opus/dnn/torch/lpcnet/utils/sparsification/gru_sparsifier.py

"""
/* 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,
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   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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*/
"""

import torch

from .common import sparsify_matrix


class GRUSparsifier:
    def __init__(self, task_list, start, stop, interval, exponent=3):
        """ Sparsifier for torch.nn.GRUs

            Parameters:
            -----------
            task_list : list
                task_list contains a list of tuples (gru, sparsify_dict), where gru is an instance
                of torch.nn.GRU and sparsify_dic is a dictionary with keys in {'W_ir', 'W_iz', 'W_in',
                'W_hr', 'W_hz', 'W_hn'} corresponding to the input and recurrent weights for the reset,
                update, and new gate. The values of sparsify_dict are tuples (density, [m, n], keep_diagonal),
                where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which
                sparsification is applied and keep_diagonal is a bool variable indicating whether the diagonal
                should be kept.

            start : int
                training step after which sparsification will be started.

            stop : int
                training step after which sparsification will be completed.

            interval : int
                sparsification interval for steps between start and stop. After stop sparsification will be
                carried out after every call to GRUSparsifier.step()

            exponent : float
                Interpolation exponent for sparsification interval. In step i sparsification will be carried out
                with density (alpha + target_density * (1 * alpha)), where
                alpha = ((stop - i) / (start - stop)) ** exponent

            Example:
            --------
            >>> import torch
            >>> gru = torch.nn.GRU(10, 20)
            >>> sparsify_dict = {
            ...         'W_ir' : (0.5, [2, 2], False),
            ...         'W_iz' : (0.6, [2, 2], False),
            ...         'W_in' : (0.7, [2, 2], False),
            ...         'W_hr' : (0.1, [4, 4], True),
            ...         'W_hz' : (0.2, [4, 4], True),
            ...         'W_hn' : (0.3, [4, 4], True),
            ...     }
            >>> sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 50)
            >>> for i in range(100):
            ...         sparsifier.step()
        """
        # just copying parameters...
        self.start      = start
        self.stop       = stop
        self.interval   = interval
        self.exponent   = exponent
        self.task_list  = task_list

        # ... and setting counter to 0
        self.step_counter = 0

        self.last_masks = {key : None for key in ['W_ir', 'W_in', 'W_iz', 'W_hr', 'W_hn', 'W_hz']}

    def step(self, verbose=False):
        """ carries out sparsification step

            Call this function after optimizer.step in your
            training loop.

            Parameters:
            ----------
            verbose : bool
                if true, densities are printed out

            Returns:
            --------
            None

        """
        # compute current interpolation factor
        self.step_counter += 1

        if self.step_counter < self.start:
            return
        elif self.step_counter < self.stop:
            # update only every self.interval-th interval
            if self.step_counter % self.interval:
                return

            alpha = ((self.stop - self.step_counter) / (self.stop - self.start)) ** self.exponent
        else:
            alpha = 0


        with torch.no_grad():
            for gru, params in self.task_list:
                hidden_size = gru.hidden_size

                # input weights
                for i, key in enumerate(['W_ir', 'W_iz', 'W_in']):
                    if key in params:
                        density = alpha + (1 - alpha) * params[key][0]
                        if verbose:
                            print(f"[{self.step_counter}]: {key} density: {density}")

                        gru.weight_ih_l0[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix(
                            gru.weight_ih_l0[i * hidden_size : (i + 1) * hidden_size, : ],
                            density, # density
                            params[key][1], # block_size
                            params[key][2], # keep_diagonal (might want to set this to False)
                            return_mask=True
                        )

                        if type(self.last_masks[key]) != type(None):
                            if not torch.all(self.last_masks[key] == new_mask) and self.step_counter > self.stop:
                                print(f"sparsification mask {key} changed for gru {gru}")

                        self.last_masks[key] = new_mask

                # recurrent weights
                for i, key in enumerate(['W_hr', 'W_hz', 'W_hn']):
                    if key in params:
                        density = alpha + (1 - alpha) * params[key][0]
                        if verbose:
                            print(f"[{self.step_counter}]: {key} density: {density}")
                        gru.weight_hh_l0[i * hidden_size : (i+1) * hidden_size, : ], new_mask = sparsify_matrix(
                            gru.weight_hh_l0[i * hidden_size : (i + 1) * hidden_size, : ],
                            density,
                            params[key][1], # block_size
                            params[key][2], # keep_diagonal (might want to set this to False)
                            return_mask=True
                        )

                        if type(self.last_masks[key]) != type(None):
                            if not torch.all(self.last_masks[key] == new_mask) and self.step_counter > self.stop:
                                print(f"sparsification mask {key} changed for gru {gru}")

                        self.last_masks[key] = new_mask



if __name__ == "__main__":
    print("Testing sparsifier")

    gru = torch.nn.GRU(10, 20)
    sparsify_dict = {
        'W_ir' : (0.5, [2, 2], False),
        'W_iz' : (0.6, [2, 2], False),
        'W_in' : (0.7, [2, 2], False),
        'W_hr' : (0.1, [4, 4], True),
        'W_hz' : (0.2, [4, 4], True),
        'W_hn' : (0.3, [4, 4], True),
    }

    sparsifier = GRUSparsifier([(gru, sparsify_dict)], 0, 100, 10)

    for i in range(100):
        sparsifier.step(verbose=True)