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  • I’m only interested in testing out the attention mechanisms that are hosted here

I’m only interested in testing out the attention mechanisms that are hosted here

That’s completely fine! There are two paths to do this:

  • Either you import the attention mechanisms that you’re interested in directly

    in your code base, their API should be very similar and you would own everything. The dimensions expectations are that, depending on whether the attentions expose the requires_head_dimension flag, the input data would be either [Batch, Heads, Sequence, Head dimension], or [Batch x Heads, Sequence, Head dimension].

  • Alternatively, a build_attention helper is provided, which takes a dict as an input.

    In that case, you defer a lot of the instantiation work to xFormers, which makes it a little more obscure although the parameters are hopefully straightforward. This was initially built for internal use in xFormers, to make sure that we can programatically build and test all possible combinations. In turn this should allow you to do sweeps or architecture search, given that the multihead attention definition becomes something like:

from xformers.components import MultiHeadDispatch, build_attention
SEQ = 1024
MODEL = 384
HEADS = 16

my_config = {
    "name": attention_name,  # you can easily make this dependent on a file, sweep,..
    "dropout": DROPOUT,
    "seq_len": SEQ,
    "attention_query_mask": torch.rand((SEQ, 1)) < 0.3, # some dummy mask

attention = build_attention(my_config)

# build a multi head dispatch to test this attention mechanism
multi_head = MultiHeadDispatch(

# do something with my new multi-head attention