Docs: c++11 -> c++14 (#30530)

Summary: Pull Request resolved: #30530 Switch some mentions of "C++11" in the docs to "C++14" ghstack-source-id: 95812049 Test Plan: testinprod Differential Revision: D18733733 fbshipit-source-id: b9d0490eb3f72bad974d134bbe9eb563f6bc8775
pytorch · Dec 17, 2019 · 5554e5b · 5554e5b
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diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -573,7 +573,7 @@ If you are working on the CUDA code, here are some useful CUDA debugging tips:
     slow down the build process for about 50% (compared to only `DEBUG=1`), so use wisely.
 2. `cuda-gdb` and `cuda-memcheck` are your best CUDA debugging friends. Unlike`gdb`,
    `cuda-gdb` can display actual values in a CUDA tensor (rather than all zeros).
-3. CUDA supports a lot of C++11 features such as, `std::numeric_limits`, `std::nextafter`,
+3. CUDA supports a lot of C++11/14 features such as, `std::numeric_limits`, `std::nextafter`,
    `std::tuple` etc. in device code. Many of such features are possible because of the
    [--expt-relaxed-constexpr](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#constexpr-functions)
    nvcc flag. There is a known [issue](https://github.com/ROCm-Developer-Tools/HIP/issues/374)

diff --git a/aten/conda/meta.yaml b/aten/conda/meta.yaml
@@ -26,7 +26,7 @@ requirements:
 about:
   home: https://github.com/zdevito/ATen
   license: BSD
-  summary: A TENsor library for C++11
+  summary: A TENsor library for C++14
 
 extra:
   recipe-maintainers:

diff --git a/caffe2/contrib/aten/README.md b/caffe2/contrib/aten/README.md
@@ -1,7 +1,7 @@
 # An ATen operator for Caffe2
 
 [ATen](https://github.com/zdevito/aten) is a simple tensor library thats exposes the Tensor operations in Torch
-and PyTorch directly in C++11. This library provides a generated wrapper around the ATen API
+and PyTorch directly in C++14. This library provides a generated wrapper around the ATen API
 that makes these functions available in Caffe2 as an operator. It also makes it accessible using the
 ToffeeIR.
 

diff --git a/cmake/Modules_CUDA_fix/upstream/FindCUDA.cmake b/cmake/Modules_CUDA_fix/upstream/FindCUDA.cmake
@@ -1472,7 +1472,7 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
     string(APPEND _cuda_nvcc_flags_config "\nset(CUDA_NVCC_FLAGS_${config_upper} ${CUDA_NVCC_FLAGS_${config_upper}} ;; ${CUDA_WRAP_OPTION_NVCC_FLAGS_${config_upper}})")
   endforeach()
 
-  # Process the C++11 flag.  If the host sets the flag, we need to add it to nvcc and
+  # Process the C++14 flag.  If the host sets the flag, we need to add it to nvcc and
   # remove it from the host. This is because -Xcompile -std=c++ will choke nvcc (it uses
   # the C preprocessor).  In order to get this to work correctly, we need to use nvcc's
   # specific c++14 flag.

diff --git a/docs/cpp/source/frontend.rst b/docs/cpp/source/frontend.rst
@@ -1,7 +1,7 @@
 The C++ Frontend
 ================
 
-The PyTorch C++ frontend is a C++11 library for CPU and GPU
+The PyTorch C++ frontend is a C++14 library for CPU and GPU
 tensor computation, with automatic differentiation and high level building
 blocks for state of the art machine learning applications.
 

diff --git a/docs/cpp/source/notes/tensor_basics.rst b/docs/cpp/source/notes/tensor_basics.rst
@@ -2,7 +2,7 @@ Tensor Basics
 =============
 
 The ATen tensor library backing PyTorch is a simple tensor library thats exposes
-the Tensor operations in Torch directly in C++11. ATen's API is auto-generated
+the Tensor operations in Torch directly in C++14. ATen's API is auto-generated
 from the same declarations PyTorch uses so the two APIs will track each other
 over time.