[Bug]: Illegal memory access for MoE kernel with large workloads

[comaniac]

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 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 3.29.3
Libc version: glibc-2.31

Python version: 3.10.14 (main, May  6 2024, 19:42:50) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-1022-gcp-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 L4
Nvidia driver version: 535.183.01
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:                      46 bits physical, 48 bits virtual
CPU(s):                             32
On-line CPU(s) list:                0-31
Thread(s) per core:                 2
Core(s) per socket:                 16
Socket(s):                          1
NUMA node(s):                       1
Vendor ID:                          GenuineIntel
CPU family:                         6
Model:                              85
Model name:                         Intel(R) Xeon(R) CPU @ 2.20GHz
Stepping:                           7
CPU MHz:                            2200.174
BogoMIPS:                           4400.34
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          512 KiB
L1i cache:                          512 KiB
L2 cache:                           16 MiB
L3 cache:                           38.5 MiB
NUMA node0 CPU(s):                  0-31
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:      Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
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; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI Syscall hardening, KVM SW loop
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
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 rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat avx512_vnni md_clear arch_capabilities

Versions of relevant libraries:
[pip3] mypy==1.9.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.4
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] torch==2.3.0
[pip3] torchvision==0.18.0
[pip3] transformers==4.42.1
[pip3] triton==2.3.0
[conda] numpy                     1.24.4                   pypi_0    pypi
[conda] nvidia-nccl-cu12          2.20.5                   pypi_0    pypi
[conda] torch                     2.3.0                    pypi_0    pypi
[conda] torchvision               0.18.0                   pypi_0    pypi
[conda] transformers              4.42.1                   pypi_0    pypi
[conda] triton                    2.3.0                    pypi_0    pypi
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    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      0-31    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

Illegal memory access for MoE triton kernel when the workload (e.g., batch size) is too large. To reproduce:

python3 ./benchmarks/kernels/benchmark_moe.py --tp-size 2 --batch-size 74899

Output

  File "/home/ray/default/vllm/./benchmarks/kernels/benchmark_moe.py", line 70, in run
    fused_moe(
  File "/tmp/ray/session_2024-06-27_10-06-48_118980_5595/runtime_resources/working_dir_files/_ray_pkg_ef0e5109bc8b4140628503119c10e0b2c9ea3f17/vllm/model_executor/layers/fused_moe/fused_moe.py", line 519, in fused_moe
    return fused_experts(hidden_states,
  File "/tmp/ray/session_2024-06-27_10-06-48_118980_5595/runtime_resources/working_dir_files/_ray_pkg_ef0e5109bc8b4140628503119c10e0b2c9ea3f17/vllm/model_executor/layers/fused_moe/fused_moe.py", line 449, in fused_experts
    invoke_fused_moe_kernel(intermediate_cache2,
  File "/tmp/ray/session_2024-06-27_10-06-48_118980_5595/runtime_resources/working_dir_files/_ray_pkg_ef0e5109bc8b4140628503119c10e0b2c9ea3f17/vllm/model_executor/layers/fused_moe/fused_moe.py", line 245, in invoke_fused_moe_kernel
    fused_moe_kernel[grid](
  File "/home/ray/anaconda3/lib/python3.10/site-packages/triton/runtime/jit.py", line 167, in <lambda>
    return lambda *args, **kwargs: self.run(grid=grid, warmup=False, *args, **kwargs)
  File "/home/ray/anaconda3/lib/python3.10/site-packages/triton/runtime/jit.py", line 425, in run
    kernel.run(grid_0, grid_1, grid_2, kernel.num_warps, kernel.num_ctas,  # number of warps/ctas per instance
  File "/home/ray/anaconda3/lib/python3.10/site-packages/triton/compiler/compiler.py", line 255, in __getattribute__
    self._init_handles()
  File "/home/ray/anaconda3/lib/python3.10/site-packages/triton/compiler/compiler.py", line 250, in _init_handles
    self.module, self.function, self.n_regs, self.n_spills = driver.utils.load_binary(
RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered

We have seen this problem on L4 and A100 GPUs. I also tried to tune this particular workload using different block sizes, but none of the configs could bypass the error. Since we usually don't use such a large batch size (number of tokens), this bug should not be critical at least for now.

Also cc @pcmoritz @WoosukKwon @Yard1

[Minami-su]

+1,me too.
tp_size 2
Qwen2_72b
2 X A800 80G