[Bug]: Can't load gemma-2-9b-it with vllm 0.5.2

[vlsav]

Your current environment

PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: RED OS release MUROM (7.3.4) Standard Edition (x86_64)
GCC version: (GCC) 11.4.1 20230605 (Red Soft 11.4.0-1)
Clang version: Could not collect
CMake version: version 3.29.2
Libc version: glibc-2.28

Python version: 3.10.9 (main, Jan 11 2023, 15:21:40) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.1.52-1.el7.3.x86_64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: 12.1.66
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 4090
Nvidia driver version: 530.30.02
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Архитектура:                        x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      46 bits physical, 48 bits virtual
Порядок байт:                       Little Endian
CPU(s):                             32
On-line CPU(s) list:                0-31
ID прроизводителя:                  GenuineIntel
Имя модели:                         13th Gen Intel(R) Core(TM) i9-13900K
Семейство ЦПУ:                      6
Модель:                             183
Thread(s) per core:                 2
Ядер на сокет:                      24
Сокетов:                            1
Степпинг:                           1
CPU max MHz:                        5800,0000
CPU min MHz:                        800,0000
BogoMIPS:                           5990.40
Флаги:                              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 tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Виртуализация:                      VT-x
L1d cache:                          896 KiB (24 instances)
L1i cache:                          1,3 MiB (24 instances)
L2 cache:                           32 MiB (12 instances)
L3 cache:                           36 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-31
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
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] flashinfer==0.0.8+cu121torch2.3
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] onnxruntime==1.18.0
[pip3] sentence-transformers==2.2.2
[pip3] torch==2.3.1
[pip3] torchvision==0.18.1
[pip3] transformers==4.42.4
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==2.3.1
[pip3] vllm-nccl-cu12==2.18.1.0.4.0
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] sentence-transformers     2.2.2                    pypi_0    pypi
[conda] transformers              4.40.1                   pypi_0    pypi
[conda] vllm-nccl-cu12            2.18.1.0.4.0             pypi_0    pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    CPU Affinity    NUMA Affinity
GPU0     X      0-31            N/A

🐛 Describe the bug

Successfully launched gemma-2-9b-it with vlmm 0.5.1.
Following script was used
export VLLM_ATTENTION_BACKEND=FLASHINFER
python -m vllm.entrypoints.openai.api_server --port=8080 --host=0.0.0.0 --model /models/gemma-2-9b-it --quantization fp8 --enforce-eager --seed 1234 --served-model-name gemma-2-9b
no issues (except sliding window warning and capping the max length to the sliding window size (4096).
Same script after installing vllm 0.5.2 gives error message:

[rank0]: Traceback (most recent call last):
[rank0]:   File "/opt/llm/miniconda3/lib/python3.10/runpy.py", line 196, in _run_module_as_main
[rank0]:     return _run_code(code, main_globals, None,
[rank0]:   File "/opt/llm/miniconda3/lib/python3.10/runpy.py", line 86, in _run_code
[rank0]:     exec(code, run_globals)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 282, in <module>
[rank0]:     run_server(args)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 224, in run_server
[rank0]:     if llm_engine is not None else AsyncLLMEngine.from_engine_args(
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 444, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 373, in __init__
[rank0]:     self.engine = self._init_engine(*args, **kwargs)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 520, in _init_engine
[rank0]:     return engine_class(*args, **kwargs)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 249, in __init__
[rank0]:     self.model_executor = executor_class(
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 150, in __init__
[rank0]:     super().__init__(model_config, cache_config, parallel_config,
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 46, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 25, in _init_executor
[rank0]:     self.driver_worker.load_model()
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/worker/worker.py", line 139, in load_model
[rank0]:     self.model_runner.load_model()
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 256, in load_model
[rank0]:     self.model = get_model(model_config=self.model_config,
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/model_loader/__init__.py", line 21, in get_model
[rank0]:     return loader.load_model(model_config=model_config,
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 267, in load_model
[rank0]:     model = _initialize_model(model_config, self.load_config,
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 104, in _initialize_model
[rank0]:     return model_class(config=model_config.hf_config,
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/models/gemma2.py", line 323, in __init__
[rank0]:     self.model = Gemma2Model(config, cache_config, quant_config)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/models/gemma2.py", line 251, in __init__
[rank0]:     self.layers = nn.ModuleList([
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/models/gemma2.py", line 252, in <listcomp>
[rank0]:     Gemma2DecoderLayer(layer_idx, config, cache_config, quant_config)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/models/gemma2.py", line 178, in __init__
[rank0]:     self.self_attn = Gemma2Attention(
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/models/gemma2.py", line 115, in __init__
[rank0]:     self.qkv_proj = QKVParallelLinear(
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/layers/linear.py", line 517, in __init__
[rank0]:     super().__init__(input_size=input_size,
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/layers/linear.py", line 244, in __init__
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/layers/linear.py", line 244, in __init__
[rank0]:     super().__init__(input_size, output_size, skip_bias_add, params_dtype,
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/layers/linear.py", line 158, in __init__
[rank0]:     self.quant_method = quant_config.get_quant_method(self)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/fp8.py", line 74, in get_quant_method
[rank0]:     return Fp8LinearMethod(self)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/fp8.py", line 105, in __init__
[rank0]:     self.cutlass_fp8_supported = cutlass_fp8_supported()
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/model_executor/layers/quantization/utils/w8a8_utils.py", line 15, in cutlass_fp8_supported
[rank0]:     return ops.cutlass_scaled_mm_supports_fp8(capability)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/vllm/_custom_ops.py", line 220, in cutlass_scaled_mm_supports_fp8
[rank0]:     return torch.ops._C.cutlass_scaled_mm_supports_fp8(cuda_device_capability)
[rank0]:   File "/home/testvllm/.local/lib/python3.10/site-packages/torch/_ops.py", line 921, in __getattr__
[rank0]:     raise AttributeError(
[rank0]: AttributeError: '_OpNamespace' '_C' object has no attribute 'cutlass_scaled_mm_supports_fp8'

[youkaichao]

cc @tlrmchlsmth @mgoin for cutlass and fp8

[ArlanCooper]

the same operation, get this error:

[rank0]: Traceback (most recent call last):
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/runpy.py", line 196, in _run_module_as_main
[rank0]:     return _run_code(code, main_globals, None,
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/runpy.py", line 86, in _run_code
[rank0]:     exec(code, run_globals)
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 216, in <module>
[rank0]:     engine = AsyncLLMEngine.from_engine_args(
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 431, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 360, in __init__
[rank0]:     self.engine = self._init_engine(*args, **kwargs)
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 507, in _init_engine
[rank0]:     return engine_class(*args, **kwargs)
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 256, in __init__
[rank0]:     self._initialize_kv_caches()
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 353, in _initialize_kv_caches
[rank0]:     self.model_executor.determine_num_available_blocks())
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 76, in determine_num_available_blocks
[rank0]:     return self.driver_worker.determine_num_available_blocks()
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/worker/worker.py", line 173, in determine_num_available_blocks
[rank0]:     self.model_runner.profile_run()
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 874, in profile_run
[rank0]:     self.execute_model(model_input, kv_caches, intermediate_tensors)
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/home/powerop/work/conda/envs/qwen2/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 1201, in execute_model
[rank0]:     BatchDecodeWithPagedKVCacheWrapper(
[rank0]: TypeError: 'NoneType' object is not callable

[wlwqq]

the same operation, get this error:
`TypeError Traceback (most recent call last)
Cell In[1], line 5
3 print('param = ',os.environ.get('VLLM_ATTENTION_BACKEND'))
4 from vllm import LLM,SamplingParams
----> 5 llm = LLM(model='/data/wlh/gemma_2_9b_it_prod')
6 prompts = ["Hello , who are you?"]
7 sampling_params = SamplingParams(temperature=0.8, top_p=0.95,tensor_parallel_size=1)

File ~/.local/lib/python3.8/site-packages/vllm/entrypoints/llm.py:150, in LLM.init(self, model, tokenizer, tokenizer_mode, skip_tokenizer_init, trust_remote_code, tensor_parallel_size, dtype, quantization, revision, tokenizer_revision, seed, gpu_memory_utilization, swap_space, enforce_eager, max_context_len_to_capture, max_seq_len_to_capture, disable_custom_all_reduce, **kwargs)
128 raise TypeError(
129 "There is no need to pass vision-related arguments anymore.")
130 engine_args = EngineArgs(
131 model=model,
132 tokenizer=tokenizer,
(...)
148 **kwargs,
149 )
--> 150 self.llm_engine = LLMEngine.from_engine_args(
151 engine_args, usage_context=UsageContext.LLM_CLASS)
152 self.request_counter = Counter()

File ~/.local/lib/python3.8/site-packages/vllm/engine/llm_engine.py:421, in LLMEngine.from_engine_args(cls, engine_args, usage_context)
419 executor_class = GPUExecutor
420 # Create the LLM engine.
--> 421 engine = cls(
422 **engine_config.to_dict(),
423 executor_class=executor_class,
424 log_stats=not engine_args.disable_log_stats,
425 usage_context=usage_context,
426 )
427 return engine

File ~/.local/lib/python3.8/site-packages/vllm/engine/llm_engine.py:263, in LLMEngine.init(self, model_config, cache_config, parallel_config, scheduler_config, device_config, load_config, lora_config, multimodal_config, speculative_config, decoding_config, observability_config, prompt_adapter_config, executor_class, log_stats, usage_context, stat_loggers)
249 self.model_executor = executor_class(
250 model_config=model_config,
251 cache_config=cache_config,
(...)
259 prompt_adapter_config=prompt_adapter_config,
260 )
262 if not self.model_config.embedding_mode:
--> 263 self._initialize_kv_caches()
265 # If usage stat is enabled, collect relevant info.
266 if is_usage_stats_enabled():

File ~/.local/lib/python3.8/site-packages/vllm/engine/llm_engine.py:362, in LLMEngine._initialize_kv_caches(self)
355 def _initialize_kv_caches(self) -> None:
356 """Initialize the KV cache in the worker(s).
357
358 The workers will determine the number of blocks in both the GPU cache
359 and the swap CPU cache.
360 """
361 num_gpu_blocks, num_cpu_blocks = (
--> 362 self.model_executor.determine_num_available_blocks())
364 if self.cache_config.num_gpu_blocks_override is not None:
365 num_gpu_blocks_override = self.cache_config.num_gpu_blocks_override

File ~/.local/lib/python3.8/site-packages/vllm/executor/gpu_executor.py:78, in GPUExecutor.determine_num_available_blocks(self)
74 def determine_num_available_blocks(self) -> Tuple[int, int]:
75 """Determine the number of available KV blocks by invoking the
76 underlying worker.
77 """
---> 78 return self.driver_worker.determine_num_available_blocks()

File ~/.local/lib/python3.8/site-packages/torch/utils/_contextlib.py:115, in context_decorator..decorate_context(*args, **kwargs)
112 @functools.wraps(func)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)

File ~/.local/lib/python3.8/site-packages/vllm/worker/worker.py:179, in Worker.determine_num_available_blocks(self)
175 torch.cuda.empty_cache()
177 # Execute a forward pass with dummy inputs to profile the memory usage
178 # of the model.
--> 179 self.model_runner.profile_run()
181 # Calculate the number of blocks that can be allocated with the
182 # profiled peak memory.
183 torch.cuda.synchronize()

File ~/.local/lib/python3.8/site-packages/torch/utils/_contextlib.py:115, in context_decorator..decorate_context(*args, **kwargs)
112 @functools.wraps(func)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)

File ~/.local/lib/python3.8/site-packages/vllm/worker/model_runner.py:923, in GPUModelRunnerBase.profile_run(self)
918 if not get_pp_group().is_first_rank:
919 intermediate_tensors = self.model.make_empty_intermediate_tensors(
920 batch_size=batch_size,
921 dtype=self.model_config.dtype,
922 device=self.device)
--> 923 self.execute_model(model_input, kv_caches, intermediate_tensors)
924 torch.cuda.synchronize()
925 return

File ~/.local/lib/python3.8/site-packages/torch/utils/_contextlib.py:115, in context_decorator..decorate_context(*args, **kwargs)
112 @functools.wraps(func)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)

File ~/.local/lib/python3.8/site-packages/vllm/worker/model_runner.py:1299, in ModelRunner.execute_model(self, model_input, kv_caches, intermediate_tensors, num_steps)
1293 if self.flashinfer_decode_workspace_buffer is None:
1294 self.flashinfer_decode_workspace_buffer = torch.empty(
1295 FLASHINFER_WORKSPACE_BUFFER_SIZE,
1296 dtype=torch.uint8,
1297 device=self.device)
1298 self.flashinfer_decode_wrapper =
-> 1299 BatchDecodeWithPagedKVCacheWrapper(
1300 self.flashinfer_decode_workspace_buffer, "NHD")
1301 self.flashinfer_prefill_workspace_buffer = torch.empty(
1302 FLASHINFER_WORKSPACE_BUFFER_SIZE,
1303 dtype=torch.uint8,
1304 device=self.device)
1305 self.flashinfer_prefill_wrapper =
1306 BatchPrefillWithPagedKVCacheWrapper(
1307 self.flashinfer_prefill_workspace_buffer, "NHD")

TypeError: 'NoneType' object is not callable
`

[tlrmchlsmth]

@vlsav How did you install vLLM? '_OpNamespace' '_C' object has no attribute 'cutlass_scaled_mm_supports_fp8' would show up if there is a version mismatch between the vLLM Python and the compiled binaries for the kernels.

If you installed vLLM from source like:

pip install -e .

Then either try rerunning pip install -e . or the following to recompile the kernels and resolve your issue:

python setup.py build_ext --inplace

@wlwqq and @ArlanCooper, those are separate errors -- please open up a new issue to keep the conversation focused.

[vlsav]

@tlrmchlsmth By pip from pypi, not from sources:
pip install vllm==0.5.2

[tlrmchlsmth]

Update: 3f3b6b2 made it into 0.5.1, which kind of breaks the version mismatch theory, unfortunately.

[tlrmchlsmth]

@vlsav what do you see when you run the following?
nm <your venv here>/lib/python3.10/site-packages/vllm/_C.abi3.so | grep cutlass_scaled_mm_supports_fp8

[vlsav]

@tlrmchlsmth
00000000002a0b00 T _Z30cutlass_scaled_mm_supports_fp8l
but this is right now, after I reverted back to vllm 0.5.1
after pip install vllm==0.5.2:
000000000029cf70 T _Z30cutlass_scaled_mm_supports_fp8l

[jueming0312]

I also encountered the same problem when deploying the gemma-2-9b-it model with VLLM 0.5.2.

[twright8]

+1

[tlrmchlsmth]

00000000002a0b00 T _Z30cutlass_scaled_mm_supports_fp8l
but this is right now, after I reverted back to vllm 0.5.1
after pip install vllm==0.5.2:
000000000029cf70 T _Z30cutlass_scaled_mm_supports_fp8l

This looks right to me -- at least the function is present in the .so file. I'll try to reproduce the problem.

[tlrmchlsmth]

@jueming0312 and @twright8 it'd be helpful if you could share the output of collect_env.py

[twright8]

`PyTorch version: 2.3.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.30.0
Libc version: glibc-2.31

Python version: 3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:36:39) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.15.154+-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: Tesla T4
GPU 1: Tesla T4

Nvidia driver version: 550.90.07
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 4
On-line CPU(s) list: 0-3
Thread(s) per core: 2
Core(s) per socket: 2
Socket(s): 1
NUMA node(s): 1
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) CPU @ 2.00GHz
Stepping: 3
CPU MHz: 2000.216
BogoMIPS: 4000.43
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 64 KiB
L1i cache: 64 KiB
L2 cache: 2 MiB
L3 cache: 38.5 MiB
NUMA node0 CPU(s): 0-3
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; IBRS
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; IBRS; IBPB conditional; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT Host state unknown
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 rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities

Versions of relevant libraries:
[pip3] flake8==7.0.0
[pip3] flashinfer==0.1.0+cu121torch2.3
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] onnx==1.16.1
[pip3] optree==0.12.1
[pip3] pytorch-ignite==0.5.0.post2
[pip3] pytorch-lightning==2.3.3
[pip3] torch==2.3.0
[pip3] torchaudio==2.1.2
[pip3] torchdata==0.7.1
[pip3] torchinfo==1.8.0
[pip3] torchmetrics==1.4.0.post0
[pip3] torchtext==0.16.2
[pip3] torchvision==0.18.0
[pip3] transformers==4.42.3
[pip3] triton==2.3.0
[conda] flashinfer 0.1.0+cu121torch2.3 pypi_0 pypi
[conda] magma-cuda121 2.6.1 1 pytorch
[conda] mkl 2022.1.0 hc2b9512_224
[conda] nccl 2.22.3.1 hbc370b7_0 conda-forge
[conda] numpy 1.26.4 py310hb13e2d6_0 conda-forge
[conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi
[conda] optree 0.12.1 pypi_0 pypi
[conda] pytorch-ignite 0.5.0.post2 pypi_0 pypi
[conda] pytorch-lightning 2.3.3 pypi_0 pypi
[conda] torch 2.3.0 pypi_0 pypi
[conda] torchaudio 2.1.2 pypi_0 pypi
[conda] torchdata 0.7.1 pypi_0 pypi
[conda] torchinfo 1.8.0 pypi_0 pypi
[conda] torchmetrics 1.4.0.post0 pypi_0 pypi
[conda] torchtext 0.16.2 pypi_0 pypi
[conda] torchvision 0.18.0 pypi_0 pypi
[conda] transformers 4.42.3 pypi_0 pypi
[conda] triton 2.3.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PHB 0-3 0 N/A
GPU1 PHB X 0-3 0 N/A

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`

[twright8]

Think i needed to upgrade flashinfer. But I cant use it as it only supports ampere 8+. There's a solution proposed here: #6173