Warnings when learning on tpu

Nobody has claimed this yet.

### Bug description

WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.
WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.
WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.

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

v2.5

### Reproduced in studio

_No response_

### How to reproduce the bug

```python
import torch
from torch.utils.data import DataLoader, Dataset
import lightning as pl
from lightning import Trainer
from lightning.pytorch.callbacks import ModelCheckpoint
from lightning.pytorch.loggers import WandbLogger
from transformers import AutoModelForCausalLM, AutoTokenizer
from datasets import load_dataset
import os

os.environ["WANDB_API_KEY"] = "652be9a335ccff9372ec8e5b16946c34163f0ff5"
os.environ["HF_TOKEN"] = "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"

torch.set_float32_matmul_precision('high')

class ChatDataset(Dataset):
def __init__(self, dataset, tokenizer, max_length=1024):
self.dataset = dataset
self.tokenizer = tokenizer
self.max_length = max_length

def __len__(self):
return len(self.dataset)

def __getitem__(self, idx):
data = self.dataset[idx]

chat = ("<|im_start|>user\n" + data["input"] + "<|im_end|>\n" +
"<|im_start|>assistant\n\n \n\n" + data["output"] + "<|im_end|>\n")

encoding = self.tokenizer(
chat,
padding='max_length',
truncation=True,
max_length=self.max_length,
return_tensors='pt'
)

input_ids = encoding['input_ids'].squeeze()
attention_mask = encoding['attention_mask'].squeeze()

labels = input_ids.clone()

return {
'input_ids': input_ids,
'attention_mask': attention_mask,
'labels': labels
}

class LanguageModelLightning(pl.LightningModule):
def __init__(self, model_name, learning_rate=2e-5, weight_decay=0.01):
super().__init__()
self.save_hyperparameters()

self.model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto"
)
self.model.train()
self.tokenizer = AutoTokenizer.from_pretrained(model_name)

self.learning_rate = learning_rate
self.weight_decay = weight_decay

def forward(self, input_ids, attention_mask, labels=None):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
labels=labels
)
return outputs

def training_step(self, batch, batch_idx):
outputs = self.forward(
input_ids=batch['input_ids'],
attention_mask=batch['attention_mask'],
labels=batch['labels']
)

loss = outputs.loss
self.log('train_loss', loss)
return loss

def validation_step(self, batch, batch_idx):
outputs = self.forward(
input_ids=batch['input_ids'],
attention_mask=batch['attention_mask'],
labels=batch['labels']
)

loss = outputs.loss
self.log('val_loss', loss)
return loss

def configure_optimizers(self):
optimizer = torch.optim.AdamW(
self.parameters(),
lr=self.learning_rate,
weight_decay=self.weight_decay
)
return optimizer

dataset = load_dataset('intexcp/russian-llm-training-dataset')

model = LanguageModelLightning("Qwen/Qwen3-0.6B")
#model = torch.compile(model)

train_dataset = ChatDataset(dataset["train"], model.tokenizer)
val_dataset = ChatDataset(dataset["test"], model.tokenizer)

train_loader = DataLoader(
train_dataset,
batch_size=8,
shuffle=True,
num_workers=16,
pin_memory=True
)

val_loader = DataLoader(
val_dataset,
batch_size=8,
shuffle=False,
num_workers=16,
pin_memory=True
)

wandb_logger = WandbLogger(
project="IGen",
name="IGen"
)

checkpoint_callback = ModelCheckpoint(
dirpath="IGen/checkpoints",
filename='{epoch}-{val_loss:.2f}',
monitor='val_loss',
mode='min',
save_top_k=1,
save_last=True
)

trainer = Trainer(
max_epochs=2,
precision="bf16-true",
accelerator="auto",
strategy="auto",
devices="auto",
callbacks=[checkpoint_callback],
check_val_every_n_epoch=1,
log_every_n_steps=50,
enable_model_summary=True,
enable_progress_bar=True,
)

trainer.fit(model, train_loader, val_loader)

model.model.save_pretrained("IGen/final_model")
model.tokenizer.save_pretrained("IGen/final_model")
```

### Error messages and logs

```
# Error messages and logs here please
```WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.
WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.
WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xla_model.get_ordinal() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.global_ordinal instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.
WARNING:root:torch_xla.core.xla_model.xrt_world_size() will be removed in release 2.7. is deprecated. Use torch_xla.runtime.world_size instead.

### Environment

Current environment

```
#- PyTorch Lightning Version (e.g., 2.5.0):
#- PyTorch Version (e.g., 2.5):
#- Python version (e.g., 3.12):
#- OS (e.g., Linux):
#- CUDA/cuDNN version:
#- GPU models and configuration:
#- How you installed Lightning(`conda`, `pip`, source):
```

### More info

_No response_

Search the repository for uses of torch_xla.core.xla_model.get_ordinal() and xrt_world_size(), then inspect the TPU training entry points used by the reported Lightning Trainer example. Run the relevant TPU checks or reproduce the example; done means training no longer emits these deprecation warnings.

Written by the indexing model from the issue text.