[Bug]: torch.compile padding causes IMA on Hopper + DBO #25623
Description
Your current environment
The output of python collect_env.py
Collecting environment information...
uv is set
==============================
System Info
==============================
OS : Ubuntu 22.04.4 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
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PyTorch Info
==============================
PyTorch version : 2.8.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.10.12 (main, Jan 17 2025, 14:35:34) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-5.15.0-113-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.8.61
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 : 570.86.10
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 384
On-line CPU(s) list: 0-383
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9654 96-Core Processor
CPU family: 25
Model: 17
Thread(s) per core: 2
Core(s) per socket: 96
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU max MHz: 3707.8120
CPU min MHz: 1500.0000
BogoMIPS: 4799.98
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d
Virtualization: AMD-V
L1d cache: 6 MiB (192 instances)
L1i cache: 6 MiB (192 instances)
L2 cache: 192 MiB (192 instances)
L3 cache: 768 MiB (24 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-95,192-287
NUMA node1 CPU(s): 96-191,288-383
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: Mitigation; safe RET
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; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.8.0+cu128
[pip3] torchaudio==2.8.0+cu128
[pip3] torchvision==0.23.0+cu128
[pip3] transformers==4.56.2
[pip3] triton==3.4.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.11.0rc2.dev71+ge18b714b2 (git sha: e18b714b2)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X NV18 NV18 NV18 NV18 NV18 NV18 NV18 NODE NODE PIX NODE SYS SYS SYS SYS 0-95,192-287 0 N/A
GPU1 NV18 X NV18 NV18 NV18 NV18 NV18 NV18 NODE NODE NODE PIX SYS SYS SYS SYS 0-95,192-287 0 N/A
GPU2 NV18 NV18 X NV18 NV18 NV18 NV18 NV18 NODE PIX NODE NODE SYS SYS SYS SYS 0-95,192-287 0 N/A
GPU3 NV18 NV18 NV18 X NV18 NV18 NV18 NV18 PIX NODE NODE NODE SYS SYS SYS SYS 0-95,192-287 0 N/A
GPU4 NV18 NV18 NV18 NV18 X NV18 NV18 NV18 SYS SYS SYS SYS NODE NODE PIX NODE 96-191,288-383 1 N/A
GPU5 NV18 NV18 NV18 NV18 NV18 X NV18 NV18 SYS SYS SYS SYS NODE NODE NODE PIX 96-191,288-383 1 N/A
GPU6 NV18 NV18 NV18 NV18 NV18 NV18 X NV18 SYS SYS SYS SYS NODE PIX NODE NODE 96-191,288-383 1 N/A
GPU7 NV18 NV18 NV18 NV18 NV18 NV18 NV18 X SYS SYS SYS SYS PIX NODE NODE NODE 96-191,288-383 1 N/A
NIC0 NODE NODE NODE PIX SYS SYS SYS SYS X NODE NODE NODE SYS SYS SYS SYS
NIC1 NODE NODE PIX NODE SYS SYS SYS SYS NODE X NODE NODE SYS SYS SYS SYS
NIC2 PIX NODE NODE NODE SYS SYS SYS SYS NODE NODE X NODE SYS SYS SYS SYS
NIC3 NODE PIX NODE NODE SYS SYS SYS SYS NODE NODE NODE X SYS SYS SYS SYS
NIC4 SYS SYS SYS SYS NODE NODE NODE PIX SYS SYS SYS SYS X NODE NODE NODE
NIC5 SYS SYS SYS SYS NODE NODE PIX NODE SYS SYS SYS SYS NODE X NODE NODE
NIC6 SYS SYS SYS SYS PIX NODE NODE NODE SYS SYS SYS SYS NODE NODE X NODE
NIC7 SYS SYS SYS SYS NODE PIX NODE NODE SYS SYS SYS SYS NODE NODE NODE 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
==============================
Environment Variables
==============================
LD_LIBRARY_PATH=/usr/local/cuda-12.8/lib64
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
torch.compile generated padding kernel causes an Illegal Memory Access when padding inouts to block-quantized fp8 cutlass kernels on Hopper and DBO enabled. This bug was introduced in #24666.
^[[1;36m(APIServer pid=1)^[[0;0m ^[[1;36m(EngineCore_DP0 pid=276)^[[0;0m ERROR 09-24 13:00:49 [core.py:708] RuntimeError: Failed: CUDA error /tmp/deepep/csrc/kernels/internode_ll.cu:391 'an illegal memory access was encountered'
Minimal Repro instructions:
Credit to @ElizaWszola for finding this smaller repro.
VLLM_USE_DEEP_GEMM=0 VLLM_ALL2ALL_BACKEND=deepep_low_latency vllm serve \
Qwen/Qwen3-30B-A3B-FP8 \
--port 7557 \
--disable-uvicorn-access-log \
--trust-remote-code \
--enable-expert-parallel \
--data-parallel-hybrid-lb \
--tensor-parallel-size 2 \
--data-parallel-size 2 \
--data-parallel-size-local 2 \
--data-parallel-address localhost \
--data-parallel-rpc-port 5555 \
--data-parallel-start-rank 0 \
--enable-eplb \
--eplb-config '{"window_size":"1000",
"step_interval":"3000",
"num_redundant_experts":"32",
"log_balancedness":"False"}' \
--enable-dbo \
--dbo-decode-token-threshold 32 \
--kv_transfer_config '{"kv_connector":"NixlConnector",
"kv_role":"kv_both"}' \
--max_num_seqs 256
Original repro instructions:
I'm deploying vLLM using the llm-d WideEP well-lit-path
See the decoder manifest here:
https://github.com/llm-d/llm-d/blob/4970c7c2703dc23605719491c4fb380973b13517/guides/wide-ep-lws/manifests/modelserver/base/decode.yaml
In particular this is the vLLM launch command.
exec vllm serve \
deepseek-ai/DeepSeek-R1-0528 \
--port 8200 \
--disable-uvicorn-access-log \
--trust-remote-code \
--enable-expert-parallel \
--data-parallel-hybrid-lb \
--tensor-parallel-size $TP_SIZE \
--data-parallel-size $((LWS_GROUP_SIZE * DP_SIZE_LOCAL)) \
--data-parallel-size-local $DP_SIZE_LOCAL \
--data-parallel-address ${LWS_LEADER_ADDRESS} \
--data-parallel-rpc-port 5555 \
--data-parallel-start-rank $START_RANK \
--enable-eplb \
--eplb-config '{"window_size":"1000",
"step_interval":"3000",
"num_redundant_experts":"32",
"log_balancedness":"False"}' \
--enable-dbo \
--dbo-decode-token-threshold 32 \
--kv_transfer_config '{"kv_connector":"NixlConnector",
"kv_role":"kv_both"}'
From investigations of @LucasWilkinson:
the weird part is it is failing in triton_poi_fused.to_copy_add_constant_pad_nd_mean_mul_pow_rsqrt_2
and DBO isnt even running
the fishy thing is torch compile appears to be rounding the input up to 4?
triton_poi_fused__to_copy_add_constant_pad_nd_mean_mul_pow_rsqrt_2_xnumel = 7168*s72 + 7168*(((-1)*s72) % 4)
stream0 = get_raw_stream(0)
triton_poi_fused__to_copy_add_constant_pad_nd_mean_mul_pow_rsqrt_2.run(buf17, buf13, buf12, arg4_1, buf14, arg7_1, s72, triton_poi_fused__to_copy_add_constant_pad_nd_mean_mul_pow_rsqrt_2_xnumel, stream=stream0)
possibly related to
vllm/vllm/model_executor/layers/quantization/utils/fp8_utils.py
Lines 229 to 233 in e6750d0
This is failing on a store:
| Address | Instruction |
|---|---|
| 0x0000002ad1ea96c0 <+3520> | PRMT R15, R8, 0x5410, R9 |
| 0x0000002ad1ea96d0 <+3536> | @p2 EXIT |
| *> 0x0000002ad1ea96e0 <+3552> | STG.E.128 desc[UR6][R26.64], R12 |
| => 0x0000002ad1ea96f0 <+3568> | EXIT |
Also another failure for DBO
Both TP and DBO need to be enabled for the issue to be triggered:
$ vllm serve deepseek-ai/DeepSeek-V2-Lite --disable-uvicorn-access-log --trust-remote-code --enable-dbo --dbo-decode-token-threshold 32 --tensor-parallel 2
...
Failed: Cuda error /workspace/csrc/custom_all_reduce.cuh:455 'an illegal memory access was encountered'
Failed: Cuda error /workspace/csrc/custom_all_reduce.cuh:455 'an illegal memory access was encountered'
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