Remove None from return values of grad.

Also change checks to verify the output dtype of the Op itself, not of the inputs or gradient, because it can depend on different things. The idea is that if the Op's output is continuous, then the gradient should be propagated to the inputs, regardless of whether they are continuous or discrete. However, if the output is discrete, then the gradient wrt the inputs will be a continuous zero.

Remove None from return values of grad.
002872ad · Pascal Lamblin · 37ce26a3 · 002872ad · 002872ad
--- a/theano/scalar/basic.py
+++ b/theano/scalar/basic.py
--- a/theano/scalar/basic_scipy.py
+++ b/theano/scalar/basic_scipy.py
@@ -2,11 +2,12 @@
 #as scipy is not always available, we treat them separatly
 import numpy
+import theano
 from theano.scalar.basic import (UnaryScalarOp, BinaryScalarOp,
                                 exp, upgrade_to_float,
                                 float_types)
 from theano.scalar.basic import (upgrade_to_float_no_complex,
-                                 complex_types,
+                                 complex_types, discrete_types,
                                 upcast)
 imported_scipy_special = False
@@ -32,12 +33,15 @@ class Erf(UnaryScalarOp):
        gz, = grads
        if x.type in complex_types:
            raise NotImplementedError()
-        elif x.type in float_types:
+        if self(x).type in discrete_types:
+            if x.type in discrete_types:
+                return [x.zeros_like(dtype=theano.config.floatX)]
+            else:
+                return [x.zeros_like()]
        cst = numpy.asarray(2. / numpy.sqrt(numpy.pi),
                            dtype=upcast(x.type.dtype, gz.type.dtype))
        return gz * cst * exp(-x * x),
-        else:
-            return None,
    def c_code(self, node, name, inp, out, sub):
        x, = inp
@@ -60,12 +64,15 @@ class Erfc(UnaryScalarOp):
        gz, = grads
        if x.type in complex_types:
            raise NotImplementedError()
-        elif x.type in float_types:
+        if self(x).type in discrete_types:
+            if x.type in discrete_types:
+                return [x.zeros_like(dtype=theano.config.floatX)]
+            else:
+                return [x.zeros_like()]
        cst = numpy.asarray(2. / numpy.sqrt(numpy.pi),
                            dtype=upcast(x.type.dtype, gz.type.dtype))
        return - gz * cst * exp(-x * x),
-        else:
-            return None,
    def c_code(self, node, name, inp, out, sub):
        x, = inp
@@ -99,12 +106,15 @@ class Erfinv(UnaryScalarOp):
        gz, = grads
        if x.type in complex_types:
            raise NotImplementedError()
-        elif x.type in float_types:
+        if self(x).type in discrete_types:
+            if x.type in discrete_types:
+                return [x.zeros_like(dtype=theano.config.floatX)]
+            else:
+                return [x.zeros_like()]
        cst = numpy.asarray(numpy.sqrt(numpy.pi) / 2.,
                            dtype=upcast(x.type.dtype, gz.type.dtype))
        return gz * cst * exp(erfinv(x) ** 2),
-        else:
-            return None,
    # TODO: erfinv() is not provided by the C standard library
    #def c_code(self, node, name, inp, out, sub):
@@ -129,12 +139,15 @@ class Erfcinv(UnaryScalarOp):
        gz, = grads
        if x.type in complex_types:
            raise NotImplementedError()
-        elif x.type in float_types:
+        if self(x).type in discrete_types:
+            if x.type in discrete_types:
+                return [x.zeros_like(dtype=theano.config.floatX)]
+            else:
+                return [x.zeros_like()]
        cst = numpy.asarray(numpy.sqrt(numpy.pi) / 2.,
                            dtype=upcast(x.type.dtype, gz.type.dtype))
        return - gz * cst * exp(erfcinv(x) ** 2),
-        else:
-            return None,
    # TODO: erfcinv() is not provided by the C standard library
    #def c_code(self, node, name, inp, out, sub):
@@ -159,6 +172,14 @@ class Gamma(UnaryScalarOp):
            super(Gamma, self).impl(x)
    def grad(self, (x, ), (gz, )):
+        if x.type in complex_types:
+            raise NotImplementedError()
+        if self(x).type in discrete_types:
+            if x.type in discrete_types:
+                return [x.zeros_like(dtype=theano.config.floatX)]
+            else:
+                return [x.zeros_like()]
        return gz * gamma(x) * psi(x),
    def c_code(self, node, name, (x, ), (z, ), sub):
@@ -190,6 +211,14 @@ class GammaLn(UnaryScalarOp):
    def grad(self, inp, grads):
        x, = inp
        gz, = grads
+        if x.type in complex_types:
+            raise NotImplementedError()
+        if self(x).type in discrete_types:
+            if x.type in discrete_types:
+                return [x.zeros_like(dtype=theano.config.floatX)]
+            else:
+                return [x.zeros_like()]
        return [gz * psi(x)]
    def c_code(self, node, name, inp, out, sub):
@@ -224,7 +253,6 @@ class Psi(UnaryScalarOp):
    def grad(self, inputs, outputs_gradients):
        raise NotImplementedError()
-        return [None]
    def c_support_code(self):
        return (