AWS Trainium fails number of device validation when using more than 1 accelerator on the instances

Nobody has claimed this yet.

### Bug description

in lightning/fabric/accelerators/tpu.py there is a `_parse_tpu_devices` function that hard codes a maximum of 8 devices. in `torch_xla/distributed/xla_multiprocessing.py` there's a validator that allows for either 1 or $WORLD_SIZE devices to be used.

When working with Amazon Trainium, the large trn1.32xlarge instances come equipped with 16 accelerators with 2 cores each for a total of 32 devices. Setting of both 8 and 32 cause a validation error to occur before training starts. This problem is not see when only 1 acclerator is in use since it falls under the <=8 threshold.

### What version are you seeing the problem on?

v2.0, v2.1, v2.2

### How to reproduce the bug

```python
import lightning as pl
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision import transforms
from torchvision.datasets import FashionMNIST
from torchvision import transforms
from torchmetrics import Accuracy
import mlflow
import torch_xla.core.xla_model as xm
import os
import torch
import uuid

class MNISTClassifier(pl.LightningModule):
def __init__(self):
super(MNISTClassifier, self).__init__()
self.accuracy = Accuracy(task="multiclass", num_classes=10, top_k=1)
self.layer_1_size = 128
self.layer_2_size = 256
self.lr = .01

# mnist images are (1, 28, 28) (channels, width, height)
self.layer_1 = torch.nn.Linear(28 * 28, self.layer_1_size)
self.layer_2 = torch.nn.Linear(self.layer_1_size, self.layer_2_size)
self.layer_3 = torch.nn.Linear(self.layer_2_size, 10)
self.eval_loss = []
self.eval_accuracy = []

def cross_entropy_loss(self, logits, labels):
return F.nll_loss(logits, labels)

def forward(self, x):
batch_size, channels, width, height = x.size()
x = x.view(batch_size, -1)

x = self.layer_1(x)
x = torch.relu(x)

x = self.layer_2(x)
x = torch.relu(x)

x = self.layer_3(x)
x = torch.log_softmax(x, dim=1)

return x

def training_step(self, train_batch, batch_idx):
x, y = train_batch
logits = self.forward(x)
loss = self.cross_entropy_loss(logits, y)
accuracy = self.accuracy(logits, y)

self.log("train_loss", loss)
self.log("train_accuracy", accuracy)
return loss

def validation_step(self, val_batch, batch_idx):
x, y = val_batch
logits = self.forward(x)
loss = self.cross_entropy_loss(logits, y)
accuracy = self.accuracy(logits, y)
self.eval_loss.append(loss)
self.eval_accuracy.append(accuracy)
return {"val_loss": loss, "val_accuracy": accuracy}

def on_validation_epoch_end(self):
avg_loss = torch.stack(self.eval_loss).mean()
avg_acc = torch.stack(self.eval_accuracy).mean()
self.log("val_loss", avg_loss, sync_dist=True)
self.log("val_accuracy", avg_acc, sync_dist=True)
self.eval_loss.clear()
self.eval_accuracy.clear()

def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
return optimizer

def train_func():

mnist_train = FashionMNIST('~/torchdata/', train=True, download=True, transform=transforms.ToTensor())
mnist_train = DataLoader(mnist_train, batch_size=512, num_workers=4)
mnist_val = FashionMNIST('~/torchdata/', download=True, transform=transforms.ToTensor())
mnist_val = DataLoader(mnist_val, batch_size=512, num_workers=4)

# model
mnist_model = MNISTClassifier()

trainer = pl.Trainer(devices=$NUM_DEVICES, precision='bf16-true', max_epochs=2)
trainer.fit(mnist_model, mnist_train, mnist_val)

train_func()
```

### Error messages and logs

```
File ~/miniconda3/lib/python3.10/site-packages/torch_xla/distributed/xla_multiprocessing.py:201, in _pre_fork_setup(num_devices)
199 num_devices = dev_count
200 elif num_devices not in [1, dev_count]:
--> 201 raise ValueError(
202 'The number of devices must be either 1 or {}, got {} instead'.format(
203 dev_count, num_devices))
204 total_devices = _get_world_size() * num_devices
205 if total_devices > 1 and not os.environ.get(xenv.SERVICE_ADDRESS, None):
206 # In multi-processing mode, even if there is only one XLA host, we still
207 # bring up the mesh service.

ValueError: The number of devices must be either 1 or 32, got 8 instead
```

and

```
File ~/miniconda3/lib/python3.10/site-packages/lightning/fabric/accelerators/tpu.py:158, in _parse_tpu_devices(devices)
155 devices = _parse_tpu_devices_str(devices.strip())
157 if not _tpu_devices_valid(devices):
--> 158 raise TypeError("`devices` can only be 1, 8 or [<1-8>] for TPUs.")
160 return devices

TypeError: `devices` can only be 1, 8 or [<1-8>] for TPUs.
```

### Environment

* CUDA:
- GPU: None
- available: False
- version: 11.7
* Lightning:
- lightning: 2.2.3
- lightning-cloud: 0.5.68
- lightning-utilities: 0.11.2
- pytorch-lightning: 2.2.3
- torch: 1.13.0
- torch-neuronx: 1.13.1.1.14.0
- torch-xla: 1.13.1+torchneurone
- torchmetrics: 1.3.2
- torchvision: 0.14.0
* Packages:
- absl-py: 2.1.0
- aiohttp: 3.9.5
- aiohttp-cors: 0.7.0
- aiosignal: 1.3.1
- alembic: 1.13.1
- anaconda-anon-usage: 0.4.4
- aniso8601: 9.0.1
- annotated-types: 0.6.0
- anyio: 4.3.0
- archspec: 0.2.3
- argon2-cffi: 23.1.0
- argon2-cffi-bindings: 21.2.0
- arrow: 1.3.0
- asttokens: 2.4.1
- async-lru: 2.0.4
- async-timeout: 4.0.3
- attrs: 23.2.0
- aws-neuronx-runtime-discovery: 2.9
- babel: 2.14.0
- beautifulsoup4: 4.12.3
- bio: 1.7.0
- biopython: 1.83
- biothings-client: 0.3.1
- bleach: 6.1.0
- blessed: 1.20.0
- blinker: 1.8.1
- boltons: 23.0.0
- boto3: 1.34.93
- botocore: 1.34.93
- brotli: 1.0.9
- cachetools: 5.3.3
- certifi: 2024.2.2
- cffi: 1.16.0
- charset-normalizer: 2.0.4
- click: 8.1.7
- cloud-tpu-client: 0.10
- cloudpickle: 3.0.0
- colorful: 0.5.6
- comm: 0.2.2
- conda: 24.4.0
- conda-content-trust: 0.2.0
- conda-libmamba-solver: 24.1.0
- conda-package-handling: 2.2.0
- conda-package-streaming: 0.9.0
- contourpy: 1.2.1
- croniter: 1.3.15
- cryptography: 42.0.5
- cycler: 0.12.1
- datasets: 2.19.0
- dateutils: 0.6.12
- debugpy: 1.8.1
- decorator: 5.1.1
- deepdiff: 7.0.1
- defusedxml: 0.7.1
- deprecated: 1.2.14
- dill: 0.3.8
- distlib: 0.3.8
- distro: 1.8.0
- dm-tree: 0.1.8
- docker: 7.0.0
- docutils: 0.21.2
- ec2-metadata: 2.10.0
- editor: 1.6.6
- entrypoints: 0.4
- exceptiongroup: 1.2.1
- executing: 2.0.1
- farama-notifications: 0.0.4
- fastapi: 0.88.0
- fastjsonschema: 2.19.1
- filelock: 3.14.0
- flask: 3.0.3
- fonttools: 4.51.0
- fqdn: 1.5.1
- frozenlist: 1.4.1
- fsspec: 2023.12.2
- gitdb: 4.0.11
- gitpython: 3.1.43
- google-api-core: 1.34.1
- google-api-python-client: 1.8.0
- google-auth: 2.29.0
- google-auth-httplib2: 0.2.0
- googleapis-common-protos: 1.63.0
- gprofiler-official: 1.0.0
- graphene: 3.3
- graphql-core: 3.2.3
- graphql-relay: 3.2.0
- greenlet: 3.0.3
- grpcio: 1.62.2
- gunicorn: 21.2.0
- gymnasium: 0.28.1
- h11: 0.14.0
- httpcore: 1.0.5
- httplib2: 0.22.0
- httptools: 0.6.1
- httpx: 0.27.0
- huggingface-hub: 0.22.2
- idna: 3.7
- imageio: 2.34.1
- importlib-metadata: 7.0.0
- inquirer: 3.2.4
- ipykernel: 6.29.4
- ipython: 8.24.0
- ipywidgets: 8.1.2
- islpy: 2023.1
- isoduration: 20.11.0
- itsdangerous: 2.2.0
- jax-jumpy: 1.0.0
- jedi: 0.19.1
- jinja2: 3.1.3
- jmespath: 1.0.1
- joblib: 1.4.0
- json5: 0.9.25
- jsonpatch: 1.33
- jsonpointer: 2.1
- jsonschema: 4.21.1
- jsonschema-specifications: 2023.12.1
- jupyter: 1.0.0
- jupyter-client: 8.6.1
- jupyter-console: 6.6.3
- jupyter-core: 5.7.2
- jupyter-events: 0.10.0
- jupyter-lsp: 2.2.5
- jupyter-server: 2.14.0
- jupyter-server-terminals: 0.5.3
- jupyterlab: 4.1.8
- jupyterlab-pygments: 0.3.0
- jupyterlab-server: 2.27.1
- jupyterlab-widgets: 3.0.10
- kiwisolver: 1.4.5
- lazy-loader: 0.4
- libmambapy: 1.5.8
- libneuronxla: 0.5.971
- lightning: 2.2.3
- lightning-cloud: 0.5.68
- lightning-utilities: 0.11.2
- linkify-it-py: 2.0.3
- lockfile: 0.12.2
- lz4: 4.3.3
- mako: 1.3.3
- markdown: 3.6
- markdown-it-py: 3.0.0
- markupsafe: 2.1.5
- matplotlib: 3.8.4
- matplotlib-inline: 0.1.7
- mdit-py-plugins: 0.4.0
- mdurl: 0.1.2
- memray: 1.12.0
- menuinst: 2.0.2
- mistune: 3.0.2
- mlflow: 2.12.1
- mpmath: 1.3.0
- msgpack: 1.0.8
- multidict: 6.0.5
- multiprocess: 0.70.16
- mygene: 3.2.2
- nbclient: 0.10.0
- nbconvert: 7.16.4
- nbformat: 5.10.4
- nest-asyncio: 1.6.0
- networkx: 2.6.3
- neuronx-cc: 2.13.72.0+78a426937
- notebook: 7.1.3
- notebook-shim: 0.2.4
- numpy: 1.25.2
- nvidia-cublas-cu11: 11.10.3.66
- nvidia-cublas-cu12: 12.1.3.1
- nvidia-cuda-cupti-cu12: 12.1.105
- nvidia-cuda-nvrtc-cu11: 11.7.99
- nvidia-cuda-nvrtc-cu12: 12.1.105
- nvidia-cuda-runtime-cu11: 11.7.99
- nvidia-cuda-runtime-cu12: 12.1.105
- nvidia-cudnn-cu11: 8.5.0.96
- nvidia-cudnn-cu12: 8.9.2.26
- nvidia-cufft-cu12: 11.0.2.54
- nvidia-curand-cu12: 10.3.2.106
- nvidia-cusolver-cu12: 11.4.5.107
- nvidia-cusparse-cu12: 12.1.0.106
- nvidia-nccl-cu12: 2.20.5
- nvidia-nvjitlink-cu12: 12.4.127
- nvidia-nvtx-cu12: 12.1.105
- oauth2client: 4.1.3
- opencensus: 0.11.4
- opencensus-context: 0.1.3
- opentelemetry-api: 1.24.0
- opentelemetry-exporter-otlp: 1.24.0
- opentelemetry-exporter-otlp-proto-common: 1.24.0
- opentelemetry-exporter-otlp-proto-grpc: 1.24.0
- opentelemetry-exporter-otlp-proto-http: 1.24.0
- opentelemetry-proto: 1.24.0
- opentelemetry-sdk: 1.24.0
- opentelemetry-semantic-conventions: 0.45b0
- ordered-set: 4.1.0
- overrides: 7.7.0
- packaging: 23.2
- pandas: 2.2.2
- pandocfilters: 1.5.1
- parso: 0.8.4
- pexpect: 4.9.0
- pgzip: 0.3.5
- pillow: 10.3.0
- pip: 23.3.1
- platformdirs: 3.10.0
- pluggy: 1.0.0
- polars: 0.20.23
- pooch: 1.8.1
- prometheus-client: 0.20.0
- prompt-toolkit: 3.0.43
- proto-plus: 1.23.0
- protobuf: 3.19.6
- psutil: 5.9.8
- ptyprocess: 0.7.0
- pure-eval: 0.2.2
- py-spy: 0.3.14
- pyarrow: 15.0.2
- pyarrow-hotfix: 0.6
- pyasn1: 0.6.0
- pyasn1-modules: 0.4.0
- pycosat: 0.6.6
- pycparser: 2.21
- pydantic: 1.10.15
- pydantic-core: 2.18.2
- pygments: 2.17.2
- pyjwt: 2.8.0
- pyparsing: 3.1.2
- pysocks: 1.7.1
- python-daemon: 3.0.1
- python-dateutil: 2.9.0.post0
- python-dotenv: 1.0.1
- python-json-logger: 2.0.7
- python-multipart: 0.0.9
- pytorch-lightning: 2.2.3
- pytz: 2024.1
- pyyaml: 6.0.1
- pyzmq: 26.0.2
- qtconsole: 5.5.1
- qtpy: 2.4.1
- querystring-parser: 1.2.4
- ray: 2.12.0
- ray-cpp: 2.12.0
- readchar: 4.0.6
- referencing: 0.35.0
- regex: 2024.4.28
- requests: 2.31.0
- requests-unixsocket: 0.3.0
- rfc3339-validator: 0.1.4
- rfc3986-validator: 0.1.1
- rich: 13.7.1
- rpds-py: 0.18.0
- rsa: 4.9
- ruamel.yaml: 0.17.21
- ruamel.yaml.clib: 0.2.6
- runs: 1.2.2
- s3transfer: 0.10.1
- safetensors: 0.4.3
- scikit-image: 0.23.2
- scikit-learn: 1.4.2
- scipy: 1.11.2
- send2trash: 1.8.3
- setuptools: 68.2.2
- shellingham: 1.5.4
- six: 1.16.0
- smart-open: 7.0.4
- smmap: 5.0.1
- sniffio: 1.3.1
- soupsieve: 2.5
- sqlalchemy: 2.0.29
- sqlparse: 0.5.0
- stack-data: 0.6.3
- starlette: 0.22.0
- starsessions: 1.3.0
- sympy: 1.12
- tensorboardx: 2.6.2.2
- terminado: 0.18.1
- textual: 0.58.0
- threadpoolctl: 3.5.0
- tifffile: 2024.4.24
- tinycss2: 1.3.0
- tokenizers: 0.19.1
- tomli: 2.0.1
- torch: 1.13.0
- torch-neuronx: 1.13.1.1.14.0
- torch-xla: 1.13.1+torchneurone
- torchmetrics: 1.3.2
- torchvision: 0.14.0
- tornado: 6.4
- tqdm: 4.65.0
- traitlets: 5.14.3
- transformers: 4.40.1
- triton: 2.3.0
- truststore: 0.8.0
- typer: 0.12.3
- types-python-dateutil: 2.9.0.20240316
- typing-extensions: 4.11.0
- tzdata: 2024.1
- uc-micro-py: 1.0.3
- uri-template: 1.3.0
- uritemplate: 3.0.1
- urllib3: 2.1.0
- uvicorn: 0.29.0
- uvloop: 0.19.0
- virtualenv: 20.26.1
- watchfiles: 0.21.0
- wcwidth: 0.2.13
- webcolors: 1.13
- webencodings: 0.5.1
- websocket-client: 1.8.0
- websockets: 11.0.3
- werkzeug: 3.0.2
- wheel: 0.41.2
- widgetsnbextension: 4.0.10
- wrapt: 1.16.0
- xmod: 1.8.1
- xxhash: 3.4.1
- yarl: 1.9.4
- zipp: 3.18.1
- zstandard: 0.19.0
* System:
- OS: Linux
- architecture:
- 64bit
- ELF
- processor: x86_64
- python: 3.10.14
- release: 5.10.214-202.855.amzn2.x86_64
- version: #1 SMP Tue Apr 9 06:57:12 UTC 2024

### More info

_No response_

cc @JackCaoG @Liyang90 @gkroiz

Start in lightning/fabric/accelerators/tpu.py at _parse_tpu_devices, then inspect the validator in torch_xla/distributed/xla_multiprocessing.py. Reproduce the issue with the supplied Trainium configuration using 8 and 32 devices. Done means valid multi-accelerator Trainium configurations no longer fail device-count validation before training starts.

Written by the indexing model from the issue text.