型Triton算子从GPU迁移到昇腾NPU。当用户需要迁移Triton代码到NPU、提到GPU到NPU迁移、Triton迁移、昇腾适配时使用。注意：无法自动迁移存在编译问题的算子。
Deep Performance Optimization Skill for Triton Operators on Ascend NPU, dedicated to achieving the Triton operator performance improvement required by users. Core technologies include but are not limited to Unified Buffer (UB) capacity planning, multi-Tokens parallel processing, MTE/Vector pipeline parallelism, mask optimization, etc. This Skill must be triggered when the user mentions the following: performance optimization of Vector-type Triton operators on Ascend NPU.
Generate interface documents for Triton operators of Ascend NPU. Used when users need to create or update interface documents for Triton operators of Ascend NPU. Core capabilities: (1) Generate standardized documents based on templates (2) Support the list of Ascend NPU product models (3) Provide specifications for operator parameter descriptions (4) Generate call example frameworks.
vLLM Ascend plugin for LLM inference serving on Huawei Ascend NPU. Use for offline batch inference, API server deployment, quantization inference (with msmodelslim quantized models), tensor/pipeline parallelism for distributed serving, and OpenAI-compatible API endpoints. Supports Qwen, DeepSeek, GLM, LLaMA models with Ascend-optimized kernels.