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"""
/* 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)
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