def logpmf(k, p, loc=0):
k, p, loc = jnp._promote_args_inexact("geom.logpmf", k, p, loc)
zero = lax._const(k, 0)
one = lax._const(k, 1)
x = lax.sub(k, loc)
log_probs = xlog1py(lax.sub(x, one), -p) + lax.log(p)
return jnp.where(lax.le(x, zero), -jnp.inf, log_probs)
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 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 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 _logaddexp2_jvp(primals, tangents):
x1, x2 = primals
t1, t2 = tangents
x1, x2, t1, t2 = _promote_args_inexact("logaddexp2_jvp", x1, x2, t1, t2)
primal_out = logaddexp2(x1, x2)
tangent_out = lax.add(lax.mul(t1, exp2(lax.sub(_replace_inf(x1), _replace_inf(primal_out)))),
lax.mul(t2, exp2(lax.sub(_replace_inf(x2), _replace_inf(primal_out)))))
return primal_out, tangent_out
def logpmf(k, p, loc=0):
k, p, loc = jnp._promote_args_inexact("bernoulli.logpmf", k, p, loc)
zero = lax._const(k, 0)
one = lax._const(k, 1)
x = lax.sub(k, loc)
log_probs = xlogy(x, p) + xlog1py(lax.sub(one, x), -p)
return jnp.where(jnp.logical_or(lax.lt(x, zero), lax.gt(x, one)),
-jnp.inf, log_probs)
def logpdf(x, a, loc=0, scale=1):
x, a, loc, scale = _promote_args_inexact("gamma.logpdf", x, a, loc, scale)
one = _lax_const(x, 1)
y = lax.div(lax.sub(x, loc), scale)
log_linear_term = lax.sub(xlogy(lax.sub(a, one), y), y)
shape_terms = lax.add(gammaln(a), lax.log(scale))
log_probs = lax.sub(log_linear_term, shape_terms)
return where(lax.lt(x, loc), -inf, log_probs)
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 logpmf(k, n, p, loc=0):
"""JAX implementation of scipy.stats.nbinom.logpmf."""
k, n, p, loc = _promote_args_inexact("nbinom.logpmf", k, n, p, loc)
one = _lax_const(k, 1)
y = lax.sub(k, loc)
comb_term = lax.sub(lax.sub(gammaln(lax.add(y, n)), gammaln(n)),
gammaln(lax.add(y, one)))
log_linear_term = lax.add(xlogy(n, p), xlogy(y, lax.sub(one, p)))
log_probs = lax.add(comb_term, log_linear_term)
return where(lax.lt(k, loc), -inf, log_probs)
def logpdf(x, alpha):
args = (np.ones((0, ), lax.dtype(x)), np.ones((1, ), lax.dtype(alpha)))
to_dtype = lax.dtype(osp_stats.dirichlet.logpdf(*args))
x, alpha = [lax.convert_element_type(arg, to_dtype) for arg in (x, alpha)]
one = jnp._constant_like(x, 1)
normalize_term = jnp.sum(gammaln(alpha), axis=-1) - gammaln(
jnp.sum(alpha, axis=-1))
log_probs = lax.sub(jnp.sum(xlogy(lax.sub(alpha, one), x), axis=-1),
normalize_term)
return jnp.where(_is_simplex(x), log_probs, -jnp.inf)
def logpdf(x, a, b, loc=0, scale=1):
x, a, b, loc, scale = _promote_args_inexact("beta.logpdf", x, a, b, loc,
scale)
one = lax._const(x, 1)
shape_term = lax.neg(betaln(a, b))
y = lax.div(lax.sub(x, loc), scale)
log_linear_term = lax.add(xlogy(lax.sub(a, one), y),
xlog1py(lax.sub(b, one), lax.neg(y)))
log_probs = lax.sub(lax.add(shape_term, log_linear_term), lax.log(scale))
return where(logical_or(lax.gt(x, lax.add(loc, scale)), lax.lt(x, loc)),
-inf, log_probs)
def logpdf(x, df, loc=0, scale=1):
x, df, loc, scale = _promote_args_inexact("t.logpdf", x, df, loc, scale)
two = _lax_const(x, 2)
scaled_x = lax.div(lax.sub(x, loc), scale)
df_over_two = lax.div(df, two)
df_plus_one_over_two = lax.add(df_over_two, _lax_const(x, 0.5))
normalize_term_const = lax.mul(lax.mul(scale, scale), _lax_const(x, np.pi))
normalize_term_tmp = lax.div(lax.log(lax.mul(normalize_term_const, df)), two)
normalize_term = lax.sub(lax.add(lax.lgamma(df_over_two), normalize_term_tmp),
lax.lgamma(df_plus_one_over_two))
quadratic = lax.div(lax.mul(scaled_x, scaled_x), df)
return lax.neg(lax.add(normalize_term, lax.mul(df_plus_one_over_two, lax.log1p(quadratic))))
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 logpdf(x, df, loc=0, scale=1):
x, df, loc, scale = _promote_args_inexact("chi2.logpdf", x, df, loc, scale)
one = _constant_like(x, 1)
two = _constant_like(x, 2)
y = lax.div(lax.sub(x, loc), scale)
df_on_two = lax.div(df, two)
kernel = lax.sub(lax.mul(lax.sub(df_on_two, one), lax.log(y)), lax.div(y,two))
nrml_cnst = lax.neg(lax.add(lax.lgamma(df_on_two),lax.div(lax.mul(lax.log(two), df),two)))
log_probs = lax.add(lax.sub(nrml_cnst, lax.log(scale)), kernel)
return where(lax.lt(x, loc), -inf, log_probs)
def logpdf(x, loc=0, scale=1):
x, loc, scale = _promote_args_inexact("cauchy.logpdf", x, loc, scale)
pi = _constant_like(x, np.pi)
scaled_x = lax.div(lax.sub(x, loc), scale)
normalize_term = lax.log(lax.mul(pi, scale))
return lax.neg(
lax.add(normalize_term, lax.log1p(lax.mul(scaled_x, scaled_x))))
def logpdf(x, loc=0, scale=1):
x, loc, scale = _promote_args_inexact("norm.logpdf", x, loc, scale)
two = _constant_like(x, 2)
scale_sqrd = lax.pow(scale, two)
log_normalizer = lax.log(lax.mul(_constant_like(x, 2 * np.pi), scale_sqrd))
quadratic = lax.div(lax.pow(lax.sub(x, loc), two), scale_sqrd)
return lax.div(lax.neg(lax.add(log_normalizer, quadratic)), two)
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 _wrap_between(x, _a):
"""Wraps `x` between `[-a, a]`."""
a = _constant_like(x, _a)
two_a = _constant_like(x, 2 * _a)
zero = _constant_like(x, 0)
rem = lax.rem(lax.add(x, a), two_a)
rem = lax.select(lax.lt(rem, zero), lax.add(rem, two_a), rem)
return lax.sub(rem, a)
def logpdf(x, b, loc=0, scale=1):
x, b, loc, scale = _promote_args_inexact("pareto.logpdf", x, b, loc, scale)
one = _constant_like(x, 1)
scaled_x = lax.div(lax.sub(x, loc), scale)
normalize_term = lax.log(lax.div(scale, b))
log_probs = lax.neg(
lax.add(normalize_term, lax.mul(lax.add(b, one), lax.log(scaled_x))))
return where(lax.lt(x, lax.add(loc, scale)), -inf, log_probs)
def logpmf(k, n, a, b, loc=0):
"""JAX implementation of scipy.stats.betabinom.logpmf."""
k, n, a, b, loc = _promote_args_inexact("betabinom.logpmf", k, n, a, b,
loc)
y = lax.sub(lax.floor(k), loc)
one = _lax_const(y, 1)
zero = _lax_const(y, 0)
combiln = lax.neg(
lax.add(lax.log1p(n),
betaln(lax.add(lax.sub(n, y), one), lax.add(y, one))))
beta_lns = lax.sub(betaln(lax.add(y, a), lax.add(lax.sub(n, y), b)),
betaln(a, b))
log_probs = lax.add(combiln, beta_lns)
y_cond = logical_or(lax.lt(y, lax.neg(loc)), lax.gt(y, lax.sub(n, loc)))
log_probs = where(y_cond, -inf, log_probs)
n_a_b_cond = logical_or(logical_or(lax.lt(n, one), lax.lt(a, zero)),
lax.lt(b, zero))
return where(n_a_b_cond, nan, log_probs)
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 logpdf(x, alpha):
x, alpha = _promote_dtypes_inexact(x, alpha)
if alpha.ndim != 1:
raise ValueError(
f"`alpha` must be one-dimensional; got alpha.shape={alpha.shape}"
)
if x.shape[0] not in (alpha.shape[0], alpha.shape[0] - 1):
raise ValueError(
"`x` must have either the same number of entries as `alpha` "
f"or one entry fewer; got x.shape={x.shape}, alpha.shape={alpha.shape}"
)
one = lax._const(x, 1)
if x.shape[0] != alpha.shape[0]:
x = jnp.concatenate([x, lax.sub(one, x.sum(0, keepdims=True))], axis=0)
normalize_term = jnp.sum(gammaln(alpha)) - gammaln(jnp.sum(alpha))
if x.ndim > 1:
alpha = lax.broadcast_in_dim(alpha, alpha.shape + (1,) * (x.ndim - 1), (0,))
log_probs = lax.sub(jnp.sum(xlogy(lax.sub(alpha, one), x), axis=0), normalize_term)
return jnp.where(_is_simplex(x), log_probs, -jnp.inf)
def _float_divmod(x1, x2):
# see float_divmod in floatobject.c of CPython
mod = lax.rem(x1, x2)
div = lax.div(lax.sub(x1, mod), x2)
ind = lax.bitwise_and(mod != 0, lax.sign(x2) != lax.sign(mod))
mod = lax.select(ind, mod + x2, mod)
div = lax.select(ind, div - _constant_like(div, 1), div)
return lax.round(div), mod
def multigammaln(a, d):
d = core.concrete_or_error(int, d, "d argument of multigammaln")
a, d = _promote_args_inexact("multigammaln", a, d)
constant = lax.mul(lax.mul(lax.mul(_constant_like(a, 0.25), d),
lax.sub(d, _constant_like(a, 1))),
lax.log(_constant_like(a, np.pi)))
res = jnp.sum(gammaln(jnp.expand_dims(a, axis=-1) -
lax.div(jnp.arange(d), _constant_like(a, 2))),
axis=-1)
return res + constant
def _ptp(a,
axis: Optional[Union[int, Tuple[int, ...]]] = None,
out=None,
keepdims=False):
_check_arraylike("ptp", a)
if out is not None:
raise NotImplementedError(
"The 'out' argument to jnp.ptp is not supported.")
x = amax(a, axis=axis, keepdims=keepdims)
y = amin(a, axis=axis, keepdims=keepdims)
return lax.sub(x, y)
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 multigammaln(a, d):
a, = _promote_args_inexact("multigammaln", a)
d = lax.convert_element_type(d, lax.dtype(a))
constant = lax.mul(
lax.mul(lax.mul(_constant_like(a, 0.25), d),
lax.sub(d, _constant_like(a, 1))),
lax.log(_constant_like(a, np.pi)))
res = jnp.sum(gammaln(
jnp.expand_dims(a, axis=-1) -
lax.div(jnp.arange(d), _constant_like(a, 2))),
axis=-1)
return res + constant
def multigammaln(a, d):
d = core.concrete_or_error(int, d, "d argument of multigammaln")
a, d_ = _promote_args_inexact("multigammaln", a, d)
constant = lax.mul(
lax.mul(lax.mul(_lax_const(a, 0.25), d_),
lax.sub(d_, _lax_const(a, 1))), lax.log(_lax_const(a, np.pi)))
b = lax.div(jnp.arange(d, dtype=d_.dtype), _lax_const(a, 2))
res = jnp.sum(gammaln(
jnp.expand_dims(a, axis=-1) -
jnp.expand_dims(b, axis=tuple(range(a.ndim)))),
axis=-1)
return res + constant
def reciprocal(tensor, dtype, recip_hparams):
"""Generates a reciprocal function based on recip hyper params."""
if recip_hparams is not None and recip_hparams.linear_gradient != 0:
# Want: max(low_bound, -a*x+b) such that (-a*x+b) goes through
# (1, 1)
# Solution: max(low_bound, a+1- a*x) for arbitrary a>0.
afull = jnp.full(tensor.shape,
recip_hparams.linear_gradient).astype(dtype)
aplus1full = jnp.full(tensor.shape,
1 + recip_hparams.linear_gradient).astype(dtype)
arecip = jnp.clip(lax.sub(aplus1full, lax.mul(afull, tensor)),
recip_hparams.low_bound, 1.).astype(dtype)
else:
arecip = lax.reciprocal(tensor)
return arecip
def _eye(cls, N, M, k, *, dtype=None, index_dtype='int32'):
if k > 0:
diag_size = min(N, M - k)
else:
diag_size = min(N + k, M)
if diag_size <= 0:
# if k is out of range, return an empty matrix.
return cls._empty((N, M), dtype=dtype, index_dtype=index_dtype)
k = jnp.asarray(k)
data = jnp.ones(diag_size, dtype=dtype)
idx = jnp.arange(diag_size, dtype=index_dtype)
zero = _const(idx, 0)
k = _const(idx, k)
row = lax.sub(idx, lax.cond(k >= 0, lambda: zero, lambda: k))
col = lax.add(idx, lax.cond(k <= 0, lambda: zero, lambda: k))
return cls((data, row, col),
shape=(N, M),
rows_sorted=True,
cols_sorted=True)
