NCCL backend can't be used with a dataset that is IterDataPipe
Labels: oncall: distributed, module: dataloader

### 🐛 Describe the bug

Distributed training with NCCL backend doesn't work with a dataset extending `IterDataPipe`. This is because `_BaseDataLoaderIter` attempts to use the `gloo` backend in this case, see an excerpt from the master branch below. Note "gloo" backend being hardcoded in the middle.

```
class _BaseDataLoaderIter(object):
    def __init__(self, loader: DataLoader) -> None:
        self._dataset = loader.dataset
        self._shared_seed = None
        self._pg = None
        if isinstance(self._dataset, IterDataPipe):
            if dist.is_available() and dist.is_initialized():
                self._pg = dist.new_group(backend="gloo")
            self._shared_seed = _share_dist_seed(loader.generator, self._pg)
            shared_rng = torch.Generator()
            shared_rng.manual_seed(self._shared_seed)
            self._dataset = torch.utils.data.graph_settings.apply_random_seed(self._dataset, shared_rng)
```

This leads to failures like this:
```
Traceback (most recent call last):
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/trainer/call.py", line 38, in _call_and_handle_interrupt
    return trainer_fn(*args, **kwargs)
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 650, in _fit_impl
    self._run(model, ckpt_path=self.ckpt_path)
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1112, in _run
    results = self._run_stage()
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1191, in _run_stage
    self._run_train()
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1204, in _run_train
    self._run_sanity_check()
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py", line 1276, in _run_sanity_check
    val_loop.run()
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 199, in run
    self.advance(*args, **kwargs)
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 152, in advance
    dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/loops/loop.py", line 194, in run
    self.on_run_start(*args, **kwargs)
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 84, in on_run_start
    self._data_fetcher = iter(data_fetcher)
  File "/mnt/venv/lib/python3.9/site-packages/pytorch_lightning/utilities/fetching.py", line 178, in __iter__
    self.dataloader_iter = iter(self.dataloader)
  File "/mnt/venv/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 435, in __iter__
    return self._get_iterator()
  File "/mnt/venv/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 381, in _get_iterator
    return _MultiProcessingDataLoaderIter(self)
  File "/mnt/venv/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 988, in __init__
    super(_MultiProcessingDataLoaderIter, self).__init__(loader)
  File "/mnt/venv/lib/python3.9/site-packages/torch/utils/data/dataloader.py", line 567, in __init__
    self._pg = dist.new_group(backend="gloo")
  File "/mnt/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 3335, in new_group
    pg = _new_process_group_helper(
  File "/mnt/venv/lib/python3.9/site-packages/torch/distributed/distributed_c10d.py", line 862, in _new_process_group_helper
    pg = ProcessGroupGloo(prefix_store, group_rank, group_size, timeout=timeout)
RuntimeError: [../third_party/gloo/gloo/transport/tcp/pair.cc:799] connect [127.0.0.1]:31115: Connection refused
```

### Versions

Collecting environment information...
PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
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: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.9.10 (main, Feb 16 2023, 20:03:54)  [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-1022-aws-x86_64-with-glibc2.31
Is CUDA available: N/A
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration:
GPU 0: Tesla V100-SXM2-16GB
GPU 1: Tesla V100-SXM2-16GB
GPU 2: Tesla V100-SXM2-16GB
GPU 3: Tesla V100-SXM2-16GB

Nvidia driver version: 520.61.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.1.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.1.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: N/A

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):                          32
On-line CPU(s) list:             0-31
Thread(s) per core:              2
Core(s) per socket:              16
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           79
Model name:                      Intel(R) Xeon(R) CPU E5-2686 v4 @ 2.30GHz
Stepping:                        1
CPU MHz:                         3000.000
CPU max MHz:                     3000.0000
CPU min MHz:                     1200.0000
BogoMIPS:                        4600.04
Hypervisor vendor:               Xen
Virtualization type:             full
L1d cache:                       512 KiB
L1i cache:                       512 KiB
L2 cache:                        4 MiB
L3 cache:                        45 MiB
NUMA node0 CPU(s):               0-31
Vulnerability Itlb multihit:     KVM: Mitigation: VMX unsupported
Vulnerability L1tf:              Mitigation; PTE Inversion
Vulnerability Mds:               Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed:          Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
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 pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single pti fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx xsaveopt

Versions of relevant libraries:
[pip3] numpy==1.23.4
[conda] Could not collect

cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu @SsnL @VitalyFedyunin @ejguan @NivekT @dzhulgakov