### Set up environment Source: https://github.com/wzzll123/multikernelbench/blob/main/README.md Commands to create a conda environment and install necessary dependencies for different hardware backends. ```bash conda create --name multi-kernel-bench python=3.10 conda activate multi-kernel-bench pip install -r requirements.txt # For NPU users: pip install torch-npu==2.7.1 # For Intel GPU (torch xpu) users: pip install torch==2.7.0 --index-url https://download.pytorch.org/whl/xpu # For Moore Threads GPU (MUSA) users: # Install the torch_musa package matching your MUSA runtime. ``` -------------------------------- ### Set API keys Source: https://github.com/wzzll123/multikernelbench/blob/main/README.md Environment variable exports for LLM service authentication. ```bash export DEEPSEEK_API_KEY= export DASHSCOPE_API_KEY= export OPEN_ROUNTER_KEY= ``` -------------------------------- ### Evaluate kernels Source: https://github.com/wzzll123/multikernelbench/blob/main/README.md Command to run evaluation on generated kernels. ```bash python evaluation.py --model deepseek-chat --language ascendc --strategy add_shot --categories activation ``` -------------------------------- ### Generate kernels Source: https://github.com/wzzll123/multikernelbench/blob/main/README.md Command to trigger kernel generation using a specified LLM model and strategy. ```bash python generate_and_write.py --model deepseek-chat --language ascendc --strategy add_shot --categories activation ``` -------------------------------- ### Evaluate a single response Source: https://github.com/wzzll123/multikernelbench/blob/main/README.md Command to evaluate a specific generated response file directly. ```bash python eval_single_runner.py \ --input prompts/ascendc_direct_launch_model_relu.json \ --op relu \ --language ascendc_direct_launch \ --result relu_result.json ``` === COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.