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A high-throughput and memory-efficient inference and serving engine for LLMs
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[Bug]: Qwen2 MoE: AttributeError: 'MergedColumnParallelLinear' object has no attribute 'weight'. Did you mean: 'qweight'? #5343

Closed geekwish closed 4 months ago

geekwish commented 4 months ago

Your current environment

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: 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 3.29.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 2080 Ti
GPU 1: NVIDIA GeForce RTX 2080 Ti
GPU 2: NVIDIA GeForce RTX 2080 Ti
GPU 3: NVIDIA GeForce RTX 4090

Nvidia driver version: 535.154.05
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
架构：                              x86_64
CPU 运行模式：                      32-bit, 64-bit
Address sizes:                      43 bits physical, 48 bits virtual
字节序：                            Little Endian
CPU:                                64
在线 CPU 列表：                     0-63
厂商 ID：                           AuthenticAMD
型号名称：                          AMD Ryzen Threadripper 2990WX 32-Core Processor
CPU 系列：                          23
型号：                              8
每个核的线程数：                    2
每个座的核数：                      32
座：                                1
步进：                              2
Frequency boost:                    enabled
CPU 最大 MHz：                      3000.0000
CPU 最小 MHz：                      2200.0000
BogoMIPS：                          5999.32
标记：                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sev sev_es
虚拟化：                            AMD-V
L1d 缓存：                          1 MiB (32 instances)
L1i 缓存：                          2 MiB (32 instances)
L2 缓存：                           16 MiB (32 instances)
L3 缓存：                           64 MiB (8 instances)
NUMA 节点：                         4
NUMA 节点0 CPU：                    0-7,32-39
NUMA 节点1 CPU：                    16-23,48-55
NUMA 节点2 CPU：                    8-15,40-47
NUMA 节点3 CPU：                    24-31,56-63
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:             Mitigation; untrained return thunk; SMT vulnerable
Vulnerability Spec rstack overflow: Mitigation; safe RET
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; Retpolines, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] transformers==4.41.2
[pip3] triton==2.3.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.3
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PHB     SYS     SYS     0-7,32-39       0               N/A
GPU1    PHB      X      SYS     SYS     0-7,32-39       0               N/A
GPU2    SYS     SYS      X      PHB     8-15,40-47      2               N/A
GPU3    SYS     SYS     PHB      X      8-15,40-47      2               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

I have an error when run Qwen2-57B-A14B-Instruct-GPTQ-Int4 with vllm. This seems to be a problem with the code.

$ CUDA_VISIBLE_DEVICES=1,2 python -m vllm.entrypoints.openai.api_server \
    --served-model-name Qwen2-57B-A14B-Instruct-GPTQ-Int4 \
    --model /AIGC/Qwen/hf/Qwen2-57B-A14B-Instruct-GPTQ-Int4

Output:

WARNING 06-07 16:41:21 config.py:213] gptq quantization is not fully optimized yet. The speed can be slower than non-quantized models.
INFO 06-07 16:41:21 llm_engine.py:161] Initializing an LLM engine (v0.4.3) with config: model='/AIGC/Qwen/hf/Qwen2-57B-A14B-Instruct-GPTQ-Int4', speculative_config=None, tokenizer='/AIGC/Qwen/hf/Qwen2-57B-A14B-Instruct-GPTQ-Int4', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, rope_scaling=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.float16, max_seq_len=32768, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, disable_custom_all_reduce=False, quantization=gptq, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=Qwen2-57B-A14B-Instruct-GPTQ-Int4)
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
INFO 06-07 16:41:22 selector.py:120] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
INFO 06-07 16:41:22 selector.py:51] Using XFormers backend.
INFO 06-07 16:41:23 selector.py:120] Cannot use FlashAttention-2 backend for Volta and Turing GPUs.
INFO 06-07 16:41:23 selector.py:51] Using XFormers backend.
[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 "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/entrypoints/openai/api_server.py", line 186, in <module>
[rank0]:     engine = AsyncLLMEngine.from_engine_args(
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 386, in from_engine_args
[rank0]:     engine = cls(
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 340, in __init__
[rank0]:     self.engine = self._init_engine(*args, **kwargs)
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/engine/async_llm_engine.py", line 462, in _init_engine
[rank0]:     return engine_class(*args, **kwargs)
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/engine/llm_engine.py", line 222, in __init__
[rank0]:     self.model_executor = executor_class(
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/executor/executor_base.py", line 41, in __init__
[rank0]:     self._init_executor()
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/executor/gpu_executor.py", line 24, in _init_executor
[rank0]:     self.driver_worker.load_model()
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/worker/worker.py", line 121, in load_model
[rank0]:     self.model_runner.load_model()
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 134, in load_model
[rank0]:     self.model = get_model(
[rank0]:   File "/AIGC/venvs/vllm/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 "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 240, in load_model
[rank0]:     model = _initialize_model(model_config, self.load_config,
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/model_executor/model_loader/loader.py", line 91, in _initialize_model
[rank0]:     return model_class(config=model_config.hf_config,
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_moe.py", line 389, in __init__
[rank0]:     self.model = Qwen2MoeModel(config, cache_config, quant_config)
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_moe.py", line 349, in __init__
[rank0]:     self.layers = nn.ModuleList([
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_moe.py", line 350, in <listcomp>
[rank0]:     Qwen2MoeDecoderLayer(config,
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_moe.py", line 290, in __init__
[rank0]:     self.mlp = Qwen2MoeSparseMoeBlock(config=config,
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_moe.py", line 114, in __init__
[rank0]:     self.pack_params()
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/vllm/model_executor/models/qwen2_moe.py", line 138, in pack_params
[rank0]:     w1.append(expert.gate_up_proj.weight)
[rank0]:   File "/AIGC/venvs/vllm/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1709, in __getattr__
[rank0]:     raise AttributeError(f"'{type(self).__name__}' object has no attribute '{name}'")
[rank0]: AttributeError: 'MergedColumnParallelLinear' object has no attribute 'weight'. Did you mean: 'qweight'?
robertgshaw2-neuralmagic commented 4 months ago

We do not currently support quantization for MoE's except Mixtral

robertgshaw2-neuralmagic commented 4 months ago

Im going to make an RFC for this

jcxcer commented 4 months ago

+1