Update on "qnn end to end flow"

Patch a few changes including:
- support bool tensor type
- support fp16 and fix the 8w8a quantization.
- add two non-supported ops (slice_scatter and index_put) in common_defs.py

stories model working end to end:
AOT:
fp16:
```
python -m examples.models.llama2.export_llama -kv --qnn -c stories110M.pt -p params.json
```
quantize:
```
python -m examples.models.llama2.export_llama -kv --qnn --pt2e_quantize -c stories110M.pt -p params.json
```

Runtime:
```
/llama_main --model_path=llama2_fp16_qnn_2.21.pte  --tokenizer_path=tokenizer.bin --prompt="Once"
```
Output:
```
Once upon a time, there was a boy named Tim. Tim had a pet dog named Max. Max was a big, strong dog. They liked to play and run in the park.
One day, Tim and Max went to the park to play. They saw a cat. The cat was up in a tree. Max wanted to help the cat. He tried to climb the tree, but he could not.
Then, something unexpected happened. Max started to climb the tree! He was very strong. Max helped the cat come down. The cat was happy. Tim was so proud of his pet.
```

Stories model is too small and sensitive to qunatization.

Differential Revision: [D56119738](https://our.internmc.facebook.com/intern/diff/D56119738/)

[ghstack-poisoned]

Author: cccclai

examples/models/llama2/export_llama_lib.py

@@ -19,7 +19,6 @@
 
 import pkg_resources
 import torch
-import torch.nn.functional as F
 from executorch.backends.vulkan.partitioner.vulkan_partitioner import VulkanPartitioner
 from executorch.backends.xnnpack.partition.xnnpack_partitioner import (
     XnnpackDynamicallyQuantizedPartitioner,