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Pytorch slowdown test 2

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Oct 26th, 2017
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  1. import torch
  2. import time
  3. from torch.autograd import Variable
  4. import numpy as np
  5.  
  6. """
  7. This program gets slower and slower by time. No noticeable change in memory
  8. consumption seen on nvidia-smi.
  9. """
  10.  
  11. usecuda = 1
  12.  
  13. dtype = torch.FloatTensor
  14. N=1
  15. if usecuda:
  16.   N = 100*N
  17.   dtype = torch.cuda.FloatTensor
  18.  
  19. xdim = (100,50,2)
  20.  
  21. x0 = torch.randn(*xdim).type(dtype)
  22. z0 = torch.randn(N, xdim[0]).type(dtype)
  23.  
  24. x = Variable(x0, requires_grad=True)
  25. z = Variable(z0, requires_grad=False)
  26.  
  27.  
  28. t0 = time.clock()
  29. tsum = np.zeros(5)
  30. tsump = np.zeros(5)
  31. i = 0
  32. M=1000
  33. while True:
  34.   t1 = time.clock()
  35.   b = x.repeat(N,1,1,1).view(N, -1, 2).sum(2).view(N, xdim[0], xdim[1])
  36.   c = z.view(N, xdim[0], 1).expand(N, xdim[0], xdim[1])/b
  37.   loss = c.sum()
  38.   t2 = time.clock()
  39.   loss.backward()
  40.   t3 = time.clock()
  41.   z -= 0.1*b.view(N, -1 , xdim[1]).lt(0.1).sum(2).type(dtype)
  42.   t4 = time.clock()
  43.   x.data -= 0.0000000001*x.grad.data
  44.   x.grad.data.zero_()
  45.   t5 = time.clock()
  46.   tsum += np.array((t1 - t0, t2 - t1, t3 - t2, t4 - t3, t5 - t4))
  47.   t0 = t5
  48.   if i%M == M-1:
  49.     print(tsum, tsum - tsump)
  50.     #print("%.2fs +%.1f%%" % (tsum, tsum*100/tsump - 100))
  51.     tsump = tsum
  52.     tsum = np.zeros(5)
  53.   i = i + 1
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