当前环境
Collecting environment information...
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 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.22.1
Libc version: glibc-2.35
Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-89-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla V100-SXM2-16GB
Nvidia driver version: 535.161.07
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
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 48
On-line CPU(s) list: 0-47
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Silver 4214R CPU @ 2.40GHz
CPU family: 6
Model: 85
Thread(s) per core: 2
Core(s) per socket: 12
Socket(s): 2
Stepping: 7
CPU max MHz: 3500.0000
CPU min MHz: 1000.0000
BogoMIPS: 4800.00
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 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 cdp_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 mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization: VT-x
L1d cache: 768 KiB (24 instances)
L1i cache: 768 KiB (24 instances)
L2 cache: 24 MiB (24 instances)
L3 cache: 33 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-11,24-35
NUMA node1 CPU(s): 12-23,36-47
Vulnerability Gather data sampling: Mitigation; Microcode
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable
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 IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Mitigation; TSX disabled
Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.1.2
[pip3] triton==2.1.0
[conda] No relevant packagesROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.2.6
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 NIC0 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X SYS 0,24 0 N/A
NIC0 SYS X
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
NIC Legend:
NIC0: mlx5_0如何使用vllm
我想运行一个我自己训练的llama2模型的推理。我使用以下代码保存训练好的模型：
'''
if args.output_dir is not None:
output_dir = os.path.join(args.output_dir, output_dir)
accelerator.save_state(output_dir)
accelerator.wait_for_everyone()
if accelerator.is_main_process:
tokenizer.save_pretrained(args.output_dir)
unwrapped_model = accelerator.unwrap_model(model)
# When doing multi-gpu training, we need to use accelerator.get_state_dict(model) to get the state_dict.
# Otherwise, sometimes the model will be saved with only part of the parameters.
# Also, accelerator needs to use the wrapped model to get the state_dict.
state_dict = accelerator.get_state_dict(model)
if args.use_lora:
# When using lora, the unwrapped model is a PeftModel, which doesn't support the is_main_process
# and has its own save_pretrained function for only saving lora modules.
# We have to mannually specify the is_main_process outside the save_pretrained function.
if accelerator.is_main_process:
unwrapped_model.save_pretrained(args.output_dir, state_dict=state_dict)
else:
unwrapped_model.save_pretrained(
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save, state_dict=state_dict
)
'''
然后我得到了以下训练好的模型文件：
```python
当我尝试用 model = LLM(model={my_model_dir}, download_dir={my_model_dir}, dtype=args.dtype, tensor_parallel_size=args.world_size,) 加载模型时，我遇到了以下AssertionError:
File "run_short_form.py", line 374, in main()
model = LLM(model=gpt, download_dir=args.download_dir,
File "run_short_form.py", line 302, in main
model = LLM(model=gpt, download_dir=args.download_dir,
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/entrypoints/llm.py", line 105, in init
self.llm_engine = LLMEngine.from_engine_args(engine_args)
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 250, in from_engine_args
engine = cls(*engine_configs,
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 110, in init
self._init_workers(distributed_init_method)
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 146, in _init_workers
self._run_workers(
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 755, in _run_workers
self._run_workers_in_batch(workers, method, *args, **kwargs))
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/engine/llm_engine.py", line 729, in _run_workers_in_batch
output = executor(*args, **kwargs)
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/worker/worker.py", line 79, in load_model
self.model_runner.load_model()
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/worker/model_runner.py", line 57, in load_model
self.model = get_model(self.model_config)
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm/model_executor/model_loader.py", line 72, in get_model
model.load_weights(model_config.model, model_config.download_dir,
File "/home/xxx/anaconda3/envs/rag/lib/python3.8/site-packages/vllm
3条答案
按热度按时间iqxoj9l91#
我也遇到了这个问题。有什么解决方案吗？
mlnl4t2r2#
我也遇到了这个问题，并解决了它。
在"model.safetensors"中有一个空的Tensor。我不确定为什么这个权重会被保存，但请检查一下，删除文件，然后再试一次运行。以下是我用来检查safetensor的代码：
gcuhipw93#
我也遇到了这个问题。有什么解决方法吗？
我通过删除'model.safetensors'解决了这个问题。希望能帮到你。