[Bug]: enable_chunked_prefill feature hangs on AMD Radeon PRO W7900 (gfx1100) about vllm HOT 5 OPEN

INFO 05-23 21:41:39 pynccl.py:58] Loading nccl from library librccl.so.1
Namespace(model='/dockerx/data/llama-2-13b-chat-hf', tokenizer=None, quantization=None, tensor_parallel_size=1, input_len=1024, output_len=256, batch_size=1, n=1, use_beam_search=False, num_iters_warmup=10, num_iters=10, trust_remote_code=False, dtype='auto', enforce_eager=False, kv_cache_dtype='auto', quantization_param_path=None, profile=False, profile_result_dir=None, device='cuda', block_size=16, enable_chunked_prefill=True, ray_workers_use_nsight=False, download_dir=None)
INFO 05-23 21:41:40 config.py:581] Chunked prefill is enabled (EXPERIMENTAL).
INFO 05-23 21:41:40 llm_engine.py:84] Initializing an LLM engine (v0.4.0.post1) with config: model='/dockerx/data/llama-2-13b-chat-hf', speculative_config=None, tokenizer='/dockerx/data/llama-2-13b-chat-hf', tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=2048, download_dir=None, load_format=auto, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0)
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML
  warnings.warn("Can't initialize NVML")
INFO 05-23 21:41:41 selector.py:59] flash_atten is not supported on NAVI GPUs.
INFO 05-23 21:41:41 selector.py:38] Using ROCmFlashAttention backend.
INFO 05-23 21:41:50 model_runner.py:169] Loading model weights took 24.2835 GB
INFO 05-23 21:41:52 gpu_executor.py:61] # GPU blocks: 1270, # CPU blocks: 327
INFO 05-23 21:41:52 model_runner.py:967] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 05-23 21:41:52 model_runner.py:971] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
INFO 05-23 21:41:57 model_runner.py:1048] Graph capturing finished in 5 secs.
SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=1.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=[], stop_token_ids=[], include_stop_str_in_output=False, ignore_eos=True, max_tokens=256, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None)
Warming up...
Warmup iterations: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [05:01<00:00, 30.15s/it]
Profiling iterations: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [05:01<00:00, 30.12s/it]
Avg latency: xxx seconds

root@26a65a87bce7:/data/vllm-latest/vllm-240507/benchmarks# python benchmark_throughput.py --dataset /data/ShareGPT_V3_unfiltered_cleaned_split.json \
> --model /data/THUDM/.chatglm2-6b --enable-chunked-prefill --max-num-batched-tokens 1024 --trust-remote-code --num-prompts 100
Namespace(backend='vllm', dataset='/data/ShareGPT_V3_unfiltered_cleaned_split.json', input_len=None, output_len=None, model='/data/THUDM/chatglm2-6b', tokenizer='/data/THUDM/chatglm2-6b', quantization=None, tensor_parallel_size=1, n=1, use_beam_search=False, num_prompts=100, seed=0, hf_max_batch_size=None, trust_remote_code=True, max_model_len=None, dtype='auto', gpu_memory_utilization=0.9, enforce_eager=False, kv_cache_dtype='auto', quantization_param_path=None, device='cuda', enable_prefix_caching=False, enable_chunked_prefill=True, max_num_batched_tokens=1024, download_dir=None)
INFO 05-24 02:35:06 config.py:627] Chunked prefill is enabled (EXPERIMENTAL).
INFO 05-24 02:35:06 llm_engine.py:100] Initializing an LLM engine (v0.4.2) with config: model='/data/THUDM/chatglm2-6b', speculative_config=None, tokenizer='/data/THUDM/chatglm2-6b', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.float16, max_seq_len=32768, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/data/THUDM/chatglm2-6b)
WARNING 05-24 02:35:06 tokenizer.py:126] Using a slow tokenizer. This might cause a significant slowdown. Consider using a fast tokenizer instead.
INFO 05-24 02:35:06 utils.py:660] Found nccl from library /opt/rocm-6.0.0/lib/librccl.so.1
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML
  warnings.warn("Can't initialize NVML")
INFO 05-24 02:35:09 selector.py:63] flash_atten is not supported on NAVI GPUs.
INFO 05-24 02:35:09 selector.py:37] Using ROCmFlashAttention backend.
INFO 05-24 02:35:23 model_runner.py:175] Loading model weights took 11.6608 GB
INFO 05-24 02:35:25 gpu_executor.py:114] # GPU blocks: 65192, # CPU blocks: 9362
INFO 05-24 02:35:27 model_runner.py:937] Capturing the model for CUDA graphs. This may lead to unexpected consequences if the model is not static. To run the model in eager mode, set 'enforce_eager=True' or use '--enforce-eager' in the CLI.
INFO 05-24 02:35:27 model_runner.py:941] CUDA graphs can take additional 1~3 GiB memory per GPU. If you are running out of memory, consider decreasing `gpu_memory_utilization` or enforcing eager mode. You can also reduce the `max_num_seqs` as needed to decrease memory usage.
INFO 05-24 02:35:30 model_runner.py:1017] Graph capturing finished in 4 secs.
Processed prompts:   0%|                                                                                                                                                          | 0/100 [00:00<?, ?it/s]

root@26a65a87bce7:/app/vllm# python collect_env.py
Collecting environment information...
/opt/conda/envs/py_3.9/lib/python3.9/site-packages/torch/cuda/__init__.py:611: UserWarning: Can't initialize NVML
  warnings.warn("Can't initialize NVML")
PyTorch version: 2.1.1+git011de5c
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 6.0.32830-d62f6a171

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: 17.0.0 (https://github.com/RadeonOpenCompute/llvm-project roc-6.0.0 23483 7208e8d15fbf218deb74483ea8c549c67ca4985e)
CMake version: version 3.29.2
Libc version: glibc-2.31

Python version: 3.9.18 (main, Sep 11 2023, 13:41:44)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-92-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 10.1.243
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: AMD Radeon PRO W7900NoGCNArchNameOnOldPyTorch
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: 6.0.32830
MIOpen runtime version: 3.0.0
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Byte Order:                         Little Endian
Address sizes:                      46 bits physical, 57 bits virtual
CPU(s):                             64
On-line CPU(s) list:                0-63
Thread(s) per core:                 1
Core(s) per socket:                 32
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              106
Model name:                         Intel(R) Xeon(R) Platinum 8338C CPU @ 2.60GHz
Stepping:                           6
CPU MHz:                            800.000
CPU max MHz:                        3500.0000
CPU min MHz:                        800.0000
BogoMIPS:                           5200.00
Virtualization:                     VT-x
L1d cache:                          3 MiB
L1i cache:                          2 MiB
L2 cache:                           80 MiB
L3 cache:                           96 MiB
NUMA node0 CPU(s):                  0-31
NUMA node1 CPU(s):                  32-63
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 disabled
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: 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
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 intel_ppin 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 split_lock_detect wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] mypy==1.4.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.22.4
[pip3] torch==2.1.1+git011de5c
[pip3] torchvision==0.16.1+fdea156
[pip3] triton==2.1.0
[conda] No relevant packagesROCM Version: 6.0.32830-d62f6a171
Neuron SDK Version: N/A
vLLM Version: 0.4.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
Could not collect