7 ms·

But u/imjonse's reasoning seems right. I haven't run either version of the code, but when reading it I expected that to be False. The output is still a list wit

• FL33TW00D 2y ago
  Run the code and look at the values!
  • jmmcd 2y ago
    Well, yes, I deserved that reply! And yes the code is printing True. It's not that I disbelieved you... but something is wrong here. Investigation below, thanks to Claude.ai for walking me through it! In [10]: o1[0, :, :3] Out[10]: tensor([[ 0.0053, 0.0017, -0.0012], [ 0.0053, 0.0017, -0.0012], [ 0.0053, 0.0017, -0.0012], [ 0.0053, 0.0017, -0.0012], [ 0.0053, 0.0017, -0.0012], [ 0.0053, 0.0017, -0.0012]], grad_fn=<SliceBackward0>) Every token has the same attention values. I expect attention(cat, everything) to differ from attention(dog, everything), even without positional encoding. Further, the attention weights are uniform and identical for both sentences: In [46]: o1, aw1 = mha(W_q(e1), W_k(e1), W_v(e1)) In [47]: o2, aw2 = mha(W_q(e2), W_k(e2), W_v(e2)) In [48]: aw1.shape Out[48]: torch.Size([1, 6, 6]) In [49]: aw2.shape Out[49]: torch.Size([1, 6, 6]) In [50]: aw1 Out[50]: tensor([[[0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667]]], grad_fn=<MeanBackward1>) In [51]: aw2 Out[51]: tensor([[[0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667], [0.1667, 0.1667, 0.1667, 0.1667, 0.1667, 0.1667]]], grad_fn=<MeanBackward1>) That is not expected. It's because the Linear layers are initialised with such small values. And the softmax causes a collapse. Trying random weights on a larger scale: In [52]: W_q.weight.data *= 100 W_k.weight.data *= 100 W_v.weight.data *= 100 In [55]: o1, aw1 = mha(W_q(e1), W_k(e1), W_v(e1)) In [56]: o2, aw2 = mha(W_q(e2), W_k(e2), W_v(e2)) In [57]: aw1 Out[57]: tensor([[[0.2049, 0.1606, 0.1256, 0.1095, 0.1723, 0.2270], [0.0883, 0.2047, 0.1544, 0.2776, 0.1405, 0.1345], [0.1196, 0.1719, 0.1831, 0.1541, 0.1374, 0.2339], [0.1413, 0.2399, 0.1617, 0.2056, 0.1634, 0.0880], [0.1455, 0.1432, 0.2432, 0.1239, 0.1494, 0.1948], [0.1897, 0.1817, 0.1920, 0.1478, 0.1618, 0.1270]]], grad_fn=<MeanBackward1>) In [58]: aw2 Out[58]: tensor([[[0.2049, 0.1606, 0.2270, 0.1095, 0.1723, 0.1256], [0.0883, 0.2047, 0.1345, 0.2776, 0.1405, 0.1544], [0.1897, 0.1817, 0.1270, 0.1478, 0.1618, 0.1920], [0.1413, 0.2399, 0.0880, 0.2056, 0.1634, 0.1617], [0.1455, 0.1432, 0.1948, 0.1239, 0.1494, 0.2432], [0.1196, 0.1719, 0.2339, 0.1541, 0.1374, 0.1831]]], grad_fn=<MeanBackward1>) In [60]: o1[:, :, :5] Out[60]: tensor([[[ 0.0145, 0.3128, -0.3659, -0.1884, 0.1724], [-0.2319, 0.1407, -0.6010, -0.4064, 0.4259], [-0.3231, 0.1622, -0.6351, -0.1711, 0.4014], [-0.0596, 0.2610, -0.7388, -0.2987, 0.3214], [-0.2750, 0.0676, -0.4140, -0.2024, 0.3383], [-0.1434, 0.0871, -0.3154, -0.0755, 0.3314]]], grad_fn=<SliceBackward0>) In [61]: o2[:, :, :5] Out[61]: tensor([[[ 0.0145, 0.3128, -0.3659, -0.1884, 0.1724], [-0.2319, 0.1407, -0.6010, -0.4064, 0.4259], [-0.1434, 0.0871, -0.3154, -0.0755, 0.3314], [-0.0596, 0.2610, -0.7388, -0.2987, 0.3214], [-0.2750, 0.0676, -0.4140, -0.2024, 0.3383], [-0.3231, 0.1622, -0.6351, -0.1711, 0.4014]]], grad_fn=<SliceBackward0>) In [62]: print("Matches: ", torch.allclose(o1, o2, atol=1e-6)) Matches: False
    • FL33TW00D 2y ago
      Hm! Very interesting! Thank you for taking the time to debug that. I'm going to have to think hard about how to rewrite the motivating example to explain this best. Edit: updated the post, thanks for pointing out the pernicious init values!