[Bug]: vLLM with Neuron performance degrades dramatically if request concurrency is >= max_num_seqs

Your current environment

The output of `python collect_env.py`

```text collecting environment information... WARNING 08-29 18:36:46 _custom_ops.py:18] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'") /usr/local/lib/python3.10/dist-packages/vllm/connections.py:8: RuntimeWarning: Failed to read commit hash: No module named 'vllm.commit_id' from vllm.version import __version__ as VLLM_VERSION 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 22.04.4 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: version 3.27.7 Libc version: glibc-2.35 Python version: 3.10.12 (main, Jul 29 2024, 16:56:48) [GCC 11.4.0] (64-bit runtime) Python platform: Linux-6.2.0-1017-aws-x86_64-with-glibc2.35 Is CUDA available: False CUDA runtime version: No CUDA CUDA_MODULE_LOADING set to: N/A GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA 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): 128 On-line CPU(s) list: 0-127 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz CPU family: 6 Model: 106 Thread(s) per core: 2 Core(s) per socket: 32 Socket(s): 2 Stepping: 6 BogoMIPS: 5799.87 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd ida arat avx512vbmi pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear flush_l1d arch_capabilities Hypervisor vendor: KVM Virtualization type: full L1d cache: 3 MiB (64 instances) L1i cache: 2 MiB (64 instances) L2 cache: 80 MiB (64 instances) L3 cache: 108 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: Unknown: Dependent on hypervisor status 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 Host state unknown 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; 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] numpy==1.25.2 [pip3] nvidia-cublas-cu12==12.1.3.1 [pip3] nvidia-cuda-cupti-cu12==12.1.105 [pip3] nvidia-cuda-nvrtc-cu12==12.1.105 [pip3] nvidia-cuda-runtime-cu12==12.1.105 [pip3] nvidia-cudnn-cu12==8.9.2.26 [pip3] nvidia-cufft-cu12==11.0.2.54 [pip3] nvidia-curand-cu12==10.3.2.106 [pip3] nvidia-cusolver-cu12==11.4.5.107 [pip3] nvidia-cusparse-cu12==12.1.0.106 [pip3] nvidia-nccl-cu12==2.18.1 [pip3] nvidia-nvjitlink-cu12==12.6.20 [pip3] nvidia-nvtx-cu12==12.1.105 [pip3] pynvml==11.5.3 [pip3] pyzmq==26.2.0 [pip3] torch==2.1.2 [pip3] torch-neuronx==2.1.2.2.2.0 [pip3] torch-xla==2.1.3 [pip3] torchvision==0.16.2 [pip3] transformers==4.44.2 [pip3] transformers-neuronx==0.11.351 [pip3] triton==2.2.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: (0, '+--------+--------+--------+-------------+---------+--------+---------+\n| NEURON | NEURON | NEURON | CONNECTED | PCI | PID | RUNTIME |\n| DEVICE | CORES | MEMORY | DEVICES | BDF | | VERSION |\n+--------+--------+--------+-------------+---------+--------+---------+\n| 12 | 2 | 32 GB | 12, 3, 4, 1 | 10:1e.0 | 865036 | 2.21.41 |\n| 13 | 2 | 32 GB | 13, 0, 5, 2 | 10:1b.0 | 865036 | 2.21.41 |\n| 14 | 2 | 32 GB | 14, 1, 6, 3 | a0:1e.0 | 865036 | 2.21.41 |\n| 15 | 2 | 32 GB | 15, 2, 7, 0 | a0:1b.0 | 865036 | 2.21.41 |\n+--------+--------+--------+-------------+---------+--------+---------+', '') vLLM Version: 0.5.5@COMMIT_HASH_PLACEHOLDER vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: Could not collect ```

🐛 Describe the bug

When vLLM is used with Neuron, as long as number of concurrent requests are less than max_num_seqs, performance is nominal, but if number of concurrent requests is >= max_num_seqs, performance degrades dramatically. For example, in one test with max_num_seqs=4, request latency for up to 3 concurrent requests is in the 20 second range, but with 4 concurrent requests, it jumps to over 500 seconds. Logs show continual preemption, even when gpu_memory_utilization = 0.9.

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vllm-project / vllm