Torch compile generates incorrect graph on Llama model
Labels: high priority, triaged, module: regression, oncall: pt2, module: dynamic shapes

### 🐛 Describe the bug

We have Dynamo backend defined similar to IPEX which traces and freezes the model (however the problem is general):

```python
import importlib
import logging

import torch
from torch._dynamo import register_backend
from .common import fake_tensor_unsupported

@register_backend
@fake_tensor_unsupported
def aio(model, input):
    model.print_readable()
    try:
        with torch.no_grad():
            traced_model = torch.jit.trace(model.eval(), inputs)
            frozen_model = torch.jit.freeze(traced_model)
        return frozen_model
    except Exception as ex:
        log.warning("JIT trace failed during the optimize process.")
        log.warning(print(ex))
        return model
```

I'm running the Llama model from Transformers repo tag `tag: v4.30.1` with following script:

```python
import argparse
import os
import sys
import time
import datetime
import torch._dynamo.config
import transformers
import torch
import torch._dynamo

from torch.autograd.profiler import profile
import traceback as tb
import logging

default_input_texts = ("Below is an instruction that describes a task." 
                      "Write a response that appropriately completes the request.\r\n\r\n" 
                      "### Instruction:\r\nList three technologies that make life easier.\r\n\r\n### Response:")


def parse_args():
  parser = argparse.ArgumentParser()
  parser.add_argument("-m", "--model_path",
                      type=str, required=None,
                      help="Recovered Model path")
  parser.add_argument("-a", "--aio",
                      dest="aio", action='store_true',
                      help="Use AIO backend")
  parser.set_defaults(aio=False)
  parser.add_argument("-i", "--input_prompt",
                      type=str, default=default_input_texts,
                      help="Input prompt")
  return parser.parse_args()

def main():
  args = parse_args()

  torch._dynamo.config.cache_size_limit = 128

  print("Loading model and tokenizer...")
  alpaca_model = transformers.LlamaForCausalLM.from_pretrained(args.model_path)
  alpaca_tokenizer = transformers.LlamaTokenizer.from_pretrained(args.model_path)
  alpaca_model.config.pad_token_id = alpaca_tokenizer.pad_token_id = 0 #unk
  alpaca_model.config.bos_token_id = 1
  alpaca_model.config.eos_token_id = 2

  print("Torch compile...")
  alpaca_model = alpaca_model.eval()
  alpaca_model = torch.compile(alpaca_model, backend="air", dynamic=True, fullgraph=False)

  inputs = alpaca_tokenizer(args.input_prompt, return_tensors="pt")
  outputs = alpaca_model.generate(inputs=inputs.input_ids, max_new_tokens=100)

  output_text = alpaca_tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]

  print("-- Alpaca output --")
  print("{}\n\n".format(output_text))


```

When model gets to the our dynamo backend Pytorch throws an error (inside `torch.jit.trace`):

```
INTERNAL ASSERT FAILED at "../torch/csrc/jit/ir/alias_analysis.cpp":621, please report a bug to PyTorch. We don't have an op for aten::__and__ but it isn't a special case.  Argument types: Tensor, bool, 

Candidates:
	aten::__and__.Scalar(Tensor self, Scalar other) -> Tensor
	aten::__and__.Tensor(Tensor self, Tensor other) -> Tensor
	aten::__and__.bool(bool a, bool b) -> bool
	aten::__and__.int(int a, int b) -> int

```

The problem lies in this part (I printed the model with `print_readable()` call in our backend:

```
        # File: /onspecta/transformers/src/transformers/models/llama/modeling_llama.py:234, code: if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
        size_12 = matmul_1.size()
        getitem_47 = size_12[2];  size_12 = None
        eq_4 = getitem_47 == getitem_7;  getitem_47 = None
        and__6 = True & True
        and__7 = 1 & eq_4;  eq_4 = None
        and__8 = and__7 & True;  and__7 = None
        not__2 = _operator_not_(and__8);  and__8 = None
```

and__7 = 1 & eq_4;  eq_4 = None <----- this line is wrong there is an Tensor on left hand side and bool on the right hand side, the code generated by torch.dynamo is incorrect. Problem doesn't occur on `v2.0.0` tag, but happens on `400c4de53bb7b36066aef381313ed71e4a877e95`

The original code from the model in this place is:

```
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
            raise ValueError(
                f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
                f" {attn_output.size()}"
            )
```

### Versions

main branch

cc @ezyang @gchanan @zou3519 @kadeng @msaroufim @wconstab @bdhirsh @anijain2305