Add and Mul now take an arbitrary number of inputs

_test_scalar.py

@@ -66,9 +66,9 @@ class _test_composite(unittest.TestCase):
         assert c.outputs[0].data == 6.0
         assert c.outputs[1].data == 7.0
         assert c.outputs[2].data == 0.5
-        g = env([x, y], c.outputs)
+        g = env([x, y, z], c.outputs)
         fn = gof.DualLinker(g).make_function()
-        assert fn(1.0, 2.0) == [6.0, 7.0, 0.5]
+        assert fn(1.0, 2.0, 3.0) == [6.0, 7.0, 0.5]
         
 
 if __name__ == '__main__':

elemwise.py

@@ -136,8 +136,11 @@ class Broadcast(Op, Destroyer):
             assert len(set([len(input.broadcastable) for input in inputs])) == 1
         except (AssertionError, AttributeError):
             raise TypeError("All inputs to a Broadcast subclass must be Tensor instances and their broadcastable fields must all have the same length.", self.__class__)
-        self.nin = scalar_opclass.nin
-        self.nout = scalar_opclass.nout
+        
+        self.shadow = scalar_opclass(*[Scalar(dtype = t.dtype) for t in inputs])
+        
+        self.nin = self.shadow.nin
+        self.nout = self.shadow.nout
         out_broadcastables = [[1*all(bcast) for bcast in zip(*[input.broadcastable for input in inputs])]] * self.nout
 
         if inplace_pattern:
@@ -158,8 +161,7 @@ class Broadcast(Op, Destroyer):
         self.outputs = [Tensor(dtype = dtype, broadcastable = broadcastable) for dtype, broadcastable in zip(out_dtypes, out_broadcastables)]
         self.inplace_pattern = inplace_pattern
         self.scalar_opclass = scalar_opclass
-        self.shadow = scalar_opclass(*[Scalar(dtype = t.dtype) for t in self.inputs])
-        self.ufunc = numpy.frompyfunc(self.shadow.impl, scalar_opclass.nin, scalar_opclass.nout)
+        self.ufunc = numpy.frompyfunc(self.shadow.impl, self.shadow.nin, self.shadow.nout)
 
     def clone_with_new_inputs(self, *new_inputs):
         return Broadcast(self.scalar_opclass, new_inputs, self.inplace_pattern)
@@ -389,8 +391,10 @@ class CAReduce(Op):
     
     def __init__(self, scalar_opclass, inputs, dimensions_to_reduce = None):
         inputs = map(astensor, inputs)
+
+        self.shadow = scalar_opclass(*[Scalar(dtype = inputs[0].dtype) for i in xrange(len(inputs) + 1)])
         
-        if scalar_opclass.nin != 2 or scalar_opclass.nout != 1:
+        if self.shadow.nin != 2 or self.shadow.nout != 1:
             raise NotImplementedError("CAReduce only supports binary functions with a single output.")
         if len(inputs) != 1:
             raise TypeError("Only one argument expected.")
@@ -403,8 +407,7 @@ class CAReduce(Op):
 
         self.dimensions_to_reduce = dimensions_to_reduce
         self.scalar_opclass = scalar_opclass
-        self.shadow = scalar_opclass(*[Scalar(dtype = inputs[0].dtype) for i in xrange(scalar_opclass.nin)])
-        self.ufunc = numpy.frompyfunc(self.shadow.impl, scalar_opclass.nin, scalar_opclass.nout)
+        self.ufunc = numpy.frompyfunc(self.shadow.impl, self.shadow.nin, self.shadow.nout)
 
     def desc(self):
         return (self.__class__, self.scalar_opclass, tuple(self.dimensions_to_reduce))

gof/utils.py

@@ -36,6 +36,16 @@ def difference(seq1, seq2):
         # -> use O(len(seq1) * len(seq2)) algo
         return [x for x in seq1 if x not in seq2]
 
+def partition(f, seq):
+    seqt = []
+    seqf = []
+    for elem in seq:
+        if f(elem):
+            seqt.append(elem)
+        else:
+            seqf.append(elem)
+    return seqt, seqf
+    
 def attr_checker(*attrs):
     def f(candidate):
         for attr in attrs:

scalar.py

@@ -186,28 +186,32 @@ class Scalar(Result):
 
 
 
-class ScalarMixedOp(GuardedOp):
-    """Olivier: document this stuff! -JB"""
+def upcast(dtype, *dtypes):
+    z = numpy.zeros((), dtype = dtype)
+    for dtype in dtypes:
+        z = z + numpy.zeros((), dtype = dtype)
+    return str(z.dtype)
+
+class ScalarOp(GuardedOp):
 
     nin = -1
     nout = 1
-    
+        
     def __init__(self, *inputs):
         if self.nin >= 0:
             if len(inputs) != self.nin:
                 raise TypeError("Wrong number of inputs for %s (got %i, expected %i)" \
                                     % (self.__class__.__name__, len(inputs), self.nin))
+        else:
+            self.nin = len(inputs)
         
         inputs = [as_scalar(input) for input in inputs]
         i_dtypes = [getattr(input, 'dtype', None) for input in inputs]
-        o_dtypes = self.propagate_dtypes(*i_dtypes)
+        o_dtypes = [upcast(*i_dtypes)] * self.nout
 
         self.inputs = inputs
         self.outputs = [Scalar(dtype) for dtype in o_dtypes]
 
-    def propagate_dtypes(self, *inputs):
-        raise AbstractFunctionError()
-    
     def impl(self, *inputs):
         raise AbstractFunctionError()
     
@@ -215,43 +219,45 @@ class ScalarMixedOp(GuardedOp):
         raise AbstractFunctionError()
     
     def perform(self):
-        self.outputs[0].data = self.impl(*[input.data for input in self.inputs])
-
-
-def upcast(dtype, *dtypes):
-    z = numpy.zeros((), dtype = dtype)
-    for dtype in dtypes:
-        z = z + numpy.zeros((), dtype = dtype)
-    return str(z.dtype)
-
-
-class PureScalarOp(ScalarMixedOp):
-
-    cast_method = lambda self, *args: upcast(*args)
-    
-    def propagate_dtypes(self, *i_dtypes):
-        for dtype in i_dtypes:
-            if dtype is None:
-                raise TypeError("Expected a Scalar.")
-        return [self.cast_method(*i_dtypes)] * self.nout
-
+        if self.nout == 1:
+            self.outputs[0].data = self.impl(*[input.data for input in self.inputs])
+        else:
+            results = utils.from_return_values(self.impl(*[input.data for input in self.inputs]))
+            for output, result in zip(self.outputs, results):
+                output.data = result
 
-class UnaryScalarOp(PureScalarOp):
+class UnaryScalarOp(ScalarOp):
     nin = 1
 
-class BinaryScalarOp(PureScalarOp):
+class BinaryScalarOp(ScalarOp):
     nin = 2
 
 
-
-class Add(BinaryScalarOp):
+class Add(ScalarOp):
     identity = 0
-    def impl(self, x, y):
-        return x + y
-    def c_code(self, (x, y), (z, ), sub):
-        return "%(z)s = %(x)s + %(y)s;" % locals()
-    def grad(self, (x, y), (gz, )):
-        return gz, gz
+    def impl(self, *inputs):
+        return sum(inputs)
+    def c_code(self, inputs, (z, ), sub):
+        if not inputs:
+            return z + " = 0;"
+        else:
+            return z + " = " + " + ".join(inputs) + ";"
+    def grad(self, inputs, (gz, )):
+        return (gz, ) * len(inputs)
+
+class Mul(ScalarOp):
+    identity = 1
+    def impl(self, *inputs):
+        return numpy.product(inputs)
+    def c_code(self, inputs, (z, ), sub):
+        if not inputs:
+            return z + " = 1;"
+        else:
+            return z + " = " + " * ".join(inputs) + ";"
+    def grad(self, inputs, (gz, )):
+        return [mul(*([gz] + utils.difference(inputs, [input])))
+                for input in inputs]
+
 
 class Sub(BinaryScalarOp):
     def impl(self, x, y):
@@ -261,14 +267,6 @@ class Sub(BinaryScalarOp):
     def grad(self, (x, y), (gz, )):
         return gz, -gz
 
-class Mul(BinaryScalarOp):
-    def impl(self, x, y):
-        return x * y
-    def c_code(self, (x, y), (z, ), sub):
-        return "%(z)s = %(x)s * %(y)s;" % locals()
-    def grad(self, (x, y), (gz, )):
-        return gz * y, gz * x
-
 class Div(BinaryScalarOp):
     def impl(self, x, y):
         return x / y
@@ -302,6 +300,7 @@ class Second(BinaryScalarOp):
         return None, gz
 
 
+
 class Identity(UnaryScalarOp):
     def impl(self, x):
         return x
@@ -333,7 +332,8 @@ class Sgn(UnaryScalarOp):
     def grad(self, (x, ), (gz, )):
         return None,
     def c_code(self, (x, ), (z, ), sub):
-        return "%(z)s = %(x)s/abs(%(x)s);" % locals() # TODO: C use copysign
+        return "%(z)s = %(x)s/%(prefix)sabs(%(x)s);" \
+            % dict(locals(), prefix = 'float' in self.inputs[0].dtype and 'f' or '') # TODO: C use copysign
 
 class Inv(UnaryScalarOp):
     def impl(self, x):
@@ -405,7 +405,7 @@ def composite(inputs, outputs):
     
     The operations between inputs and outputs (as given by
     Env(inputs, outputs).ops()) must all be instances of
-    PureScalarOp.
+    ScalarOp.
 
     Examples:
       x, y = Scalar(), Scalar()
@@ -420,8 +420,8 @@ def composite(inputs, outputs):
     inputs, outputs = env.inputs, env.outputs
 
     for op in env.ops():
-        if not isinstance(op, PureScalarOp):
-            raise ValueError("The input env to composite must be exclusively composed of PureScalarOp instances.")
+        if not isinstance(op, ScalarOp):
+            raise ValueError("The input env to composite must be exclusively composed of ScalarOp instances.")
 
     subd = dict(zip(inputs,
                     ["%%(i%i)s"%i for i in range(len(inputs))]) +
@@ -460,7 +460,7 @@ def composite(inputs, outputs):
         # this is not optimal at all eg in add(*1 -> mul(x, y), *1)
         # it will calculate *1 twice
         # it also doesn't follow env.toposort but that's (presumably)
-        # still correct since we only have pure scalar ops
+        # still correct since we only have scalar ops
         if r in env.inputs:
             idx = env.inputs.index(r)
             return lambda inputs: inputs[idx]
@@ -472,7 +472,7 @@ def composite(inputs, outputs):
 
     _impls = [compose_impl(r) for r in env.outputs]
     
-    class Composite(PureScalarOp):
+    class Composite(ScalarOp):
 
         nin = len(inputs)
         nout = len(outputs)