Repository: pytorch
Issue: PyTorch's Execution Trace Observer Producing Invalid JSON
Labels: oncall: profiler

### 🐛 Describe the bug

I am using PyTorch's execution trace observer to collect traces about a basic MNIST program. While the program **does** produce valid data (everything is there), it **does not** produce valid **json**.

```python
from torch.profiler import profile, schedule, tensorboard_trace_handler, ProfilerActivity, ExecutionTraceObserver

tracing_schedule = schedule(skip_first=5, wait=5, warmup=2, active=2, repeat=1)

et = ExecutionTraceObserver()
et.register_callback("pytorch_et.json")
et.start()
with profile(
  activities = [ProfilerActivity.CPU, ProfilerActivity.CUDA],
  schedule = tracing_schedule,
  on_trace_ready=lambda x: x.export_chrome_trace("./kineto_trace.json"),
  profile_memory = True,
  record_shapes = True,
  with_stack = True
) as prof:
  for epoch in range(1): # number of epochs
    for step, batch_data in list(enumerate(train_data))[:1]:
        train(batch_data)
        prof.step()

et.stop()
et.unregister_callback()
```

The JSON produced will be contain extra commas at the end of the file and omitted commas between objects. Despite these formatting errors, the data is still there and cleaning the trace with a basic script to add or remove commas yields correct data. 

@briancoutinho

The [full Google Collab of my issue](https://colab.research.google.com/drive/1t0IgZ8eZ5N7zwZci-jOMXycv78isUWS2?usp=sharing) is available for consideration.

### Versions

PyTorch version: 2.2.1+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: 14.0.0-1ubuntu1.1
CMake version: version 3.27.9
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.85+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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):                               2
On-line CPU(s) list:                  0,1
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) CPU @ 2.30GHz
CPU family:                           6
Model:                                63
Thread(s) per core:                   2
Core(s) per socket:                   1
Socket(s):                            1
Stepping:                             0
BogoMIPS:                             4599.99
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
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            32 KiB (1 instance)
L1i cache:                            32 KiB (1 instance)
L2 cache:                             256 KiB (1 instance)
L3 cache:                             45 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0,1
Vulnerability Gather data sampling:   Not affected
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 Reg file data sampling: Not affected
Vulnerability Retbleed:               Vulnerable
Vulnerability Spec rstack overflow:   Not affected
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; BHI: Vulnerable (Syscall hardening enabled)
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] torch==2.2.1+cu121
[pip3] torchaudio==2.2.1+cu121
[pip3] torchdata==0.7.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.17.1
[pip3] torchvision==0.17.1+cu121
[pip3] triton==2.2.0

cc @robieta @chaekit @aaronenyeshi @guotuofeng @guyang3532 @dzhulgakov @davidberard98 @briancoutinho @sraikund16 @sanrise