fix functionalization <> resnet18, make ProxyTensor work with tensor-less decomps (#83207)
This should fix a few of the errors I was seeing when I turned on functionalization in torchbench. It also fixes this AOTAutograd repro with resnet18:
```
import torch
from torchvision.models import resnet18
from functorch._src.compilers import nop
from functorch._src.aot_autograd import aot_module
from functorch.compile import config
config.use_functionalize = True
model = resnet18().cuda().half().to(memory_format=torch.channels_last)
input = torch.randn(256, 3, 224, 224, device='cuda', dtype=torch.float16) \
.to(memory_format=torch.channels_last).detach().requires_grad_(True)
input_expected = input.clone().detach().requires_grad_(True)
fn = aot_module(model, nop)
out = fn(input)
out_expected = model(input_expected)
print(torch.allclose(out, out_expected))
out.sum().backward()
out_expected.sum().backward()
print(torch.allclose(input.grad, input_expected.grad))
```
The problem was that functorch adds a decomp to the decomp table for `new_zeros`:
```
@register_decomposition(aten.new_zeros, aot_autograd_decompositions)
def new_zeros(inp, size, dtype=None, layout=None, device=None, pin_memory=None):
return torch.zeros(size, dtype=inp.dtype, device=inp.device)
```
When calling that decomp from inside of `ProxyTensorDispatchMode`, the ProxyTensorMode is already disabled, and `torch.zeros` doesn't take in any tensor-like arguments, so we never end up dispatching back into python again.
The way that manifests is that the output of `new_zeros()` gets baked as a constant into the AOTAutograd FX graph.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83207
Approved by: https://github.com/ezyang
