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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]: Mixtral 8x7b FP8 encounters illegal memory access in custom_all_reduce.cuh #6116

Open ferdiko opened 4 months ago

ferdiko commented 4 months ago

Your current environment

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 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 3.26.3
Libc version: glibc-2.31

Python version: 3.11.9 (main, Apr  6 2024, 17:59:24) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 12.1.105
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: 535.129.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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
Byte Order:                         Little Endian
Address sizes:                      48 bits physical, 57 bits virtual
CPU(s):                             176
On-line CPU(s) list:                0-175
Thread(s) per core:                 2
Core(s) per socket:                 44
Socket(s):                          2
NUMA node(s):                       2
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              143
Model name:                         Intel(R) Xeon(R) Platinum 8468V
Stepping:                           8
CPU MHz:                            2400.000
BogoMIPS:                           4800.00
Virtualization:                     VT-x
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          2.8 MiB
L1i cache:                          2.8 MiB
L2 cache:                           352 MiB
L3 cache:                           32 MiB
NUMA node0 CPU(s):                  0-87
NUMA node1 CPU(s):                  88-175
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:      Unknown: No mitigations
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:      Mitigation; TSX disabled
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk amx_bf16 avx512_fp16 amx_tile amx_int8 arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] torchvision==0.18.0
[pip3] transformers==4.42.3
[pip3] triton==2.3.0
[conda] Could not collect
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.5.0.post1
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0    GPU1    GPU2    GPU3    GPU4    GPU5    GPU6    GPU7    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6    NIC7    NIC8    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    NV18    NV18    NV18    NV18    SYS     SYS     SYS     SYS     SYS     PHB     PHB     PHB     PHB     0-87    0               N/A
GPU1    NV18     X      NV18    NV18    NV18    NV18    NV18    NV18    SYS     SYS     SYS     SYS     SYS     PHB     PHB     PHB     PHB     0-87    0               N/A
GPU2    NV18    NV18     X      NV18    NV18    NV18    NV18    NV18    SYS     SYS     SYS     SYS     SYS     PHB     PHB     PHB     PHB     0-87    0               N/A
GPU3    NV18    NV18    NV18     X      NV18    NV18    NV18    NV18    SYS     SYS     SYS     SYS     SYS     PHB     PHB     PHB     PHB     0-87    0               N/A
GPU4    NV18    NV18    NV18    NV18     X      NV18    NV18    NV18    PHB     PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS     88-175  1               N/A
GPU5    NV18    NV18    NV18    NV18    NV18     X      NV18    NV18    PHB     PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS     88-175  1               N/A
GPU6    NV18    NV18    NV18    NV18    NV18    NV18     X      NV18    PHB     PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS     88-175  1               N/A
GPU7    NV18    NV18    NV18    NV18    NV18    NV18    NV18     X      PHB     PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS     88-175  1               N/A
NIC0    SYS     SYS     SYS     SYS     PHB     PHB     PHB     PHB      X      PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS
NIC1    SYS     SYS     SYS     SYS     PHB     PHB     PHB     PHB     PHB      X      PHB     PHB     SYS     SYS     SYS     SYS     SYS
NIC2    SYS     SYS     SYS     SYS     PHB     PHB     PHB     PHB     PHB     PHB      X      PHB     SYS     SYS     SYS     SYS     SYS
NIC3    SYS     SYS     SYS     SYS     PHB     PHB     PHB     PHB     PHB     PHB     PHB      X      SYS     SYS     SYS     SYS     SYS
NIC4    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      X      SYS     SYS     SYS     SYS
NIC5    PHB     PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      X      PHB     PHB     PHB
NIC6    PHB     PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB      X      PHB     PHB
NIC7    PHB     PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB     PHB      X      PHB
NIC8    PHB     PHB     PHB     PHB     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB     PHB     PHB      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
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8

🐛 Describe the bug

I'm running the examples/offline_inference.py script with Mixtral 8x7b and FP8 quantization (tp=2), i.e., the following script:

from vllm import LLM, SamplingParams

prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

llm = LLM(model="mistralai/Mixtral-8x7B-Instruct-v0.1", 
          tensor_parallel_size=2, 
          quantization="fp8"
          )

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

I'm on main on the latest commit as of now (47f0954af0a5aefd0db19875f6bdcbe933d055a9).

I get the following error. I only get it if I enable FP8 quantization (otherwise, the script runs fine).

Failed: Cuda error /mnt/workdisk/ferdiko/exploratory/vllm/csrc/custom_all_reduce.cuh:330 'an illegal memory access was encountered'
[rank1]:[W CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors]
Failed: Cuda error /mnt/workdisk/ferdiko/exploratory/vllm/csrc/custom_all_reduce.cuh:330 'an illegal memory access was encountered'
[rank0]:[W CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors]
/usr/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 2 leaked shared_memory objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '

If I run Llama3-8B, the script runs fine even with FP8 quantization. However, I still see the following warning:

[rank0]:[W CudaIPCTypes.cpp:16] Producer process has been terminated before all shared CUDA tensors released. See Note [Sharing CUDA tensors]
/usr/lib/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 2 leaked shared_memory objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '
ferdiko commented 4 months ago

Might be related to #6088

github-actions[bot] commented 2 weeks ago

This issue has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this issue should remain open. Thank you!