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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]: local variable 'lora_b_k' referenced before assignment #4605

Closed LucienShui closed 4 months ago

LucienShui 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: Could not collect
CMake version: version 3.27.6
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-97-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 H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3

Nvidia driver version: 535.104.12
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5
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, 57 bits virtual
Byte Order:                         Little Endian
CPU(s):                             192
On-line CPU(s) list:                0-191
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Platinum 8468
CPU family:                         6
Model:                              143
Thread(s) per core:                 2
Core(s) per socket:                 48
Socket(s):                          2
Stepping:                           6
Frequency boost:                    enabled
CPU max MHz:                        2101.0000
CPU min MHz:                        800.0000
BogoMIPS:                           4200.00
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 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 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
Virtualization:                     VT-x
L1d cache:                          4.5 MiB (96 instances)
L1i cache:                          3 MiB (96 instances)
L2 cache:                           192 MiB (96 instances)
L3 cache:                           210 MiB (2 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-47,96-143
NUMA node1 CPU(s):                  48-95,144-191
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:             Not affected
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:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.22.2
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] onnx==1.14.0
[pip3] pytorch-quantization==2.1.2
[pip3] torch==2.3.0
[pip3] torch-tensorrt==0.0.0
[pip3] torchdata==0.7.0a0
[pip3] torchtext==0.16.0a0
[pip3] torchvision==0.16.0a0
[pip3] triton==2.3.0
[pip3] vllm-nccl-cu12==2.18.1.0.4.0
[conda] Could not collectROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.4.2
vLLM Build Flags:
CUDA Archs: 5.2 6.0 6.1 7.0 7.2 7.5 8.0 8.6 8.7 9.0+PTX; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X  NV18    NV18    NV18    NV18    NV18    NV18    NV18    0-47,96-143 0       N/A
GPU1    NV18     X  NV18    NV18    NV18    NV18    NV18    NV18    0-47,96-143 0       N/A
GPU2    NV18    NV18     X  NV18    NV18    NV18    NV18    NV18    0-47,96-143 0       N/A
GPU3    NV18    NV18    NV18     X  NV18    NV18    NV18    NV18    0-47,96-143 0       N/A
GPU4    NV18    NV18    NV18    NV18     X  NV18    NV18    NV18    48-95,144-191   1       N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X  NV18    NV18    48-95,144-191   1       N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X  NV18    48-95,144-191   1       N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X  48-95,144-191   1       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

After installed 0.4.2 using pip:

python3 -m pip install "vllm==0.4.2"

Then I tried to start a service using entrypoint as usual.

python3 -m vllm.entrypoints.openai.api_server \
        --model /root/model/Qwen1.5-110B-Chat \
        --max-model-len 32768 \
        --host 0.0.0.0 \
        --port 8000 \
        --enable-lora \
        --tensor-parallel-size 4 \
        --max-lora-rank 64 \
        --served-model-name qwen1.5-110b-chat \
        --lora-modules "lora_1=/root/saved_adapters/lora_1/"

I got the error when I make a request:

  File "/root/miniconda3/lib/python3.10/site-packages/vllm/worker/worker.py", line 249, in execute_model
    output = self.model_runner.execute_model(seq_group_metadata_list,
  File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 790, in execute_model
    self.set_active_loras(lora_requests, lora_mapping)
  File "/root/miniconda3/lib/python3.10/site-packages/vllm/worker/model_runner.py", line 901, in set_active_loras
    self.lora_manager.set_active_loras(lora_requests, lora_mapping)
  File "/root/miniconda3/lib/python3.10/site-packages/vllm/lora/worker_manager.py", line 113, in set_active_loras
    self._apply_loras(lora_requests)
  File "/root/miniconda3/lib/python3.10/site-packages/vllm/lora/worker_manager.py", line 235, in _apply_loras
    self.add_lora(lora)
  File "/root/miniconda3/lib/python3.10/site-packages/vllm/lora/worker_manager.py", line 250, in add_lora
    self._lora_manager.activate_lora(lora_request.lora_int_id)
  File "/root/miniconda3/lib/python3.10/site-packages/vllm/lora/models.py", line 615, in activate_lora
    result = super().activate_lora(lora_id)
  File "/root/miniconda3/lib/python3.10/site-packages/vllm/lora/models.py", line 355, in activate_lora
    module.set_lora(index, module_lora.lora_a, module_lora.lora_b,
  File "/root/miniconda3/lib/python3.10/site-packages/vllm/lora/layers.py", line 800, in set_lora
    lora_b = self.slice_lora_b(lora_b)
  File "/root/miniconda3/lib/python3.10/site-packages/vllm/lora/layers.py", line 786, in slice_lora_b
    lora_b = [lora_b_q, lora_b_k, lora_b_v]
UnboundLocalError: local variable 'lora_b_k' referenced before assignment

Then check the code of tag v0.4.2, I found this:

https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/lora/layers.py#L773-L787

Neither of lora_b_q, lora_b_k, lora_b_v has a default value, unfortunately my adapter only applied to q and v at the time, then the error occured, I guess.

yyccli commented 4 months ago

after solving above issues, can you run a full tp lora model now? When enable full sharded lora, i always get a dim check error

yyccli commented 4 months ago

problem solved after applying the new pr's patch, for it's not merged now