def Init(shape, rng):
"""Returns orthogonalized random normal values with the given `shape`."""
# Have at least 2 elements in shape.
cur_shape = list(shape)
while len(cur_shape) < 2:
cur_shape = [1] + cur_shape
# Flatten the input shape with the last dimension remaining.
n_rows = 1
for dim in cur_shape[:-1]:
n_rows *= dim
n_cols = cur_shape[-1]
flat_shape = (n_cols, n_rows) if n_rows < n_cols else (n_rows, n_cols)
# Generate a random matrix
a = random.normal(rng, flat_shape, dtype=jnp.float32)
# Compute the qr factorization
q, r = jnp.linalg.qr(a)
# Make Q uniform
d = jnp.diag(r)
q *= jnp.sign(d)
# Transpose and reshape back q if needed.
if n_rows < n_cols:
q = jnp.transpose(q)
q = jnp.reshape(q, shape)
# Return scaled as requested.
return stddev * q
def CombineHeadsPos(x, n_heads=1, **unused_kwargs):
"""Mix x = (x0, p0, ..., xH, pH) into (x0, ...., xH), p_combined.
The positions are averaged as vectors.
Args:
x: input vector, concatenated (x0, p0, ..., xH, pH).
n_heads: number of heads.
Returns:
the vector with combined xs and one with combined positions.
"""
seqlen = x.shape[1]
d_head = x.shape[2]
x = np.reshape(x, (-1, n_heads, seqlen, d_head))
x = np.transpose(x, (0, 2, 1, 3)) # -> n_batch, seqlen, n_heads, d_head
x = np.reshape(x, (-1, seqlen, n_heads * d_head))
head_size = int(d_head) - POS_VECTOR_SIZE
res, positions, idx = [], [], 0
for _ in range(n_heads):
res.append(x[:, :, idx:idx + head_size])
idx += head_size
positions.append(x[:, :, idx:idx + POS_VECTOR_SIZE])
idx += POS_VECTOR_SIZE
combined_position = sum(positions) / float(len(positions))
return np.concatenate(res, axis=-1), combined_position
def compute_attention_heads(x): batch_size = x.shape[0] seqlen = x.shape[1] # n_batch, seqlen, n_heads*d_head -> n_batch, seqlen, n_heads, d_head x = jnp.reshape(x, (batch_size, seqlen, n_heads, d_head)) # n_batch, seqlen, n_heads, d_head -> n_batch, n_heads, seqlen, d_head x = jnp.transpose(x, (0, 2, 1, 3)) # n_batch, n_heads, seqlen, d_head -> n_batch*n_heads, seqlen, d_head return jnp.reshape(x, (-1, seqlen, d_head))
def create_weights_unbatched(self, input_signature, rng): d_model = input_signature[0].shape[-1] d_kv_antecedent = input_signature[1].shape[-1] rng_q, rng_k, rng_v, rng_o = jax.random.split(rng, 4) w_q = self._kernel_initializer((d_model, self.d_qk), rng_q) w_k = self._kernel_initializer((d_kv_antecedent, self.d_qk), rng_k) w_v = self._kernel_initializer((d_kv_antecedent, self.d_v), rng_v) w_o = np.transpose(self._kernel_initializer((d_model, self.d_v), rng_o)) return (w_q, w_k, w_v, w_o)
def forward(self, x, weights):
seqlen = x.shape[1]
d_head = x.shape[2]
x = np.reshape(x, (-1, self._n_heads, seqlen, d_head))
x = np.transpose(x, (0, 2, 1, 3)) # -> n_batch, seqlen, n_heads, d_head
x = np.reshape(x, (-1, seqlen, self._n_heads * d_head))
return np.dot(x, weights)
def forward(self, x, weights):
seqlen = x.shape[1]
res = np.dot(x, weights)
# n_batch, seqlen, n_heads*d_head -> n_batch, seqlen, n_heads, d_head
res = np.reshape(res, (x.shape[0], seqlen, self._n_heads, self._d_head))
# n_batch, seqlen, n_heads, d_head -> n_batch, n_heads, seqlen, d_head
res = np.transpose(res, (0, 2, 1, 3))
# n_batch, n_heads, seqlen, d_head -> n_batch*n_heads, seqlen, d_head
res = np.reshape(res, (-1, seqlen, self._d_head))
return res
def create_weights_unbatched(self, input_signature, rng):
d_model = input_signature.shape[-1]
rng_q, rng_k, rng_v, rng_o = jax.random.split(rng, 4)
w_q = self._kernel_initializer((d_model, self.d_qk), rng_q)
if not self.share_qk:
w_k = self._kernel_initializer((d_model, self.d_qk), rng_k)
w_v = self._kernel_initializer((d_model, self.d_v), rng_v)
w_o = np.transpose(self._kernel_initializer((d_model, self.d_v),
rng_o))
if self.share_qk:
return (w_q, w_v, w_o)
else:
return (w_q, w_k, w_v, w_o)
def forward(self, inp, weights):
"""Reshape input to have heads dimension and concatenate positions there."""
x = inp[0]
n_batches, seqlen = x.shape[0], x.shape[1]
d_head = x.shape[-1] // self._n_heads
res = np.reshape(x, (n_batches, seqlen, self._n_heads, d_head))
res = np.transpose(res, (0, 2, 1, 3)) # (batch, heads, len, depth)
if self._n_pos == 1: # Just one position given, tile into each head.
pos_shape = list(res.shape)[:-1] + [inp[1].shape[-1]]
pos = inp[1][:, None, :, :] + np.zeros(pos_shape) # Add 0 to broadcast.
else: # As many positions as heads, concatenate them in.
pos = [p[:, None, :, :] for p in inp[1:]]
pos = np.concatenate(pos, axis=1)
res = np.concatenate([res, pos], axis=-1)
# n_batch, n_heads, seqlen, d_head -> n_batch*n_heads, seqlen, d_head
res = np.reshape(res, (-1, seqlen, d_head + POS_VECTOR_SIZE))
return res
def JoinHeads(x): # pylint: disable=invalid-name
return np.reshape(np.transpose(x, (0, 2, 1, 3)),
(nbatch, -1, n_heads * d_head))
def SplitHeads(x):
return np.transpose(np.reshape(x, (nbatch, -1, n_heads, d_head)),
(0, 2, 1, 3))
def compute_attention_output(x): seqlen = x.shape[1] x = jnp.reshape(x, (-1, n_heads, seqlen, d_head)) x = jnp.transpose(x, (0, 2, 1, 3)) # -> n_batch, seqlen, n_heads, d_head return jnp.reshape(x, (-1, seqlen, n_heads * d_head))
