def forward(self, inputs):
weights = self.weights[0]
if isinstance(inputs, list):
inputs = tuple(inputs) # so that inputs structure matches outputs
n_carry = self._n_carry
def scannable_fn(x, carry_and_state): # pylint: disable=invalid-name
carry, state = carry_and_state
x_and_carry = x + carry if n_carry > 0 else x
res, new_state = self.sublayer.pure_fn(x_and_carry,
weights,
state,
self.rng,
use_cache=True)
if n_carry > 0:
return (res[:-n_carry], (res[-n_carry:], new_state))
else:
return (res, ([], new_state))
if n_carry > 0:
xs = inputs[:-n_carry] # Split input stack into inputs and carry.
init = (inputs[-n_carry:], self.state[0])
else:
xs, init = inputs, ([], self.state[0])
ys, (carry, new_state) = fastmath.scan(scannable_fn,
xs,
init,
axis=self._axis,
remat=self._remat)
res = ys + carry if n_carry > 0 else ys
self.state = (new_state, )
return res # Put outputs and carry back on stack.
def favor_denominator_fwd(init_prefix_sum_value, precision,
query_prime, key_prime):
def body(p, qk):
q, k = qk
p += k
x = jnp.einsum('...m,...m->...', q, p, precision=precision)
return p, x
p, r = fastmath.scan(body, init_prefix_sum_value, (query_prime, key_prime))
return r, (precision, query_prime, key_prime, p)
def favor_numerator_fwd(init_prefix_sum_value, precision,
query_prime, key_prime, value):
def body(p, qkv):
(q, k, v) = qkv
p += jnp.einsum('...m,...d->...md', k, v, precision=precision)
x_slice = jnp.einsum('...m,...md->...d', q, p, precision=precision)
return p, x_slice
p, w = fastmath.scan(body, init_prefix_sum_value,
(query_prime, key_prime, value))
return w, (precision, p, query_prime, key_prime, value)
def favor_denominator_bwd(qkp, r_ct):
precision, qs, ks, p = qkp
def body(carry, qkx):
p, p_ct = carry
q, k, x_ct = qkx
q_ct = jnp.einsum('...,...m->...m', x_ct, p, precision=precision)
p_ct += jnp.einsum('...,...m->...m', x_ct, q, precision=precision)
k_ct = p_ct
p -= k
return (p, p_ct), (q_ct, k_ct)
_, (qs_ct, ks_ct) = fastmath.scan(
body, (p, jnp.zeros_like(p)), (qs, ks, r_ct), reverse=True)
return (None, None, qs_ct, ks_ct)
def favor_numerator_bwd(pqkv, w_ct):
precision, p, qs, ks, vs = pqkv
def body(carry, qkv_xct):
p, p_ct = carry
q, k, v, x_ct = qkv_xct
q_ct = jnp.einsum('...d,...md->...m', x_ct, p, precision=precision)
p_ct += jnp.einsum('...d,...m->...md', x_ct, q, precision=precision)
k_ct = jnp.einsum('...md,...d->...m', p_ct, v, precision=precision)
v_ct = jnp.einsum('...md,...m->...d', p_ct, k, precision=precision)
p -= jnp.einsum('...m,...d->...md', k, v, precision=precision)
return (p, p_ct), (q_ct, k_ct, v_ct)
_, (qs_ct, ks_ct, vs_ct) = fastmath.scan(
body, (p, jnp.zeros_like(p)), (qs, ks, vs, w_ct), reverse=True)
return (None, None, qs_ct, ks_ct, vs_ct)
def favor_denominator_bwd(init_prefix_sum_value, precision, qkp, r_ct):
del init_prefix_sum_value
def body(carry, qkx):
p, p_ct = carry
q, k, x_ct = qkx
q_ct = np.einsum('...,...m->...m', x_ct, p, precision=precision)
p_ct += np.einsum('...,...m->...m', x_ct, q, precision=precision)
k_ct = p_ct
p -= k
return (p, p_ct), (q_ct, k_ct)
qs, ks, p = qkp
_, (qs_ct, ks_ct) = fastmath.scan(body, (p, np.zeros_like(p)),
(qs, ks, r_ct),
reverse=True)
return (qs_ct, ks_ct)
def favor_numerator_bwd(init_prefix_sum_value, precision, pqkv, w_ct):
del init_prefix_sum_value
def body(carry, qkv_xct):
p, p_ct = carry
q, k, v, x_ct = qkv_xct
q_ct = np.einsum('...d,...md->...m', x_ct, p, precision=precision)
p_ct += np.einsum('...d,...m->...md', x_ct, q, precision=precision)
k_ct = np.einsum('...md,...d->...m', p_ct, v, precision=precision)
v_ct = np.einsum('...md,...m->...d', p_ct, k, precision=precision)
p -= np.einsum('...m,...d->...md', k, v, precision=precision)
return (p, p_ct), (q_ct, k_ct, v_ct)
p, qs, ks, vs = pqkv
_, (qs_ct, ks_ct, vs_ct) = fastmath.scan(body, (p, np.zeros_like(p)),
(qs, ks, vs, w_ct),
reverse=True)
return qs_ct, ks_ct, vs_ct
def _scan(f, xs, init_value, axis=0, remat=False):
"""Scans the f over the given axis of xs.
In pseudo-python, the scan function would look as follows:
def scan(f, xs, init_value, axis):
xs = [xs[..., i, ...] for i in range(xs.shape[axis])]
cur_value = init_value
ys = []
for x in xs:
y, cur_value = f(x, cur_value)
ys.append(y)
return np.stack(ys, axis), cur_value
Args:
f: function (x, carry) -> (y, new_carry)
xs: tensor, x will be xs slices on axis
init_value: tensor, initial value of the carry-over
axis: int, the axis on which to slice xs
remat: whether to re-materialize f
Returns:
A pair (ys, last_value) as described above.
"""
def swapaxes(x):
transposed_axes = list(range(len(x.shape)))
transposed_axes[axis] = 0
transposed_axes[0] = axis
return jnp.transpose(x, axes=transposed_axes)
if axis != 0:
xs = fastmath.nested_map(swapaxes, xs)
def transposed_f(c, x):
y, d = f(x, c)
return d, y
if remat:
transposed_f = fastmath.remat(transposed_f)
last_value, ys = fastmath.scan(transposed_f, init_value, xs)
if axis != 0:
ys = fastmath.nested_map(swapaxes, ys)
return ys, last_value
