I created an embedding and passing out of index tensors doesn’t work on CPU, but on GPU it is returning a tensor(all except first embedding keep changing with every call, and even the first tensor is not equal to embedding.weight).
Torch version = 1.5.0
a = torch.nn.Embedding(1,768)
>>> a(torch.LongTensor([1,2,3]))
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<path>/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in __call__
    result = self.forward(*input, **kwargs)
  File "<path>/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/sparse.py", line 114, in forward
    self.norm_type, self.scale_grad_by_freq, self.sparse)
  File "<path>/vl-bert/lib/python3.6/site-packages/torch/nn/functional.py", line 1724, in embedding
    return torch.embedding(weight, input, padding_idx, scale_grad_by_freq, sparse)
IndexError: index out of range in self

a.cuda()(torch.LongTensor([1,2,3]).cuda())
tensor([[1.4013e-45, 0.0000e+00, 2.8026e-45,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00],
        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00],
        [0.0000e+00, 0.0000e+00, 0.0000e+00,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00]], device='cuda:0', grad_fn=<EmbeddingBackward>)
a.cuda()(torch.LongTensor([1,2,3]).cuda())
tensor([[ 1.4013e-45,  0.0000e+00,  2.8026e-45,  ...,  1.0869e+00,
         -1.7131e+00, -6.9908e-01],
        [-5.6997e-01,  1.6486e+00,  1.7096e+00,  ...,  1.0869e+00,
          1.7131e+00,  6.9908e-01],
        [ 5.6996e-01,  1.6486e+00,  1.7096e+00,  ...,  1.7427e+00,
          2.0000e+00,  1.7774e+00]], device='cuda:0',
       grad_fn=<EmbeddingBackward>)
a.cuda()(torch.LongTensor([1,2,3]).cuda())
tensor([[1.4013e-45, 0.0000e+00, 2.8026e-45,  ..., 0.0000e+00, 0.0000e+00,
         0.0000e+00],
        [0.0000e+00, 2.0000e+00, 0.0000e+00,  ..., 1.0869e+00, 1.7131e+00,
         6.9908e-01],
        [6.5202e+06, 4.3207e+00, 8.6057e-02,  ..., 1.7427e+00, 2.0000e+00,
         1.7774e+00]], device='cuda:0', grad_fn=<EmbeddingBackward>)

>>> a.weight
Parameter containing:
tensor([[ 0.7804,  1.5051,  0.0861, -0.9269, -0.8105, -2.7018, -1.2860, -0.4517,
          0.6019,  1.2832,  2.1942,  0.3216,  1.9599,  0.8146,  0.0085,  0.6976,
          1.9618,  0.0783,  1.3515,  0.8830,  0.8101, -2.4665,  2.6164,  1.1543,
         -0.8128, -0.9217,  1.3534, -0.3387,  0.1712,  1.1185, -0.5681,  0.2406,
          1.8387,  0.7704,  1.6712,  0.4060, -1.2792, -0.3