ns for fx: add support for shadowing linear fp16 patterns (#54275)
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/54275
Adds support for NS shadow activations path for the fp16 emulation
pattern such as
```
... -> dequantize -> linear -> relu -> to(torch.float16) -> ...
```
There are a couple of changes necessary here:
1. removing the restriction on the shadowing graph pass that the B
subgraph is a single node (since this subgraph is four nodes), and
modifying the code to correctly add the relevant inputs versus output
loggers (input loggers and subgraph copy if we are at start_node,
and output logger if we are at end_node)
2. modifying the logic for calculating node input and output type
to work correcty for the `to` and `dequantize` nodes:
2a. make the function return the first input and output, instead of just
the first input
2b. make the function handle `dequantize` correctly by recursively
using the output if its input
2c. make the function handle `to` correctyl by recursively using the
output of its input and the target dtype
3. a bug fix to handle observers in kwargs, while copying subgraphs
Note: input logging for these patterns is not tested yet,
this will be in the next PR.
Test Plan:
```
python test/test_quantization.py TestFXNumericSuiteCoreAPIs.test_linear_fp16
```
Imported from OSS
Reviewed By: jerryzh168
Differential Revision: D27172655
fbshipit-source-id: 3bdc86618b2a5782627fcf303d58af7f47fbc30d
