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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 .base_sparsifier import BaseSparsifier
from .common import sparsify_matrix, debug
class ConvTranspose1dSparsifier(BaseSparsifier):
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 (conv1d, params), where conv1d is an instance
of torch.nn.Conv1d and params is a tuple (density, [m, n]),
where density is the target density in [0, 1], [m, n] is the shape sub-blocks to which
sparsification is applied.
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
>>> conv = torch.nn.ConvTranspose1d(8, 16, 8)
>>> params = (0.2, [8, 4])
>>> sparsifier = ConvTranspose1dSparsifier([(conv, params)], 0, 100, 50)
>>> for i in range(100):
... sparsifier.step()
"""
super().__init__(task_list, start, stop, interval, exponent=3)
self.last_mask = None
def sparsify(self, alpha, verbose=False):
""" carries out sparsification step
Call this function after optimizer.step in your
training loop.
Parameters:
----------
alpha : float
density interpolation parameter (1: dense, 0: target density)
verbose : bool
if true, densities are printed out
Returns:
--------
None
"""
with torch.no_grad():
for conv, params in self.task_list:
# reshape weight
if hasattr(conv, 'weight_v'):
weight = conv.weight_v
else:
weight = conv.weight
i, o, k = weight.shape
w = weight.permute(2, 1, 0).reshape(k * o, i)
target_density, block_size = params
density = alpha + (1 - alpha) * target_density
w, new_mask = sparsify_matrix(w, density, block_size, return_mask=True)
w = w.reshape(k, o, i).permute(2, 1, 0)
weight[:] = w
if self.last_mask is not None:
if not torch.all(self.last_mask * new_mask == new_mask) and debug:
print("weight resurrection in conv.weight")
self.last_mask = new_mask
if verbose:
print(f"convtrans1d_sparsier[{self.step_counter}]: {density=}")
if __name__ == "__main__":
print("Testing sparsifier")
import torch
conv = torch.nn.ConvTranspose1d(8, 16, 4, 4)
params = (0.2, [8, 4])
sparsifier = ConvTranspose1dSparsifier([(conv, params)], 0, 100, 5)
for i in range(100):
sparsifier.step(verbose=True)
print(conv.weight)
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