[Bug]: TypeError: 'NoneType' object is not callable when loading Gemma 2 9B with new 0.5.1 version

[DanielusG]

Your current environment

Idk how to run it inside a docker

🐛 Describe the bug

Simply run the following command
docker run --runtime nvidia --gpus all -v ~/.cache/huggingface:/root/.cache/huggingface --env "HUGGING_FACE_HUB_TOKEN=<my secret>" --env "VLLM_ATTENTION_BACKEND=FLASHINFER" -p 8000:8000 --ipc=host vllm/vllm-openai:latest --model google/gemma-2-9b-it

It doesn't work.

Log:

INFO 07-06 07:29:27 api_server.py:206] vLLM API server version 0.5.1
INFO 07-06 07:29:27 api_server.py:207] args: Namespace(host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], model='google/gemma-2-9b-it', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=False, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, device='auto', scheduler_delay_factor=0.0, enable_chunked_prefill=False, speculative_model=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, model_loader_extra_config=None, preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None)
WARNING 07-06 07:29:28 utils.py:562] Gemma 2 uses sliding window attention for every odd layer, which is currently not supported by vLLM. Disabling sliding window and capping the max length to the sliding window size (4096).
INFO 07-06 07:29:28 llm_engine.py:169] Initializing an LLM engine (v0.5.1) with config: model='google/gemma-2-9b-it', speculative_config=None, tokenizer='google/gemma-2-9b-it', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_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'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=google/gemma-2-9b-it, use_v2_block_manager=False, enable_prefix_caching=False)
INFO 07-06 07:29:29 selector.py:79] Using Flashinfer backend.
WARNING 07-06 07:29:29 selector.py:80] Flashinfer will be stuck on llama-2-7b, please avoid using Flashinfer as the backend when running on llama-2-7b.
INFO 07-06 07:29:29 selector.py:79] Using Flashinfer backend.
WARNING 07-06 07:29:29 selector.py:80] Flashinfer will be stuck on llama-2-7b, please avoid using Flashinfer as the backend when running on llama-2-7b.
INFO 07-06 07:29:29 weight_utils.py:218] Using model weights format ['*.safetensors']
INFO 07-06 07:29:55 model_runner.py:255] Loading model weights took 17.3781 GB
[rank0]: Traceback (most recent call last):
[rank0]:   File "/usr/lib/python3.10/runpy.py", line 196, in _run_module_as_main
[rank0]:     return _run_code(code, main_globals, None,
[rank0]:   File "/usr/lib/python3.10/runpy.py", line 86, in _run_code
[rank0]:     exec(code, run_globals)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/api_server.py", line 216, in <module>
[rank0]:     engine = AsyncLLMEngine.from_engine_args(
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 431, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 360, in __init__
[rank0]:     self.engine = self._init_engine(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 507, in _init_engine
[rank0]:     return engine_class(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 256, in __init__
[rank0]:     self._initialize_kv_caches()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 353, in _initialize_kv_caches
[rank0]:     self.model_executor.determine_num_available_blocks())
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/executor/gpu_executor.py", line 76, in determine_num_available_blocks
[rank0]:     return self.driver_worker.determine_num_available_blocks()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 173, in determine_num_available_blocks
[rank0]:     self.model_runner.profile_run()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 874, in profile_run
[rank0]:     self.execute_model(model_input, kv_caches, intermediate_tensors)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 1201, in execute_model
[rank0]:     BatchDecodeWithPagedKVCacheWrapper(
[rank0]: TypeError: 'NoneType' object is not callable

I'm on a Google Cloud VM with Nvidia L4, 4 cores and 32GB RAM, using the DeepLearning 12.1 CUDA image provided by Google. The same error come out when running via standard python using the following command:
python -m vllm.entrypoints.openai.api_server --model "google/gemma-2-9b-it"
Obviously already setted the env for enable FlashInfer backend and my hugging face key

[simon-mo]

Hmmm which version are you using for FlashInfer? Is it the latest release? https://github.com/flashinfer-ai/flashinfer/releases/tag/v0.0.8?

[DanielusG]

In the docker i assume yes, also in my python env i use v0.0.8

[DanielusG]

Using python and installing manually the v0.0.7 of flashinfer i got this:

@instance-20240703-140522:~/vllm$ python -m vllm.entrypoints.openai.api_server --model "google/gemma-2-9b-it"
INFO 07-06 07:51:34 api_server.py:206] vLLM API server version 0.5.1
INFO 07-06 07:51:34 api_server.py:207] args: Namespace(host=None, port=8000, uvicorn_log_level='info', allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, chat_template=None, response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, ssl_cert_reqs=0, root_path=None, middleware=[], model='google/gemma-2-9b-it', tokenizer=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, download_dir=None, load_format='auto', dtype='auto', kv_cache_dtype='auto', quantization_param_path=None, max_model_len=None, guided_decoding_backend='outlines', distributed_executor_backend=None, worker_use_ray=False, pipeline_parallel_size=1, tensor_parallel_size=1, max_parallel_loading_workers=None, ray_workers_use_nsight=False, block_size=16, enable_prefix_caching=False, disable_sliding_window=False, use_v2_block_manager=False, num_lookahead_slots=0, seed=0, swap_space=4, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_seqs=256, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, enforce_eager=False, max_context_len_to_capture=None, max_seq_len_to_capture=8192, disable_custom_all_reduce=False, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, enable_lora=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, device='auto', scheduler_delay_factor=0.0, enable_chunked_prefill=False, speculative_model=None, num_speculative_tokens=None, speculative_draft_tensor_parallel_size=None, speculative_max_model_len=None, speculative_disable_by_batch_size=None, ngram_prompt_lookup_max=None, ngram_prompt_lookup_min=None, spec_decoding_acceptance_method='rejection_sampler', typical_acceptance_sampler_posterior_threshold=None, typical_acceptance_sampler_posterior_alpha=None, model_loader_extra_config=None, preemption_mode=None, served_model_name=None, qlora_adapter_name_or_path=None, otlp_traces_endpoint=None, engine_use_ray=False, disable_log_requests=False, max_log_len=None)
WARNING 07-06 07:51:34 utils.py:562] Gemma 2 uses sliding window attention for every odd layer, which is currently not supported by vLLM. Disabling sliding window and capping the max length to the sliding window size (4096).
INFO 07-06 07:51:34 llm_engine.py:169] Initializing an LLM engine (v0.5.1) with config: model='google/gemma-2-9b-it', speculative_config=None, tokenizer='google/gemma-2-9b-it', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, rope_theta=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=4096, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_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'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None), seed=0, served_model_name=google/gemma-2-9b-it, use_v2_block_manager=False, enable_prefix_caching=False)
INFO 07-06 07:51:35 selector.py:79] Using Flashinfer backend.
WARNING 07-06 07:51:35 selector.py:80] Flashinfer will be stuck on llama-2-7b, please avoid using Flashinfer as the backend when running on llama-2-7b.
INFO 07-06 07:51:36 selector.py:79] Using Flashinfer backend.
WARNING 07-06 07:51:36 selector.py:80] Flashinfer will be stuck on llama-2-7b, please avoid using Flashinfer as the backend when running on llama-2-7b.
INFO 07-06 07:51:36 weight_utils.py:218] Using model weights format ['*.safetensors']
INFO 07-06 07:51:43 model_runner.py:255] Loading model weights took 17.3781 GB
[rank0]: Traceback (most recent call last):
[rank0]:   File "/opt/conda/lib/python3.10/runpy.py", line 196, in _run_module_as_main
[rank0]:     return _run_code(code, main_globals, None,
[rank0]:   File "/opt/conda/lib/python3.10/runpy.py", line 86, in _run_code
[rank0]:     exec(code, run_globals)
[rank0]:   File "/home/daniele/ComfyUI/.vev/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 216, in <module>
[rank0]:     engine = AsyncLLMEngine.from_engine_args(
[rank0]:   File "/home/daniele/ComfyUI/.vev/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 431, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/home/daniele/ComfyUI/.vev/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/daniele/ComfyUI/.vev/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/daniele/ComfyUI/.vev/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 256, in __init__
[rank0]:     self._initialize_kv_caches()
[rank0]:   File "/home/daniele/ComfyUI/.vev/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/daniele/ComfyUI/.vev/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/daniele/ComfyUI/.vev/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/home/daniele/ComfyUI/.vev/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/daniele/ComfyUI/.vev/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/home/daniele/ComfyUI/.vev/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/daniele/ComfyUI/.vev/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
[rank0]:     return func(*args, **kwargs)
[rank0]:   File "/home/daniele/ComfyUI/.vev/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 1221, in execute_model
[rank0]:     model_input.attn_metadata.begin_forward()
[rank0]:   File "/home/daniele/ComfyUI/.vev/lib/python3.10/site-packages/vllm/attention/backends/flashinfer.py", line 132, in begin_forward
[rank0]:     self.prefill_wrapper.begin_forward(
[rank0]:   File "/home/daniele/ComfyUI/.vev/lib/python3.10/site-packages/flashinfer/prefill.py", line 778, in begin_forward
[rank0]:     self._wrapper.begin_forward(
[rank0]: RuntimeError: qo_indptr must be contiguous

This is the result of collect env:

Collecting environment information...
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: Debian GNU/Linux 11 (bullseye) (x86_64)
GCC version: (Debian 10.2.1-6) 10.2.1 20210110
Clang version: Could not collect
CMake version: version 3.30.0
Libc version: glibc-2.31

Python version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.10.0-30-cloud-amd64-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: NVIDIA L4
Nvidia driver version: 550.90.07
cuDNN version: Could not collect
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):                               8
On-line CPU(s) list:                  0-7
Thread(s) per core:                   2
Core(s) per socket:                   4
Socket(s):                            1
NUMA node(s):                         1
Vendor ID:                            GenuineIntel
CPU family:                           6
Model:                                85
Model name:                           Intel(R) Xeon(R) CPU @ 2.20GHz
Stepping:                             7
CPU MHz:                              2200.222
BogoMIPS:                             4400.44
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            128 KiB
L1i cache:                            128 KiB
L2 cache:                             4 MiB
L3 cache:                             38.5 MiB
NUMA node0 CPU(s):                    0-7
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown:               Not affected
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; Enhanced 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; Enhanced / Automatic IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
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 ssbd ibrs ibpb stibp ibrs_enhanced 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 avx512_vnni md_clear arch_capabilities

Versions of relevant libraries:
[pip3] flashinfer==0.0.7+cu121torch2.2
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] onnxruntime==1.18.1
[pip3] torch==2.3.0
[pip3] torchaudio==2.3.1+cu121
[pip3] torchsde==0.2.6
[pip3] torchvision==0.18.0
[pip3] transformers==4.42.3
[pip3] triton==2.3.0
[conda] numpy                     1.25.2                   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	CPU Affinity	NUMA Affinity	GPU NUMA ID
GPU0	 X 	0-7	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

[simon-mo]

In a python shell, can you run

from flashinfer import BatchDecodeWithPagedKVCacheWrapper
from flashinfer.decode import CUDAGraphBatchDecodeWithPagedKVCacheWrapper
from flashinfer.prefill import BatchPrefillWithPagedKVCacheWrapper

to see what error it raises? It must be some ImportError that caused these to be set to None.

[simon-mo]

Actually I can repro this, it seems that we missed FlashInfer installation in Docker Image. Adding in now.

[DanielusG]

Python 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from flashinfer import BatchDecodeWithPagedKVCacheWrapper
>>> from flashinfer.decode import CUDAGraphBatchDecodeWithPagedKVCacheWrapper
>>> from flashinfer.prefill import BatchPrefillWithPagedKVCacheWrapper
>>> 

No problem executing this

[DanielusG]

The RuntimeError: qo_indptr must be contiguous can be an error of FlashInfer or vllm?

[simon-mo]

The flash infer version must be 0.8.0, not 0.7.0

[simon-mo]

Ok I have updated the docker image on the hub with pre-installed FlashInfer 0.8.0. It should help resolve this!

[DanielusG]

Thanks, i can confirm, it works now :)
really thx for the help

Also my local installation with python now works (i missdownloaded the right version of flashinfer)

A little OOT but is it normal that the speed in tokens/s is the same as llama.cpp version of gemma 9b fp16?