def logaddexp(x1, x2):
x1, x2 = _promote_to_result_dtype(onp.logaddexp, *_promote_shapes(x1, x2))
amax = lax.max(x1, x2)
return lax.add(
amax,
lax.log(lax.add(lax.exp(lax.sub(x1, amax)), lax.exp(lax.sub(x2,
amax)))))
def logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False):
if b is not None:
a, b = jnp.broadcast_arrays(a, b)
dims = _reduction_dims(a, axis)
dimadd = lambda x: lax.expand_dims(x, dims)
amax = lax.reduce(a, _constant_like(a, -np.inf), lax.max, dims)
amax = lax.stop_gradient(
lax.select(lax.is_finite(amax), amax, lax.full_like(amax, 0)))
amax_singletons = dimadd(amax)
if b is None:
out = lax.add(
lax.log(
lax.reduce(lax.exp(lax.sub(a, amax_singletons)),
_constant_like(a, 0), lax.add, dims)), amax)
sign = jnp.where(jnp.isnan(out), np.nan, 1.0).astype(out.dtype)
sign = jnp.where(out == -np.inf, 0.0, sign)
else:
sumexp = lax.reduce(lax.mul(lax.exp(lax.sub(a, amax_singletons)), b),
_constant_like(a, 0), lax.add, dims)
sign = lax.stop_gradient(lax.sign(sumexp))
out = lax.add(lax.log(lax.abs(sumexp)), amax)
if return_sign:
return (dimadd(out), dimadd(sign)) if keepdims else (out, sign)
if b is not None:
out = jnp.where(sign < 0, np.nan, out)
return dimadd(out) if keepdims else out
def logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False):
if b is not None:
a, b = _promote_args_inexact("logsumexp", a, b)
a = jnp.where(b != 0, a, -jnp.inf)
pos_dims, dims = _reduction_dims(a, axis)
amax = jnp.max(a, axis=dims, keepdims=keepdims)
amax = lax.stop_gradient(
lax.select(lax.is_finite(amax), amax, lax.full_like(amax, 0)))
amax_with_dims = amax if keepdims else lax.expand_dims(amax, pos_dims)
if b is None:
out = lax.add(
lax.log(
jnp.sum(lax.exp(lax.sub(a, amax_with_dims)),
axis=dims,
keepdims=keepdims)), amax)
sign = jnp.where(jnp.isnan(out), np.nan, 1.0).astype(out.dtype)
sign = jnp.where(out == -np.inf, 0.0, sign)
else:
sumexp = jnp.sum(lax.mul(lax.exp(lax.sub(a, amax_with_dims)), b),
axis=dims,
keepdims=keepdims)
sign = lax.stop_gradient(lax.sign(sumexp))
out = lax.add(lax.log(lax.abs(sumexp)), amax)
if return_sign:
return (out, sign)
if b is not None:
out = jnp.where(sign < 0, np.nan, out)
return out
def cdf(x, loc=0, scale=1):
x, loc, scale = _promote_args_inexact("laplace.cdf", x, loc, scale)
half = _constant_like(x, 0.5)
one = _constant_like(x, 1)
zero = _constant_like(x, 0)
diff = lax.div(lax.sub(x, loc), scale)
return lax.select(lax.le(diff, zero), lax.mul(half, lax.exp(diff)),
lax.sub(one, lax.mul(half, lax.exp(lax.neg(diff)))))
def logaddexp(x1, x2):
x1, x2 = _promote_args_inexact("logaddexp", x1, x2)
amax = lax.max(x1, x2)
if dtypes.issubdtype(x1.dtype, np.floating):
delta = lax.sub(x1, x2)
return lax.select(lax_internal._isnan(delta),
lax.add(x1, x2), # NaNs or infinities of the same sign.
lax.add(amax, lax.log1p(lax.exp(lax.neg(lax.abs(delta))))))
else:
delta = lax.sub(lax.add(x1, x2), lax.mul(amax, _constant_like(amax, 2)))
out = lax.add(amax, lax.log1p(lax.exp(delta)))
return lax.complex(lax.real(out), _wrap_between(lax.imag(out), np.pi))
def exponential(tensor, dtype, exp_hparams):
"""Calculates an exponential approximation based on exp hyper params."""
# If low_bound defined, it clips x-M.
if exp_hparams.low_bound != 0:
tensor = jnp.clip(tensor, exp_hparams.low_bound, 0.)
# TODO(luispazos) Use standard calls to top level jnp functions.
# pylint: disable=protected-access
def make_constant(c):
return lax_numpy._constant_like(tensor, c).astype(dtype)
# If clip_and_subtract, replace exp(clip(x-M,low_bound)) term with
# exp(clip(x-M,low_bound))-exp(low_bound).'
if exp_hparams.clip_and_subtract:
tensor = lax.sub(tensor, make_constant(onp.exp(exp_hparams.low_bound)))
# If linear_gradient: use this gradient as linear approximation of
# exponential.
if exp_hparams.linear_gradient is not None and exp_hparams.linear_gradient != 0:
# Want: max(0, a*x+b) such that a*x+b goes through (0, 1).
#
# This comes out to: max(0, a*x+1), for arbitrary a>0.
one = jnp.full(tensor.shape, 1.).astype(dtype)
gradient = jnp.full(tensor.shape,
exp_hparams.linear_gradient).astype(dtype)
approx_exp = jnp.clip(lax.add(lax.mul(tensor, gradient), one), 0, 1)
else:
approx_exp = lax.exp(tensor)
return approx_exp
def log1m_exp(val):
"""Numerically stable implementation of `log(1 - exp(val))`."""
return lax.cond(
lax.gt(val, lax.log(2.0)),
lambda _: lax.log(-lax.expm1(val)),
lambda _: lax.log1p(-lax.exp(val)),
operand=None,
)
def _logaddexp(x1, x2):
"""
Logaddexp while ignoring the custom_jvp rule.
"""
amax = lax.max(x1, x2)
delta = lax.sub(x1, x2)
return lax.select(jnp.isnan(delta),
lax.add(x1, x2), # NaNs or infinities of the same sign.
lax.add(amax, lax.log1p(lax.exp(-lax.abs(delta)))))
def _exp_taylor(primals_in, series_in):
x, = primals_in
series, = series_in
u = [x] + series
v = [lax.exp(x)] + [None] * len(series)
for k in range(1,len(v)):
v[k] = fact(k-1) * sum([_scale(k, j) * v[k-j] * u[j] for j in range(1, k+1)])
primal_out, *series_out = v
return primal_out, series_out
def _erf_inv_rule(primals_in, series_in):
x, = primals_in
series, = series_in
u = [x] + series
primal_out = lax.erf_inv(x)
v = [primal_out] + [None] * len(series)
# derivative on co-domain for caching purposes
deriv_const = np.sqrt(np.pi) / 2.
deriv_y = lambda y: lax.mul(deriv_const, lax.exp(lax.square(y)))
# manually propagate through deriv_y since we don't have lazy evaluation of sensitivities
c = [deriv_y(primal_out)] + [None] * (len(series) - 1)
tmp_sq = [lax.square(v[0])] + [None] * (len(series) - 1)
tmp_exp = [lax.exp(tmp_sq[0])] + [None] * (len(series) - 1)
for k in range(1, len(series)):
# we know c[:k], we compute c[k]
# propagate c to get v
v[k] = fact(k - 1) * sum(
_scale(k, j) * c[k - j] * u[j] for j in range(1, k + 1))
# propagate v to get next c
# square
tmp_sq[k] = fact(k) * sum(
_scale2(k, j) * v[k - j] * v[j] for j in range(k + 1))
# exp
tmp_exp[k] = fact(k - 1) * sum(
_scale(k, j) * tmp_exp[k - j] * tmp_sq[j] for j in range(1, k + 1))
# const
c[k] = deriv_const * tmp_exp[k]
# we can't, and don't need, to compute c[k+1], just need to get the last v[k]
k = len(series)
v[k] = fact(k - 1) * sum(
_scale(k, j) * c[k - j] * u[j] for j in range(1, k + 1))
primal_out, *series_out = v
return primal_out, series_out
def logsumexp(a, axis=None, b=None, keepdims=False, return_sign=False):
if b is not None:
a, b = _promote_args_inexact("logsumexp", a, b)
a = jnp.where(b != 0, a, -jnp.inf)
else:
a, = _promote_args_inexact("logsumexp", a)
pos_dims, dims = _reduction_dims(a, axis)
amax = jnp.max(a, axis=dims, keepdims=keepdims)
amax = lax.stop_gradient(
lax.select(jnp.isfinite(amax), amax, lax.full_like(amax, 0)))
amax_with_dims = amax if keepdims else lax.expand_dims(amax, pos_dims)
# fast path if the result cannot be negative.
if b is None and not np.issubdtype(a.dtype, np.complexfloating):
out = lax.add(
lax.log(
jnp.sum(lax.exp(lax.sub(a, amax_with_dims)),
axis=dims,
keepdims=keepdims)), amax)
sign = jnp.where(jnp.isnan(out), out, 1.0)
sign = jnp.where(jnp.isneginf(out), 0.0, sign).astype(out.dtype)
else:
expsub = lax.exp(lax.sub(a, amax_with_dims))
if b is not None:
expsub = lax.mul(expsub, b)
sumexp = jnp.sum(expsub, axis=dims, keepdims=keepdims)
sign = lax.stop_gradient(jnp.sign(sumexp))
if np.issubdtype(sumexp.dtype, np.complexfloating):
if return_sign:
sumexp = sign * sumexp
out = lax.add(lax.log(sumexp), amax)
else:
out = lax.add(lax.log(lax.abs(sumexp)), amax)
if return_sign:
return (out, sign)
if b is not None:
if not np.issubdtype(out.dtype, np.complexfloating):
with jax.debug_nans(False):
out = jnp.where(sign < 0, jnp.array(np.nan, dtype=out.dtype),
out)
return out
def _random_poisson(rng_key, lmbda, shape):
"""
References
----------
.. [1] Knuth, Donald E. Art of computer programming, volume 2:
Seminumerical algorithms. Addison-Wesley Professional, 2014 (p 137).
"""
L = lax.exp(lax.neg(lmbda))
k = np.zeros(shape=shape)
p = np.ones(shape=shape)
is_done = p < L
while not is_done.all():
_, rng_key = random.split(rng_key)
u = random.uniform(rng_key, shape=shape)
p = np.where(is_done, p, u * p)
k = np.where(is_done, k, k + 1)
is_done = p < L
return k
series, = series_in
primal_out = prim.bind(x)
c0, cs = jet(deriv, primals_in, series_in)
c = [c0] + cs
u = [x] + series
v = [primal_out] + [None] * len(series)
for k in range(1, len(v)):
v[k] = fact(k - 1) * sum(
_scale(k, j) * c[k - j] * u[j] for j in range(1, k + 1))
primal_out, *series_out = v
return primal_out, series_out
def_deriv(
lax.erf_p, lambda x: lax.mul(lax._const(x, 2. / np.sqrt(np.pi)),
lax.exp(lax.neg(lax.square(x)))))
def def_comp(prim, comp):
"""
Define the jet rule for a primitive in terms of a composition of simpler primitives.
"""
jet_rules[prim] = partial(jet, comp)
def_comp(lax.expm1_p, lambda x: lax.exp(x) - 1)
def_comp(lax.log1p_p, lambda x: lax.log(1 + x))
def_comp(lax.sqrt_p, lambda x: x**0.5)
def_comp(lax.rsqrt_p, lambda x: x**-0.5)
def_comp(lax.asinh_p, lambda x: lax.log(x + lax.sqrt(lax.square(x) + 1)))
def_comp(lax.acosh_p, lambda x: lax.log(x + lax.sqrt(lax.square(x) - 1)))
def pdf(x, mean, cov):
return lax.exp(logpdf(x, mean, cov))
def dot_product_attention(query,
key,
value,
dtype=jnp.float32,
bias=None,
axis=None,
broadcast_dropout=True,
dropout_rng=None,
dropout_rate=0.,
deterministic=False,
precision=None):
"""Computes dot-product attention given query, key, and value.
This is the core function for applying attention based on
https://arxiv.org/abs/1706.03762. It calculates the attention weights given
query and key and combines the values using the attention weights. This
function supports multi-dimensional inputs. This version is modified to
move the softmax division after the dot product.
Args:
query: queries for calculating attention with shape of `[batch_size, dim1,
dim2, ..., dimN, num_heads, mem_channels]`.
key: keys for calculating attention with shape of `[batch_size, dim1, dim2,
..., dimN, num_heads, mem_channels]`.
value: values to be used in attention with shape of `[batch_size, dim1,
dim2,..., dimN, num_heads, value_channels]`.
dtype: the dtype of the computation (default: float32)
bias: bias for the attention weights. This can be used for incorporating
autoregressive mask, padding mask, proximity bias.
axis: axises over which the attention is applied.
broadcast_dropout: bool: use a broadcasted dropout along batch dims.
dropout_rng: JAX PRNGKey: to be used for dropout
dropout_rate: dropout rate
deterministic: bool, deterministic or not (to apply dropout)
precision: numerical precision of the computation see `jax.lax.Precision`
for details.
Returns:
Output of shape `[bs, dim1, dim2, ..., dimN,, num_heads, value_channels]`.
"""
assert key.shape[:-1] == value.shape[:-1]
assert (query.shape[0:1] == key.shape[0:1]
and query.shape[-1] == key.shape[-1])
if axis is None:
axis = tuple(range(1, key.ndim - 2))
if not isinstance(axis, Iterable):
axis = (axis, )
assert key.ndim == query.ndim
assert key.ndim == value.ndim
for ax in axis:
if not (query.ndim >= 3 and 1 <= ax < query.ndim - 2):
raise ValueError('Attention axis must be between the batch '
'axis and the last-two axes.')
depth = query.shape[-1]
n = key.ndim
# batch_dims is <bs, <non-attention dims>, num_heads>
batch_dims = tuple(np.delete(range(n), axis + (n - 1, )))
# q & k -> (bs, <non-attention dims>, num_heads, <attention dims>, channels)
qk_perm = batch_dims + axis + (n - 1, )
key = key.transpose(qk_perm)
query = query.transpose(qk_perm)
# v -> (bs, <non-attention dims>, num_heads, channels, <attention dims>)
v_perm = batch_dims + (n - 1, ) + axis
value = value.transpose(v_perm)
query = query / jnp.sqrt(depth).astype(dtype)
batch_dims_t = tuple(range(len(batch_dims)))
attn_weights = lax.dot_general(query,
key, (((n - 1, ), (n - 1, )),
(batch_dims_t, batch_dims_t)),
precision=precision)
# apply attention bias: masking, droput, proximity bias, ect.
if bias is not None:
attn_weights = attn_weights + bias
# normalize the attention weights
norm_dims = tuple(range(attn_weights.ndim - len(axis), attn_weights.ndim))
decoding = attn_weights.shape[-2] != 256
if decoding:
attn_weights = lax.exp(attn_weights - jax.scipy.special.logsumexp(
attn_weights, axis=norm_dims, keepdims=True))
else:
# move the division by the softmax denominator to after the dot product
attn_weights = jnp.exp(attn_weights - lax.stop_gradient(
jnp.max(attn_weights, axis=norm_dims, keepdims=True)))
softmax_denominator = jnp.sum(attn_weights,
axis=norm_dims,
keepdims=False)
attn_weights = attn_weights.astype(dtype)
# apply dropout
if not deterministic and dropout_rate > 0.:
if dropout_rng is None:
dropout_rng = nn.make_rng()
keep_prob = jax.lax.tie_in(attn_weights, 1.0 - dropout_rate)
if broadcast_dropout:
# dropout is broadcast across the batch+head+non-attention dimension
dropout_dims = attn_weights.shape[-(2 * len(axis)):]
dropout_shape = (tuple([1] * len(batch_dims_t)) + dropout_dims)
keep = random.bernoulli(dropout_rng, keep_prob, dropout_shape)
else:
keep = random.bernoulli(dropout_rng, keep_prob, attn_weights.shape)
multiplier = (keep.astype(attn_weights.dtype) /
jnp.asarray(keep_prob, dtype=dtype))
attn_weights = attn_weights * multiplier
# compute the new values given the attention weights
wv_contracting_dims = (norm_dims, range(value.ndim - len(axis),
value.ndim))
y = lax.dot_general(attn_weights,
value,
(wv_contracting_dims, (batch_dims_t, batch_dims_t)),
precision=precision)
if not decoding:
# divide by the denominator of the attention softmax now, when the array is
# O(N*H) rather than O(N^2)
y = y / jnp.expand_dims(softmax_denominator, -1)
# back to (bs, dim1, dim2, ..., dimN, num_heads, channels)
perm_inv = _invert_perm(qk_perm)
y = y.transpose(perm_inv)
return y
def i1(x):
x, = _promote_args_inexact("i1", x)
return lax.mul(lax.exp(lax.abs(x)), lax.bessel_i1e(x))
def pdf(x, b, loc=0, scale=1):
return lax.exp(logpdf(x, b, loc, scale))
def pdf(x, p):
return lax.exp(logpdf(x, p))
def cosh(x):
x, = _promote_to_result_dtype(onp.cosh, x)
return lax.div(lax.add(lax.exp(x), lax.exp(lax.neg(x))),
_constant_like(x, 2))
def sinh(x):
x, = _promote_to_result_dtype(onp.sinh, x)
return lax.div(lax.sub(lax.exp(x), lax.exp(lax.neg(x))),
_constant_like(x, 2))
def _exp(x):
return lax.exp(x)
def unquantized_softmax(a):
a = lax.exp(
a - jax.scipy.special.logsumexp(a, axis=norm_dims, keepdims=True))
return a.astype(dtype)
def fact(n):
return lax.exp(lax.lgamma(n + 1.))
def exp2(x):
x, = _promote_args_inexact("exp2", x)
return lax.exp(lax.mul(lax.log(_constant_like(x, 2)), x))
def expit(x):
x, = _promote_args_inexact("expit", x)
one = _lax_const(x, 1)
return lax.div(one, lax.add(one, lax.exp(lax.neg(x))))
def pmf(k, n, a, b, loc=0):
"""JAX implementation of scipy.stats.betabinom.pmf."""
return lax.exp(logpmf(k, n, a, b, loc))
def _log_ndtr_jvp(series_order, primals, tangents):
(x, ), (t, ) = primals, tangents
ans = log_ndtr(x, series_order=series_order)
t_out = lax.mul(t, lax.exp(lax.sub(_norm_logpdf(x), ans)))
return ans, t_out
def pdf(x, alpha): return lax.exp(logpdf(x, alpha))
def expit(x):
x = asarray(x)
one = lax._const(x, 1)
return lax.div(one, lax.add(one, lax.exp(lax.neg(x))))
def pdf(x):
return lax.exp(logpdf(x))
