toolbox/torch/sparsification/common.py

#!/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