Repository: pytorch
Issue: Mismatch flat_outputs and out_spec in torch 2.3
Labels: triaged,oncall: pt2,module: aotinductor

### 🐛 Describe the bug

in this code https://github.com/pytorch/pytorch/blob/63d5e9221bedd1546b7d364b5ce4171547db12a9/torch/_export/__init__.py#L404

after AOT compile, the flat_outputs is a list of 7 tensors and out_spec is a single node/leaf.  

The same python model is compiled with torch.compile and the output is a single tensor.  The output of AOT model has the shape of 
[torch.Size([1024]), torch.Size([1024]), torch.Size([]), torch.Size([512]), torch.Size([512]), torch.Size([]), torch.Size([128, 1])]
and the last tensor has the correct values as the torch.compile output. 


Attach code here
```python
def optimized(*args, **kwargs):
        call_spec = runner.get_call_spec()  # type: ignore[attr-defined]
        in_spec = pytree.treespec_loads(call_spec[0])
        out_spec = pytree.treespec_loads(call_spec[1])
        flat_inputs = pytree.tree_flatten((args, reorder_kwargs(kwargs, in_spec)))[0]
        flat_outputs = runner.run(flat_inputs)  # type: ignore[attr-defined]
        return pytree.tree_unflatten(flat_outputs, out_spec)

``` 



### Error logs

Exception has occurred: ValueError       (note: full exception trace is shown but execution is paused at: _run_module_as_main)
treespec.unflatten(leaves): `leaves` has length 7 but the spec refers to a pytree that holds 1 items (*).

### Minified repro

Will do later but want to to get a sense if this is our internal issue or a pytorch issue. 

### Versions

python3 collect_env.py
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 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: version 3.29.1
Libc version: glibc-2.35

Python version: 3.11.7 (main, Apr 20 2024, 22:01:03) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.5.0-1016-gcp-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA L4
GPU 1: NVIDIA L4

Nvidia driver version: 550.54.15
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:                      46 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             24
On-line CPU(s) list:                0-23
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 2
Core(s) per socket:                 12
Socket(s):                          1
Stepping:                           7
BogoMIPS:                           4400.39
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
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          384 KiB (12 instances)
L1i cache:                          384 KiB (12 instances)
L2 cache:                           12 MiB (12 instances)
L3 cache:                           38.5 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-23
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT Host state unknown
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
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT Host state unknown

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.25.0
[pip3] pytorch-lightning==2.0.2
[pip3] torch==2.3.0+cu121
[pip3] torchmetrics==0.11.0
[pip3] torchvision==0.18.0+cu121
[pip3] triton==2.3.0
[conda] Could not collect

cc @ezyang @chauhang @penguinwu @desertfire @chenyang78