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Make floatX and intX always return TensorVariables #6636

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Make floatX and intX always return TensorVariables #6636

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4 changes: 2 additions & 2 deletions docs/source/contributing/implementing_distribution.md
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Original file line number Diff line number Diff line change
Expand Up @@ -146,12 +146,12 @@ class Blah(PositiveContinuous):
# We pass the standard parametrizations to super().dist
@classmethod
def dist(cls, param1, param2=None, alt_param2=None, **kwargs):
param1 = pt.as_tensor_variable(intX(param1))
param1 = intX(param1)
if param2 is not None and alt_param2 is not None:
raise ValueError("Only one of param2 and alt_param2 is allowed.")
if alt_param2 is not None:
param2 = 1 / alt_param2
param2 = pt.as_tensor_variable(floatX(param2))
param2 = floatX(param2)

# The first value-only argument should be a list of the parameters that
# the rv_op needs in order to be instantiated
Expand Down
143 changes: 65 additions & 78 deletions pymc/distributions/continuous.py
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Original file line number Diff line number Diff line change
Expand Up @@ -304,8 +304,8 @@ class Uniform(BoundedContinuous):

@classmethod
def dist(cls, lower=0, upper=1, **kwargs):
lower = pt.as_tensor_variable(floatX(lower))
upper = pt.as_tensor_variable(floatX(upper))
lower = floatX(lower)
upper = floatX(upper)
return super().dist([lower, upper], **kwargs)

def moment(rv, size, lower, upper):
Expand Down Expand Up @@ -509,12 +509,8 @@ class Normal(Continuous):
@classmethod
def dist(cls, mu=0, sigma=None, tau=None, **kwargs):
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
sigma = pt.as_tensor_variable(sigma)

# tau = pt.as_tensor_variable(tau)
# mean = median = mode = mu = pt.as_tensor_variable(floatX(mu))
# variance = 1.0 / self.tau

mu = floatX(mu)
sigma = floatX(sigma)
return super().dist([mu, sigma], **kwargs)

def moment(rv, size, mu, sigma):
Expand Down Expand Up @@ -670,12 +666,11 @@ def dist(
**kwargs,
) -> RandomVariable:
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
sigma = pt.as_tensor_variable(sigma)
tau = pt.as_tensor_variable(tau)
mu = pt.as_tensor_variable(floatX(mu))
sigma = floatX(sigma)
mu = floatX(mu)

lower = pt.as_tensor_variable(floatX(lower)) if lower is not None else pt.constant(-np.inf)
upper = pt.as_tensor_variable(floatX(upper)) if upper is not None else pt.constant(np.inf)
lower = floatX(lower) if lower is not None else pt.constant(-np.inf)
upper = floatX(upper) if upper is not None else pt.constant(np.inf)
return super().dist([mu, sigma, lower, upper], **kwargs)

def moment(rv, size, mu, sigma, lower, upper):
Expand Down Expand Up @@ -948,9 +943,9 @@ class Wald(PositiveContinuous):
@classmethod
def dist(cls, mu=None, lam=None, phi=None, alpha=0.0, **kwargs):
mu, lam, phi = cls.get_mu_lam_phi(mu, lam, phi)
alpha = pt.as_tensor_variable(floatX(alpha))
mu = pt.as_tensor_variable(floatX(mu))
lam = pt.as_tensor_variable(floatX(lam))
alpha = floatX(alpha)
mu = floatX(mu)
lam = floatX(lam)
return super().dist([mu, lam, alpha], **kwargs)

def moment(rv, size, mu, lam, alpha):
Expand Down Expand Up @@ -1115,8 +1110,8 @@ class Beta(UnitContinuous):
@classmethod
def dist(cls, alpha=None, beta=None, mu=None, sigma=None, nu=None, *args, **kwargs):
alpha, beta = cls.get_alpha_beta(alpha, beta, mu, sigma, nu)
alpha = pt.as_tensor_variable(floatX(alpha))
beta = pt.as_tensor_variable(floatX(beta))
alpha = floatX(alpha)
beta = floatX(beta)

return super().dist([alpha, beta], **kwargs)

Expand Down Expand Up @@ -1242,8 +1237,8 @@ class Kumaraswamy(UnitContinuous):

@classmethod
def dist(cls, a, b, *args, **kwargs):
a = pt.as_tensor_variable(floatX(a))
b = pt.as_tensor_variable(floatX(b))
a = floatX(a)
b = floatX(b)

return super().dist([a, b], *args, **kwargs)

Expand Down Expand Up @@ -1328,7 +1323,7 @@ class Exponential(PositiveContinuous):

@classmethod
def dist(cls, lam, *args, **kwargs):
lam = pt.as_tensor_variable(floatX(lam))
lam = floatX(lam)

# PyTensor exponential op is parametrized in terms of mu (1/lam)
return super().dist([pt.reciprocal(lam)], **kwargs)
Expand Down Expand Up @@ -1409,8 +1404,8 @@ class Laplace(Continuous):

@classmethod
def dist(cls, mu, b, *args, **kwargs):
b = pt.as_tensor_variable(floatX(b))
mu = pt.as_tensor_variable(floatX(mu))
b = floatX(b)
mu = floatX(mu)

return super().dist([mu, b], *args, **kwargs)

Expand Down Expand Up @@ -1518,9 +1513,9 @@ class AsymmetricLaplace(Continuous):
@classmethod
def dist(cls, kappa=None, mu=None, b=None, q=None, *args, **kwargs):
kappa = cls.get_kappa(kappa, q)
b = pt.as_tensor_variable(floatX(b))
kappa = pt.as_tensor_variable(floatX(kappa))
mu = pt.as_tensor_variable(floatX(mu))
b = floatX(b)
kappa = floatX(kappa)
mu = floatX(mu)

return super().dist([b, kappa, mu], *args, **kwargs)

Expand Down Expand Up @@ -1634,8 +1629,8 @@ class LogNormal(PositiveContinuous):
def dist(cls, mu=0, sigma=None, tau=None, *args, **kwargs):
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)

mu = pt.as_tensor_variable(floatX(mu))
sigma = pt.as_tensor_variable(floatX(sigma))
mu = floatX(mu)
sigma = floatX(sigma)

return super().dist([mu, sigma], *args, **kwargs)

Expand Down Expand Up @@ -1759,9 +1754,9 @@ class StudentT(Continuous):

@classmethod
def dist(cls, nu, mu=0, *, sigma=None, lam=None, **kwargs):
nu = pt.as_tensor_variable(floatX(nu))
nu = floatX(nu)
lam, sigma = get_tau_sigma(tau=lam, sigma=sigma)
sigma = pt.as_tensor_variable(sigma)
sigma = floatX(sigma)

return super().dist([nu, mu, sigma], **kwargs)

Expand Down Expand Up @@ -1856,8 +1851,8 @@ class Pareto(BoundedContinuous):

@classmethod
def dist(cls, alpha, m, **kwargs):
alpha = pt.as_tensor_variable(floatX(alpha))
m = pt.as_tensor_variable(floatX(m))
alpha = floatX(alpha)
m = floatX(m)

return super().dist([alpha, m], **kwargs)

Expand Down Expand Up @@ -1953,8 +1948,8 @@ class Cauchy(Continuous):

@classmethod
def dist(cls, alpha, beta, *args, **kwargs):
alpha = pt.as_tensor_variable(floatX(alpha))
beta = pt.as_tensor_variable(floatX(beta))
alpha = floatX(alpha)
beta = floatX(beta)

return super().dist([alpha, beta], **kwargs)

Expand Down Expand Up @@ -2024,7 +2019,7 @@ class HalfCauchy(PositiveContinuous):

@classmethod
def dist(cls, beta, *args, **kwargs):
beta = pt.as_tensor_variable(floatX(beta))
beta = floatX(beta)
return super().dist([0.0, beta], **kwargs)

def moment(rv, size, loc, beta):
Expand Down Expand Up @@ -2119,8 +2114,8 @@ class Gamma(PositiveContinuous):
@classmethod
def dist(cls, alpha=None, beta=None, mu=None, sigma=None, **kwargs):
alpha, beta = cls.get_alpha_beta(alpha, beta, mu, sigma)
alpha = pt.as_tensor_variable(floatX(alpha))
beta = pt.as_tensor_variable(floatX(beta))
alpha = floatX(alpha)
beta = floatX(beta)

# The PyTensor `GammaRV` `Op` will invert the `beta` parameter itself
return super().dist([alpha, beta], **kwargs)
Expand Down Expand Up @@ -2228,8 +2223,8 @@ class InverseGamma(PositiveContinuous):
@classmethod
def dist(cls, alpha=None, beta=None, mu=None, sigma=None, *args, **kwargs):
alpha, beta = cls._get_alpha_beta(alpha, beta, mu, sigma)
alpha = pt.as_tensor_variable(floatX(alpha))
beta = pt.as_tensor_variable(floatX(beta))
alpha = floatX(alpha)
beta = floatX(beta)

return super().dist([alpha, beta], **kwargs)

Expand Down Expand Up @@ -2332,7 +2327,7 @@ class ChiSquared(PositiveContinuous):

@classmethod
def dist(cls, nu, *args, **kwargs):
nu = pt.as_tensor_variable(floatX(nu))
nu = floatX(nu)
return super().dist([nu], *args, **kwargs)

def moment(rv, size, nu):
Expand Down Expand Up @@ -2417,8 +2412,8 @@ class Weibull(PositiveContinuous):

@classmethod
def dist(cls, alpha, beta, *args, **kwargs):
alpha = pt.as_tensor_variable(floatX(alpha))
beta = pt.as_tensor_variable(floatX(beta))
alpha = floatX(alpha)
beta = floatX(beta)

return super().dist([alpha, beta], *args, **kwargs)

Expand Down Expand Up @@ -2537,9 +2532,9 @@ class HalfStudentT(PositiveContinuous):

@classmethod
def dist(cls, nu, sigma=None, lam=None, *args, **kwargs):
nu = pt.as_tensor_variable(floatX(nu))
nu = floatX(nu)
lam, sigma = get_tau_sigma(lam, sigma)
sigma = pt.as_tensor_variable(sigma)
sigma = floatX(sigma)

return super().dist([nu, sigma], *args, **kwargs)

Expand Down Expand Up @@ -2657,9 +2652,9 @@ class ExGaussian(Continuous):

@classmethod
def dist(cls, mu=0.0, sigma=None, nu=None, *args, **kwargs):
mu = pt.as_tensor_variable(floatX(mu))
sigma = pt.as_tensor_variable(floatX(sigma))
nu = pt.as_tensor_variable(floatX(nu))
mu = floatX(mu)
sigma = floatX(sigma)
nu = floatX(nu)

return super().dist([mu, sigma, nu], *args, **kwargs)

Expand Down Expand Up @@ -2762,8 +2757,8 @@ class VonMises(CircularContinuous):

@classmethod
def dist(cls, mu=0.0, kappa=1.0, *args, **kwargs):
mu = pt.as_tensor_variable(floatX(mu))
kappa = pt.as_tensor_variable(floatX(kappa))
mu = floatX(mu)
kappa = floatX(kappa)
return super().dist([mu, kappa], *args, **kwargs)

def moment(rv, size, mu, kappa):
Expand Down Expand Up @@ -2865,10 +2860,9 @@ class SkewNormal(Continuous):
@classmethod
def dist(cls, alpha=1, mu=0.0, sigma=None, tau=None, *args, **kwargs):
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
alpha = pt.as_tensor_variable(floatX(alpha))
mu = pt.as_tensor_variable(floatX(mu))
tau = pt.as_tensor_variable(tau)
sigma = pt.as_tensor_variable(sigma)
alpha = floatX(alpha)
mu = floatX(mu)
sigma = floatX(sigma)

return super().dist([mu, sigma, alpha], *args, **kwargs)

Expand Down Expand Up @@ -2954,9 +2948,9 @@ class Triangular(BoundedContinuous):

@classmethod
def dist(cls, lower=0, upper=1, c=0.5, *args, **kwargs):
lower = pt.as_tensor_variable(floatX(lower))
upper = pt.as_tensor_variable(floatX(upper))
c = pt.as_tensor_variable(floatX(c))
lower = floatX(lower)
upper = floatX(upper)
c = floatX(c)

return super().dist([lower, c, upper], *args, **kwargs)

Expand Down Expand Up @@ -3061,8 +3055,8 @@ class Gumbel(Continuous):

@classmethod
def dist(cls, mu, beta, **kwargs):
mu = pt.as_tensor_variable(floatX(mu))
beta = pt.as_tensor_variable(floatX(beta))
mu = floatX(mu)
beta = floatX(beta)

return super().dist([mu, beta], **kwargs)

Expand Down Expand Up @@ -3171,8 +3165,8 @@ class Rice(PositiveContinuous):
@classmethod
def dist(cls, nu=None, sigma=None, b=None, *args, **kwargs):
nu, b, sigma = cls.get_nu_b(nu, b, sigma)
b = pt.as_tensor_variable(floatX(b))
sigma = pt.as_tensor_variable(floatX(sigma))
b = floatX(b)
sigma = floatX(sigma)

return super().dist([b, sigma], *args, **kwargs)

Expand Down Expand Up @@ -3270,8 +3264,8 @@ class Logistic(Continuous):

@classmethod
def dist(cls, mu=0.0, s=1.0, *args, **kwargs):
mu = pt.as_tensor_variable(floatX(mu))
s = pt.as_tensor_variable(floatX(s))
mu = floatX(mu)
s = floatX(s)
return super().dist([mu, s], *args, **kwargs)

def moment(rv, size, mu, s):
Expand Down Expand Up @@ -3365,11 +3359,9 @@ class LogitNormal(UnitContinuous):

@classmethod
def dist(cls, mu=0, sigma=None, tau=None, **kwargs):
mu = pt.as_tensor_variable(floatX(mu))
mu = floatX(mu)
tau, sigma = get_tau_sigma(tau=tau, sigma=sigma)
sigma = pt.as_tensor_variable(sigma)
tau = pt.as_tensor_variable(tau)

sigma = floatX(sigma)
return super().dist([mu, sigma], **kwargs)

def moment(rv, size, mu, sigma):
Expand Down Expand Up @@ -3499,11 +3491,6 @@ def dist(cls, x_points, pdf_points, *args, **kwargs):
x_points = pt.constant(floatX(x_points))
pdf_points = pt.constant(floatX(pdf_points))
cdf_points = pt.constant(floatX(cdf_points))

# lower = pt.as_tensor_variable(x_points[0])
# upper = pt.as_tensor_variable(x_points[-1])
# median = _interpolated_argcdf(0.5, pdf_points, cdf_points, x_points)

return super().dist([x_points, pdf_points, cdf_points], **kwargs)

def moment(rv, size, x_points, pdf_points, cdf_points):
Expand Down Expand Up @@ -3607,8 +3594,8 @@ class Moyal(Continuous):

@classmethod
def dist(cls, mu=0, sigma=1.0, *args, **kwargs):
mu = pt.as_tensor_variable(floatX(mu))
sigma = pt.as_tensor_variable(floatX(sigma))
mu = floatX(mu)
sigma = floatX(sigma)

return super().dist([mu, sigma], *args, **kwargs)

Expand Down Expand Up @@ -3694,9 +3681,9 @@ def __init__(self, get_pdf=False):
self.get_pdf = get_pdf

def make_node(self, x, h, z):
x = pt.as_tensor_variable(floatX(x))
h = pt.as_tensor_variable(floatX(h))
z = pt.as_tensor_variable(floatX(z))
x = floatX(x)
h = floatX(h)
z = floatX(z)
bshape = broadcast_shape(x, h, z)
shape = [None] * len(bshape)
return Apply(self, [x, h, z], [pt.TensorType(pytensor.config.floatX, shape)()])
Expand Down Expand Up @@ -3795,8 +3782,8 @@ class PolyaGamma(PositiveContinuous):

@classmethod
def dist(cls, h=1.0, z=0.0, **kwargs):
h = pt.as_tensor_variable(floatX(h))
z = pt.as_tensor_variable(floatX(z))
h = floatX(h)
z = floatX(z)

msg = f"The variable {h} specified for PolyaGamma has non-positive "
msg += "values, making it unsuitable for this parameter."
Expand Down
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