Sparse benchmarking utils (#48397)
Summary:
This is a benchmarking tooling to work with sparse tensors. To implement this, we extended PR `benchmarking util` [https://github.com/pytorch/pytorch/issues/38338](https://github.com/pytorch/pytorch/pull/38338) for sparse tensors. In order to extend the proposed utility library the **FuzzedTensor** class was extended by creating the new **FuzzedSparseTensor** class. In addition two new operator classes were added, the `UnaryOpSparseFuzzer` and `BinaryOpSparseFuzzer`.
The class `FuzzedSparseTensor` adds new input parameters to the constructor:
1. `sparse_dim`: The number of sparse dimensions in a sparse tensor.
2. `nnz`: Number of non-zero elements in the sparse tensor.
3. `density`: The density of the sparse tensor.
4. `coalesced`: As we know the sparse tensor format permits coalesced/uncoalesced sparse tensors.
and removes `probability_contiguous`, `max_allocation_bytes`, `roll_parameter`, `tensor_constructor` as they are dense-tensors related parameters.
In addition, I've extended the `torch.utils.benchmark.examples` to work with the new classes `FuzzedSparseTensor`, `UnaryOpSparseFuzzer` and `BinaryOpSparseFuzzer`.
Hopefully, this tooling and these examples will help to make other benchmarks in other PRs. Looking forward to your thoughts and feedback. cc robieta, mruberry, ngimel
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48397
Reviewed By: ejguan
Differential Revision: D26008137
Pulled By: mruberry
fbshipit-source-id: 2f37811c7c3eaa3494a0f2500e519267f2186dfb
