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import torch
from . import base_sparsifier
class NearlyDiagonalSparsifier(base_sparsifier.BaseSparsifier):
r"""Nearly Diagonal Sparsifier
This sparsifier creates a nearly diagonal mask to be applied to the weight matrix.
Nearly Diagonal Matrix is a matrix that contains non-zero elements near the diagonal and the rest are zero.
An example of a nearly diagonal matrix with degree (or nearliness) 3 and 5 are follows respectively.
1 1 0 0 1 1 1 0
1 1 1 0 1 1 1 1
0 1 1 1 1 1 1 1
0 0 1 1 0 1 1 1
Note that a nearly diagonal matrix with degree 1 is just a matrix with main diagonal populated
This sparsifier is controlled by one variable:
1. `nearliness` defines the number of non-zero diagonal lines that are closest to the main diagonal.
Currently - supports only odd number
Note:
This can be accelerated (vectorized) once the Spdiagonal feature (PR: #78439) is landed or the banded matrix
feature is landed: https://stackoverflow.com/questions/52463972/generating-banded-matrices-using-numpy
Args:
nearliness: The degree of nearliness (default = 1)
"""
def __init__(self, nearliness: int = 1):
defaults = {'nearliness': nearliness}
super().__init__(defaults=defaults)
def update_mask(self, module, tensor_name, nearliness,
**kwargs):
mask = getattr(module.parametrizations, tensor_name)[0].mask
mask.data = torch.zeros_like(mask)
if nearliness <= 0:
return
tensor = getattr(module, tensor_name)
height, width = tensor.shape
if nearliness % 2 == 0:
raise ValueError("nearliness can only be an odd number")
dist_to_diagonal = nearliness // 2
# check
if dist_to_diagonal >= min(height, width):
raise ValueError("nearliness cannot be larger than the dimensions of tensor.")
for row in range(0, height):
# Bounds of entries that needs to be set to 1
low = max(0, row - dist_to_diagonal)
high = min(width, row + dist_to_diagonal + 1)
mask[row, low:high].fill_(1)
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