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!6382 Use op level erf op in distributions

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Merge pull request !6382 from peixu_ren/custom_bijector
pull/6382/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent b3c7515ba0 7fc9a1d192
commit cc795083de
3 changed files with 3 additions and 162 deletions
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158
mindspore/nn/probability/distribution/_utils/custom_ops.py
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@ -67,161 +67,3 @@ def log1p_generic(x):
Log1p ops on GPU device or when device_target == GPU.
"""
return log_generic(x + 1.0)
def _evaluate_polynomial(x, coefficients):
poly = 0
for co in coefficients:
poly = poly * x + co
return poly
def erf_f32_generic(x):
"""
Calculate erf for dtype of f32
"""
k_erf_tcoefficient = [+7.853861353153693e-5,
-8.010193625184903e-4,
+5.188327685732524e-3,
-2.685381193529856e-2,
+1.128358514861418e-1,
-3.761262582423300e-1,
+1.128379165726710e+0]
poly = _evaluate_polynomial(x * x, k_erf_tcoefficient)
return x * poly
def erf_f64_generic(x):
"""
Calculate erf for dtype of f64
"""
k_erf_tcoefficient = [9.60497373987051638749e0,
9.00260197203842689217e1,
2.23200534594684319226e3,
7.00332514112805075473e3,
5.55923013010394962768e4]
k_erf_ucoefficient = [1.00000000000000000000e0,
3.35617141647503099647e1,
5.21357949780152679795e2,
4.59432382970980127987e3,
2.26290000613890934246e4,
4.92673942608635921086e4]
z = x * x
poly1 = _evaluate_polynomial(z, k_erf_tcoefficient)
poly2 = _evaluate_polynomial(z, k_erf_ucoefficient)
return x * poly1 / poly2
def erfc_f32_generic(x):
"""
Calculate erfc for dtype of f32
"""
k_maxlog = 88.72283905206835
k_erfc_pcoefficient = [+2.326819970068386e-2,
-1.387039388740657e-1,
+3.687424674597105e-1,
-5.824733027278666e-1,
+6.210004621745983e-1,
-4.944515323274145e-1,
+3.404879937665872e-1,
-2.741127028184656e-1,
+5.638259427386472e-1]
k_erfc_rcoefficient = [-1.047766399936249e+1,
+1.297719955372516e+1,
-7.495518717768503e+0,
+2.921019019210786e+0,
-1.015265279202700e+0,
+4.218463358204948e-1,
-2.820767439740514e-1,
+5.641895067754075e-1]
abs_cal = P.Abs()
select = P.Select()
less = P.Less()
fill = P.Fill()
dtype = P.DType()
shape = P.Shape()
abs_x = abs_cal(x)
z = exp_generic(-x * x)
q = 1 / abs_x
y = q * q
poly1 = _evaluate_polynomial(y, k_erfc_pcoefficient)
poly2 = _evaluate_polynomial(y, k_erfc_rcoefficient)
p = select(less(abs_x, 2.0), poly1, poly2)
y = z * q * p
zeros = fill(dtype(x), shape(x), 0)
y_clamp = select(less(z, -k_maxlog), zeros, y)
return select(less(x, 0), 2.0 - y_clamp, y_clamp)
def erfc_f64_generic(x):
"""
Calculate erfc for dtype of f64
"""
k_maxlog = 7.09782712893383996843e2
k_erfc_pcoefficient = [2.46196981473530512524e-10,
5.64189564831068821977e-1,
7.46321056442269912687e0,
4.86371970985681366614e1,
1.96520832956077098242e2,
5.26445194995477358631e2,
9.34528527171957607540e2,
1.02755188689515710272e3,
5.57535335369399327526e2]
k_erfc_qcoefficient = [1.00000000000000000000e0,
1.32281951154744992508e1,
8.67072140885989742329e1,
3.54937778887819891062e2,
9.75708501743205489753e2,
1.82390916687909736289e3,
2.24633760818710981792e3,
1.65666309194161350182e3,
5.57535340817727675546e2]
k_erfc_rcoefficient = [5.64189583547755073984e-1,
1.27536670759978104416e0,
5.01905042251180477414e0,
6.16021097993053585195e0,
7.40974269950448939160e0,
2.97886665372100240670e0]
k_erfc_scoefficient = [1.00000000000000000000e0,
2.26052863220117276590e0,
9.39603524938001434673e0,
1.20489539808096656605e1,
1.70814450747565897222e1,
9.60896809063285878198e0,
3.36907645100081516050e02]
abs_cal = P.Abs()
select = P.Select()
less = P.Less()
fill = P.Fill()
dtype = P.DType()
shape = P.Shape()
abs_x = abs_cal(x)
z = -x * x
exp_z = exp_generic(z)
temp1 = exp_z * _evaluate_polynomial(abs_x, k_erfc_pcoefficient) / _evaluate_polynomial(abs_x, k_erfc_qcoefficient)
temp2 = exp_z * _evaluate_polynomial(abs_x, k_erfc_rcoefficient) / _evaluate_polynomial(abs_x, k_erfc_scoefficient)
y = select(less(abs_x, 8.0), temp1, temp2)
zeros = fill(dtype(x), shape(x), 0)
y_clamp = select(less(z, k_maxlog), zeros, y)
poly2 = _evaluate_polynomial(y, k_erfc_rcoefficient)
p = select(less(abs_x, 2.0), poly1, poly2)
y = z * q * p
zeros = fill(dtype(x), shape(x), 0)
y_clamp = select(less(z, -k_maxlog), zeros, y)
return select(less(x, 0), 2.0 - y_clamp, y_clamp)
def erfc_generic(x):
select = P.Select()
greater = P.Greater()
abs_cal = P.Abs()
return select(greater(abs_cal(x), 1), erfc_f32_generic(x), 1 - erf_f32_generic(x))
def erf_generic(x):
select = P.Select()
less = P.Less()
abs_cal = P.Abs()
return select(less(abs_cal(x), 1), erf_f32_generic(x), 1 - erfc_f32_generic(x))

3
mindspore/nn/probability/distribution/bernoulli.py
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@ -18,7 +18,7 @@ from mindspore.ops import operations as P
from mindspore.ops import composite as C
from .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_prob, check_type, check_distribution_name, set_param_type
from ._utils.custom_ops import exp_generic, log_generic, erf_generic
from ._utils.custom_ops import exp_generic, log_generic
class Bernoulli(Distribution):
@ -132,7 +132,6 @@ class Bernoulli(Distribution):
# ops needed for the class
self.exp = exp_generic
self.log = log_generic
self.erf = erf_generic
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.const = P.ScalarToArray()

4
mindspore/nn/probability/distribution/normal.py
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@ -20,7 +20,7 @@ from mindspore.common import dtype as mstype
from .distribution import Distribution
from ._utils.utils import cast_to_tensor, check_greater_zero, check_type, check_distribution_name,\
set_param_type
from ._utils.custom_ops import exp_generic, expm1_generic, log_generic, erf_generic
from ._utils.custom_ops import exp_generic, expm1_generic, log_generic
class Normal(Distribution):
@ -147,7 +147,7 @@ class Normal(Distribution):
self.exp = exp_generic
self.expm1 = expm1_generic
self.log = log_generic
self.erf = erf_generic
self.erf = P.Erf()
self.squeeze = P.Squeeze(0)
self.cast = P.Cast()
self.const = P.ScalarToArray()

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