torch.cuda.graph "Invalid capture" with torch.linalg.solve
Labels: triaged, module: cuda graphs

### 🐛 Describe the bug

I'm trying a simple CUDAGraph capture example. It works with a torch.matmul operation but reported "Invalid Capture" with torch.linalg.solve. See below. I'm hoping to understand what is ok and what is not ok with torch.cuda.graph. If this is due to un-determined number of iterations inside torch.linalg.solve, if we can set a fix number of iterations.

```
import torch
N = 640

# Placeholders used for capture
A = torch.randn(N, N, device='cuda')
b = torch.randn(N, 1, device='cuda')

# warmup
s = torch.cuda.Stream()
s.wait_stream(torch.cuda.current_stream())
with torch.cuda.stream(s):
    for i in range(3):
        #dat = torch.matmul(A, b) # this command runs
        dat = torch.linalg.solve(A, b) # this command does NOT run
torch.cuda.current_stream().wait_stream(s)

# capture
g = torch.cuda.CUDAGraph()
with torch.cuda.graph(g):
    #dat = torch.matmul(A, b) # this command runs
    dat = torch.linalg.solve(A, b) # this command does NOT run

real_inputs = [torch.rand_like(A) for _ in range(10)]
real_targets = [torch.rand_like(b) for _ in range(10)]

for data, target in zip(real_inputs, real_targets):
    # Fills the graph's input memory with new data to compute on
    A.copy_(data)
    b.copy_(target)
    g.replay()
#print(dat - torch.matmul(A, b))
```

The returned information is
```
---------------------------------------------------------------------------
_LinAlgError                              Traceback (most recent call last)
[<ipython-input-2-f302cf725f49>](https://localhost:8080/#) in <module>
     22     #dat = torch.matmul(A, b) # this command runs
---> 23     dat = torch.linalg.solve(A, b) # this command does NOT run
     24 

_LinAlgError: torch.linalg.solve: The solver failed because the input matrix is singular.

During handling of the above exception, another exception occurred:

RuntimeError                              Traceback (most recent call last)
2 frames
[<ipython-input-2-f302cf725f49>](https://localhost:8080/#) in <module>
     21 with torch.cuda.graph(g):
     22     #dat = torch.matmul(A, b) # this command runs
---> 23     dat = torch.linalg.solve(A, b) # this command does NOT run
     24 
     25 real_inputs = [torch.rand_like(A) for _ in range(10)]

[/usr/local/lib/python3.9/dist-packages/torch/cuda/graphs.py](https://localhost:8080/#) in __exit__(self, exc_type, exc_value, traceback)
    157 
    158     def __exit__(self, exc_type, exc_value, traceback):
--> 159         self.cuda_graph.capture_end()
    160         self.stream_ctx.__exit__(exc_type, exc_value, traceback)
    161         # returning None should propagate exceptions from either capture_end or stream_ctx.__exit__()

[/usr/local/lib/python3.9/dist-packages/torch/cuda/graphs.py](https://localhost:8080/#) in capture_end(self)
     79         which call ``capture_end`` internally.
     80         """
---> 81         super(CUDAGraph, self).capture_end()
     82 
     83     def replay(self):

RuntimeError: Invalid capture.
```


### Versions

--2023-03-13 01:51:20--  https://raw.githubusercontent.com/pytorch/pytorch/master/torch/utils/collect_env.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 21415 (21K) [text/plain]
Saving to: ‘collect_env.py’

collect_env.py      100%[===================>]  20.91K  --.-KB/s    in 0.001s  

2023-03-13 01:51:20 (23.4 MB/s) - ‘collect_env.py’ saved [21415/21415]

Collecting environment information...
PyTorch version: 1.13.1+cu116
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: 10.0.0-4ubuntu1 
CMake version: version 3.22.6
Libc version: glibc-2.31

Python version: 3.9.16 (main, Dec  7 2022, 01:11:51)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.10.147+-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 525.85.12
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU(s):                          2
On-line CPU(s) list:             0,1
Thread(s) per core:              2
Core(s) per socket:              1
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           63
Model name:                      Intel(R) Xeon(R) CPU @ 2.30GHz
Stepping:                        0
CPU MHz:                         2299.998
BogoMIPS:                        4599.99
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       32 KiB
L1i cache:                       32 KiB
L2 cache:                        256 KiB
L3 cache:                        45 MiB
NUMA node0 CPU(s):               0,1
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Mitigation; PTE Inversion
Vulnerability Mds:               Vulnerable; SMT Host state unknown
Vulnerability Meltdown:          Vulnerable
Vulnerability Mmio stale data:   Vulnerable
Vulnerability Retbleed:          Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
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 invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid xsaveopt arat md_clear arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.22.4
[pip3] torch==1.13.1+cu116
[pip3] torchaudio==0.13.1+cu116
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.14.1
[pip3] torchvision==0.14.1+cu116
[conda] Could not collect

cc @mcarilli @ezyang