def forward_unbatched(self, x, mask=None, *, weights, state, update_state):
del update_state
if self.share_qk:
w_q, w_v, w_o = weights
else:
w_q, w_k, w_v, w_o = weights
q = np.matmul(x, w_q)
k = None
if not self.share_qk:
k = np.matmul(x, w_k)
v = np.matmul(x, w_v)
mask_fn = functools.partial(mask_self_attention,
causal=self.causal,
exclude_self=self.share_qk,
masked=self.masked)
q_info = kv_info = jax.lax.tie_in(x, np.arange(q.shape[-2]))
assert (mask is not None) == self.masked
if self.masked:
# mask is a boolean array (True means "is valid token")
ones_like_mask = jax.lax.tie_in(x,
np.ones_like(mask, dtype=np.int32))
kv_info = kv_info * np.where(mask, ones_like_mask, -ones_like_mask)
o, _ = attend(
q,
k,
v,
q_chunk_len=self.chunk_len,
kv_chunk_len=self.chunk_len,
n_chunks_before=self.n_chunks_before,
n_chunks_after=self.n_chunks_after,
mask_fn=mask_fn,
q_info=q_info,
kv_info=kv_info,
dropout=self.attention_dropout,
rng=None, # TODO(kitaev): support RNG
)
out = np.matmul(o, w_o)
return out, state
def init(self, params): return np.ones_like(params)
def WeightMask(target, mask_id=0, **unused_kwargs):
if mask_id is None:
return np.ones_like(target)
return 1.0 - np.equal(target, mask_id).astype(np.float32)
def _ElementMask(target, id_to_mask=0, **unused_kwargs):
"""Returns a mask with zeros marking elements to exclude from calculations."""
if id_to_mask is None:
return np.ones_like(target)
return 1.0 - np.equal(target, id_to_mask).astype(np.float32)
def WeightMask(target, mask_id=0, **kw):
del kw
if mask_id is None:
return np.ones_like(target)
return 1.0 - np.equal(target, mask_id).astype(np.float32)
def init(self, weights):
return np.ones_like(weights)
