norm="ortho" does not take into account the actual transformed axes #336

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1. vincefn commented on Jun 25, 2026

   

   vincefn

   on Jun 25, 2026

   Last edited by vincefn

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   I made more tests - the same behaviour is seen with fftn, ifftn and rfftn. However irfftn does not have the issue - the norm is correctly computed.

   Here are snippets to compare numpy and mkl normalisation, for even and odd sizes (only matters for r2c):

   for c2c:

   import numpy as np
   import mkl_fft
   
   for s in [(16, 18), (15,17)]:
       ax = -1,
   
       a = np.random.uniform(0,1,s)
       a /= np.sqrt((abs(a)**2).sum()) # normalise
       a0 = a.copy()
   
       a_np = np.fft.fftn(a, axes=ax, norm="ortho")
       n_np = (abs(a_np) ** 2).sum()
   
       a_mkl = mkl_fft.fftn(a, axes=ax, norm="ortho")
       n_mkl = (abs(a_mkl) ** 2).sum()
   
       a_mkl = mkl_fft.ifftn(a_np, axes=ax, norm="ortho")
       n_mkli = (abs(a_mkl) ** 2).sum()
   
       a_np = np.fft.ifftn(a_np, axes=ax, norm="ortho")
       n_npi = (abs(a_np) ** 2).sum()
   
   
       print(f"   Array norm: {(abs(a)**2).sum():.6f}")
       print(f"  np-fft norm: {n_np:.6f}")
       print(f" mkl_fft norm: {n_mkl:.6f}")
       print(f" np-ifft norm: {n_npi:.6f}")
       print(f"mkl_ifft norm: {n_mkli:.6f}")

   Gives:

      Array norm: 1.000000
     np-fft norm: 1.000000
    mkl_fft norm: 0.062500
    np-ifft norm: 1.000000
   mkl_ifft norm: 16.000000
      Array norm: 1.000000
     np-fft norm: 1.000000
    mkl_fft norm: 0.066667
    np-ifft norm: 1.000000
   mkl_ifft norm: 15.000000
   

   for r2c

   import numpy as np
   import mkl_fft
   
   for s in [(16, 18), (15,17)]:
       ax = -1,
   
       a = np.random.uniform(0,1,s)
       a /= np.sqrt((abs(a)**2).sum()) # normalise
       a0 = a.copy()
   
       a_np = np.fft.rfftn(a, axes=ax, norm="ortho")
       print(s, "->", a_np.shape)
       if s[-1] % 2 != 0:
           n_np = (abs(a_np) ** 2).sum() + (abs(a_np[..., 1:]) ** 2).sum()
       else:
           n_np = (abs(a_np) ** 2).sum() + (abs(a_np[..., 1:-1]) ** 2).sum()
   
       a_mkl = mkl_fft.rfftn(a, axes=ax, norm="ortho")
       if s[-1] % 2 != 0:
           n_mkl = (abs(a_mkl) ** 2).sum() + (abs(a_mkl[..., 1:]) ** 2).sum()
       else:
           n_mkl = (abs(a_mkl) ** 2).sum() + (abs(a_mkl[..., 1:-1]) ** 2).sum()
   
       a_mkl = mkl_fft.irfftn(a_np, axes=ax, norm="ortho", s=[s[i] for i in ax])
       n_mkli = (abs(a_mkl) ** 2).sum()
   
       a_np = np.fft.irfftn(a_np, axes=ax, norm="ortho", s=[s[i] for i in ax])
       n_npi = (abs(a_np) ** 2).sum()
       print(a_np.shape)
   
   
       print(f"   Array norm: {(abs(a)**2).sum():.6f}")
       print(f"  np-rfft norm: {n_np:.6f}")
       print(f" mkl_rfft norm: {n_mkl:.6f}")
       print(f" np-irfft norm: {n_npi:.6f}")
       print(f"mkl_irfft norm: {n_mkli:.6f}")

   gives:

   (16, 18) -> (16, 10)
   (16, 18)
      Array norm: 1.000000
     np-rfft norm: 1.000000
    mkl_rfft norm: 0.062500
    np-irfft norm: 1.000000
   mkl_irfft norm: 1.000000
   (15, 17) -> (15, 9)
   (15, 17)
      Array norm: 1.000000
     np-rfft norm: 1.000000
    mkl_rfft norm: 0.066667
    np-irfft norm: 1.000000
   mkl_irfft norm: 1.000000
   
2. ndgrigorian

   self-assigned this

   on Jul 30, 2026
3. ndgrigorian

   added

   bug

   on Jul 30, 2026
4. jharlow-intel

   added a commit that references this issue on Sep 15, 2026

   task: tests to cover #336

   277deb4
5. ndgrigorian

   closed this as completedin #370on Sep 17, 2026