""" /* 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 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)