`hybrid_ep_cpp` extension hardcodes `-std=c++17`; should inherit torch's C++ standard like `deep_ep_cpp`

[janbernloehr]

Describe the bug

DeepEP's hybrid-ep branch hardcodes -std=c++17 for the hybrid_ep_cpp CUDAExtension in setup.py (get_extension_hybrid_ep_cpp()). Recent PyTorch builds require C++20 — their headers emit #error C++20 or later compatible compiler is required to use PyTorch when included from a C++17 translation unit. As a result, building hybrid_ep_cpp against a current-PyTorch environment fails at compile time.

The sibling deep_ep_cpp extension (get_extension_deep_ep_cpp()) sets no explicit -std flag — its nvcc_flags = ['-O3', '-Xcompiler', '-O3'] — and therefore inherits torch's default standard (now c++20). That is why deep_ep_cpp still builds while hybrid_ep_cpp does not.

To Reproduce

1. Install a recent PyTorch whose headers require C++20 (e.g. a current nightly).
2. Build DeepEP hybrid-ep with the hybrid-EP multinode extension enabled:

   HYBRID_EP_MULTINODE=1 CUDA_HOME=/usr/local/cuda pip install --no-build-isolation .
   

3. Compilation of hybrid_ep_cpp aborts with the C++20 #error shown below.

Expected behavior

hybrid_ep_cpp builds against current PyTorch, the same way the sibling deep_ep_cpp extension already does.

Actual behavior

Building the hybrid_ep_cpp extension aborts at compile time. nvcc invokes the host compiler with -std=c++17 for the hybrid_ep_cpp sources, which include PyTorch headers (torch/all.h:5, ATen/ATen.h:5); those headers hard-#error under C++17:

error: #error C++20 or later compatible compiler is required to use PyTorch.
error: #error C++20 or later compatible compiler is required to use ATen.
...
ERROR: Failed building wheel for deep_ep

The build log shows a mix of -std=c++17 invocations (for hybrid_ep_cpp) and -std=c++20 invocations (for the sibling deep_ep_cpp, which inherits torch's standard); only the C++17 ones fail. pip install . then exits with code 1, so DeepEP is not installed and every downstream consumer is blocked.

Proposed fix

Remove the hardcoded -std=c++17 entry from the hybrid_ep_cpp nvcc compile args in setup.py, so the extension inherits torch's C++ standard from CUDAExtension/BuildExtension — exactly like deep_ep_cpp. This is portable: it builds against both C++17 torch (older) and C++20 torch (current), rather than swapping one hardcoded standard for another (a hardcoded -std=c++20 would instead break users still on a C++17 torch). The runtime JIT compiler (csrc/hybrid_ep/jit/compiler.cu) can keep -std=c++17, since its JIT-compiled kernels do not include torch headers and it relies on std::any.

Validated experimentally: with the hardcoded flag removed, the extension compiles cleanly and a DeepSeek-V3 MXFP8 expert-parallel (EP=8) training run completes end-to-end with healthy convergence (loss 12.02 decreasing to 6.60 across 50 steps) and no runtime regressions on GB200-class hardware (CUDA arch 10.0/9.0, PyTorch built for C++20).

Environment

• DeepEP: hybrid-ep branch, commit 17cfb817bccec3a9c247013360cc550c2bac441e
• PyTorch: current build requiring C++20 (arm64/aarch64)
• CUDA arch list: 10.0 9.0
• OS/arch: Linux aarch64

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This issue was drafted with assistance from the opus AI model.