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MegEngine/dnn/scripts/cutlass_generator
Megvii Engine Team e0d505e6bd fix(mgb/dnn): fix bug that some cutlass file compile very slowly on SM86 2 years ago
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BUILD feat(dnn/cuda): add implicit bmm kernels for large kernel depthwise convolution backward filter opr 3 years ago
README.md feat(dnn/cuda): generate cutlass kimpls using cmake and bazel 3 years ago
conv2d_operation.py build(mgb/cutlass): merge partial headers 3 years ago
gemm_operation.py build(mgb/cutlass): merge partial headers 3 years ago
gen_list.py fix(mgb/dnn): fix bug that some cutlass file compile very slowly on SM86 2 years ago
generator.py fix(mgb/dnn): fix bug that some cutlass file compile very slowly on SM86 2 years ago
library.py feat(dnn/cuda): add implicit bmm kernels for large kernel depthwise convolution backward filter opr 3 years ago
list.bzl fix(mgb/dnn): fix bug that some cutlass file compile very slowly on SM86 2 years ago
manifest.py feat(dnn/cuda): add implicit bmm large kernel dwconv2d fprop impl 3 years ago

README.md

Generate device kernel registration code for CUTLASS kernels

Usage

python3 generator.py [--operations {gemm, gemv, conv2d, deconv}] [--type {simt, tensorop8816, tensorop8832}]
                     output
  • operations: operation kind, including gemm|gemv|conv2d|deconv
  • type: opcode class, simt|tensorop8816|tensorop8832
  • output: the output directory for CUTLASS kernels

Generate file list for bazel

We generate list.bzl because the genrule method of bazel requires that the output file list be specified in the analysis phase.

Please call gen_list.py when new operations are added.

python3 gen_list.py
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MegEngine 安装包中集成了使用 GPU 运行代码所需的 CUDA 环境，不用区分 CPU 和 GPU 版。 如果想要运行 GPU 程序，请确保机器本身配有 GPU 硬件设备并安装好驱动。 如果你想体验在云端 GPU 算力平台进行深度学习开发的感觉，欢迎访问 MegStudio 平台

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