Comments (2)
just switching this up for JAX with channels first (for performance) and without batch size (jax.vmap automatically batches)
x: The input features of shape (N, L, E) where N is the batch size,
L is the sequence length (padded) and E is d_model passed in the
constructor.
https://github.com/idiap/fast-transformers/blob/master/fast_transformers/transformers.py
"BLHC,BLHc->BHCc"
then without batch, it's
"LHC,LHc->HCc"
and with channels first it's:
"CHL,cHL->hCc"
from fast-transformers.
# D/d: d_head; H: n_heads; L: sequence length
key_value = np.einsum("DHL,dHL->HDd", key, value)
norm = 1.0 / (np.einsum("DHL,DH->HL", query, key.sum(2)) + 1e-6)
attended = np.einsum("DHL,HDd,HL->DHL", query, key_value, norm)
attended = attended.reshape((d_head * self.n_heads, length))
works like a charm! a lot faster too! thanks for your work
from fast-transformers.
Related Issues (20)
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from fast-transformers.