[Bug]: Mixtral 8x7b FP8 encounters illegal memory access in custom_all_reduce.cuh

[ferdiko]

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 (47f0954).

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]

Might be related to #6088

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[github-actions]

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