def loss(q, k, scale, proj, attn_dist):
qp = q
kp = k
ra, _ = fat.relu_rff_attn(qp, kp, scale * proj)
return fat.kl(attn_dist, ra).mean()
def loss(q, k, scale, proj, attn_dist):
qp = q
kp = k
ra, _ = fat.relu_rff_attn(qp, kp, jax.lax.stop_gradient(proj))
return fat.kl(attn_dist, ra).mean()
def loss(q, k, scale, proj, attn_dist):
qp = renorm(q, axis=-1)
kp = renorm(k, axis=-1)
ra, _ = fat.rff_attn(qp, kp, jax.lax.stop_gradient(proj))
return fat.kl(attn_dist, ra).mean()
def loss(q, k, scale, proj, attn_dist):
qp = renorm(q, axis=-1)
kp = renorm(k, axis=-1)
ra, _ = fat.rff_attn(qp, kp, proj)
return fat.kl(attn_dist, ra).mean()
def loss(q, k, dummy_proj, attn):
logits = q @ k.T
probs = softmax(logits)
return fat.kl(attn, probs).mean()
