[WIP] Implement XBM

[monatis]

This is still WIP, so consider it as a draft for now.

Closes #163.

I'm adding commits after small refactoring and polishing comments. Will request a review afterwards.

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[monatis]

It's zero-size tensor (scalar), so regular equal and not-equal operators should work --we use torch.allclose and the similar to compare vectors and embeddings to avoid ambiguous meaning of comparing them.

[generall]

trying to compare xbm_loss and regular loss:

torch.random.manual_seed(42)
embeddings = torch.rand(64, 100)
groups = torch.randint(0, 10, (64,))

loss = TripletLoss(margin=1.0, mining="hard")

print(loss(embeddings, groups))
print(loss._compute_xbm_loss(embeddings, groups, embeddings, groups))

If I understand the approach right, loss and _compute_xbm_loss should give same result in this case, but in fact I got

tensor(1.5443)
tensor(50.5432)

Order of magnitude different. I am not sure how the implementation was validated and if this is an expected result, but in this case xbm weight constant is especially tricky to select.