Fixed bugs with float 32 and Fast_compile

theano/typed_list/basic.py

@@ -63,11 +63,12 @@ class GetItem(Op):
                 index = Constant(SliceType(), index)
                 return Apply(self, [x, index], [x.type()])
             else:
-                index = T.constant(index, ndim=0)
+                index = T.constant(index, ndim=0, dtype='int32')
                 return Apply(self, [x, index], [x.ttype()])
         if isinstance(index.type, SliceType):
             return Apply(self, [x, index], [x.type()])
         elif isinstance(index, T.TensorVariable) and index.ndim == 0:
+            assert index.dtype == 'int32'
             return Apply(self, [x, index], [x.ttype()])
         else:
             raise TypeError('Expected scalar or slice as index.')
@@ -85,7 +86,7 @@ class GetItem(Op):
         output_name = out[0]
         fail = sub['fail']
         return """
-        %(output_name)s = (typeof %(output_name)s) PyList_GetItem( (PyObject*) %(x_name)s, *((double *) PyArray_DATA(%(index)s)));
+        %(output_name)s = (typeof %(output_name)s) PyList_GetItem( (PyObject*) %(x_name)s, *((int *) PyArray_DATA(%(index)s)));
         if(%(output_name)s == NULL){
             %(fail)s
         }
@@ -228,8 +229,9 @@ class Insert(Op):
         assert isinstance(x.type, TypedListType)
         assert x.ttype == toInsert.type
         if not isinstance(index, Variable):
-            index = T.constant(index, ndim=0)
+            index = T.constant(index, ndim=0, dtype='int32')
         else:
+            assert index.dtype == 'int32'
             assert isinstance(index, T.TensorVariable) and index.ndim == 0
         return Apply(self, [x, index, toInsert], [x.type()])
 
@@ -259,7 +261,7 @@ class Insert(Op):
         if(%(output_name)s==NULL){
                 %(fail)s
         };
-        if(PyList_Insert((PyObject*) %(output_name)s, *((double *) PyArray_DATA(%(index)s)), (PyObject*) %(toInsert)s)==-1){
+        if(PyList_Insert((PyObject*) %(output_name)s, *((int *) PyArray_DATA(%(index)s)), (PyObject*) %(toInsert)s)==-1){
             %(fail)s
         };
         Py_INCREF(%(output_name)s);

theano/typed_list/tests/test_basic.py

@@ -68,7 +68,7 @@ class test_get_item(unittest.TestCase):
         mySymbolicMatricesList = TypedListType(T.TensorType(
                             theano.config.floatX, (False, False)))()
 
-        mySymbolicScalar = T.scalar()
+        mySymbolicScalar = T.scalar(dtype='int32')
 
         z = GetItem()(mySymbolicMatricesList, mySymbolicScalar)
 
@@ -79,12 +79,12 @@ class test_get_item(unittest.TestCase):
         y = rand_ranged_matrix(-1000, 1000, [100, 101])
 
         self.assertTrue(numpy.array_equal(f([x], numpy.asarray(0,
-                                    dtype=theano.config.floatX)), x))
+                                    dtype='int32')), x))
 
     def test_interface(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
                             theano.config.floatX, (False, False)))()
-        mySymbolicScalar = T.scalar()
+        mySymbolicScalar = T.scalar(dtype='int32')
 
         z = mySymbolicMatricesList[mySymbolicScalar]
 
@@ -94,14 +94,14 @@ class test_get_item(unittest.TestCase):
         x = rand_ranged_matrix(-1000, 1000, [100, 101])
 
         self.assertTrue(numpy.array_equal(f([x], numpy.asarray(0,
-                                dtype=theano.config.floatX)), x))
+                                dtype='int32')), x))
 
-        z = mySymbolicMatricesList[0: 1: 1]
+        z = mySymbolicMatricesList[0]
 
         f = theano.function([mySymbolicMatricesList],
                             z)
 
-        self.assertTrue(numpy.array_equal(f([x]), [x]))
+        self.assertTrue(numpy.array_equal(f([x]), x))
 
     def test_wrong_input(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
@@ -241,7 +241,7 @@ class test_insert(unittest.TestCase):
         mySymbolicMatricesList = TypedListType(T.TensorType(
                             theano.config.floatX, (False, False)))()
         myMatrix = T.matrix()
-        myScalar = T.scalar()
+        myScalar = T.scalar(dtype='int32')
 
         z = Insert(True)(mySymbolicMatricesList, myScalar, myMatrix)
 
@@ -253,13 +253,13 @@ class test_insert(unittest.TestCase):
         y = rand_ranged_matrix(-1000, 1000, [100, 101])
 
         self.assertTrue(numpy.array_equal(f([x], numpy.asarray(1,
-                                dtype=theano.config.floatX), y), [x, y]))
+                                dtype='int32'), y), [x, y]))
 
     def test_sanity_check(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
                                 theano.config.floatX, (False, False)))()
         myMatrix = T.matrix()
-        myScalar = T.scalar()
+        myScalar = T.scalar(dtype='int32')
 
         z = Insert()(mySymbolicMatricesList, myScalar, myMatrix)
 
@@ -270,13 +270,13 @@ class test_insert(unittest.TestCase):
         y = rand_ranged_matrix(-1000, 1000, [100, 101])
 
         self.assertTrue(numpy.array_equal(f([x], numpy.asarray(1,
-                        dtype=theano.config.floatX), y), [x, y]))
+                        dtype='int32'), y), [x, y]))
 
     def test_interface(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
                                 theano.config.floatX, (False, False)))()
         myMatrix = T.matrix()
-        myScalar = T.scalar()
+        myScalar = T.scalar(dtype='int32')
 
         z = mySymbolicMatricesList.insert(myScalar, myMatrix)
 
@@ -287,7 +287,7 @@ class test_insert(unittest.TestCase):
         y = rand_ranged_matrix(-1000, 1000, [100, 101])
 
         self.assertTrue(numpy.array_equal(f([x], numpy.asarray(1,
-                                    dtype=theano.config.floatX), y), [x, y]))
+                                    dtype='int32'), y), [x, y]))
 
 
 class test_remove(unittest.TestCase):

theano/typed_list/tests/test_opt.py

@@ -26,9 +26,9 @@ class test_inplace(unittest.TestCase):
                                 theano.config.floatX, (False, False)))()
 
         z = Reverse()(mySymbolicMatricesList)
-
+        m = theano.compile.mode.get_default_mode().including("typed_list_inplace_opt")
         f = theano.function([In(mySymbolicMatricesList, borrow=True,
-                        mutable=True)], z, accept_inplace=True)
+                        mutable=True)], z, accept_inplace=True, mode=m)
         self.assertTrue(f.maker.fgraph.toposort()[0].op.inplace)
 
         x = rand_ranged_matrix(-1000, 1000, [100, 101])
@@ -42,10 +42,10 @@ class test_inplace(unittest.TestCase):
                                 theano.config.floatX, (False, False)))()
         mySymbolicMatrix = T.matrix()
         z = Append()(mySymbolicMatricesList, mySymbolicMatrix)
-
+        m = theano.compile.mode.get_default_mode().including("typed_list_inplace_opt")
         f = theano.function([In(mySymbolicMatricesList, borrow=True,
                         mutable=True), In(mySymbolicMatrix, borrow=True,
-                        mutable=True)], z, accept_inplace=True)
+                        mutable=True)], z, accept_inplace=True, mode=m)
         self.assertTrue(f.maker.fgraph.toposort()[0].op.inplace)
 
         x = rand_ranged_matrix(-1000, 1000, [100, 101])
@@ -62,10 +62,10 @@ class test_inplace(unittest.TestCase):
                                 theano.config.floatX, (False, False)))()
 
         z = Extend()(mySymbolicMatricesList1, mySymbolicMatricesList2)
-
+        m = theano.compile.mode.get_default_mode().including("typed_list_inplace_opt")
         f = theano.function([In(mySymbolicMatricesList1, borrow=True,
                     mutable=True), mySymbolicMatricesList2],
-                            z)
+                            z, mode=m)
         self.assertTrue(f.maker.fgraph.toposort()[0].op.inplace)
 
         x = rand_ranged_matrix(-1000, 1000, [100, 101])
@@ -77,14 +77,15 @@ class test_inplace(unittest.TestCase):
     def test_insert_inplace(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
                                 theano.config.floatX, (False, False)))()
-        mySymbolicIndex = T.scalar()
+        mySymbolicIndex = T.scalar(dtype='int32')
         mySymbolicMatrix = T.matrix()
 
         z = Insert()(mySymbolicMatricesList, mySymbolicIndex, mySymbolicMatrix)
+        m = theano.compile.mode.get_default_mode().including("typed_list_inplace_opt")
 
         f = theano.function([In(mySymbolicMatricesList, borrow=True,
                         mutable=True), mySymbolicIndex, mySymbolicMatrix],
-                        z, accept_inplace=True)
+                        z, accept_inplace=True, mode=m)
         self.assertTrue(f.maker.fgraph.toposort()[0].op.inplace)
 
         x = rand_ranged_matrix(-1000, 1000, [100, 101])
@@ -92,21 +93,21 @@ class test_inplace(unittest.TestCase):
         y = rand_ranged_matrix(-1000, 1000, [100, 101])
 
         self.assertTrue(numpy.array_equal(f([x], numpy.asarray(1,
-                                dtype=theano.config.floatX), y), [x, y]))
+                                dtype='int32'), y), [x, y]))
 
     def test_remove_inplace(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
                                     theano.config.floatX, (False, False)))()
         mySymbolicMatrix = T.matrix()
         z = Remove()(mySymbolicMatricesList, mySymbolicMatrix)
-
+        m = theano.compile.mode.get_default_mode().including("typed_list_inplace_opt")
         f = theano.function([In(mySymbolicMatricesList, borrow=True,
                             mutable=True), In(mySymbolicMatrix, borrow=True,
-                            mutable=True)], z, accept_inplace=True)
+                            mutable=True)], z, accept_inplace=True, mode=m)
         self.assertTrue(f.maker.fgraph.toposort()[0].op.inplace)
 
         x = rand_ranged_matrix(-1000, 1000, [100, 101])
 
         y = rand_ranged_matrix(-1000, 1000, [100, 101])
 
-        self.assertTrue(numpy.array_equal(f([x, y], y), [x,]))
+        self.assertTrue(numpy.array_equal(f([x, y], y), [x]))