[dynamo][profiler] console spew of ..."torch._dynamo.variables.torch: [WARNING] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored" for pages...

### 🐛 Describe the bug

when compiling either NanoGPT or T5 using FSDP, get pages of console spew of this warning:
`[rank1]:[2023-10-20 04:13:04,946] [6/21] torch._dynamo.variables.torch: [WARNING] Profiler function <class 'torch.autograd.profiler.record_function'> will be ignored`

Request that this be modified to a warn once type of warning - it is warning the same thing over and over, and then on every rank, spamming up pages of console view.  

for example:
<img width="1434" alt="console_prof_function_warning" src="https://github.com/pytorch/pytorch/assets/46302957/bc3451ed-5770-4814-ae5d-b8d47f0d0fa6">

you can repro using latest nightly and run this for T5 example (nanogpt one is worse in terms of total spew):
https://github.com/lessw2020/transformer_framework/blob/main/run_training.sh (adjust to 8 gpus if needed)
*you may need to edit config/t5_config and ensure use_torch_compile  = True

### Versions

PyTorch version: 2.2.0.dev20231019+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: version 3.25.0
Libc version: glibc-2.31

Python version: 3.9.16 | packaged by conda-forge | (main, Feb  1 2023, 21:39:03)  [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-1036-aws-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.7.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA A10G
GPU 1: NVIDIA A10G
GPU 2: NVIDIA A10G
GPU 3: NVIDIA A10G

Nvidia driver version: 525.85.12
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
Byte Order:                      Little Endian
Address sizes:                   48 bits physical, 48 bits virtual
CPU(s):                          48
On-line CPU(s) list:             0-47
Thread(s) per core:              2
Core(s) per socket:              24
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       AuthenticAMD
CPU family:                      23
Model:                           49
Model name:                      AMD EPYC 7R32
Stepping:                        0
CPU MHz:                         2799.998
BogoMIPS:                        5599.99
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       768 KiB
L1i cache:                       768 KiB
L2 cache:                        12 MiB
L3 cache:                        96 MiB
NUMA node0 CPU(s):               0-47
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 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
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 tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save rdpid

Versions of relevant libraries:
[pip3] flake8==4.0.1
[pip3] flake8-bugbear==22.4.25
[pip3] flake8-polyfill==1.0.2
[pip3] memory-efficient-attention-pytorch==0.1.6
[pip3] mypy==1.0.1
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.3
[pip3] pytorch-triton==2.1.0+6e4932cda8
[pip3] torch==2.2.0.dev20231019+cu121
[pip3] torch-model-archiver==0.5.3b20220226
[pip3] torch-workflow-archiver==0.2.8b20230512
[pip3] torchaudio==2.2.0.dev20231019+cu121
[pip3] torchmultimodal==0.1.0b0
[pip3] torchserve==0.6.0b20220513
[pip3] torchtext==0.14.1
[pip3] torchvision==0.17.0.dev20231019+cu121
[pip3] triton==2.0.0.dev20221202
[pip3] triton-nightly==2.1.0.dev20231012235740
[pip3] vit-pytorch==1.2.2
[conda] blas                      1.0                         mkl    conda-forge
[conda] captum                    0.5.0                         0    pytorch
[conda] cudatoolkit               11.7.0              hd8887f6_11    conda-forge
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] libblas                   3.9.0            16_linux64_mkl    conda-forge
[conda] libcblas                  3.9.0            16_linux64_mkl    conda-forge
[conda] liblapack                 3.9.0            16_linux64_mkl    conda-forge
[conda] magma-cuda117             2.6.1                         1    pytorch
[conda] mkl                       2022.2.1         h84fe81f_16997    conda-forge
[conda] numpy                     1.24.3           py39h6183b62_0    conda-forge
[conda] pytorch-cuda              11.7                 h778d358_5    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] pytorch-triton            2.1.0+6e4932cda8          pypi_0    pypi
[conda] torch                     2.2.0.dev20231019+cu121          pypi_0    pypi
[conda] torch-model-archiver      0.5.3                    py39_0    pytorch
[conda] torch-workflow-archiver   0.2.8                    py39_0    pytorch
[conda] torchaudio                2.2.0.dev20231019+cu121          pypi_0    pypi
[conda] torchmultimodal           0.1.0b0                  pypi_0    pypi
[conda] torchserve                0.6.0                    py39_0    pytorch
[conda] torchtext                 0.14.1                     py39    pytorch
[conda] torchvision               0.17.0.dev20231019+cu121          pypi_0    pypi
[conda] triton                    2.0.0.dev20221202          pypi_0    pypi
[conda] triton-nightly            2.1.0.dev20231012235740          pypi_0    pypi
[conda] vit-pytorch               1.2.2                    pypi_0    pypi