Repository: pytorch
Issue: `torch.compile` fails with `fullgraph=True` when accessing `getitem` of a `Tensor` subclass
Labels: triaged,tensor subclass,oncall: pt2,module: dynamo,module: pt2-dispatcher

### 🐛 Describe the bug

The following snippet

```python
import torch
import operator


torch.manual_seed(11)


def f(x):
    return operator.getitem(x, [slice(0, 3), slice(0, 4)])


class BasicTensorSubclass(torch.Tensor):
    pass


x = BasicTensorSubclass(torch.rand(5, 5))

cf = torch.compile(f, fullgraph=False)
print(cf(x))  # SUCCEEDS!

torch.compiler.reset()

cf = torch.compile(f, fullgraph=True)
print(cf(x))  # FAILS!

```
fails with `fullgraph=True` and prints
```
torch._dynamo.exc.Unsupported: call_method UserDefinedObjectVariable(BasicTensorSubclass) __getitem__ (ListVariable(length=2),) {}
```

### Versions

```
Collecting environment information...
PyTorch version: 2.5.0.dev20240617+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

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: version 3.27.2
Libc version: glibc-2.35

Python version: 3.10.9 (main, Mar  8 2023, 10:47:38) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.3.107
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB
GPU 2: NVIDIA A100-SXM4-40GB
GPU 3: NVIDIA A100-SXM4-40GB
GPU 4: NVIDIA A100-SXM4-40GB
GPU 5: NVIDIA A100-SXM4-40GB
GPU 6: NVIDIA A100-SXM4-40GB
GPU 7: NVIDIA A100-SXM4-40GB

Nvidia driver version: 545.23.08
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.0.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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
Address sizes:                      43 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             256
On-line CPU(s) list:                0-254
Off-line CPU(s) list:               255
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7742 64-Core Processor
CPU family:                         23
Model:                              49
Thread(s) per core:                 2
Core(s) per socket:                 64
Socket(s):                          2
Stepping:                           0
Frequency boost:                    enabled
CPU max MHz:                        2250.0000
CPU min MHz:                        0.0000
BogoMIPS:                           4500.20
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 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 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 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 avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es
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 (32 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-63,128-191
NUMA node1 CPU(s):                  64-127,192-254
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:             Mitigation; untrained return thunk; SMT enabled with STIBP protection
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, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] optree==0.9.2
[pip3] pytorch-lightning==2.2.1
[pip3] pytorch-triton==3.0.0+45fff310c8
[pip3] torch==2.5.0.dev20240617+cu121
[pip3] torchaudio==2.4.0.dev20240617+cu121
[pip3] torchmetrics==1.4.0.post0
[pip3] torchvision==0.19.0.dev20240617+cu121
[pip3] triton==2.2.0
[conda] numpy                     1.24.4                   pypi_0    pypi
[conda] numpy-base                1.24.3          py310h375b286_0  
[conda] optree                    0.9.2                    pypi_0    pypi
[conda] pytorch-lightning         2.2.1                    pypi_0    pypi
[conda] pytorch-triton            3.0.0+45fff310c8          pypi_0    pypi
[conda] torch                     2.5.0.dev20240617+cu121          pypi_0    pypi
[conda] torchaudio                2.4.0.dev20240617+cu121          pypi_0    pypi
[conda] torchmetrics              1.4.0.post0              pypi_0    pypi
[conda] torchvision               0.19.0.dev20240617+cu121          pypi_0    pypi
[conda] triton                    2.2.0                    pypi_0    pypi
```


cc @ezyang @albanD @anijain2305 @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng @zou3519 @bdhirsh