how to compile with GLIBCXX_USE_CXX11_ABI=1

[demonatic]

Your current environment

Collecting environment information...
PyTorch version: 2.2.0
Is debug build: False
CUDA used to build PyTorch: 12.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.28.3
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-165-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB

Nvidia driver version: 525.125.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 128
On-line CPU(s) list: 0-127
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8358 CPU @ 2.60GHz
CPU family: 6
Model: 106
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
Stepping: 6
Frequency boost: enabled
CPU max MHz: 3400.0000
CPU min MHz: 800.0000
BogoMIPS: 5200.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 3 MiB (64 instances)
L1i cache: 2 MiB (64 instances)
L2 cache: 80 MiB (64 instances)
L3 cache: 96 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-31,64-95
NUMA node1 CPU(s): 32-63,96-127
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.22.2
[pip3] torch==2.2.0
[pip3] triton==2.2.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.3
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 CPU Affinity NUMA Affinity
GPU0 X NV12 NV12 NV12 NV12 NV12 NV12 NV12 PXB NODE SYS NODE NODE SYS SYS 0-31,64-95 0
GPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 PXB NODE SYS NODE NODE SYS SYS 0-31,64-95 0
GPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 NODE PXB SYS NODE NODE SYS SYS 0-31,64-95 0
GPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 NODE PXB SYS NODE NODE SYS SYS 0-31,64-95 0
GPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 SYS SYS NODE SYS SYS PXB NODE 32-63,96-127 1
GPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 SYS SYS NODE SYS SYS PXB NODE 32-63,96-127 1
GPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 SYS SYS PXB SYS SYS NODE PXB 32-63,96-127 1
GPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X SYS SYS PXB SYS SYS NODE PXB 32-63,96-127 1
NIC0 PXB PXB NODE NODE SYS SYS SYS SYS X NODE SYS NODE NODE SYS SYS
NIC1 NODE NODE PXB PXB SYS SYS SYS SYS NODE X SYS NODE NODE SYS SYS
NIC2 SYS SYS SYS SYS NODE NODE PXB PXB SYS SYS X SYS SYS NODE PXB
NIC3 NODE NODE NODE NODE SYS SYS SYS SYS NODE NODE SYS X PIX SYS SYS
NIC4 NODE NODE NODE NODE SYS SYS SYS SYS NODE NODE SYS PIX X SYS SYS
NIC5 SYS SYS SYS SYS PXB PXB NODE NODE SYS SYS NODE SYS SYS X NODE
NIC6 SYS SYS SYS SYS NODE NODE PXB PXB SYS SYS PXB SYS SYS NODE X

Legend:

X = Self
SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
PIX = Connection traversing at most a single PCIe bridge
NV# = Connection traversing a bonded set of # NVLinks

NIC Legend:

NIC0: mlx5_0
NIC1: mlx5_1
NIC2: mlx5_2
NIC3: mlx5_3
NIC4: mlx5_4
NIC5: mlx5_5
NIC6: mlx5_6

How you are installing vllm

my torch library was compiled with gcc with cxx11 abi，so I need to compile vllm with cxx11 abi either. When I try to add add_definitions(-D_GLIBCXX_USE_CXX11_ABI=1) in CMakeLists.txt and use pip install -e . to compile vllm from source, it seems atomatically add another -D_GLIBCXX_USE_CXX11_ABI=0 at last which override my config

/usr/local/cuda/bin/nvcc -forward-unknown-to-host-compiler -DTORCH_EXTENSION_NAME=_C -DUSE_C10D_GLOO -DUSE_C10D_NCCL -DUSE_DISTRIBUTED -DUSE_RPC -DUSE_TENSORPIPE -D_C_EXPORTS -D_GLIBCXX_USE_CXX11_ABI=1 -I/home/vllm/csrc -isystem /usr/include/python3.10 -isystem /tmp/pip-build-env-ro4dn3pv/overlay/local/lib/python3.10/dist-packages/torch/include -isystem /tmp/pip-build-env-ro4dn3pv/overlay/local/lib/python3.10/dist-packages/torch/include/torch/csrc/api/include -isystem /usr/local/cuda/include -DONNX_NAMESPACE=onnx_c2 -Xcudafe --diag_suppress=cc_clobber_ignored,--diag_suppress=field_without_dll_interface,--diag_suppress=base_class_has_different_dll_interface,--diag_suppress=dll_interface_conflict_none_assumed,--diag_suppress=dll_interface_conflict_dllexport_assumed,--diag_suppress=bad_friend_decl --expt-relaxed-constexpr --expt-extended-lambda -O2 -g -DNDEBUG -std=c++17 "--generate-code=arch=compute_80,code=[sm_80]" -Xcompiler=-fPIC --expt-relaxed-constexpr -DENABLE_FP8_E5M2 --threads=1 -D_GLIBCXX_USE_CXX11_ABI=0 -MD -MT CMakeFiles/_C.dir/csrc/pos_encoding_ke
rnels.cu.o -MF CMakeFiles/_C.dir/csrc/pos_encoding_kernels.cu.o.d -x cu -c /home/vllm/csrc/pos_encoding_kernels
.cu -o CMakeFiles/_C.dir/csrc/pos_encoding_kernels.cu.o

[demonatic]

It finds out that vllm pip install -e . will download a torch wheel and import it in setup.py with torch._C._GLIBCXX_USE_CXX11_ABI = False, which does not use my torch in environment with torch._C._GLIBCXX_USE_CXX11_ABI = True, so is there any convenient way to compile with torch in my environment ?

[demonatic]

is there any way to compile VLLM with GLIBCXX_USE_CXX11_ABI=1 ?

[nightflight-dk]

@njhill Nick, I believe this issue is related and could help #5084

[github-actions]

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