Repository: pytorch
Issue: Getting "RuntimeError: Trying to backward through the graph a second time" when calling backward on compiled unbind / split ops
Labels: triaged,bug,oncall: pt2,module: aotdispatch,module: pt2-dispatcher

### 🐛 Describe the bug

Since Pytorch 2.2 (until now - current PT nightly) trying to run backward() on compiled unbind or split ops, a RuntimeError is thrown. As suggested in the error message, calling backward() with retain_graph=True prevents the error from occuring. However previous PT versions (2.1) did not require that to work. 
Is this an expected bahaviour or a new bug that a workaround with retain_graph=True hides?

### Error logs

➜  ~ python3 unbind_repro.py
Traceback (most recent call last):
  File "/home/ubu/unbind_repro.py", line 14, in <module>
    i.backward() #retain_graph=True)
    ^^^^^^^^^^^^
  File "/home/ubu/.local/lib/python3.11/site-packages/torch/_tensor.py", line 524, in backward
    torch.autograd.backward(
  File "/home/ubu/.local/lib/python3.11/site-packages/torch/autograd/__init__.py", line 267, in backward
    _engine_run_backward(
  File "/home/ubu/.local/lib/python3.11/site-packages/torch/autograd/graph.py", line 744, in _engine_run_backward
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubu/.local/lib/python3.11/site-packages/torch/autograd/function.py", line 294, in apply
    return user_fn(self, *args)
           ^^^^^^^^^^^^^^^^^^^^
  File "/home/ubu/.local/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 620, in backward
    *ctx.saved_tensors,
     ^^^^^^^^^^^^^^^^^
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.

### Minified repro
```
import torch

input = torch.Tensor([1, 2, 3, 4, 8])
input.requires_grad = True

def foo(input):
  outputs = torch.unbind(input, 0)
  return outputs

fooCompiled = torch.compile(foo)
out = fooCompiled(input)

for i in out:
  i.backward() #retain_graph=True)

print(out)
```
### Versions

PyTorch version: 2.3.0.dev20240129+cpu
Is debug build: False
CUDA used to build PyTorch: Could not collect
ROCM used to build PyTorch: N/A

OS: Ubuntu 23.04 (x86_64)
GCC version: (Ubuntu 12.3.0-1ubuntu1~23.04) 12.3.0
Clang version: 15.0.7
CMake version: version 3.25.1
Libc version: glibc-2.37

Python version: 3.11.4 (main, Dec  7 2023, 15:43:41) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.37
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
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:                      39 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             8
On-line CPU(s) list:                0-7
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Core(TM) i7-8665U CPU @ 1.90GHz
CPU family:                         6
Model:                              142
Thread(s) per core:                 2
Core(s) per socket:                 4
Socket(s):                          1
Stepping:                           12
BogoMIPS:                           4224.01
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 arch_perfmon rep_good nopl xtopology cpuid pni pclmulqdq vmx ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi ept vpid ept_ad fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt xsaveopt xsavec xgetbv1 xsaves flush_l1d arch_capabilities
Virtualization:                     VT-x
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          128 KiB (4 instances)
L1i cache:                          128 KiB (4 instances)
L2 cache:                           1 MiB (4 instances)
L3 cache:                           8 MiB (1 instance)
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
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 and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Unknown: Dependent on hypervisor status
Vulnerability Tsx async abort:      Mitigation; TSX disabled

Versions of relevant libraries:
[pip3] numpy==1.24.2
[pip3] torch==2.3.0.dev20240129+cpu
[pip3] torchaudio==2.2.0.dev20240129+cpu
[pip3] torchvision==0.18.0.dev20240129+cpu
[conda] Could not collect

cc @ezyang @msaroufim @bdhirsh @anijain2305 @zou3519 @chauhang @gchanan @kadeng