moved elemwise2 to elemwise

_test_elemwise.py

 
+import time
 import unittest
-import numpy
 
-from tensor import astensor, Tensor
+from gof import ResultBase, Op, Env, modes
 import gof
-from gof import modes, Env
 
-from elemwise import *
-
-
-class ElemwiseAdd(Elemwise):
-
-    def __init__(self, x, y):
-        self.inputs = (x, y)
-        self.outputs = [Tensor(dtype = x.dtype, broadcastable = x.broadcastable)]
-    
-    def var_desc(self):
-        return [('x', 1), ('y', 1)], [('z', 1)]
-
-#     def destroy_map(self):
-#         return {self.out: [self.inputs[0]]}
+from scalar import *
 
-    def c_code_foreach(self):
-        return "%(z)s_i = %(x)s_i + %(y)s_i;"
+from elemwise import *
 
 
 def inputs():
-    l1 = [[1.0, 2.0], [3.0, 4.0]]
-    l2 = [[3.0, 4.0], [1.0, 2.0]]
-    l3 = numpy.ones((2, 3))
-    x = modes.build(astensor(l1, name = 'x'))
-    y = modes.build(astensor(l2, name = 'y'))
-    z = modes.build(astensor(l3, name = 'z'))
+    x = modes.build(Tensor('float64', (0, 0), name = 'x'))
+    y = modes.build(Tensor('float64', (1, 0), name = 'y'))
+    z = modes.build(Tensor('float64', (0, 0), name = 'z'))
     return x, y, z
 
 def env(inputs, outputs, validate = True, features = []):
     return Env(inputs, outputs, features = features, consistency_check = validate)
 
 
-class _test_Elemwise(unittest.TestCase):
+class _test_DimShuffle(unittest.TestCase):
 
-    def test_0(self):
-        x, y, z = inputs()
-        e = ElemwiseAdd(x, y).out
-        fn, i, o = gof.CLinker(env([x, y], [e])).make_thunk(True)
-        fn()
-        assert (e.data == numpy.array([[4, 6], [4, 6]])).all()
-        x.data.resize((1, 4))
-        y.data.resize((1, 4))
-        fn()
-        assert (e.data == numpy.array([[4, 6, 4, 6]])).all()
+    def with_linker(self, linker):
+        for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)),
+                                  ((1, 2, 3), (1, 2), (2, 3)),
+                                  ((1, 2, 1, 3), (1, 3), (2, 3)),
+                                  ((2, 3, 4), (2, 1, 0), (4, 3, 2)),
+                                  ((2, 3, 4), ('x', 2, 1, 0, 'x'), (1, 4, 3, 2, 1)),
+                                  ((1, 4, 3, 2, 1), (3, 2, 1), (2, 3, 4)),
+                                  ((1, 1, 4), (1, 2), (1, 4))]:
+            x = modes.build(Tensor('float64', [1 * (entry == 1) for entry in xsh], name = 'x'))
+            e = DimShuffle(x, shuffle).out
+#             print shuffle, e.owner.grad(e.owner.inputs, e.owner.outputs).owner.new_order
+            f = linker(env([x], [e])).make_function(inplace=False)
+            assert f(numpy.ones(xsh)).shape == zsh
 
-#     def test_1(self):
-#         x, y, z = inputs()
-#         e = ElemwiseAdd(x, y).out
-#         fn, i, o = gof.CLinker(env([x, y], [e])).make_thunk(True)
-#         fn()
-#         assert (e.data == numpy.array([[4, 6], [4, 6]])).all()
-#         x.data.resize((1, 4))
-#         y.data.resize((1, 4))
-#         fn()
-#         assert (e.data == numpy.array([[4, 6, 4, 6]])).all()
+    def test_perform(self):
+        self.with_linker(gof.PerformLinker)
 
 
+#     def test_straightforward(self):
+#         x, y, z = inputs()
+#         e0 = DimShuffle(x, [1, 'x', 0]).out
+#         f = gof.PerformLinker(env([x], [e0])).make_function(inplace=True)
+#         assert f(numpy.ones((2, 3))).shape == (3, 1, 2)
+
+
+class _test_Broadcast(unittest.TestCase):
+
+    def with_linker(self, linker):
+        for xsh, ysh in [((3, 5), (3, 5)),
+                         ((3, 5), (1, 5)),
+                         ((3, 5), (3, 1)),
+                         ((1, 5), (5, 1)),
+                         ((1, 1), (1, 1)),
+                         ((2, 3, 4, 5), (2, 3, 4, 5)),
+                         ((2, 3, 4, 5), (1, 3, 1, 5)),
+                         ((2, 3, 4, 5), (1, 1, 1, 1)),
+                         ((), ())]:
+            x = modes.build(Tensor('float64', [1 * (entry == 1) for entry in xsh], name = 'x'))
+            y = modes.build(Tensor('float64', [1 * (entry == 1) for entry in ysh], name = 'y'))
+            e = Broadcast(Add, (x, y)).out
+            f = linker(env([x, y], [e])).make_function(inplace = False)
+#             xv = numpy.array(range(numpy.product(xsh)))
+#             xv = xv.reshape(xsh)
+#             yv = numpy.array(range(numpy.product(ysh)))
+#             yv = yv.reshape(ysh)
+            xv = numpy.asarray(numpy.random.rand(*xsh))
+            yv = numpy.asarray(numpy.random.rand(*ysh))
+            zv = xv + yv
+
+#             print "AAAAAAAAAAAAAAAAAA"
+#             print f(xv, yv)
+#             print zv
+#             print "BBBBBBBBBBBBBBBBBB"
+            self.failUnless((f(xv, yv) == zv).all())
+
+    def with_linker_inplace(self, linker):
+        for xsh, ysh in [((5, 5), (5, 5)),
+                         ((5, 5), (1, 5)),
+                         ((5, 5), (5, 1)),
+                         ((1, 1), (1, 1)),
+                         ((2, 3, 4, 5), (2, 3, 4, 5)),
+                         ((2, 3, 4, 5), (1, 3, 1, 5)),
+                         ((2, 3, 4, 5), (1, 1, 1, 1)),
+                         ((), ())]:
+            x = modes.build(Tensor('float64', [1 * (entry == 1) for entry in xsh], name = 'x'))
+            y = modes.build(Tensor('float64', [1 * (entry == 1) for entry in ysh], name = 'y'))
+            e = Broadcast(Add, (x, y), {0:0}).out
+            f = linker(env([x, y], [e])).make_function(inplace = False)
+            xv = numpy.asarray(numpy.random.rand(*xsh))
+            yv = numpy.asarray(numpy.random.rand(*ysh))
+            zv = xv + yv
+
+            f(xv, yv)
+
+            self.failUnless((xv == zv).all())
+
+    def test_perform(self):
+        self.with_linker(gof.PerformLinker)
+
+    def test_c(self):
+        self.with_linker(gof.CLinker)
+
+    def test_perform_inplace(self):
+        self.with_linker_inplace(gof.PerformLinker)
+
+    def test_c_inplace(self):
+        self.with_linker_inplace(gof.CLinker)
+
+    def test_fill(self):
+        x = modes.build(Tensor('float64', [0, 0], name = 'x'))
+        y = modes.build(Tensor('float64', [1, 1], name = 'y'))
+        e = Broadcast(Second, (x, y), {0:0}).out
+        f = gof.CLinker(env([x, y], [e])).make_function(inplace = False)
+        xv = numpy.ones((5, 5))
+        yv = numpy.random.rand(1, 1)
+        f(xv, yv)
+        assert (xv == yv).all()
+
+
+class _test_CAReduce(unittest.TestCase):
+
+    def with_linker(self, linker):
+        for xsh, tosum in [((5, 6), (0, 1)),
+                           ((5, 6), (0, )),
+                           ((5, 6), (1, )),
+                           ((5, 6), ()),
+                           ((2, 3, 4, 5), (0, 1, 3)),
+                           ((), ())]:
+            x = modes.build(Tensor('float64', [1 * (entry == 1) for entry in xsh], name = 'x'))
+            e = CAReduce(Add, [x], dimensions_to_reduce = tosum).out
+            f = linker(env([x], [e])).make_function(inplace = False)
+            xv = numpy.asarray(numpy.random.rand(*xsh))
+            zv = xv
+            for axis in reversed(sorted(tosum)):
+                zv = numpy.add.reduce(zv, axis)
+#             print "AAAAAAAAAAAAAAAAAA"
+#             print xsh, tosum
+#             print f(xv)
+#             print zv
+#             print f(xv) - zv
+#             print "BBBBBBBBBBBBBBBBBB"
+            self.failUnless((numpy.abs(f(xv) - zv) < 1e-10).all())
+
+    def test_perform(self):
+        self.with_linker(gof.PerformLinker)
+
+    def test_c(self):
+        self.with_linker(gof.CLinker)
+        
 
 if __name__ == '__main__':
     unittest.main()
+#     x = modes.build(Tensor('float64', [0, 0], name = 'x'))
+#     y = modes.build(Tensor('float64', [0, 0], name = 'y'))
+#     e = Broadcast(SquareDiff, (x, y), {0:0}).out
+#     f = gof.CLinker(env([x, y], [e])).make_function(inplace = False)
+#     xv = numpy.random.rand(1000, 1000)
+#     yv = numpy.random.rand(1000, 1000)
+#     zv = numpy.random.rand(1000, 1000)
+#     add = numpy.frompyfunc(lambda x, y: x + y, 2, 1)
+
+#     t0 = time.time()
+#     for i in xrange(100):
+#         xv -= yv
+#         xv *= xv
+# #        xv += yv
+#     print time.time() - t0
+
+#     t0 = time.time()
+#     for i in xrange(100):
+#         f(xv, yv)
+#     print time.time() - t0
+    
+
+
+
+#     def test_straightforward(self):
+#         x, y, z = inputs()
+#         e0 = CAReduce(Add, [x]).out
+# #        print e0.owner
+#         f = gof.PerformLinker(env([x], [e0])).make_function(inplace=True)
+#         assert f(numpy.ones((2, 2))) == 4.0
+##########
+
+##########
+#     def test_straightforward(self):
+#         x, y, z = inputs()
+#         e0 = Broadcast(Add, (x, y)).out
+#         f = gof.PerformLinker(env([x, y], [e0])).make_function(inplace=True)
+#         assert (f(numpy.ones((2, 2)), numpy.ones((1, 2))) == numpy.ones((2, 2))*2).all()
+# #         for result in e0.owner.grad(e0.owner.inputs, (z, )):
+# #             print env([x, y, z], [result])
+
+#     def test_c(self):
+#         x = modes.build(Tensor('float64', (0, 0), name = 'x'))
+#         y = modes.build(Tensor('float64', (0, 1), name = 'y'))
+#         z = modes.build(Tensor('float64', (0, 0), name = 'z'))
+# #        x = modes.build(Tensor('float64', (), name = 'x'))
+# #        y = modes.build(Tensor('float64', (), name = 'y'))
+# #        x, y, z = inputs()
+#         e0 = Broadcast(Add, (x, y)).out
+#         f = gof.CLinker(env([x, y], [e0])).make_function(inplace=True)
+#         print f(numpy.ones((4, 4), order = 'f'), numpy.array([[1], [2], [3], [4]]))
+# #        print f(numpy.ones(()), numpy.ones(()))
+#         assert (f(numpy.ones((2, 2)), numpy.ones((2, 1))) == numpy.ones((2, 2))*2).all()
+

_test_elemwise2.py

-
-import time
-import unittest
-
-from gof import ResultBase, Op, Env, modes
-import gof
-
-from scalar import *
-
-from elemwise2 import *
-
-
-def inputs():
-    x = modes.build(Tensor('float64', (0, 0), name = 'x'))
-    y = modes.build(Tensor('float64', (1, 0), name = 'y'))
-    z = modes.build(Tensor('float64', (0, 0), name = 'z'))
-    return x, y, z
-
-def env(inputs, outputs, validate = True, features = []):
-    return Env(inputs, outputs, features = features, consistency_check = validate)
-
-
-class _test_DimShuffle(unittest.TestCase):
-
-    def with_linker(self, linker):
-        for xsh, shuffle, zsh in [((2, 3), (1, 'x', 0), (3, 1, 2)),
-                                  ((1, 2, 3), (1, 2), (2, 3)),
-                                  ((1, 2, 1, 3), (1, 3), (2, 3)),
-                                  ((2, 3, 4), (2, 1, 0), (4, 3, 2)),
-                                  ((2, 3, 4), ('x', 2, 1, 0, 'x'), (1, 4, 3, 2, 1)),
-                                  ((1, 4, 3, 2, 1), (3, 2, 1), (2, 3, 4)),
-                                  ((1, 1, 4), (1, 2), (1, 4))]:
-            x = modes.build(Tensor('float64', [1 * (entry == 1) for entry in xsh], name = 'x'))
-            e = DimShuffle(x, shuffle).out
-#             print shuffle, e.owner.grad(e.owner.inputs, e.owner.outputs).owner.new_order
-            f = linker(env([x], [e])).make_function(inplace=False)
-            assert f(numpy.ones(xsh)).shape == zsh
-
-    def test_perform(self):
-        self.with_linker(gof.PerformLinker)
-
-
-#     def test_straightforward(self):
-#         x, y, z = inputs()
-#         e0 = DimShuffle(x, [1, 'x', 0]).out
-#         f = gof.PerformLinker(env([x], [e0])).make_function(inplace=True)
-#         assert f(numpy.ones((2, 3))).shape == (3, 1, 2)
-
-
-class _test_Broadcast(unittest.TestCase):
-
-    def with_linker(self, linker):
-        for xsh, ysh in [((3, 5), (3, 5)),
-                         ((3, 5), (1, 5)),
-                         ((3, 5), (3, 1)),
-                         ((1, 5), (5, 1)),
-                         ((1, 1), (1, 1)),
-                         ((2, 3, 4, 5), (2, 3, 4, 5)),
-                         ((2, 3, 4, 5), (1, 3, 1, 5)),
-                         ((2, 3, 4, 5), (1, 1, 1, 1)),
-                         ((), ())]:
-            x = modes.build(Tensor('float64', [1 * (entry == 1) for entry in xsh], name = 'x'))
-            y = modes.build(Tensor('float64', [1 * (entry == 1) for entry in ysh], name = 'y'))
-            e = Broadcast(Add, (x, y)).out
-            f = linker(env([x, y], [e])).make_function(inplace = False)
-#             xv = numpy.array(range(numpy.product(xsh)))
-#             xv = xv.reshape(xsh)
-#             yv = numpy.array(range(numpy.product(ysh)))
-#             yv = yv.reshape(ysh)
-            xv = numpy.asarray(numpy.random.rand(*xsh))
-            yv = numpy.asarray(numpy.random.rand(*ysh))
-            zv = xv + yv
-
-#             print "AAAAAAAAAAAAAAAAAA"
-#             print f(xv, yv)
-#             print zv
-#             print "BBBBBBBBBBBBBBBBBB"
-            self.failUnless((f(xv, yv) == zv).all())
-
-    def with_linker_inplace(self, linker):
-        for xsh, ysh in [((5, 5), (5, 5)),
-                         ((5, 5), (1, 5)),
-                         ((5, 5), (5, 1)),
-                         ((1, 1), (1, 1)),
-                         ((2, 3, 4, 5), (2, 3, 4, 5)),
-                         ((2, 3, 4, 5), (1, 3, 1, 5)),
-                         ((2, 3, 4, 5), (1, 1, 1, 1)),
-                         ((), ())]:
-            x = modes.build(Tensor('float64', [1 * (entry == 1) for entry in xsh], name = 'x'))
-            y = modes.build(Tensor('float64', [1 * (entry == 1) for entry in ysh], name = 'y'))
-            e = Broadcast(Add, (x, y), {0:0}).out
-            f = linker(env([x, y], [e])).make_function(inplace = False)
-            xv = numpy.asarray(numpy.random.rand(*xsh))
-            yv = numpy.asarray(numpy.random.rand(*ysh))
-            zv = xv + yv
-
-            f(xv, yv)
-
-            self.failUnless((xv == zv).all())
-
-    def test_perform(self):
-        self.with_linker(gof.PerformLinker)
-
-    def test_c(self):
-        self.with_linker(gof.CLinker)
-
-    def test_perform_inplace(self):
-        self.with_linker_inplace(gof.PerformLinker)
-
-    def test_c_inplace(self):
-        self.with_linker_inplace(gof.CLinker)
-
-    def test_fill(self):
-        x = modes.build(Tensor('float64', [0, 0], name = 'x'))
-        y = modes.build(Tensor('float64', [1, 1], name = 'y'))
-        e = Broadcast(Second, (x, y), {0:0}).out
-        f = gof.CLinker(env([x, y], [e])).make_function(inplace = False)
-        xv = numpy.ones((5, 5))
-        yv = numpy.random.rand(1, 1)
-        f(xv, yv)
-        assert (xv == yv).all()
-
-
-class _test_CAReduce(unittest.TestCase):
-
-    def with_linker(self, linker):
-        for xsh, tosum in [((5, 6), (0, 1)),
-                           ((5, 6), (0, )),
-                           ((5, 6), (1, )),
-                           ((5, 6), ()),
-                           ((2, 3, 4, 5), (0, 1, 3)),
-                           ((), ())]:
-            x = modes.build(Tensor('float64', [1 * (entry == 1) for entry in xsh], name = 'x'))
-            e = CAReduce(Add, [x], dimensions_to_reduce = tosum).out
-            f = linker(env([x], [e])).make_function(inplace = False)
-            xv = numpy.asarray(numpy.random.rand(*xsh))
-            zv = xv
-            for axis in reversed(sorted(tosum)):
-                zv = numpy.add.reduce(zv, axis)
-#             print "AAAAAAAAAAAAAAAAAA"
-#             print xsh, tosum
-#             print f(xv)
-#             print zv
-#             print f(xv) - zv
-#             print "BBBBBBBBBBBBBBBBBB"
-            self.failUnless((numpy.abs(f(xv) - zv) < 1e-10).all())
-
-    def test_perform(self):
-        self.with_linker(gof.PerformLinker)
-
-    def test_c(self):
-        self.with_linker(gof.CLinker)
-        
-
-if __name__ == '__main__':
-    unittest.main()
-#     x = modes.build(Tensor('float64', [0, 0], name = 'x'))
-#     y = modes.build(Tensor('float64', [0, 0], name = 'y'))
-#     e = Broadcast(SquareDiff, (x, y), {0:0}).out
-#     f = gof.CLinker(env([x, y], [e])).make_function(inplace = False)
-#     xv = numpy.random.rand(1000, 1000)
-#     yv = numpy.random.rand(1000, 1000)
-#     zv = numpy.random.rand(1000, 1000)
-#     add = numpy.frompyfunc(lambda x, y: x + y, 2, 1)
-
-#     t0 = time.time()
-#     for i in xrange(100):
-#         xv -= yv
-#         xv *= xv
-# #        xv += yv
-#     print time.time() - t0
-
-#     t0 = time.time()
-#     for i in xrange(100):
-#         f(xv, yv)
-#     print time.time() - t0
-    
-
-
-
-#     def test_straightforward(self):
-#         x, y, z = inputs()
-#         e0 = CAReduce(Add, [x]).out
-# #        print e0.owner
-#         f = gof.PerformLinker(env([x], [e0])).make_function(inplace=True)
-#         assert f(numpy.ones((2, 2))) == 4.0
-##########
-
-##########
-#     def test_straightforward(self):
-#         x, y, z = inputs()
-#         e0 = Broadcast(Add, (x, y)).out
-#         f = gof.PerformLinker(env([x, y], [e0])).make_function(inplace=True)
-#         assert (f(numpy.ones((2, 2)), numpy.ones((1, 2))) == numpy.ones((2, 2))*2).all()
-# #         for result in e0.owner.grad(e0.owner.inputs, (z, )):
-# #             print env([x, y, z], [result])
-
-#     def test_c(self):
-#         x = modes.build(Tensor('float64', (0, 0), name = 'x'))
-#         y = modes.build(Tensor('float64', (0, 1), name = 'y'))
-#         z = modes.build(Tensor('float64', (0, 0), name = 'z'))
-# #        x = modes.build(Tensor('float64', (), name = 'x'))
-# #        y = modes.build(Tensor('float64', (), name = 'y'))
-# #        x, y, z = inputs()
-#         e0 = Broadcast(Add, (x, y)).out
-#         f = gof.CLinker(env([x, y], [e0])).make_function(inplace=True)
-#         print f(numpy.ones((4, 4), order = 'f'), numpy.array([[1], [2], [3], [4]]))
-# #        print f(numpy.ones(()), numpy.ones(()))
-#         assert (f(numpy.ones((2, 2)), numpy.ones((2, 1))) == numpy.ones((2, 2))*2).all()
-

_test_tensor.py

@@ -9,7 +9,7 @@ import gof, gof.graph
 from gof.python25 import any
 import gof
 
-from elemwise2 import DimShuffle
+from elemwise import DimShuffle
 
 def _numpy_checker(x, y):
     """

opt.py

 
 from gof import opt
-from elemwise2 import Broadcast
+from elemwise import Broadcast
 
 class InplaceOptimizer(opt.OpSpecificOptimizer):
 

tensor.py

@@ -11,7 +11,7 @@ import gof.op
 from base_tensor import BaseTensor, BaseTensorOp
 import blas # for gemm, dot
 
-import elemwise2 as s2t
+import elemwise as s2t
 import scalar as scal