Is torch.float32 different from numpy's float32?
Setting precision as 30 in PyTorch shows:
&gt;&gt;&gt; torch.set_printoptions(precision=30)
&gt;&gt;&gt; y
tensor([[-0.388252139091491699218750000000, -0.610148549079895019531250000000,
         -1.333969473838806152343750000000, -1.027917861938476562500000000000,
         -0.498563587665557861328125000000, -0.096793495118618011474609375000,
         -0.895992159843444824218750000000, -0.752071321010589599609375000000,
         -0.879653215408325195312500000000],
        [ 1.960780262947082519531250000000,  0.290681242942810058593750000000,
          0.111534759402275085449218750000, -1.412155270576477050781250000000,
          1.015806078910827636718750000000,  0.201809123158454895019531250000,
          0.131465137004852294921875000000, -1.262379050254821777343750000000,
         -0.480409622192382812500000000000],
        [ 0.111068181693553924560546875000, -2.129202365875244140625000000000,
          0.538800299167633056640625000000,  1.165832757949829101562500000000,
          0.194993987679481506347656250000, -1.110693812370300292968750000000,
         -1.451576709747314453125000000000, -3.398952484130859375000000000000,
          2.022404193878173828125000000000],
        [-2.374018669128417968750000000000, -1.442466974258422851562500000000,
         -0.406166225671768188476562500000,  0.045908458530902862548828125000,
         -0.835370421409606933593750000000, -0.302138328552246093750000000000,
         -0.421340197324752807617187500000,  0.931307554244995117187500000000,
          1.061386585235595703125000000000],
        [ 1.166660070419311523437500000000,  0.710260510444641113281250000000,
          1.008558034896850585937500000000,  1.957847237586975097656250000000,
         -1.070753335952758789062500000000,  0.319442749023437500000000000000,
         -1.140496969223022460937500000000, -1.723430752754211425781250000000,
          0.109533369541168212890625000000],
        [ 0.501820147037506103515625000000,  0.349833250045776367187500000000,
         -0.073374643921852111816406250000, -2.459295272827148437500000000000,
         -1.853959321975708007812500000000,  0.153838425874710083007812500000,
         -1.860147237777709960937500000000, -0.880943417549133300781250000000,
         -1.352821707725524902343750000000]])

but in numpy setting precision as 30 shows:
&gt;&gt;&gt; np.set_printoptions(precision=30)
&gt;&gt;&gt; y.numpy()
array([[-0.38825214 , -0.61014855 , -1.3339695  , -1.0279179  ,
        -0.4985636  , -0.096793495, -0.89599216 , -0.7520713  ,
        -0.8796532  ],
       [ 1.9607803  ,  0.29068124 ,  0.11153476 , -1.4121553  ,
         1.0158061  ,  0.20180912 ,  0.13146514 , -1.262379   ,
        -0.48040962 ],
       [ 0.11106818 , -2.1292024  ,  0.5388003  ,  1.1658328  ,
         0.19499399 , -1.1106938  , -1.4515767  , -3.3989525  ,
         2.0224042  ],
       [-2.3740187  , -1.442467   , -0.40616623 ,  0.04590846 ,
        -0.8353704  , -0.30213833 , -0.4213402  ,  0.93130755 ,
         1.0613866  ],
       [ 1.1666601  ,  0.7102605  ,  1.008558   ,  1.9578472  ,
        -1.0707533  ,  0.31944275 , -1.140497   , -1.7234308  ,
         0.10953337 ],
       [ 0.50182015 ,  0.34983325 , -0.073374644, -2.4592953  ,
        -1.8539593  ,  0.15383843 , -1.8601472  , -0.8809434  ,
        -1.3528217  ]], dtype=float32)e

Why are the results different?

By default, if it takes less digits than the configured value of precision to distinguish a floating-point value from other values of the same dtype, NumPy will only print as many digits as necessary for that. You have to set the floatmode option to 'fixed' to get the behavior you were expecting:
numpy.set_printoptions(precision=30, floatmode='fixed')

Note that even if you print 30 decimal digits, 32-bit floats don't have anywhere near that level of precision.
