Support Phi3-mini-128k

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## 🚀 Model / language coverage

Support the https://huggingface.co/microsoft/Phi-3-mini-128k-instruct model. This is a tracking issue.

Dynamo is splitting this into 13 subgraphs. The good news is that `examine` doesn't find much for us:

* _exit_autocast of torch.amp.autocast_mode
* _enter_autocast of torch.amp.autocast_mode
* and examine notes that `TensorBase.bool` is automatically registered, which is probably fine.

Issues:
* #1276
* https://github.com/NVIDIA/Fuser/issues/3228

### Pitch

This is an ask from internal NVIDIA colleagues.

### Minimal Repro

```python
import torch
from torch.utils.data import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, get_scheduler
from datasets import load_dataset
import thunder
import thunder.dynamo

# Define model and tokenizer
model_name = "microsoft/Phi-3-mini-128k-instruct" # Replace with the model you want to fine-tune
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
torch_dtype='auto'
)
model = torch.compile(model, dynamic=False,
backend=thunder.dynamo.ThunderCompiler())
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Add a padding token to the tokenizer
if tokenizer.pad_token is None:
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
model.resize_token_embeddings(len(tokenizer)) # Resize the model's embeddings to accommodate new tokens

# Load a smaller dataset
dataset = load_dataset("tiny_shakespeare", split='train')

# Tokenize the dataset
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True,
max_length=128)

tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=["text"])

# Convert the dataset to PyTorch format and specify columns to return as tensors
tokenized_dataset.set_format(type='torch', columns=['input_ids', 'attention_mask'])

# Create PyTorch DataLoader
dataloader = DataLoader(tokenized_dataset, batch_size=1, shuffle=False)

# Define optimizer and learning rate scheduler
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5)
num_epochs = 3
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_epochs * len(dataloader),
)

# Move model to GPU if available
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)

# Fine-tuning loop
model.train()
for epoch in range(num_epochs):
total_loss = 0
for batch in dataloader:
# Move input tensors to device
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)

# Forward pass
outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=input_ids)
loss = outputs.loss
print(loss)
total_loss += loss.item()

# Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()

# Update learning rate
lr_scheduler.step()

avg_loss = total_loss / len(dataloader)
print(f"Epoch {epoch + 1}/{num_epochs} completed. Average Loss: {avg_loss:.4f}")

bytes = torch.cuda.memory.max_memory_allocated()
mb: int = 1024*1024
gb: int = 1024*1024*1024
print(f"max allocated: {bytes/1024}kB {bytes/mb}mB {bytes/gb}gB")
```

cc @tfogal

Run the provided Python minimal reproduction with microsoft/Phi-3-mini-128k-instruct and inspect the Dynamo/examine output, especially the listed autocast operations and TensorBase.bool registration. Read issue #1276 and NVIDIA/Fuser issue #3228 for the tracked blockers. Done means the model is supported without the reported incompatibilities.

Written by the indexing model from the issue text.