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#!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
/* Copyright (c) 2023 Amazon
Written by Jan Buethe */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
"""
import torch
"""
https://github.com/xiph/rnnoise/blob/main/torch/sparsification/common.py
"""
def sparsify_matrix(matrix : torch.tensor, density : float, block_size, keep_diagonal : bool=False, return_mask : bool=False):
""" sparsifies matrix with specified block size
Parameters:
-----------
matrix : torch.tensor
matrix to sparsify
density : int
target density
block_size : [int, int]
block size dimensions
keep_diagonal : bool
If true, the diagonal will be kept. This option requires block_size[0] == block_size[1] and defaults to False
"""
m, n = matrix.shape
m1, n1 = block_size
if m % m1 or n % n1:
raise ValueError(f"block size {(m1, n1)} does not divide matrix size {(m, n)}")
# extract diagonal if keep_diagonal = True
if keep_diagonal:
if m != n:
raise ValueError("Attempting to sparsify non-square matrix with keep_diagonal=True")
to_spare = torch.diag(torch.diag(matrix))
matrix = matrix - to_spare
else:
to_spare = torch.zeros_like(matrix)
# calculate energy in sub-blocks
x = torch.reshape(matrix, (m // m1, m1, n // n1, n1))
x = x ** 2
block_energies = torch.sum(torch.sum(x, dim=3), dim=1)
number_of_blocks = (m * n) // (m1 * n1)
number_of_survivors = round(number_of_blocks * density)
# masking threshold
if number_of_survivors == 0:
threshold = 0
else:
threshold = torch.sort(torch.flatten(block_energies)).values[-number_of_survivors]
# create mask
mask = torch.ones_like(block_energies)
mask[block_energies < threshold] = 0
mask = torch.repeat_interleave(mask, m1, dim=0)
mask = torch.repeat_interleave(mask, n1, dim=1)
# perform masking
masked_matrix = mask * matrix + to_spare
if return_mask:
return masked_matrix, mask
else:
return masked_matrix
def calculate_gru_flops_per_step(gru, sparsification_dict=dict(), drop_input=False):
input_size = gru.input_size
hidden_size = gru.hidden_size
flops = 0
input_density = (
sparsification_dict.get('W_ir', [1])[0]
+ sparsification_dict.get('W_in', [1])[0]
+ sparsification_dict.get('W_iz', [1])[0]
) / 3
recurrent_density = (
sparsification_dict.get('W_hr', [1])[0]
+ sparsification_dict.get('W_hn', [1])[0]
+ sparsification_dict.get('W_hz', [1])[0]
) / 3
# input matrix vector multiplications
if not drop_input:
flops += 2 * 3 * input_size * hidden_size * input_density
# recurrent matrix vector multiplications
flops += 2 * 3 * hidden_size * hidden_size * recurrent_density
# biases
flops += 6 * hidden_size
# activations estimated by 10 flops per activation
flops += 30 * hidden_size
return flops
if __name__ == "__main__":
pass
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