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vllm-project / vllm

A high-throughput and memory-efficient inference and serving engine for LLMs
https://docs.vllm.ai
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[Bug]: `facebook/chameleon-30b` triggers assertion error while loading weights #7388

Closed jaywonchung closed 1 month ago

jaywonchung commented 1 month ago

Your current environment

The output of `python collect_env.py` ```text 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 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 3.30.2 Libc version: glibc-2.31 Python version: 3.10.14 (main, Apr 6 2024, 18:45:05) [GCC 9.4.0] (64-bit runtime) Python platform: Linux-6.5.0-1023-aws-x86_64-with-glibc2.31 Is CUDA available: True CUDA runtime version: 12.4.131 CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB GPU 1: NVIDIA A100-SXM4-40GB 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): 96 On-line CPU(s) list: 0-95 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz Stepping: 7 CPU MHz: 1257.578 BogoMIPS: 5999.99 Hypervisor vendor: KVM Virtualization type: full L1d cache: 1.5 MiB L1i cache: 1.5 MiB L2 cache: 48 MiB L3 cache: 71.5 MiB NUMA node0 CPU(s): 0-23,48-71 NUMA node1 CPU(s): 24-47,72-95 Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status Vulnerability Itlb multihit: KVM: Mitigation: VMX unsupported Vulnerability L1tf: Mitigation; PTE Inversion Vulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Vulnerability Meltdown: Mitigation; PTI Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown Vulnerability Retbleed: Vulnerable Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Vulnerable Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Retpoline 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 mmx fxsr sse sse2 s s ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pcl mulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm ab m 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke Versions of relevant libraries: [pip3] flashinfer==0.1.2+cu121torch2.4 [pip3] numpy==1.26.4 [pip3] nvidia-nccl-cu12==2.20.5 [pip3] pyzmq==26.1.0 [pip3] torch==2.4.0 [pip3] torchvision==0.19.0 [pip3] transformers==4.44.0 [pip3] triton==3.0.0 [conda] Could not collect ROCM Version: Could not collect Neuron SDK Version: N/A vLLM Version: 0.5.4 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled GPU Topology: GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NV12 0-23,48-71 0 N/A GPU1 NV12 X 0-23,48-71 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 ```

🐛 Describe the bug

Running vLLM v0.5.4 with tensor parallelism degree 2. facebook/chameleon-7b works well (this one with TP 1), but facebook/chameleon-30b fails to load weights while reading in the first safetensors shard. The name of the parameter that triggers the assertion is model.layers.3.self_attn.k_norm.bias.

Traceback (most recent call last):                                                                                                     
  File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap                                                       
    self.run()                                                                                                                         
  File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run                                                              
    self._target(*self._args, **self._kwargs)                                                                                          
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 217, in run_rpc_server                    
    server = AsyncEngineRPCServer(async_engine_args, usage_context, port)                                                              
  File "/usr/local/lib/python3.10/dist-packages/vllm/entrypoints/openai/rpc/server.py", line 25, in __init__                           
    self.engine = AsyncLLMEngine.from_engine_args(async_engine_args,                                                                   
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 484, in from_engine_args                        
    engine = cls(                                                                                                                      
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 394, in __init__                                
    self.engine = self._init_engine(*args, **kwargs)                                                                                   
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/async_llm_engine.py", line 565, in _init_engine
    return engine_class(*args, **kwargs)                                                                                               
  File "/usr/local/lib/python3.10/dist-packages/vllm/engine/llm_engine.py", line 274, in __init__                                      
    self.model_executor = executor_class(                                                                                              
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 215, in __init__                        
    super().__init__(*args, **kwargs)                                                                                                  
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/distributed_gpu_executor.py", line 25, in __init__
    super().__init__(*args, **kwargs)                                                                                                  
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/executor_base.py", line 47, in __init__
    self._init_executor()                                                                                                              
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 138, in _init_executor                  
    self._run_workers("load_model",                                                                                                    
  File "/usr/local/lib/python3.10/dist-packages/vllm/executor/multiproc_gpu_executor.py", line 192, in _run_workers                    
    driver_worker_output = driver_worker_method(*args, **kwargs)                                                                       
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/worker.py", line 139, in load_model
    self.model_runner.load_model()                                 
  File "/usr/local/lib/python3.10/dist-packages/vllm/worker/model_runner.py", line 722, in load_model
    self.model = get_model(model_config=self.model_config,
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/__init__.py", line 21, in get_model                   
    return loader.load_model(model_config=model_config,
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/loader.py", line 327, in load_model                   
    model.load_weights(
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/models/chameleon.py", line 1048, in load_weights
    weight_loader(param, loaded_weight)
  File "/usr/local/lib/python3.10/dist-packages/vllm/model_executor/model_loader/weight_utils.py", line 470, in default_weight_loader
    assert param.size() == loaded_weight.size()
AssertionError
Loading safetensors checkpoint shards:   0% Completed | 0/15 [00:00<?, ?it/s]
DarkLight1337 commented 1 month ago

cc @ywang96

DarkLight1337 commented 1 month ago

Please check if #7410 works on your end.