Merge pull request #1279 from jsalvatier/advinc_rebase3

theano/gof/cutils.py

-import os, sys
+import os
+import sys
 
 from theano.compat import PY3
 from theano.gof.compilelock import get_lock, release_lock
 from theano import config
+import cmodule 
 
 # TODO These two lines may be removed in the future, when we are 100% sure
 # noone has an old cutils_ext.so lying around anymore.
@@ -12,8 +14,66 @@ if os.path.exists(os.path.join(config.compiledir, 'cutils_ext.so')):
 
 def compile_cutils():
     """Do just the compilation of cutils_ext"""
-    code = """
+
+    types = ['npy_' + t for t in ['int8', 'int16', 'int32', 'int64', 'int128',
+        'int256', 'uint8', 'uint16', 'uint32', 'uint64', 'uint128', 'uint256',
+        'float16', 'float32', 'float64', 'float80', 'float96', 'float128',
+        'float256']]
+
+    complex_types = ['npy_' + t for t in ['complex32', 'complex64',
+        'complex128', 'complex160', 'complex192', 'complex512']]
+
+    inplace_map_template = """
+    #if defined(%(typen)s)
+    static void %(type)s_inplace_add(PyArrayMapIterObject *mit, PyArrayIterObject *it)
+    {
+        int index = mit->size;
+        while (index--) {
+            %(op)s
+
+            PyArray_MapIterNext(mit);
+            PyArray_ITER_NEXT(it);
+        }
+    }
+    #endif
+    """
+
+    floatadd = "((%(type)s*)mit->dataptr)[0] = ((%(type)s*)mit->dataptr)[0] + ((%(type)s*)it->dataptr)[0];"
+    complexadd = """
+    ((%(type)s*)mit->dataptr)[0].real = ((%(type)s*)mit->dataptr)[0].real + ((%(type)s*)it->dataptr)[0].real;
+    ((%(type)s*)mit->dataptr)[0].imag = ((%(type)s*)mit->dataptr)[0].imag + ((%(type)s*)it->dataptr)[0].imag;
+    """
+
+    fns = ''.join([inplace_map_template % {'type': t, 'typen': t.upper(),
+                                           'op': floatadd % {'type': t}}
+                        for t in types] +
+                  [inplace_map_template % {'type': t, 'typen': t.upper(),
+                                           'op': complexadd % {'type': t}}
+                        for t in complex_types])
+
+    fn_array = ("inplace_map_binop addition_funcs[] = {" +
+            ''.join(["""
+            #if defined(%(typen)s)
+            %(type)s_inplace_add,
+            #endif
+            """ % {'type': t, 'typen': t.upper()}
+                for t in types + complex_types]) +
+            """NULL};
+            """)
+
+    type_number_array = ("int type_numbers[] = {" +
+            ''.join(["""
+            #if defined(%(typen)s)
+            %(typen)s,
+            #endif
+            """ % {'type': t, 'typen': t.upper()}
+                for t in types + complex_types]) +
+            "-1000};")
+
+    code = ("""
         #include <Python.h>
+        #include "numpy/arrayobject.h"
+
         extern "C"{
         static PyObject *
         run_cthunk(PyObject *self, PyObject *args)
@@ -35,14 +95,130 @@ def compile_cutils():
           return Py_BuildValue("i", failure);
         }
 
+        #if NPY_API_VERSION >= 0x00000008
+        typedef void (*inplace_map_binop)(PyArrayMapIterObject *, PyArrayIterObject *);
+        """ + fns + fn_array + type_number_array +
+
+"""
+static int
+map_increment(PyArrayMapIterObject *mit, PyObject *op, inplace_map_binop add_inplace)
+{
+    PyArrayObject *arr = NULL;
+    PyArrayIterObject *it;
+    PyArray_Descr *descr;
+    if (mit->ait == NULL) {
+        return -1;
+    }
+    descr = PyArray_DESCR(mit->ait->ao);
+    Py_INCREF(descr);
+    arr = (PyArrayObject *)PyArray_FromAny(op, descr,
+                                0, 0, NPY_ARRAY_FORCECAST, NULL);
+    if (arr == NULL) {
+        return -1;
+    }
+    if ((mit->subspace != NULL) && (mit->consec)) {
+        if (mit->iteraxes[0] > 0) {
+            PyArray_MapIterSwapAxes(mit, (PyArrayObject **)&arr, 0);
+            if (arr == NULL) {
+                return -1;
+            }
+        }
+    }
+    it = (PyArrayIterObject*)
+            PyArray_BroadcastToShape((PyObject*)arr, mit->dimensions, mit->nd);
+    if (it  == NULL) {
+        Py_DECREF(arr);
+        return -1;
+    }
+
+    (*add_inplace)(mit, it);
+
+    Py_DECREF(arr);
+    Py_DECREF(it);
+    return 0;
+}
+
+
+static PyObject *
+inplace_increment(PyObject *dummy, PyObject *args)
+{
+    PyObject *arg_a = NULL, *index=NULL, *inc=NULL;
+    PyArrayObject *a;
+    inplace_map_binop add_inplace = NULL;
+    int type_number = -1;
+    int i =0;
+    PyArrayMapIterObject * mit;
+
+    if (!PyArg_ParseTuple(args, "OOO", &arg_a, &index,
+            &inc)) {
+        return NULL;
+    }
+    if (!PyArray_Check(arg_a)) {
+         PyErr_SetString(PyExc_ValueError, "needs an ndarray as first argument");
+         return NULL;
+    }
+
+    a = (PyArrayObject *) arg_a;
+
+    if (PyArray_FailUnlessWriteable(a, "input/output array") < 0) {
+        return NULL;
+    }
+
+    if (PyArray_NDIM(a) == 0) {
+        PyErr_SetString(PyExc_IndexError, "0-d arrays can't be indexed.");
+        return NULL;
+    }
+    type_number = PyArray_TYPE(a);
+
+
+
+    while (type_numbers[i] >= 0 && addition_funcs[i] != NULL){
+        if (type_number == type_numbers[i]) {
+            add_inplace = addition_funcs[i];
+            break;
+        }
+        i++ ;
+    }
+
+    if (add_inplace == NULL) {
+        PyErr_SetString(PyExc_TypeError, "unsupported type for a");
+        return NULL;
+    }
+    mit = (PyArrayMapIterObject *) PyArray_MapIterArray(a, index);
+    if (mit == NULL) {
+        goto fail;
+    }
+    if (map_increment(mit, inc, add_inplace) != 0) {
+        goto fail;
+    }
+
+    Py_DECREF(mit);
+
+    Py_INCREF(Py_None);
+    return Py_None;
+
+fail:
+    Py_XDECREF(mit);
+
+    return NULL;
+}
+        #endif
+
+
         static PyMethodDef CutilsExtMethods[] = {
             {"run_cthunk",  run_cthunk, METH_VARARGS|METH_KEYWORDS,
              "Run a theano cthunk."},
+            #if NPY_API_VERSION >= 0x00000008
+            {"inplace_increment",  inplace_increment,
+              METH_VARARGS,
+             "increments a numpy array inplace at the passed indexes."},
+            #endif
             {NULL, NULL, 0, NULL}        /* Sentinel */
-        };"""
+        };""")
+
     if PY3:
-        # This is not the most efficient code, but it is written this way to highlight
-        # the changes needed to make 2.x code compile under python 3.
+        # This is not the most efficient code, but it is written this way to
+        # highlight the changes needed to make 2.x code compile under python 3.
         code = code.replace("<Python.h>", '"numpy/npy_3kcompat.h"', 1)
         code = code.replace("PyCObject", "NpyCapsule")
         code += """
@@ -65,9 +241,10 @@ def compile_cutils():
         PyMODINIT_FUNC
         initcutils_ext(void)
         {
+          import_array();
           (void) Py_InitModule("cutils_ext", CutilsExtMethods);
         }
-        }
+    } //extern C
         """
 
     loc = os.path.join(config.compiledir, 'cutils_ext')
@@ -95,8 +272,6 @@ try:
     try:
         from cutils_ext.cutils_ext import *
     except ImportError:
-        import cmodule
-
         get_lock()
     # Ensure no-one else is currently modifying the content of the compilation
     # directory. This is important to prevent multiple processes from trying to
@@ -108,7 +283,6 @@ try:
                 # and when we receive the lock
                 from cutils_ext.cutils_ext import *
             except ImportError:
-                import cmodule
 
                 compile_cutils()
                 from cutils_ext.cutils_ext import *

theano/scalar/basic.py

@@ -23,7 +23,8 @@ import numpy
 import theano
 from theano.compat import PY3
 from theano import gof
-from theano.gof import Op, utils, Variable, Constant, Type, Apply, FunctionGraph
+from theano.gof import (Op, utils, Variable, Constant, Type, Apply,
+        FunctionGraph)
 from theano.gof.python25 import partial, all, any
 from theano.configparser import config
 
@@ -1090,7 +1091,7 @@ class UnaryBitOp(UnaryScalarOp):
         return upcast_out(*input_types[0])
 
     def grad(self, inputs, output_gradients):
-        return [None]
+        return [inputs[0].zeros_like().astype(theano.config.floatX)]
 
 
 class BinaryBitOp(BinaryScalarOp):
@@ -1103,7 +1104,8 @@ class BinaryBitOp(BinaryScalarOp):
         return upcast_out(*input_types[0])
 
     def grad(self, inputs, output_gradients):
-        return [None, None]
+        a,b = inputs
+        return [a.zeros_like().astype(theano.config.floatX), b.zeros_like().astype(theano.config.floatX)]
 
 
 class OR(BinaryBitOp):
@@ -2679,7 +2681,7 @@ class Composite(ScalarOp):
         except AttributeError:
             if 0:
                 l = []
-                for n in fgraph.toposort():
+                for n in self.fgraph.toposort():
                     if hasattr(n.op, "name") and n.op.name is not None:
                         v = n.op.name
                         if v.startswith("Composite"):

theano/tensor/basic.py

@@ -8,7 +8,7 @@ from itertools import izip
 from textwrap import dedent
 
 import numpy
-#from copy import copy as python_copy
+from copy import copy as python_copy
 
 import theano
 from theano.compat import PY3
@@ -24,6 +24,12 @@ from theano import compile, printing
 from theano.printing import pprint, min_informative_str
 from theano.tensor.utils import hash_from_ndarray
 
+import theano.gof.cutils  # needed to import cutils_ext
+try:
+    from cutils_ext.cutils_ext import inplace_increment
+except ImportError:
+    inplace_increment = None
+
 # We use these exceptions as well.
 from theano.scalar import ComplexError, IntegerDivisionError
 import theano.scalar.sharedvar
@@ -43,14 +49,14 @@ python_any = any
 python_all = all
 
 # Define common subsets of dtypes (as strings).
-int_dtypes = map(str, scal.int_types)
-uint_dtypes = map(str, scal.uint_types)
-float_dtypes = map(str, scal.float_types)
 complex_dtypes = map(str, scal.complex_types)
-
 continuous_dtypes = map(str, scal.continuous_types)
+float_dtypes = map(str, scal.float_types)
 discrete_dtypes = map(str, scal.discrete_types)
 all_dtypes = map(str, scal.all_types)
+int_dtypes = map(str, scal.int_types)
+uint_dtypes = map(str, scal.uint_types)
+
 
 # Do a lazy import of the sparse module
 sparse_module_ref = None
@@ -4446,7 +4452,8 @@ class Subtensor(Op):
                 slice_c = None
 
             return slice(slice_a, slice_b, slice_c)
-        # There is a bug in numpy that results in isinstance(x, int) returning False for numpy integers.
+        # There is a bug in numpy that results in isinstance(x, int) returning
+        # False for numpy integers.
         # See <http://projects.scipy.org/numpy/ticket/2235>.
         elif isinstance(entry, (numpy.integer, int)):
             return entry
@@ -5077,6 +5084,7 @@ def inc_subtensor(x, y, inplace=False, set_instead_of_inc=False,
     """
     # First of all, y cannot have a higher dimension than x,
     # nor have non-broadcastable dimensions where x is broadcastable.
+
     x = as_tensor_variable(x)
     y = as_tensor_variable(y)
 
@@ -5117,11 +5125,11 @@ def inc_subtensor(x, y, inplace=False, set_instead_of_inc=False,
         return the_op(real_x, y, ilist)
     elif isinstance(x.owner.op, AdvancedSubtensor):
         real_x = x.owner.inputs[0]
-        coordvec_0 = x.owner.inputs[1]
-        coordvec_1 = x.owner.inputs[2]
+        ilist = x.owner.inputs[1:]
+
         the_op = AdvancedIncSubtensor(inplace,
                                       set_instead_of_inc=set_instead_of_inc)
-        return the_op(real_x, y, coordvec_0, coordvec_1)
+        return the_op(real_x, y, *ilist)
     elif isinstance(x.owner.op, DimShuffle):
         inner_x = x.owner.inputs[0]
         # In the dimshuffle case, there are in fact two dimshuffles:
@@ -5147,7 +5155,7 @@ def inc_subtensor(x, y, inplace=False, set_instead_of_inc=False,
         # Try to apply inc_subtensor on inner_x.
         # If it works, there is no need to reshape, as the inc_subtensor
         # will have the same shape as inner_x, which is what we want.
-        inner_incsubtensor = inc_subtensor(inner_x, y,
+        inner_incsubtensor = inc_subtensor(inner_x, y.flatten(),
                 inplace=inplace,
                 set_instead_of_inc=set_instead_of_inc,
                 tolerate_inplace_aliasing=tolerate_inplace_aliasing)
@@ -7147,6 +7155,15 @@ class AdvancedIncSubtensor1(Op):
         if self.set_instead_of_inc:
             x[idx] = y
         else:
+            increment = inplace_increment
+            if increment is None: 
+                increment = self.inplace_increment1d_slow
+
+            increment(x,idx, y) 
+
+        out[0] = x
+
+    def inplace_increment1d_slow(self, x, idx, y):
         # If `y` has as many dimensions as `x`, then we want to iterate
         # jointly on `x` and `y`. Otherwise, it means `y` should be
         # broadcasted to fill all relevant rows of `x`.
@@ -7158,7 +7175,6 @@ class AdvancedIncSubtensor1(Op):
         else:
             for i in idx:
                 x[i] += y
-        out[0] = x
 
     def infer_shape(self, node, ishapes):
         x, y, ilist = ishapes
@@ -7187,6 +7203,111 @@ class AdvancedIncSubtensor1(Op):
 
 advanced_inc_subtensor1 = AdvancedIncSubtensor1()
     
+def as_index_variable(idx):
+    if idx is None:
+        return NoneConst
+    if isinstance(idx, slice):
+        return make_slice(idx)
+    idx = as_tensor_variable(idx)
+    if idx.type.dtype[:3] not in ('int', 'uin'):
+        raise TypeError('index must be integers')
+    return idx
+
+
+def as_int_none_variable(x):
+    if x is None:
+        return NoneConst
+    x = as_tensor_variable(x, ndim=0)
+    if x.type.dtype[:3] not in ('int', 'uin'):
+        raise TypeError('index must be integers')
+    return x
+
+
+class MakeSlice(Op):
+    def make_node(self, slc):
+        return Apply(self,
+                     map(as_int_none_variable,
+                         [slc.start, slc.stop, slc.step]),
+                     [slicetype()])
+
+    def perform(self, node, inp, out_):
+        out, = out_
+        out[0] = slice(*inp)
+
+    def __str__(self):
+        return self.__class__.__name__
+
+    def __eq__(self, other):
+        return type(self) == type(other)
+
+    def __hash__(self):
+        return hash(type(self))
+
+    def grad(self, inputs, grads):
+        return [DisconnectedType()() for i in inputs]
+
+make_slice = MakeSlice()
+
+
+class SliceType(gof.Type):
+
+    def filter(self, x, strict=False, allow_downcast=None):
+        if isinstance(x, slice):
+            return x
+        else:
+            raise TypeError('Expected a slice!')
+
+    def __str__(self):
+        return "slice"
+
+slicetype = SliceType()
+
+class NoneTypeT(gof.Type):
+
+    def filter(self, x, strict=False, allow_downcast=None):
+        if x is None:
+            return x
+        else:
+            raise TypeError('Expected None!')
+
+    def __str__(self):
+        return "None"
+
+NoneConst = Constant(NoneTypeT(), None, name='None')
+
+
+def adv_index_broadcastable_pattern(a, idx):
+    """
+    This function is only used to determine the broadcast pattern for
+    AdvancedSubtensor output variable.
+
+    For this, we make a fake ndarray and a fake idx and call use ask numpy
+    the output. From this, we find the output broadcast pattern.
+    """
+
+    def replace_slice(v):
+        if isinstance(v, gof.Apply):
+            if len(v.outputs) != 1:
+                raise ValueError(
+                    "It is ambiguous which output of a multi-output Op has"
+                    " to be fetched.", v)
+            else:
+                v = v.outputs[0]
+
+        if NoneConst.equals(v):
+            return None
+        if isinstance(v.type, SliceType):
+            return slice(None, None)
+
+        return numpy.zeros((2,) * v.ndim, int)
+
+    newidx = tuple(map(replace_slice, idx))
+
+    #2 - True = 1; 2 - False = 2
+    fakeshape = [2 - bc for bc in a.broadcastable]
+    retshape = numpy.empty(fakeshape)[newidx].shape
+    return tuple([dim == 1 for dim in retshape])
+
 
 class AdvancedSubtensor(Op):
     """Return a subtensor copy, using advanced indexing.
@@ -7204,37 +7325,15 @@ class AdvancedSubtensor(Op):
     def __str__(self):
         return self.__class__.__name__
 
-    def make_node(self, x, *inputs):
+    def make_node(self, x, *index):
         x = as_tensor_variable(x)
-        # FIXME
-        # Note (9 Jul 2012): what does this 'FIXME' mean? Possibly that the
-        # current implementation must be generalized? Please specify.
-        if x.ndim == 2 and len(inputs) == 2:
-            ind1 = as_tensor_variable(inputs[0])
-            ind2 = as_tensor_variable(inputs[1])
-            if (not (ind1.type.dtype.startswith('int') or
-                     ind1.type.dtype.startswith('uint'))):
-                raise TypeError(
-                    'the indices into a matrix must be int or uint. It is ',
-                    ind1.type.dtype)
-            if (not (ind2.type.dtype.startswith('int') or
-                     ind2.type.dtype.startswith('uint'))):
-                raise TypeError(
-                    'the indices into a matrix must be int or uint. It is ',
-                    ind2.type.dtype)
 
-            if ind1.ndim == 1 and ind2.ndim == 1:
+        index = tuple(map(as_index_variable, index))
+        bcast = adv_index_broadcastable_pattern(x, index)
         return gof.Apply(self,
-                        (x, ind1, ind2),
+                         (x,) + index,
                          [tensor(dtype=x.type.dtype,
-                            broadcastable=[False])])
-            raise NotImplementedError(
-                'Advanced indexing of x (of dimension %i) with these argument'
-                ' dimensions (%s) not supported yet'
-                    % (x.ndim, ','.join(str(input.ndim) for input in inputs)))
-        raise NotImplementedError(
-            'Advanced indexing of x with arguments (%s) not supported yet'
-                % ','.join(str(input) for input in inputs))
+                                 broadcastable=bcast)])
 
     def R_op(self, inputs, eval_points):
         if eval_points[0] is None:
@@ -7309,6 +7408,8 @@ class AdvancedIncSubtensor(Op):
             raise NotImplementedError('In place computation is not'
                                       ' implemented')
 
+        self.allow_legacy_perform = False
+
     def __hash__(self):
         return hash((type(self), self.inplace, self.set_instead_of_inc))
 
@@ -7326,24 +7427,43 @@ class AdvancedIncSubtensor(Op):
         x = as_tensor_variable(x)
         y = as_tensor_variable(y)
 
+        op = self
+        # If we are incrementing, but the increment compiled function is not
+        # available, we need to support legacy cases.
+        if not self.set_instead_of_inc and inplace_increment is None:
+            legacy_conditions = False
             if x.ndim == 2 and y.ndim == 1 and len(inputs) == 2:
                 ind1 = as_tensor_variable(inputs[0])
                 ind2 = as_tensor_variable(inputs[1])
                 if ind1.ndim == 1 and ind2.ndim == 1:
-                return gof.Apply(self,
+                    if ind1.owner and isinstance(ind1.owner.op, ARange):
+                        legacy_conditions = True
+                    elif isinstance(ind1, Constant):
+                        # Make sure no index is duplicated
+                        val = ind1.value
+                        if numpy.unique(val).size == val.size:
+                            legacy_conditions = True
+                    elif ind2.owner and isinstance(ind2.owner.op, ARange):
+                        legacy_conditions = True
+                    elif isinstance(ind2, Constant):
+                        # Make sure no index is duplicated
+                        val = ind2.value
+                        if numpy.unique(val).size == val.size:
+                            legacy_conditions = True
+            if legacy_conditions:
+                op = python_copy(self)
+                op.allow_legacy_perform = True
+            else:
+                raise NotImplementedError(
+                        'Could not import inplace_increment, so some advanced '
+                        'indexing features are disabled. They will be '
+                        'available if you update NumPy to version 1.8 or '
+                        'later, or to the latest development version.')
+
+        return gof.Apply(op,
                         (x, y) + inputs,
                         [tensor(dtype=x.type.dtype,
                             broadcastable=x.type.broadcastable)])
-            raise NotImplementedError(
-                'Advanced indexing increment/set of x (of dimension %i) by y'
-                ' (of dimension %i) with these argument dimensions (%s) not'
-                ' supported yet'
-                % (x.ndim, y.ndim,
-                   ','.join(str(input.ndim) for input in inputs)))
-        raise NotImplementedError(
-            'Advanced indexing increment/set of x (of dim %i) by y (of dim %i)'
-            ' with arguments (%s) not supported yet'
-            % (x.ndim, y.ndim, ','.join(str(input) for input in inputs)))
 
     def perform(self, node, inputs, out_):
         # TODO: 1. opt to make this in place 2. generalize as described in
@@ -7353,12 +7473,20 @@ class AdvancedIncSubtensor(Op):
         if not self.inplace:
             out[0] = inputs[0].copy()
         else:
-            raise NotImplementedError('In place computation is not'
-                                      ' implemented')
+            out[0] = inputs[0]
+
         if self.set_instead_of_inc:
             out[0][inputs[2:]] = inputs[1]
-        else:
+        elif inplace_increment is not None:
+            inplace_increment(out[0], tuple(inputs[2:]), inputs[1])
+        elif self.allow_legacy_perform:
             out[0][inputs[2:]] += inputs[1]
+        else:
+            raise NotImplementedError(
+                    'Could not import inplace_increment, so some advanced '
+                    'indexing features are disabled. They will be '
+                    'available if you update NumPy to version 1.8 or '
+                    'later, or to the latest development version.')
 
         if (numpy.__version__ <= '1.6.1' and
                 out[0].size != numpy.uint32(out[0].size)):

theano/tensor/tests/test_basic.py

@@ -43,7 +43,7 @@ from theano.tensor import (_shared, wvector, bvector, autocast_float_as,
         ScalarFromTensor, TensorFromScalar, dtensor4, Rebroadcast, Alloc,
         dtensor3, SpecifyShape, Mean, IncSubtensor, AdvancedIncSubtensor1,
         itensor3, Tile, AdvancedIncSubtensor, switch, Diagonal, Diag,
-        nonzero, flatnonzero, nonzero_values)
+        nonzero, flatnonzero, nonzero_values, inplace_increment)
 from theano.tests import unittest_tools as utt
 
 
@@ -3131,10 +3131,6 @@ class T_subtensor(unittest.TestCase, utt.TestOptimizationMixin):
         n = self.shared(numpy.asarray(5, dtype=self.dtype))
         self.assertRaises(TypeError, n.__getitem__, [0, 0])
 
-    def test_err_invalid_not_2d(self):
-        n = self.shared(numpy.ones((3, 3, 3), dtype=self.dtype) * 5)
-        self.assertRaises(NotImplementedError, n.__getitem__,
-                          ([0, 0, 0], [1, 1, 1], [2, 2, 2]))
 
     def test_err_invalid_2list_dtype(self):
         n = self.shared(numpy.ones((3, 3), dtype=self.dtype) * 5)
@@ -3725,6 +3721,109 @@ class TestIncSubtensor1(unittest.TestCase):
         self.assertRaises(TypeError,
                 lambda: inc_subtensor(self.v[self.adv1q], fmatrix()))
 
+inplace_increment_missing = SkipTest("inc_subtensor with advanced indexing not enabled. "
+                       "Installing NumPy 1.8 or the latest development version "
+                       "should make that feature available.")
+
+class TestAdvancedSubtensor(unittest.TestCase):
+    # test inc_subtensor
+    # also tests set_subtensor
+
+    def setUp(self):
+        self.s = iscalar()
+        self.v = fvector()
+        self.m = dmatrix()
+        self.t = ctensor3()
+
+        self.ix1 = lvector()  # advanced 1d query
+        self.ix12 = lvector()
+        self.ix2 = lmatrix()
+
+    def test_cant_adv_idx_into_scalar(self):
+        self.assertRaises(TypeError, lambda: self.s[self.ix1])
+
+    def test_index_into_vec_w_vec(self):
+        a = self.v[self.ix1]
+        assert a.type == self.v.type, (a.type, self.v.type)
+
+    def test_index_into_vec_w_matrix(self):
+        a = self.v[self.ix2]
+        assert a.dtype == self.v.dtype, (a.dtype, self.v.dtype)
+        assert a.broadcastable == self.ix2.broadcastable, (
+                a.broadcastable, self.ix2.broadcastable)
+
+    def test_inc_adv_subtensor_w_matrix(self):
+        if inplace_increment is None: 
+            raise inplace_increment_missing
+        
+        subt = self.v[self.ix2]
+        a = inc_subtensor(subt,subt)
+
+        assert a.type == self.v.type, (a.type, self.v.type)
+        f = theano.function([self.v, self.ix2], a, allow_input_downcast=True)
+        aval = f([.4, .9, .1], [[1, 2],
+                                [1, 2]])
+        assert numpy.allclose(aval, [.4, .9 * 3, .1 * 3])
+
+    def test_inc_adv_subtensor_w_2vec(self):
+        if inplace_increment is None: 
+            raise inplace_increment_missing
+
+        subt = self.m[self.ix1, self.ix12]
+        a = inc_subtensor(subt, subt)
+
+        typ = TensorType(self.m.type.dtype, self.ix2.type.broadcastable)
+        assert a.type == typ, (a.type, typ)
+        f = theano.function([self.m, self.ix1, self.ix12], a,
+                            allow_input_downcast=True)
+        aval = f([[.4, .9, .1],
+                  [5,   6,  7],
+                  [.5, .3, .15]],
+                 [1, 2, 1],
+                 [0, 1, 0])
+        assert numpy.allclose(aval,
+                [[.4, .9, .1],
+                  [5 * 3,   6,  7],
+                  [.5, .3 * 2, .15]]), aval
+
+    def test_inc_adv_subtensor_with_broadcasting(self):
+        if inplace_increment is None: 
+            raise inplace_increment_missing
+
+        a = inc_subtensor(self.m[self.ix1, self.ix12], 2.1)
+
+        assert a.type == self.m.type, (a.type, self.m.type)
+        f = theano.function([self.m, self.ix1, self.ix12], a,
+                            allow_input_downcast=True)
+        aval = f([[.4, .9, .1],
+                  [5,   6,  7],
+                  [.5, .3, .15]],
+                 [1, 2, 1],
+                 [0, 1, 0])
+        assert numpy.allclose(aval,
+                [[.4, .9, .1],
+                  [5 + 2.1 * 2,   6,  7],
+                  [.5, .3 + 2.1, .15]]), aval
+
+    def test_inc_adv_subtensor_with_index_broadcasting(self):
+        if inplace_increment is None: 
+            raise inplace_increment_missing
+
+        a = inc_subtensor(self.m[self.ix1, self.ix2], 2.1)
+
+        assert a.type == self.m.type, (a.type, self.m.type)
+        f = theano.function([self.m, self.ix1, self.ix2], a,
+                            allow_input_downcast=True)
+        aval = f([[.4, .9, .1],
+                  [5,   6,  7],
+                  [.5, .3, .15]],
+                 [0, 2, 0],
+                 [[0, 1, 0],
+                  [2, 2, 2]])
+        assert numpy.allclose(aval,
+                [[.4 + 2*2.1, .9, .1 + 2*2.1],
+                  [5 ,   6,  7 ],
+                  [.5, .3 + 2.1, .15 + 2.1]]), aval
 
 class T_Join_and_Split(unittest.TestCase):
     """