Is there an efficient way to create a random bit mask in Pytorch?
Question:
I want to have a random bit mask that has some specified percent of 0
s. The function I devised is:
def create_mask(shape, rate):
"""
The idea is, you take a random permutations of numbers. You then mod then
mod it by the [number of entries in the bitmask] / [percent of 0s you
want]. The number of zeros will be exactly the rate of zeros need. You
can clamp the values for a bitmask.
"""
mask = torch.randperm(reduce(operator.mul, shape, 1)).float().cuda()
# Mod it by the percent to get an even dist of 0s.
mask = torch.fmod(mask, reduce(operator.mul, shape, 1) / rate)
# Anything not zero should be put to 1
mask = torch.clamp(mask, 0, 1)
return mask.view(shape)
To illustrate:
>>> x = create_mask((10, 10), 10)
>>> x
1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 1 1
0 1 1 1 1 0 1 1 1 1
0 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 0
1 1 1 1 1 1 1 1 1 1
1 1 1 0 1 1 1 0 1 1
0 1 1 1 1 1 1 1 1 1
1 1 1 0 1 1 0 1 1 1
1 1 1 1 1 1 1 1 1 1
[torch.cuda.FloatTensor of size 10x10 (GPU 0)]
The main issue I have with this method is it requires the rate
to divide the shape
. I want a function that accepts an arbitrary decimal and gives approximately rate
percent of 0s in the bitmask. Furthermore, I am trying to find a relatively efficient way of doing so. Hence, I would rather not move a numpy
array from the CPU to the GPU. Is there an effiecient way of doing so that allows for a decimal rate
?
Answers:
Use NumPy and cudamat:
import numpy as np
import cudamat as cm
gpuMask = cm.CUDAMatrix(np.random.choice([0, 1], size=(10,10), p=[1./2, 1./2]))
where the elements of the list are fraction representations of the probabilities of your 1s and 0s.
For anyone running into this, this will create a bitmask with approximately 80% zero’s directly on GPU. (PyTorch 0.3)
torch.cuda.FloatTensor(10, 10).uniform_() > 0.8
The proper way to create a bit mask directly on GPU with Pytorch is:
import torch
tensor = torch.rand((3, 5), device=torch.device("cuda")) < 0.9
# tensor([[ True, True, False, True, True, True, True, True, True, False],
# [ True, True, True, True, True, True, True, False, False, True],
# [ True, False, False, True, True, True, True, True, True, False],
# [ True, True, True, True, True, True, True, True, True, True],
# [ True, True, False, True, True, True, True, False, True, True],
# [ True, True, False, False, True, True, True, False, True, True],
# [ True, True, True, False, True, True, True, True, True, True],
# [ True, True, True, True, True, True, False, False, True, True],
# [ True, False, True, True, True, True, True, True, True, True],
# [ True, True, True, True, True, True, True, True, False, True]],
# device='cuda:0')
I want to have a random bit mask that has some specified percent of 0
s. The function I devised is:
def create_mask(shape, rate):
"""
The idea is, you take a random permutations of numbers. You then mod then
mod it by the [number of entries in the bitmask] / [percent of 0s you
want]. The number of zeros will be exactly the rate of zeros need. You
can clamp the values for a bitmask.
"""
mask = torch.randperm(reduce(operator.mul, shape, 1)).float().cuda()
# Mod it by the percent to get an even dist of 0s.
mask = torch.fmod(mask, reduce(operator.mul, shape, 1) / rate)
# Anything not zero should be put to 1
mask = torch.clamp(mask, 0, 1)
return mask.view(shape)
To illustrate:
>>> x = create_mask((10, 10), 10)
>>> x
1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 0 1 1 1
0 1 1 1 1 0 1 1 1 1
0 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 0
1 1 1 1 1 1 1 1 1 1
1 1 1 0 1 1 1 0 1 1
0 1 1 1 1 1 1 1 1 1
1 1 1 0 1 1 0 1 1 1
1 1 1 1 1 1 1 1 1 1
[torch.cuda.FloatTensor of size 10x10 (GPU 0)]
The main issue I have with this method is it requires the rate
to divide the shape
. I want a function that accepts an arbitrary decimal and gives approximately rate
percent of 0s in the bitmask. Furthermore, I am trying to find a relatively efficient way of doing so. Hence, I would rather not move a numpy
array from the CPU to the GPU. Is there an effiecient way of doing so that allows for a decimal rate
?
Use NumPy and cudamat:
import numpy as np
import cudamat as cm
gpuMask = cm.CUDAMatrix(np.random.choice([0, 1], size=(10,10), p=[1./2, 1./2]))
where the elements of the list are fraction representations of the probabilities of your 1s and 0s.
For anyone running into this, this will create a bitmask with approximately 80% zero’s directly on GPU. (PyTorch 0.3)
torch.cuda.FloatTensor(10, 10).uniform_() > 0.8
The proper way to create a bit mask directly on GPU with Pytorch is:
import torch
tensor = torch.rand((3, 5), device=torch.device("cuda")) < 0.9
# tensor([[ True, True, False, True, True, True, True, True, True, False],
# [ True, True, True, True, True, True, True, False, False, True],
# [ True, False, False, True, True, True, True, True, True, False],
# [ True, True, True, True, True, True, True, True, True, True],
# [ True, True, False, True, True, True, True, False, True, True],
# [ True, True, False, False, True, True, True, False, True, True],
# [ True, True, True, False, True, True, True, True, True, True],
# [ True, True, True, True, True, True, False, False, True, True],
# [ True, False, True, True, True, True, True, True, True, True],
# [ True, True, True, True, True, True, True, True, False, True]],
# device='cuda:0')