High Peak Memory with CUDAGraphTransform

@t-vi is already working on this.

Since Dec 11, 2024.

Peak Memory is very high when CUDAGraphTransform is used.
```python
# With CUDAGraphTransform - 27517.101568
# Without CUDAGraphTransform - 11917.129728
```

Example -
```python
import torch
import thunder
import litgpt
from torch.testing import make_tensor
from functools import partial
from thunder.dynamo import ThunderCompiler
from thunder.transforms.cudagraph import CUDAGraphTransform

device = torch.device("cuda")

cfg = litgpt.Config.from_name("open_llama_3b", n_layer=10)
with device:
make = partial(make_tensor, low=0, high=255, device=device, dtype=torch.long, requires_grad=False)
shape = (1,) + (cfg.block_size,)

x = make(shape)
m = litgpt.GPT(cfg)

# m = thunder.jit(m)
m = thunder.jit(m, transforms=[CUDAGraphTransform()])

o = m(x)
o.sum().backward()

# With CUDAGraphTransform - 27517.101568
# Without CUDAGraphTransform - 11917.129728
print(torch.cuda.max_memory_allocated() / 1e6)

```

Tested with internal image dated `20241209` on RTX 6000 Ada.