[issue templates] add some issue templates

[youkaichao]

Currently developers are heavily overloaded and we need to classify issues first.

cc @zhuohan123 @WoosukKwon @simon-mo

[youkaichao]

And we might need to add some vllm specific env-collector in the script.

[youkaichao]

This is currently how the output looks like:

PyTorch version: 2.1.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
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: 14.0.0-1ubuntu1.1
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.9.18 (main, Sep 11 2023, 13:41:44)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-14-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: Tesla V100-SXM2-32GB-LS
GPU 1: Tesla V100-SXM2-32GB-LS
GPU 2: Tesla V100-SXM2-32GB-LS
GPU 3: Tesla V100-SXM2-32GB-LS
GPU 4: Tesla V100-SXM2-32GB-LS
GPU 5: Tesla V100-SXM2-32GB-LS
GPU 6: Tesla V100-SXM2-32GB-LS
GPU 7: Tesla V100-SXM2-32GB-LS

Nvidia driver version: 545.23.08
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, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             80
On-line CPU(s) list:                0-79
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz
CPU family:                         6
Model:                              79
Thread(s) per core:                 2
Core(s) per socket:                 20
Socket(s):                          2
Stepping:                           1
CPU max MHz:                        3600.0000
CPU min MHz:                        1200.0000
BogoMIPS:                           4390.23
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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor 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 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts vnmi md_clear flush_l1d
Virtualization:                     VT-x
L1d cache:                          1.3 MiB (40 instances)
L1i cache:                          1.3 MiB (40 instances)
L2 cache:                           10 MiB (40 instances)
L3 cache:                           100 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-19,40-59
NUMA node1 CPU(s):                  20-39,60-79
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.1.2
[pip3] triton==2.1.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] torch                     2.1.2                    pypi_0    pypi
[conda] triton                    2.1.0                    pypi_0    pypiROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.3.3
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled

Should have enough information for most of the issues!

[youkaichao]

Note: you can preview the ui at https://github.com/youkaichao/vllm/issues/new/choose .

[youkaichao]

If @simon-mo is bandwidth bounded, maybe @WoosukKwon can merge this PR?

[simon-mo]

For the env, can we also collect GPU topology like SXM or PCIe, is the cards connected by NVLINK or not? (basically some sort of output of nvidia-smi topo -m)

[simon-mo]

Suggested change

	# For security purposes, please check the contents of collect_env.py before running it.
	# For security purposes, please feel free to check the contents of collect_env.py before running it.

[simon-mo]

Change this to vLLM hello world?

[simon-mo]

cite the pytorch link?

[youkaichao]

Good suggestions, will do today.