add stride into KJT pytree (meta-pytorch#2587)

Summary:
Pull Request resolved: meta-pytorch#2587

# context
* Previously for a KJT, only the following fields and `_keys` are stored in the pytree flatten specs. All other arguments/parameters would be derived accordingly.
```
    _fields = [
        "_values",
        "_weights",
        "_lengths",
        "_offsets",
    ]
```
* Particularly, the `stride` (int) of a KJT, which represents the `batch_size`, is computed by `_maybe_compute_stride_kjt`:
```
def _maybe_compute_stride_kjt(
    keys: List[str],
    stride: Optional[int],
    lengths: Optional[torch.Tensor],
    offsets: Optional[torch.Tensor],
    stride_per_key_per_rank: Optional[List[List[int]]],
) -> int:
    if stride is None:
        if len(keys) == 0:
            stride = 0
        elif stride_per_key_per_rank is not None and len(stride_per_key_per_rank) > 0:
            stride = max([sum(s) for s in stride_per_key_per_rank])
        elif offsets is not None and offsets.numel() > 0:
            stride = (offsets.numel() - 1) // len(keys)
        elif lengths is not None:
            stride = lengths.numel() // len(keys)
        else:
            stride = 0
    return stride
```
* The previously stored pytree flatten specs are enough if the `batch_size` is static, however, this no longer holds true in a variable batch size scenario, where the `stride_per_key_per_rank` is not `None`.
* An example is that with `dedup_ebc`, where the actual batch_size is variable (depending on the dedup data), but the output of the ebc should always be the **true** `stride` (static).
* During ir_export, the output shape will be calculated from `kjt.stride()` function, which would be incorrect if the pytree specs only contains the `keys`.
* This diff adds the `stride` into the KJT pytree flatten/unflatten functions so that a fakified KJT would have the correct stride value.

Reviewed By: PaulZhang12

Differential Revision: D66400821

Author: TroyGarden

.github/scripts/install_libs.sh

@@ -28,4 +28,4 @@ elif [ "$CHANNEL" = "test" ]; then
 fi
 
 
-${CONDA_RUN} pip install importlib-metadata
+${CONDA_RUN} pip install importlib-metadata click PyYAML

requirements.txt

@@ -1,7 +1,9 @@
 black
+click
 cmake
 fbgemm-gpu
 hypothesis==6.70.1
+importlib-metadata
 iopath
 numpy
 pandas
@@ -13,6 +15,7 @@ torchx
 tqdm
 usort
 parameterized
+yaml
 
 # for tests
 # https://github.com/pytorch/pytorch/blob/b96b1e8cff029bb0a73283e6e7f6cc240313f1dc/requirements.txt#L3