Duplicate parameters (_flat_params and original params) in the state_dict when using `use_orig_params=True` and `StateDictType.LOCAL_STATE_DICT`
Labels: triaged, module: fsdp

### 🐛 Describe the bug

When you set `use_orig_params=True` and `StateDictType.LOCAL_STATE_DICT` and you try to get a model's state_dict using `with FSDP.state_dict_type(module=model state_dict_type=StateDictType.LOCAL_STATE_DICT):`, the returned state dict contains BOTH the `_flat_params*` keyed parameters and the parameters with the original name. This is an issue because it takes up unnecessary memory and causes issues if you set `strict=True` in `load_state_dict`

Minimal Repro:
```python
import torch
from torch import nn
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp import StateDictType
import torch.distributed as dist
import os

rank, world_size = int(os.environ['RANK']), int(os.environ['WORLD_SIZE'])

print((f'Torch version is {torch.__version__}' if rank==0 else ''))

torch.cuda.set_device(f'cuda:{rank}')
dist.init_process_group(backend='nccl', world_size=world_size, rank=rank)

class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.model = nn.Sequential(
            nn.Linear(4, 4, bias=False),
        )

unwrapped_model = MyModel().cuda()

wrapped_model = FSDP(
    module=unwrapped_model,
    use_orig_params=True
)

with FSDP.state_dict_type(module=wrapped_model, state_dict_type=StateDictType.LOCAL_STATE_DICT):
    sd = wrapped_model.state_dict()
    print(f'\nState Dict for Rank {rank}:')
    for k,v in sd.items():
        print('\t' + k + ': ', v.shape)
        
    # This line errors out because there are extra parameters in the state_dict.
    wrapped_model.load_state_dict(sd, strict=True)
```

I talked to @awgu over slack and he said `StateDictType.LOCAL_STATE_DICT` is not really recommended anymore, but I figured I would post this here for posterity.

### Versions

Collecting environment information...
PyTorch version: 2.0.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.26.3
Libc version: glibc-2.31

Python version: 3.10.11 (main, Apr  5 2023, 14:15:10) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-148-generic-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: NVIDIA A100-SXM4-40GB
GPU 1: NVIDIA A100-SXM4-40GB

Nvidia driver version: 515.48.07
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:                   48 bits physical, 48 bits virtual
CPU(s):                          64
On-line CPU(s) list:             0-63
Thread(s) per core:              1
Core(s) per socket:              32
Socket(s):                       2
NUMA node(s):                    8
Vendor ID:                       AuthenticAMD
CPU family:                      25
Model:                           1
Model name:                      AMD EPYC 7513 32-Core Processor
Stepping:                        1
Frequency boost:                 enabled
CPU MHz:                         1505.415
CPU max MHz:                     2600.0000
CPU min MHz:                     1500.0000
BogoMIPS:                        5199.90
Virtualization:                  AMD-V
L1d cache:                       2 MiB
L1i cache:                       2 MiB
L2 cache:                        32 MiB
L3 cache:                        256 MiB
NUMA node0 CPU(s):               0-7
NUMA node1 CPU(s):               8-15
NUMA node2 CPU(s):               16-23
NUMA node3 CPU(s):               24-31
NUMA node4 CPU(s):               32-39
NUMA node5 CPU(s):               40-47
NUMA node6 CPU(s):               48-55
NUMA node7 CPU(s):               56-63
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:          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; Retpolines, IBPB conditional, IBRS_FW, STIBP disabled, 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 pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca

Versions of relevant libraries:
[pip3] numpy==1.24.3
[pip3] pytorch-ranger==0.1.1
[pip3] torch==2.0.1
[pip3] torch-optimizer==0.3.0
[pip3] torchdata==0.6.1
[pip3] torchmetrics==0.11.3
[pip3] torchtext==0.15.2
[pip3] torchvision==0.15.2
[pip3] triton==2.0.0
[pip3] vit-pytorch==0.35.8
[conda] Could not collect

cc @zhaojuanmao @mrshenli @rohan-varma @awgu