Merge pull request #6396 from nouiz/scipy_nfunc

theano/scalar/basic_scipy.py

@@ -26,6 +26,8 @@ except (ImportError, ValueError):
 
 
 class Erf(UnaryScalarOp):
+    nfunc_spec = ('scipy.special.erf', 1, 1)
+
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erf(x)
@@ -58,6 +60,8 @@ erf = Erf(upgrade_to_float, name='erf')
 
 
 class Erfc(UnaryScalarOp):
+    nfunc_spec = ('scipy.special.erfc', 1, 1)
+
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erfc(x)
@@ -105,6 +109,8 @@ class Erfcx(UnaryScalarOp):
     running on GPU an optimization will replace it with a gpu version.
 
     """
+    nfunc_spec = ('scipy.special.erfcx', 1, 1)
+
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erfcx(x)
@@ -140,6 +146,8 @@ class Erfinv(UnaryScalarOp):
 
     (TODO) Find a C implementation of erfinv for CPU.
     """
+    nfunc_spec = ('scipy.special.erfinv', 1, 1)
+
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erfinv(x)
@@ -173,6 +181,8 @@ erfinv = Erfinv(upgrade_to_float_no_complex, name='erfinv')
 
 
 class Erfcinv(UnaryScalarOp):
+    nfunc_spec = ('scipy.special.erfcinv', 1, 1)
+
     def impl(self, x):
         if imported_scipy_special:
             return scipy.special.erfcinv(x)
@@ -206,6 +216,8 @@ erfcinv = Erfcinv(upgrade_to_float_no_complex, name='erfcinv')
 
 
 class Gamma(UnaryScalarOp):
+    nfunc_spec = ('scipy.special.gamma', 1, 1)
+
     @staticmethod
     def st_impl(x):
         return scipy.special.gamma(x)
@@ -243,6 +255,8 @@ class GammaLn(UnaryScalarOp):
     Log gamma function.
 
     """
+    nfunc_spec = ('scipy.special.gammaln', 1, 1)
+
     @staticmethod
     def st_impl(x):
         return scipy.special.gammaln(x)
@@ -287,6 +301,8 @@ class Psi(UnaryScalarOp):
     Derivative of log gamma function.
 
     """
+    nfunc_spec = ('scipy.special.psi', 1, 1)
+
     @staticmethod
     def st_impl(x):
         return scipy.special.psi(x)
@@ -472,6 +488,7 @@ class Chi2SF(BinaryScalarOp):
     https://github.com/Theano/Theano_lgpl.git
 
     """
+    nfunc_spec = ('scipy.stats.chi2.sf', 2, 1)
 
     @staticmethod
     def st_impl(x, k):
@@ -489,6 +506,7 @@ class Jv(BinaryScalarOp):
     """
     Bessel function of the first kind of order v (real).
     """
+    nfunc_spec = ('scipy.special.jv', 2, 1)
 
     @staticmethod
     def st_impl(v, x):
@@ -513,6 +531,7 @@ class J1(UnaryScalarOp):
     """
     Bessel function of the first kind of order 1.
     """
+    nfunc_spec = ('scipy.special.j1', 1, 1)
 
     @staticmethod
     def st_impl(x):
@@ -544,6 +563,7 @@ class J0(UnaryScalarOp):
     """
     Bessel function of the first kind of order 0.
     """
+    nfunc_spec = ('scipy.special.j0', 1, 1)
 
     @staticmethod
     def st_impl(x):
@@ -575,6 +595,7 @@ class Iv(BinaryScalarOp):
     """
     Modified Bessel function of the first kind of order v (real).
     """
+    nfunc_spec = ('scipy.special.iv', 2, 1)
 
     @staticmethod
     def st_impl(v, x):
@@ -599,6 +620,7 @@ class I1(UnaryScalarOp):
     """
     Modified Bessel function of the first kind of order 1.
     """
+    nfunc_spec = ('scipy.special.i1', 1, 1)
 
     @staticmethod
     def st_impl(x):
@@ -622,6 +644,7 @@ class I0(UnaryScalarOp):
     """
     Modified Bessel function of the first kind of order 0.
     """
+    nfunc_spec = ('scipy.special.i0', 1, 1)
 
     @staticmethod
     def st_impl(x):

theano/tensor/elemwise.py

@@ -392,17 +392,13 @@ second dimension
             inplace_pattern = frozendict({})
         self.name = name
         self.scalar_op = scalar_op
-        self.inplace_pattern = frozendict(inplace_pattern)
+        self.inplace_pattern = inplace_pattern
         self.destroy_map = dict((o, [i]) for o, i in self.inplace_pattern.items())
 
-        self.ufunc = None
-        self.nfunc = None
         if nfunc_spec is None:
             nfunc_spec = getattr(scalar_op, 'nfunc_spec', None)
         self.nfunc_spec = nfunc_spec
-        if nfunc_spec:
-            self.nfunc = getattr(np, nfunc_spec[0])
-
+        self.__setstate__(self.__dict__)
         super(Elemwise, self).__init__(openmp=openmp)
 
     def __getstate__(self):
@@ -417,12 +413,6 @@ second dimension
         self.ufunc = None
         self.nfunc = None
         self.inplace_pattern = frozendict(self.inplace_pattern)
-        if getattr(self, 'nfunc_spec', None):
-            self.nfunc = getattr(np, self.nfunc_spec[0])
-        elif 0 < self.scalar_op.nin < 32:
-            self.ufunc = np.frompyfunc(self.scalar_op.impl,
-                                       self.scalar_op.nin,
-                                       self.scalar_op.nout)
 
     def get_output_info(self, dim_shuffle, *inputs):
         """Return the outputs dtype and broadcastable pattern and the
@@ -655,9 +645,28 @@ second dimension
         return ret
 
     def prepare_node(self, node, storage_map, compute_map, impl):
-        # Postpone the ufunc building to the last minutes
-        # NumPy ufunc support only up to 31 inputs.
-        # But our c code support more.
+        # Postpone the ufunc building to the last minutes due to:
+        # - NumPy ufunc support only up to 31 inputs.
+        #   But our c code support more.
+        # - nfunc is reused for scipy and scipy is optional
+        if getattr(self, 'nfunc_spec', None):
+            self.nfunc = getattr(np, self.nfunc_spec[0], None)
+            if self.nfunc is None:
+                # Not inside NumPy. So probably another package like scipy.
+                symb = self.nfunc_spec[0].split(".")
+                for idx in range(1, len(self.nfunc_spec[0])):
+                    try:
+                        module = __import__('.'.join(symb[:idx]))
+                    except ImportError:
+                        break
+                for sub in symb[1:]:
+                    try:
+                        module = getattr(module, sub)
+                    except AttributeError:
+                        module = None
+                        break
+                self.nfunc = module
+
         if (len(node.inputs) < 32 and
                 (self.nfunc is None or
                  self.scalar_op.nin != len(node.inputs)) and
@@ -743,6 +752,10 @@ second dimension
 
         ufunc_args = inputs
         ufunc_kwargs = {}
+        # We supported in the past calling manually op.perform.
+        # To keep that support we need to sometimes call self.prepare_node
+        if self.nfunc is None and self.ufunc is None:
+            self.prepare_node(node, None, None, 'py')
         if self.nfunc and len(inputs) == self.nfunc_spec[1]:
             ufunc = self.nfunc
             nout = self.nfunc_spec[2]

theano/tensor/tests/test_basic.py

@@ -1782,14 +1782,16 @@ ErfcxTester = makeBroadcastTester(
     good=_good_broadcast_unary_normal_float_no_complex_small_neg_range,
     grad=_grad_broadcast_unary_normal_small_neg_range,
     eps=2e-10,
-    mode=mode_no_scipy)
+    mode=mode_no_scipy,
+    skip=skip_scipy)
 ErfcxInplaceTester = makeBroadcastTester(
     op=inplace.erfcx_inplace,
     expected=expected_erfcx,
     good=_good_broadcast_unary_normal_float_no_complex_small_neg_range,
     eps=2e-10,
     mode=mode_no_scipy,
-    inplace=True)
+    inplace=True,
+    skip=skip_scipy)
 
 ErfinvTester = makeBroadcastTester(
     op=tensor.erfinv,
@@ -2015,7 +2017,8 @@ def test_verify_jv_grad():
     # Verify Jv gradient.
     # Implemented separately due to need to fix first input for which grad is
     # not defined.
-
+    if skip_scipy:
+        raise SkipTest("SciPy needed")
     v_val, x_val = _grad_broadcast_binary_bessel['normal']
 
     def fixed_first_input_jv(x):
@@ -2082,6 +2085,8 @@ def test_verify_iv_grad():
     # Verify Iv gradient.
     # Implemented separately due to need to fix first input for which grad is
     # not defined.
+    if skip_scipy:
+        raise SkipTest("SciPy needed")
 
     v_val, x_val = _grad_broadcast_binary_bessel['normal']