Closed
Description
Your current environment
The output of `python collect_env.py`
Collecting environment information...
PyTorch version: 2.4.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.5 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35
Python version: 3.11.10 (main, Oct 3 2024, 07:29:13) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.8.0-45-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090
Nvidia driver version: 535.183.06
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.5.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.5.0
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
Address sizes: 39 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 16
On-line CPU(s) list: 0-15
Vendor ID: GenuineIntel
Model name: 13th Gen Intel(R) Core(TM) i5-13400
CPU family: 6
Model: 191
Thread(s) per core: 2
Core(s) per socket: 10
Socket(s): 1
Stepping: 2
CPU max MHz: 4600,0000
CPU min MHz: 800,0000
BogoMIPS: 4992.00
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 tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid 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 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 user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 416 KiB (10 instances)
L1i cache: 448 KiB (10 instances)
L2 cache: 9,5 MiB (7 instances)
L3 cache: 20 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-15
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 Reg file data sampling: Mitigation; Clear Register File
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 / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] numpy==1.26.4
[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==9.1.0.70
[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-ml-py==12.560.30
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.6.77
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==26.2.0
[pip3] torch==2.4.0
[pip3] torchvision==0.19.0
[pip3] transformers==4.45.1
[pip3] triton==3.0.0
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.1.3.1 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.1.105 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.0.2.54 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.2.106 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.4.5.107 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.1.0.106 pypi_0 pypi
[conda] nvidia-ml-py 12.560.30 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.20.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.6.77 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.1.105 pypi_0 pypi
[conda] pyzmq 26.2.0 pypi_0 pypi
[conda] torch 2.4.0 pypi_0 pypi
[conda] torchvision 0.19.0 pypi_0 pypi
[conda] transformers 4.45.1 pypi_0 pypi
[conda] triton 3.0.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: N/A
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 0-15 0 N/A
GPU1 PHB X 0-15 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
Model Input Dumps
No response
🐛 Describe the bug
Here is how I load my model using vllm serve
vllm serve <my-model-path> --host 0.0.0.0 --served-model-name my-model --port 8000 --enable-lora --lora-modules my-model:lora=<my-lora-path>And here is the error that I got
Process SpawnProcess-1:
Traceback (most recent call last):
File "/home/fahadh/anaconda3/envs/vllm/lib/python3.11/multiprocessing/process.py", line 314, in _bootstrap
self.run()
File "/home/fahadh/anaconda3/envs/vllm/lib/python3.11/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/fahadh/vllm/vllm/engine/multiprocessing/engine.py", line 388, in run_mp_engine
engine = MQLLMEngine.from_engine_args(engine_args=engine_args,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/engine/multiprocessing/engine.py", line 138, in from_engine_args
return cls(
^^^^
File "/home/fahadh/vllm/vllm/engine/multiprocessing/engine.py", line 78, in __init__
self.engine = LLMEngine(*args,
^^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/engine/llm_engine.py", line 338, in __init__
self.model_executor = executor_class(
^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/executor/executor_base.py", line 47, in __init__
self._init_executor()
File "/home/fahadh/vllm/vllm/executor/gpu_executor.py", line 40, in _init_executor
self.driver_worker.load_model()
File "/home/fahadh/vllm/vllm/worker/worker.py", line 183, in load_model
self.model_runner.load_model()
File "/home/fahadh/vllm/vllm/worker/model_runner.py", line 1045, in load_model
self.model = self.lora_manager.create_lora_manager(self.model)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/lora/worker_manager.py", line 173, in create_lora_manager
lora_manager = create_lora_manager(
^^^^^^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/lora/models.py", line 734, in create_lora_manager
lora_manager = lora_manager_cls(
^^^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/lora/models.py", line 658, in __init__
super().__init__(model, max_num_seqs, max_num_batched_tokens,
File "/home/fahadh/vllm/vllm/lora/models.py", line 343, in __init__
self._create_lora_modules()
File "/home/fahadh/vllm/vllm/lora/models.py", line 457, in _create_lora_modules
from_layer(module, self.lora_slots, self.lora_config,
File "/home/fahadh/vllm/vllm/lora/utils.py", line 64, in from_layer
ret = lora_cls(layer)
^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/lora/layers.py", line 694, in __init__
super().__init__(base_layer)
File "/home/fahadh/vllm/vllm/lora/layers.py", line 372, in __init__
self.device = _get_lora_device(self.base_layer)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/lora/layers.py", line 49, in _get_lora_device
raise ValueError(f"Unsupported base layer: {base_layer}")
ValueError: Unsupported base layer: QKVParallelLinear(in_features=4096, output_features=6144, bias=False, tp_size=1, gather_output=False)
Traceback (most recent call last):
File "/home/fahadh/anaconda3/envs/vllm/bin/vllm", line 8, in <module>
sys.exit(main())
^^^^^^
File "/home/fahadh/vllm/vllm/scripts.py", line 191, in main
args.dispatch_function(args)
File "/home/fahadh/vllm/vllm/scripts.py", line 40, in serve
uvloop.run(run_server(args))
File "/home/fahadh/anaconda3/envs/vllm/lib/python3.11/site-packages/uvloop/__init__.py", line 105, in run
return runner.run(wrapper())
^^^^^^^^^^^^^^^^^^^^^
File "/home/fahadh/anaconda3/envs/vllm/lib/python3.11/asyncio/runners.py", line 118, in run
return self._loop.run_until_complete(task)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "uvloop/loop.pyx", line 1517, in uvloop.loop.Loop.run_until_complete
File "/home/fahadh/anaconda3/envs/vllm/lib/python3.11/site-packages/uvloop/__init__.py", line 61, in wrapper
return await main
^^^^^^^^^^
File "/home/fahadh/vllm/vllm/entrypoints/openai/api_server.py", line 538, in run_server
async with build_async_engine_client(args) as engine_client:
File "/home/fahadh/anaconda3/envs/vllm/lib/python3.11/contextlib.py", line 210, in __aenter__
return await anext(self.gen)
^^^^^^^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/entrypoints/openai/api_server.py", line 105, in build_async_engine_client
async with build_async_engine_client_from_engine_args(
File "/home/fahadh/anaconda3/envs/vllm/lib/python3.11/contextlib.py", line 210, in __aenter__
return await anext(self.gen)
^^^^^^^^^^^^^^^^^^^^^
File "/home/fahadh/vllm/vllm/entrypoints/openai/api_server.py", line 192, in build_async_engine_client_from_engine_args
raise RuntimeError(
RuntimeError: Engine process failed to startHere is what inside config.json of my model
{
"_name_or_path": "my-model",
"architectures": [
"MistralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"eos_token_id": 32000,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 8192,
"model_type": "mistral",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"quantization_config": {
"config_groups": {
"group_0": {
"input_activations": null,
"output_activations": null,
"targets": [
"Linear"
],
"weights": {
"actorder": null,
"block_structure": null,
"dynamic": false,
"group_size": null,
"num_bits": 8,
"observer": "minmax",
"observer_kwargs": {},
"strategy": "channel",
"symmetric": true,
"type": "int"
}
}
},
"format": "pack-quantized",
"global_compression_ratio": 1.460841389791697,
"ignore": [
"lm_head"
],
"kv_cache_scheme": null,
"quant_method": "compressed-tensors",
"quantization_status": "compressed"
},
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"sliding_window": 4096,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.45.1",
"use_cache": true,
"vocab_size": 32019
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