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5dde8cd483
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/33563 When NVCC or Clang are driving CUDA compilation many math functions are declared by default, with a small difference: Clang marks them as `__device__` only, while NVCC uses both `__host__` and `__device__`. This makes every un-elaborated `min` or `max` function call from a `__host__` function generate a syntax error when Clang is used. Fix the errors by using `std::min` and `std::max` from `<algorithm>`, since C++14 they are `constexpr` and can be used in the `__device__` code [1]. 1. https://llvm.org/docs/CompileCudaWithLLVM.html#algorithm Test Plan: ```lang=bash buck build mode/opt -c fbcode.cuda_use_clang=true //fblearner/flow/projects/dper:workflow buck build mode/opt //fblearner/flow/projects/dper:workflow ``` Execute tests on devgpu: ``` buck test mode/dev-nosan -j 8 //caffe2/caffe2/python/operator_test/... //caffe2/test:cuda ``` Reviewed By: ngimel Differential Revision: D20005795 fbshipit-source-id: 98a3f35e8a96c15d3ad3d2066396591f5cca1696
Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.
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