Lift jagged -> padded dense forward / backward kernels from fbgemm_gpu (

pytorch#125946)

PyTorch can't depend on `fbgemm_gpu` as a dependency because `fbgemm_gpu` already has a dependency on PyTorch. So this PR copy / pastes kernels from `fbgemm_gpu`:
* `dense_to_jagged_forward()` as CUDA registration for new ATen op `_padded_dense_to_jagged_forward()`
* `jagged_to_padded_dense_forward()` as CUDA registration for new ATen op `_jagged_to_padded_dense_forward()`

CPU impls for these new ATen ops will be added in a follow-up PR.
Pull Request resolved: pytorch#125946
Approved by: https://github.com/davidberard98

aten/src/ATen/native/native_functions.yaml

@@ -14644,6 +14644,16 @@
     NestedTensorCUDA: NestedTensor_to_padded_tensor_cuda
   autogen: to_padded_tensor.out
 
+- func: _jagged_to_padded_dense_forward(Tensor values, Tensor[] offsets, SymInt[] max_lengths, float padding_value=0.0) -> Tensor
+  variants: function
+  dispatch:
+    CUDA: _fbgemm_jagged_to_padded_dense_forward
+
+- func: _padded_dense_to_jagged_forward(Tensor dense, Tensor[] offsets, SymInt? total_L=None) -> Tensor
+  variants: function
+  dispatch:
+    CUDA: _fbgemm_dense_to_jagged_forward_symint
+
 - func: _nested_tensor_softmax_with_shape(Tensor self, Tensor query) -> Tensor
   dispatch:
     NestedTensorCPU: NestedTensor_softmax_dropout