当前环境
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
🐛 描述错误
当工作负载(例如,批处理大小)过大时,MoE Triton内核发生非法内存访问。要重现:
python3 ./benchmarks/kernels/benchmark_moe.py --tp-size 2 --batch-size 74899
输出
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
我们在L4和A100 GPU上遇到了这个问题。我还尝试使用不同的块大小调整这个特定的工作负载,但没有一个配置可以绕过错误。因为我们通常不会使用如此大的批处理大小(标记数量),所以至少在目前这个bug应该不是关键问题。
另外,cc @pcmoritz@WoosukKwon@Yard1
1条答案
按热度按时间ru9i0ody1#
+1,我也是。
tp_size 2
Qwen2_72b
2 X A800 80G