[ao][sparsity] L1 norm based block data sparsifier
L1-Norm Sparsifier
This sparsifier computes the *L1-norm* of every sparse block and "zeroes-out" the
ones with the lowest norm. The level of sparsity defines how many of the
blocks is removed.
This sparsifier is controlled by three variables:
1. `sparsity_level` defines the number of *sparse blocks* that are zeroed-out
2. `sparse_block_shape` defines the shape of the sparse blocks. Note that
the sparse blocks originate at the zero-index of the tensor.
3. `zeros_per_block` is the number of zeros that we are expecting in each
sparse block. By default we assume that all elements within a block are
zeroed-out. However, setting this variable sets the target number of
zeros per block. The zeros within each block are chosen as the *smallest
absolute values*.
Test Plan:
```python test/test_ao_sparsity.py TestNormDataSparsifiers```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79534
Approved by: https://github.com/z-a-f
