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Merged
sklam merged 9 commits into from
Aug 17, 2022
Merged

Migrate random glue_lowering to overload where easy #8061

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bea6f65
Migrate random `glue_lowering` to `overload` where easy
apmasell May 17, 2022
ba597b0
Fix missing array overrides
apmasell Jun 1, 2022
189c9ae
Relax types on hypergeometric
apmasell Jun 2, 2022
fe6fd8e
Add missing tests and remove unncessary type checks
apmasell Jul 18, 2022
4fa48fa
Fix parameter names and test `size=None`
apmasell Jul 20, 2022
c100b2f
Create special unit tests for gamma distribution over array
apmasell Jul 25, 2022
f8684a9
Ensure logseries works on 32-bit
apmasell Jul 26, 2022
74bfeae
Add test for remaining random array implementations
apmasell Jul 27, 2022
f5c5b8f
Add test for vonmises random array
apmasell Aug 4, 2022
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Fix missing array overrides 
Add missing functions for handling random distributions in arrays
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@apmasell
apmasell committed Jun 1, 2022
commit ba597b08deebd11434793d278ab41ee95deb4dd7
194 changes: 178 additions & 16 deletions numba/cpython/randomimpl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -610,6 +610,41 @@ def _impl(alpha, beta):
return _impl


@overload(np.random.gamma)
def gamma_impl(alpha, beta, size):
if (isinstance(alpha, types.Float) and isinstance(beta, types.Float) and
isinstance(size, types.NoneType)):
return lambda alpha, beta, _size: np.random.gamma(alpha, beta)

@sklam sklam Jul 19, 2022

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Suggested change
return lambda alpha, beta, _size: np.random.gamma(alpha, beta)
return lambda alpha, beta, size: np.random.gamma(alpha, beta)

if (isinstance(alpha, types.Float) and isinstance(beta, types.Float) and
(isinstance(size, types.Integer) or (isinstance(size, types.UniTuple)
and isinstance(size.dtype,
types.Integer)))):
def _impl(alpha, beta, size):
out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.gamma(alpha, beta)
return out
return _impl
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@overload(np.random.standard_gamma)
def standard_gamma_impl(alpha, size):
if isinstance(alpha, types.Float) and isinstance(size, types.NoneType):
return lambda alpha, _size: np.random.standard_gamma(alpha)

@sklam sklam Jul 19, 2022

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Suggested change
return lambda alpha, _size: np.random.standard_gamma(alpha)
return lambda alpha, size: np.random.standard_gamma(alpha)

if isinstance(alpha, types.Float) and (isinstance(size, types.Integer) or
(isinstance(size, types.UniTuple) and isinstance(size.dtype,
types.Integer))
):
def _impl(alpha, size):
out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.standard_gamma(alpha)
return out
return _impl
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@overload(random.betavariate)
def betavariate_impl(alpha, beta):
if isinstance(alpha, types.Float) and isinstance(beta, types.Float):
Expand All @@ -636,6 +671,24 @@ def _impl(alpha, beta):
return _impl


@overload(np.random.beta)
def beta_impl(alpha, beta, size):
if (isinstance(alpha, types.Float) and isinstance(beta, types.Float) and
isinstance(size, types.NoneType)):
return lambda alpha, beta, _size: np.random.beta(alpha, beta)

@sklam sklam Jul 19, 2022

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Suggested change
return lambda alpha, beta, _size: np.random.beta(alpha, beta)
return lambda alpha, beta, size: np.random.beta(alpha, beta)

if (isinstance(alpha, types.Float) and isinstance(beta, types.Float) and
(isinstance(size, types.Integer) or (isinstance(size, types.UniTuple)
and isinstance(size.dtype,
types.Integer)))):
def _impl(alpha, beta, size):
out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.beta(alpha, beta)
return out
return _impl
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@overload(random.expovariate)
def expovariate_impl(lambd):
if isinstance(lambd, types.Float):
Expand Down Expand Up @@ -719,6 +772,24 @@ def np_log_normal_impl2(mu, sigma):
return _lognormvariate_impl(np.random.normal)


@overload(np.random.lognormal)
def lognormal_impl(mu, sigma, size):
if (isinstance(mu, types.Float) and isinstance(sigma, types.Float)
and isinstance(size, types.NoneType)):
return lambda mu, sigma, _size: np.random.lognormal(mu, sigma)

@sklam sklam Jul 19, 2022

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Suggested change
return lambda mu, sigma, _size: np.random.lognormal(mu, sigma)
return lambda mu, sigma, size: np.random.lognormal(mu, sigma)

if (isinstance(mu, types.Float) and isinstance(sigma, types.Float) and
(isinstance(size, types.Integer) or (isinstance(size, types.UniTuple)
and isinstance(size.dtype,
types.Integer)))):
def _impl(mu, sigma, size):
out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.lognormal(mu, sigma)
return out
return _impl


@overload(random.lognormvariate)
def lognormvariate_impl(mu, sigma):
if isinstance(mu, types.Float) and isinstance(sigma, types.Float):
Expand Down Expand Up @@ -752,6 +823,23 @@ def _impl(alpha):
return _impl


@overload(np.random.pareto)
def pareto_impl(alpha, size):
if isinstance(alpha, types.Float) and isinstance(size, types.NoneType):
return lambda alpha, _size: np.random.pareto(alpha)

@sklam sklam Jul 19, 2022

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Suggested change
return lambda alpha, _size: np.random.pareto(alpha)
return lambda alpha, size: np.random.pareto(alpha)

if isinstance(alpha, types.Float) and (isinstance(size, types.Integer) or
(isinstance(size, types.UniTuple) and isinstance(size.dtype,
types.Integer))
):
def _impl(alpha, size):
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out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.pareto(alpha)
return out
return _impl
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Resolved


@overload(random.weibullvariate)
def weibullvariate_impl(alpha, beta):
if isinstance(alpha, types.Float) and isinstance(beta, types.Float):
Expand All @@ -775,6 +863,23 @@ def _impl(beta):
return _impl


@overload(np.random.weibull)
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def weibull_impl2(beta, size):
if isinstance(beta, types.Float) and isinstance(size, types.NoneType):
return lambda beta, _size: np.random.weibull(beta)

@sklam sklam Jul 19, 2022

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Suggested change
return lambda beta, _size: np.random.weibull(beta)
return lambda beta, size: np.random.weibull(beta)

if isinstance(beta, types.Float) and (isinstance(size, types.Integer) or
(isinstance(size, types.UniTuple) and isinstance(size.dtype,
types.Integer))
):
def _impl(weibull, size):
out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.weibull(weibull)
return out
return _impl


@overload(random.vonmisesvariate)
def vonmisesvariate_impl(mu, kappa):
if isinstance(mu, types.Float) and isinstance(kappa, types.Float):
Expand All @@ -787,7 +892,7 @@ def vonmisesvariate_impl(mu, kappa):
return _vonmisesvariate_impl(np.random.random)


def _vonmisesvariate_impl( _random):
def _vonmisesvariate_impl(_random):
def _impl(mu, kappa):
"""Circular data distribution. Taken from CPython.
Note the algorithm in Python 2.6 and Numpy is different:
Expand Down Expand Up @@ -831,6 +936,24 @@ def _impl(mu, kappa):
return _impl


@overload(np.random.vonmises)
def vonmises_impl(mu, kappa, size):
if (isinstance(mu, types.Float) and isinstance(kappa, types.Float) and
isinstance(size, types.NoneType)):
return lambda mu, kappa, _size: np.random.vonmises(mu, kappa)

@sklam sklam Jul 19, 2022

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Suggested change
return lambda mu, kappa, _size: np.random.vonmises(mu, kappa)
return lambda mu, kappa, size: np.random.vonmises(mu, kappa)

if (isinstance(mu, types.Float) and isinstance(kappa, types.Float) and
(isinstance(size, types.Integer) or (isinstance(size, types.UniTuple)
and isinstance(size.dtype,
types.Integer)))):
def _impl(mu, kappa, size):
out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.vonmises(mu, kappa)
return out
return _impl

@sklam sklam Jul 15, 2022

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Not exercised by tests

@sklam sklam Aug 3, 2022

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This is still need a test



@overload(np.random.binomial)
def binomial_impl(n, p):
if isinstance(n, types.Integer) and isinstance(p, types.Float):
Expand Down Expand Up @@ -941,6 +1064,24 @@ def _impl(num, denom):
return _impl


@overload(np.random.f)
def f_impl(num, denom, size):
if (isinstance(num, types.Number) and isinstance(denom, types.Number) and
isinstance(size, types.NoneType)):
return lambda num, denom, _size: np.random.f(num, denom)

@sklam sklam Jul 19, 2022

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Suggested change
return lambda num, denom, _size: np.random.f(num, denom)
return lambda num, denom, size: np.random.f(num, denom)

if (isinstance(num, types.Number) and isinstance(denom, types.Number) and
(isinstance(size, types.Integer) or (isinstance(size, types.UniTuple)
and isinstance(size.dtype,
types.Integer)))):
def _impl(num, denom, size):
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out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.f(num, denom)
return out
return _impl


@overload(np.random.geometric)
def geometric_impl(p):
if isinstance(p, types.Number):
Expand All @@ -965,6 +1106,23 @@ def _impl(p):
return _impl


@overload(np.random.geometric)
def geometric_impl(p, size):
if isinstance(p, types.Float) and isinstance(size, types.NoneType):
return lambda p, _size: np.random.geometric(p)

@sklam sklam Jul 19, 2022

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Suggested change
return lambda p, _size: np.random.geometric(p)
return lambda p, size: np.random.geometric(p)

if isinstance(p, types.Float) and (isinstance(size, types.Integer) or
(isinstance(size, types.UniTuple) and isinstance(size.dtype,
types.Integer))
):
def _impl(p, size):
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out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.geometric(p)
return out
return _impl


@overload(np.random.gumbel)
def gumbel_impl(loc, scale):
if isinstance(loc, types.Number) and isinstance(scale, types.Number):
Expand Down Expand Up @@ -1016,6 +1174,25 @@ def _impl(ngood, nbad, nsamples):
return _impl


@overload(np.random.hypergeometric)
def hypergeometric_impl(ngood, nbad, nsamples, size):
if (isinstance(ngood, types.Integer) and isinstance(nbad, types.Integer) and
isinstance(nsamples, types.Integer) and isinstance(size, types.NoneType)):
return lambda ngood, nbad, nsamples, _size: np.random.hypergeometric(ngood, nbad, nsamples)
if (isinstance(ngood, types.Integer) and isinstance(nbad, types.Integer) and
isinstance(nsamples, types.Integer) and
(isinstance(size, types.Integer) or (isinstance(size, types.UniTuple)
and isinstance(size.dtype,
types.Integer)))):
def _impl(ngood, nbad, nsamples, size):
out = np.empty(size)
out_flat = out.flat
for idx in range(out.size):
out_flat[idx] = np.random.hypergeometric(ngood, nbad, nsamples)
return out
return _impl
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@overload(np.random.laplace)
def laplace_impl0():
return lambda: np.random.laplace(0.0, 1.0)
Expand Down Expand Up @@ -1467,30 +1644,15 @@ def permutation_impl(x):
# Array-producing variants of scalar random functions

for typing_key, arity in [
("np.random.beta", 3),
("np.random.binomial", 3),
("np.random.f", 3),
("np.random.gamma", 3),
("np.random.geometric", 2),
("np.random.hypergeometric", 4),
("np.random.laplace", 3),
("np.random.logistic", 3),
("np.random.lognormal", 3),
("np.random.negative_binomial", 3),
("np.random.normal", 3),
("np.random.pareto", 2),
("np.random.poisson", 2),
("np.random.random", 1),
("np.random.random_sample", 1),
("np.random.ranf", 1),
("np.random.sample", 1),
("np.random.randint", 3),
("np.random.standard_gamma", 2),
("np.random.standard_normal", 1),
("np.random.uniform", 3),
("np.random.vonmises", 3),
("np.random.weibull", 2),
("np.random.zipf", 2),
]:

@glue_lowering(typing_key, *(types.Any,) * arity)
Expand Down