This dropout always select const number of units to dropout.

steady_dropout.py

import torch

def steady_dropout(x, prob=0.2):
    assert len(x.shape) == 2, "expected data shape of (batch, feature), while getting " + x.shape
    n_batch = x.shape[0]
    n_feat = x.shape[1]
    n_select = int(round(n_feat * prob))
    prob = float(n_select) / float(n_feat)
    r = torch.randn(n_batch, n_feat)
    _, i = r.sort(dim=1)
    j = torch.arange(n_batch).view(-1, 1) * n_feat + i
    z = x.flatten()
    z[j[:, :n_select].flatten()]=0
    x = z.view(x.shape)
    return x / prob

if __name__ == '__main__':
    n_batch = 5
    n_feat = 10
    x = torch.randn(n_batch, n_feat)
    y = steady_dropout(x)
    print(y)