[Bug] [torch.compile]: torch.compile throws shared tensor guard error if data attribute is accessed #22938
Description
Your current environment
The output of python collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.3 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
==============================
PyTorch Info
==============================
PyTorch version : 2.7.1+cu126
Is debug build : False
CUDA used to build PyTorch : 12.6
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.10.12 (main, May 27 2025, 17:12:29) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-5.15.0-91-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : Could not collect
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB
Nvidia driver version : 570.133.20
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: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 256
On-line CPU(s) list: 0-255
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7763 64-Core Processor
CPU family: 25
Model: 1
Thread(s) per core: 2
Core(s) per socket: 64
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU max MHz: 3529.0520
CPU min MHz: 1500.0000
BogoMIPS: 4899.66
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 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization: AMD-V
L1d cache: 4 MiB (128 instances)
L1i cache: 4 MiB (128 instances)
L2 cache: 64 MiB (128 instances)
L3 cache: 512 MiB (16 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-63,128-191
NUMA node1 CPU(s): 64-127,192-255
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
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] mypy==1.14.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.6.4.1
[pip3] nvidia-cuda-cupti-cu12==12.6.80
[pip3] nvidia-cuda-nvrtc-cu12==12.6.77
[pip3] nvidia-cuda-runtime-cu12==12.6.77
[pip3] nvidia-cudnn-cu12==9.5.1.17
[pip3] nvidia-cufft-cu12==11.3.0.4
[pip3] nvidia-cufile-cu12==1.11.1.6
[pip3] nvidia-curand-cu12==10.3.7.77
[pip3] nvidia-cusolver-cu12==11.7.1.2
[pip3] nvidia-cusparse-cu12==12.5.4.2
[pip3] nvidia-cusparselt-cu12==0.6.3
[pip3] nvidia-ml-py==12.570.86
[pip3] nvidia-nccl-cu12==2.26.2
[pip3] nvidia-nvjitlink-cu12==12.6.85
[pip3] nvidia-nvtx-cu12==12.6.77
[pip3] pyzmq==26.2.1
[pip3] sentence-transformers==3.2.1
[pip3] torch==2.7.1
[pip3] torchaudio==2.7.1
[pip3] torchvision==0.22.1
[pip3] transformers==4.55.0
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.3.1
[pip3] tritonclient==2.51.0
[pip3] vector-quantize-pytorch==1.21.2
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
Neuron SDK Version : N/A
vLLM Version : 0.10.1.dev449+g9d1510377.d20250811 (git sha: 9d1510377, date: 20250811)
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
�[4mGPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 NIC7 CPU Affinity NUMA Affinity GPU NUMA ID�[0m
GPU0 X NV12 NV12 NV12 NV12 NV12 NV12 NV12 NODE NODE PXB PXB SYS SYS SYS SYS 0-63,128-191 0 N/A
GPU1 NV12 X NV12 NV12 NV12 NV12 NV12 NV12 NODE NODE PXB PXB SYS SYS SYS SYS 0-63,128-191 0 N/A
GPU2 NV12 NV12 X NV12 NV12 NV12 NV12 NV12 PXB PXB NODE NODE SYS SYS SYS SYS 0-63,128-191 0 N/A
GPU3 NV12 NV12 NV12 X NV12 NV12 NV12 NV12 PXB PXB NODE NODE SYS SYS SYS SYS 0-63,128-191 0 N/A
GPU4 NV12 NV12 NV12 NV12 X NV12 NV12 NV12 SYS SYS SYS SYS NODE NODE PXB PXB 64-127,192-255 1 N/A
GPU5 NV12 NV12 NV12 NV12 NV12 X NV12 NV12 SYS SYS SYS SYS NODE NODE PXB PXB 64-127,192-255 1 N/A
GPU6 NV12 NV12 NV12 NV12 NV12 NV12 X NV12 SYS SYS SYS SYS PXB PXB NODE NODE 64-127,192-255 1 N/A
GPU7 NV12 NV12 NV12 NV12 NV12 NV12 NV12 X SYS SYS SYS SYS PXB PXB NODE NODE 64-127,192-255 1 N/A
NIC0 NODE NODE PXB PXB SYS SYS SYS SYS X PIX NODE NODE SYS SYS SYS SYS
NIC1 NODE NODE PXB PXB SYS SYS SYS SYS PIX X NODE NODE SYS SYS SYS SYS
NIC2 PXB PXB NODE NODE SYS SYS SYS SYS NODE NODE X PXB SYS SYS SYS SYS
NIC3 PXB PXB NODE NODE SYS SYS SYS SYS NODE NODE PXB X SYS SYS SYS SYS
NIC4 SYS SYS SYS SYS NODE NODE PXB PXB SYS SYS SYS SYS X PXB NODE NODE
NIC5 SYS SYS SYS SYS NODE NODE PXB PXB SYS SYS SYS SYS PXB X NODE NODE
NIC6 SYS SYS SYS SYS PXB PXB NODE NODE SYS SYS SYS SYS NODE NODE X PXB
NIC7 SYS SYS SYS SYS PXB PXB NODE NODE SYS SYS SYS SYS NODE NODE PXB 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
==============================
CUDA_VISIBLE_DEVICES=0,1
CUDA_VISIBLE_DEVICES=0,1
LD_LIBRARY_PATH=/home/kyle/llm-compressor/env/lib/python3.10/site-packages/nvidia/nvjitlink/lib:/home/kyle/llm-compressor/env/lib/python3.10/site-packages/nvidia/nvjitlink/lib:/usr/local/cuda-12.3/lib64
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
NCCL_CUMEM_ENABLE=0
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY
🐛 Describe the bug
Context
For more context, see #22937. TLDR, I'm trying to pass a model's data into a linear kernel.
Bug Description
Attempting to access the data attribute of a vllm parameter causes torch.compile to raise an error about duplicate tensors, despite there being no duplicate tensors in the model.
This error does not get raised if the data attribute is accessed outside of the forward function, which leads me to thinking that this is either a torch.compile issue or related to a change to torch.compile done by vllm.
main...neuralmagic:vllm:kylesayrs/access-data
AssertionError: Guard check failed: 0/0: Duplicate tensors found: ["self._modules['layers']._modules['0']._modules['mlp']._modules['down_proj']._parameters['weight'].data", "self._modules['layers']._modules['1']._modules['mlp']._modules['down_proj']._parameters['weight'].data", "self._modules['layers']._modules['2']._modules['mlp']._modules['down_proj']._parameters['weight'].data","self._modules['layers']._modules['3']._modules['mlp']._modules['down_proj']._parameters['weight'].data",
...
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