Extending Theano with a C Op

This tutorial covers how to extend Theano with an op that offers a C implementation. It does not cover ops that run on a GPU but it does introduce many elements and concepts which are relevant for GPU ops. This tutorial is aimed at individuals who already know how to extend Theano (see tutorial Creating a new Op: Python implementation) by adding a new op with a Python implementation and will only cover the additional knowledge required to also produce ops with C implementations.

Providing a Theano op with a C implementation requires to interact with Python’s C-API and Numpy’s C-API. Thus, the first step of this tutorial is to introduce both and highlight their features which are most relevant to the task of implementing a C op. This tutorial then introduces the most important methods that the op needs to implement in order to provide a usable C implementation. Finally, it shows how to combine these elements to write a simple C op for performing the simple task of multiplying every element in a vector by a scalar.

Python C-API

Python provides a C-API to allows the manipulation of python objects from C code. In this API, all variables that represent Python objects are of type PyObject *. All objects have a pointer to their type object and a reference count field (that is shared with the python side). Most python methods have an equivalent C function that can be called on the PyObject * pointer.

As such, manipulating a PyObject instance is often straight-forward but it is important to properly manage its reference count. Failing to do so can lead to undesired behavior in the C code.

Reference counting

Reference counting is a mechanism for keeping track, for an object, of the number of references to it held by other entities. This mechanism is often used for purposes of garbage collecting because it allows to easily see if an object is still being used by other entities. When the reference count for an object drops to 0, it means it is not used by anyone any longer and can be safely deleted.

PyObjects implement reference counting and the Python C-API defines a number of macros to help manage those reference counts. The definition of these macros can be found here : Python C-API Reference Counting. Listed below are the two macros most often used in Theano C ops.

void Py_XINCREF(PyObject *o)

Increments the reference count of object o. Without effect if the object is NULL.

void Py_XDECREF(PyObject *o)

Decrements the reference count of object o. If the reference count reaches 0, it will trigger a call of the object’s deallocation function. Without effect if the object is NULL.

The general principle, in the reference counting paradigm, is that the owner of a reference to an object is responsible for disposing properly of it. This can be done by decrementing the reference count once the reference is no longer used or by transfering ownership; passing on the reference to a new owner which becomes responsible for it.

Some functions return “borrowed references”; this means that they return a reference to an object without transfering ownership of the reference to the caller of the function. This means that if you call a function which returns a borrowed reference, you do not have the burden of properly disposing of that reference. You should not call Py_XDECREF() on a borrowed reference.

Correctly managing the reference counts is important as failing to do so can lead to issues ranging from memory leaks to segmentation faults.

NumPy C-API

The NumPy library provides a C-API to allow users to create, access and manipulate NumPy arrays from within their own C routines. NumPy’s ndarrays are used extensively inside Theano and so extending Theano with a C op will require interaction with the NumPy C-API.

This sections covers the API’s elements that are often required to write code for a Theano C op. The full documentation for the API can be found here : NumPy C-API.

NumPy data types

To allow portability between platforms, the NumPy C-API defines its own data types which should be used whenever you are manipulating a NumPy array’s internal data. The data types most commonly used to implement C ops are the following : npy_int{8,16,32,64}, npy_uint{8,16,32,64} and npy_float{32,64}.

You should use these data types when manipulating a NumPy array’s internal data instead of C primitives because the size of the memory representation for C primitives can vary between platforms. For instance, a C long can be represented in memory with 4 bytes but it can also be represented with 8. On the other hand, the in-memory size of NumPy data types remains constant across platforms. Using them will make your code simpler and more portable.

The full list of defined data types can be found here : NumPy C-API data types.

NumPy ndarrays

In the NumPy C-API, NumPy arrays are represented as instances of the PyArrayObject class which is a descendant of the PyObject class. This means that, as for any other Python object that you manipulate from C code, you need to appropriatedly manage the reference counts of PyArrayObject instances.

Unlike in a standard multidimensionnal C array, a NumPy array’s internal data representation does not have to occupy a continuous region in memory. In fact, it can be C-contiguous, F-contiguous or non-contiguous. C-contiguous means that the data is not only contiguous in memory but also that it is organized such that the index of the latest dimension changes the fastest. If the following array

x = [[1, 2, 3],
     [4, 5, 6]]

is C-contiguous, it means that, in memory, the six values contained in the array x are stored in the order [1, 2, 3, 4, 5, 6] (the first value is x[0,0], the second value is x[0,1], the third value is x[0,2], the, fourth value is x[1,0], etc). F-contiguous (or Fortran Contiguous) also means that the data is contiguous but that it is organized such that the index of the latest dimension changes the slowest. If the array x is F-contiguous, it means that, in memory, the values appear in the order [1, 4, 2, 5, 3, 6] (the first value is x[0,0], the second value is x[1,0], the third value is x[0,1], etc).

Finally, the internal data can be non-contiguous. In this case, it occupies a non-contiguous region in memory but it is still stored in an organized fashion : the distance between the element x[i,j] and the element x[i+1,j] of the array is constant over all valid values of i and j, just as the distance between the element x[i,j] and the element x[i,j+1] of the array is constant over all valid values of i and j. This distance between consecutive elements of an array over a given dimension, is called the stride of that dimension.

Accessing NumPy ndarrays’ data and properties

The following macros serve to access various attributes of NumPy ndarrays.

void* PyArray_DATA(PyArrayObject* arr)

Returns a pointer to the first element of the array’s data. The returned pointer must be cast to a pointer of the proper Numpy C-API data type before use.

int PyArray_NDIM(PyArrayObject* arr)

Returns the number of dimensions in the the array pointed by arr

npy_intp* PyArray_DIMS(PyArrayObject* arr)

Returns a pointer on the first element of arr‘s internal array describing its dimensions. This internal array contains as many elements as the array arr has dimensions.

The macro PyArray_SHAPE() is a synonym of PyArray_DIMS() : it has the same effect and is used in an identical way.

npy_intp* PyArray_STRIDES(PyArrayObject* arr)

Returns a pointer on the first element of arr‘s internal array describing the stride for each of its dimension. This array has as many elements as the number of dimensions in arr. In this array, the strides are expressed in number of bytes.

PyArray_Descr* PyArray_DESCR(PyArrayObject* arr)

Returns a reference to the object representing the dtype of the array.

The macro PyArray_DTYPE() is a synonym of the PyArray_DESCR() : it has the same effect and is used in an identical way.

Note:This is a borrowed reference so you do not need to decrement its reference count once you are done with it.
int PyArray_TYPE(PyArrayObject* arr)

Returns the typenumber for the elements of the array. Like the dtype, the typenumber is a descriptor for the type of the data in the array. However, the two are not synonyms and, as such, cannot be used in place of the other.

npy_intp PyArray_SIZE(PyArrayObject* arr)

Returns to total number of elements in the array

bool PyArray_CHKFLAGS(PyArrayObject* arr, flags)

Returns true if the array has the specified flags. The variable flag should either be a NumPy array flag or an integer obtained by applying bitwise or to an ensemble of flags.

The flags that can be used in with this macro are : NPY_ARRAY_C_CONTIGUOUS, NPY_ARRAY_F_CONTIGUOUS, NPY_ARRAY_OWNDATA, NPY_ARRAY_ALIGNED, NPY_ARRAY_WRITEABLE, NPY_ARRAY_UPDATEIFCOPY.

Creating NumPy ndarrays

The following functions allow the creation and copy of NumPy arrays :

PyObject* PyArray_EMPTY(int nd, npy_intp* dims, typenum dtype,
int fortran)

Constructs a new ndarray with the number of dimensions specified by nd, shape specified by dims and data type specified by dtype. If fortran is equal to 0, the data is organized in a C-contiguous layout, otherwise it is organized in a F-contiguous layout. The array elements are not initialized in any way.

The function PyArray_Empty() performs the same function as the macro PyArray_EMPTY() but the data type is given as a pointer to a PyArray_Descr object instead of a typenum.

PyObject* PyArray_ZEROS(int nd, npy_intp* dims, typenum dtype,
int fortran)

Constructs a new ndarray with the number of dimensions specified by nd, shape specified by dims and data type specified by dtype. If fortran is equal to 0, the data is organized in a C-contiguous layout, otherwise it is organized in a F-contiguous layout. Every element in the array is initialized to 0.

The function PyArray_Zeros() performs the same function as the macro PyArray_ZEROS() but the data type is given as a pointer to a PyArray_Descr object instead of a typenum.

PyArrayObject* PyArray_GETCONTIGUOUS(PyObject* op)

Returns a C-contiguous and well-behaved copy of the array op. If op is already C-contiguous and well-behaved, this function simply returns a new reference to op.

Methods the C Op needs to define

There is a key difference between an op defining a Python implementation for its computation and defining a C implementation. In the case of a Python implementation, the op defines a function perform() which executes the required Python code to realize the op. In the case of a C implementation, however, the op does not define a function that will execute the C code; it instead defines functions that will return the C code to the caller.

This is because calling C code from Python code comes with a significant overhead. If every op was responsible for executing its own C code, every time a Theano function was called, this overhead would occur as many times as the number of ops with C implementations in the function’s computational graph.

To maximize performance, Theano instead requires the C ops to simply return the code needed for their execution and takes upon itself the task of organizing, linking and compiling the code from the various ops. Through this, Theano is able to minimize the number of times C code is called from Python code.

The following is a very simple example to illustrate how it’s possible to obtain performance gains with this process. Suppose you need to execute, from Python code, 10 different ops, each one having a C implementation. If each op was responsible for executing its own C code, the overhead of calling C code from Python code would occur 10 times. Consider now the case where the ops instead return the C code for their execution. You could get the C code from each op and then define your own C module that would call the C code from each op in succession. In this case, the overhead would only occur once; when calling your custom module itself.

Moreover, the fact that Theano itself takes care of compiling the C code, instead of the individual ops, allows Theano to easily cache the compiled C code. This allows for faster compilation times.

See Implementing the arithmetic Ops in C for the full documentation of the various methods of the class Op that are related to the C implementation. Of particular interest are:

This section describes the methods Op.c_code(), Op.c_support_code(), Op.c_support_code_apply() and Op.c_code_cache_version() because they are the ones that are most commonly used.

c_code(node, name, input_names, output_names, sub)

This method returns a string containing the C code to perform the computation required by this op.

The node argument is an Apply node representing an application of the current Op on a list of inputs, producing a list of outputs.

input_names is a sequence of strings which contains as many strings as the op has inputs. Each string contains the name of the C variable to which the corresponding input has been assigned. For example, the name of the C variable representing the first input of the op is given by input_names[0]. You should therefore use this name in your C code to interact with that variable. output_names is used identically to input_names, but for the op’s outputs.

Finally, sub is a dictionary of extras parameters to the c_code method. Among other things, it contains sub['fail'] which is a string of C code that you should include in your C code (after ensuring that a Python exception is set) if it needs to raise an exception. Ex:

c_code = """
    PyErr_Format(PyExc_ValueError, "X does not have the right value");
    %(fail)s;
""" % {'fail' : sub['fail']}

to raise a ValueError Python exception with the specified message. The function PyErr_Format() supports string formatting so it is possible to tailor the error message to the specifics of the error that occured. If PyErr_Format() is called with more than two arguments, the subsequent arguments are used to format the error message with the same behavior as the function PyString_FromFormat(). The % characters in the format characters need to be escaped since the C code itself is defined in a string which undergoes string formatting.

c_code = """
    PyErr_Format(PyExc_ValueError,
                 "X==%%i but it should be greater than 0", X);
    %(fail)s;
""" % {'fail' : sub['fail']}
Note:Your C code should not return the output of the computation but rather put the results in the C variables whose names are contained in the output_names.
c_support_code()

Returns a string or a list of strings containing some support C code for this op. This code will be included at the global scope level and can be used to define functions and structs that will be used by every apply of this op.

c_support_code_apply(node, name)

Returns a string containing some support C code for this op. This code will be included at the global scope level and can be used to define functions and structs that will be used by this op. The difference between this method and c_support_code() is that the C code specified in c_support_code_apply() should be specific to each apply of the Op, while c_support_code() is for support code that is not specific to each apply.

Both c_support_code() and c_support_code_apply () are necessary because a Theano op can be used more than once in a given Theano function. For example, an op that adds two matrices could be used at some point in the Theano function to add matrices of integers and, at another point, to add matrices of doubles. Because the dtype of the inputs and outputs can change between different applies of the op, any support code that relies on a certain dtype is specific to a given apply of the op and should therefore be defined in c_support_code_apply().

c_code_cache_version()

Returns a tuple of integers representing the version of the C code in this op. Ex : (1, 4, 0) for version 1.4.0

This tuple is used by Theano to cache the compiled C code for this op. As such, the return value MUST BE CHANGED every time the C code is altered or else Theano will disregard the change in the code and simply load a previous version of the op from the cache. If you want to avoid caching of the C code of this op, return an empty tuple or do not implement this method.

Note:Theano can handle tuples of any hashable objects as return values for this function but, for greater readability and easier management, this function should return a tuple of integers as previously described.

Important restrictions when implementing an Op

There are some important restrictions to remember when implementing an Op. Unless your Op correctly defines a view_map attribute, the perform and c_code must not produce outputs whose memory is aliased to any input (technically, if changing the output could change the input object in some sense, they are aliased). Unless your Op correctly defines a destroy_map attribute, perform and c_code must not modify any of the inputs.

TODO: EXPLAIN DESTROYMAP and VIEWMAP BETTER AND GIVE EXAMPLE.

When developing an Op, you should run computations in DebugMode, by using argument mode='DebugMode' to theano.function. DebugMode is slow, but it can catch many common violations of the Op contract.

TODO: Like what? How? Talk about Python vs. C too.

DebugMode is no silver bullet though. For example, if you modify an Op self.* during any of make_node, perform, or c_code, you are probably doing something wrong but DebugMode will not detect this.

TODO: jpt: I don’t understand the following sentence.

Ops and Types should usually be considered immutable – you should definitely not make a change that would have an impact on __eq__, __hash__, or the mathematical value that would be computed by perform or c_code.

Simple C Op example

In this section, we put together the concepts that were covered in this tutorial to generate an op which multiplies every element in a vector by a scalar and returns the resulting vector. This is intended to be a simple example so the methods c_support_code() and c_support_code_apply() are not used because they are not required.

In the C code below notice how the reference count on the output variable is managed. Also take note of how the new variables required for the op’s computation are declared in a new scope to avoid cross-initialization errors.

Also, in the C code, it is very important to properly validate the inputs and outputs storage. Theano guarantees that the inputs exist and have the right number of dimensions but it does not guarantee their exact shape. For instance, if an op computes the sum of two vectors, it needs to validate that its two inputs have the same shape. In our case, we do not need to validate the exact shapes of the inputs because we don’t have a need that they match in any way.

For the outputs, things are a little bit more subtle. Theano does not guarantee that they have been allocated but it does guarantee that, if they have been allocated, they have the right number of dimension. Again, Theano offers no guarantee on the exact shapes. This means that, in our example, we need to validate that the output storage has been allocated and has the same shape as our vector input. If it is not the case, we allocate a new output storage with the right shape and number of dimensions.

import numpy
import theano
from theano import gof
import theano.tensor as T

class VectorTimesScalar(gof.Op):
    __props__ = ()

    def make_node(self, x, y):
        # Validate the inputs' type
        if x.type.ndim != 1:
            raise TypeError('x must be a 1-d vector')
        if y.type.ndim != 0:
            raise TypeError('y must be a scalar')

        # Create an output variable of the same type as x
        output_var = x.type()

        return gof.Apply(self, [x, y], [output_var])

    def c_code_cache_version(self):
        return (1, 0)

    def c_code(self, node, name, inp, out, sub):
        x, y = inp
        z, = out

        # Extract the dtypes of the inputs and outputs storage to
        # be able to declare pointers for those dtypes in the C
        # code.
        dtype_x = node.inputs[0].dtype
        dtype_y = node.inputs[1].dtype
        dtype_z = node.outputs[0].dtype

        itemsize_x = numpy.dtype(dtype_x).itemsize
        itemsize_z = numpy.dtype(dtype_z).itemsize

        fail = sub['fail']

        c_code = """
        // Validate that the output storage exists and has the same
        // dimension as x.
        if (NULL == %(z)s ||
            PyArray_DIMS(%(x)s)[0] != PyArray_DIMS(%(z)s)[0])
        {
            /* Reference received to invalid output variable.
            Decrease received reference's ref count and allocate new
            output variable */
            Py_XDECREF(%(z)s);
            %(z)s = (PyArrayObject*)PyArray_EMPTY(1,
                                                PyArray_DIMS(%(x)s),
                                                PyArray_TYPE(%(x)s),
                                                0);

            if (!%(z)s) {
                %(fail)s;
            }
        }

        // Perform the vector multiplication by a scalar
        {
            /* The declaration of the following variables is done in a new
            scope to prevent cross initialization errors */
            npy_%(dtype_x)s* x_data_ptr =
                            (npy_%(dtype_x)s*)PyArray_DATA(%(x)s);
            npy_%(dtype_z)s* z_data_ptr =
                            (npy_%(dtype_z)s*)PyArray_DATA(%(z)s);
            npy_%(dtype_y)s y_value =
                            ((npy_%(dtype_y)s*)PyArray_DATA(%(y)s))[0];
            int x_stride = PyArray_STRIDES(%(x)s)[0] / %(itemsize_x)s;
            int z_stride = PyArray_STRIDES(%(z)s)[0] / %(itemsize_z)s;
            int x_dim = PyArray_DIMS(%(x)s)[0];

            for(int i=0; i < x_dim; i++)
            {
                z_data_ptr[i * z_stride] = (x_data_ptr[i * x_stride] *
                                            y_value);
            }
        }
        """

        return c_code % locals()

The c_code method accepts variable names as arguments (name, inp, out, sub) and returns a C code fragment that computes the expression output. In case of error, the %(fail)s statement cleans up and returns properly.

More complex C Op example

This section introduces a new example, slightly more complex than the previous one, with an op to perform an element-wise multiplication between the elements of two vectors. This new example differs from the previous one in its use of the methods c_support_code() and c_support_code_apply() (it does not need to use them but it does so to explain their use) and its capacity to support inputs of different dtypes.

Recall the method c_support_code() is meant to produce code that will be used for every apply of the op. This means that the C code in this method must be valid in every setting your op supports. If the op is meant to supports inputs of various dtypes, the C code in this method should be generic enough to work with every supported dtype. If the op operates on inputs that can be vectors or matrices, the C code in this method should be able to accomodate both kinds of inputs.

In our example, the method c_support_code() is used to declare a C function to validate that two vectors have the same shape. Because our op only supports vectors as inputs, this function is allowed to rely on its inputs being vectors. However, our op should support multiple dtypes so this function cannot rely on a specific dtype in its inputs.

The method c_support_code_apply(), on the other hand, is allowed to depend on the inputs to the op because it is apply-specific. Therefore, we use it to define a function to perform the multiplication between two vectors. Variables or functions defined in the method c_support_code_apply() will be included at the global scale for every apply of the Op. Because of this, the names of those variables and functions should include the name of the op, like in the example. Otherwise, using the op twice in the same graph will give rise to conflicts as some elements will be declared more than once.

The last interesting difference occurs in the c_code() method. Because the dtype of the output is variable and not guaranteed to be the same as any of the inputs (because of the upcast in the method make_node()), the typenum of the output has to be obtained in the Python code and then included in the C code.

class VectorTimesVector(gof.Op):
    __props__ = ()

    def make_node(self, x, y):
        # Validate the inputs' type
        if x.type.ndim != 1:
            raise TypeError('x must be a 1-d vector')
        if y.type.ndim != 1:
            raise TypeError('y must be a 1-d vector')

        # Create an output variable of the same type as x
        output_var = theano.tensor.TensorType(
                        dtype=theano.scalar.upcast(x.dtype, y.dtype),
                        broadcastable=[False])()

        return gof.Apply(self, [x, y], [output_var])

    def c_code_cache_version(self):
        return (1, 0, 2)

    def c_support_code(self):
        c_support_code = """
        bool vector_same_shape(PyArrayObject* arr1,
            PyArrayObject* arr2)
        {
            return (PyArray_DIMS(arr1)[0] == PyArray_DIMS(arr2)[0]);
        }
        """

        return c_support_code

    def c_support_code_apply(self, node, name):
        dtype_x = node.inputs[0].dtype
        dtype_y = node.inputs[1].dtype
        dtype_z = node.outputs[0].dtype

        c_support_code = """
        void vector_elemwise_mult_%(name)s(npy_%(dtype_x)s* x_ptr,
            int x_str, npy_%(dtype_y)s* y_ptr, int y_str,
            npy_%(dtype_z)s* z_ptr, int z_str, int nbElements)
        {
            for (int i=0; i < nbElements; i++){
                z_ptr[i * z_str] = x_ptr[i * x_str] * y_ptr[i * y_str];
            }
        }
        """

        return c_support_code % locals()

    def c_code(self, node, name, inp, out, sub):
        x, y = inp
        z, = out

        dtype_x = node.inputs[0].dtype
        dtype_y = node.inputs[1].dtype
        dtype_z = node.outputs[0].dtype

        itemsize_x = numpy.dtype(dtype_x).itemsize
        itemsize_y = numpy.dtype(dtype_y).itemsize
        itemsize_z = numpy.dtype(dtype_z).itemsize

        typenum_z = numpy.dtype(dtype_z).num

        fail = sub['fail']

        c_code = """
        // Validate that the inputs have the same shape
        if ( !vector_same_shape(%(x)s, %(y)s))
        {
            PyErr_Format(PyExc_ValueError, "Shape mismatch : "
                        "x.shape[0] and y.shape[0] should match but "
                        "x.shape[0] == %%i and y.shape[0] == %%i",
                        PyArray_DIMS(%(x)s)[0], PyArray_DIMS(%(y)s)[0]);
            %(fail)s;
        }

        // Validate that the output storage exists and has the same
        // dimension as x.
        if (NULL == %(z)s || !(vector_same_shape(%(x)s, %(z)s)))
        {
            /* Reference received to invalid output variable.
            Decrease received reference's ref count and allocate new
            output variable */
            Py_XDECREF(%(z)s);
            %(z)s = (PyArrayObject*)PyArray_EMPTY(1,
                                                PyArray_DIMS(%(x)s),
                                                %(typenum_z)s,
                                                0);

            if (!%(z)s) {
                %(fail)s;
            }
        }

        // Perform the vector elemwise multiplication
        vector_elemwise_mult_%(name)s(
                                (npy_%(dtype_x)s*)PyArray_DATA(%(x)s),
                                PyArray_STRIDES(%(x)s)[0] / %(itemsize_x)s,
                                (npy_%(dtype_y)s*)PyArray_DATA(%(y)s),
                                PyArray_STRIDES(%(y)s)[0] / %(itemsize_y)s,
                                (npy_%(dtype_z)s*)PyArray_DATA(%(z)s),
                                PyArray_STRIDES(%(z)s)[0] / %(itemsize_z)s,
                                PyArray_DIMS(%(x)s)[0]);
        """

        return c_code % locals()

Alternate way of defining C Ops

The two previous examples have covered the standard way of implementing C Ops in Theano by inheriting from the class Op. This process is mostly simple but it still involves defining many methods as well as mixing, in the same file, both Python and C code which tends to make the result less readable.

To help with this, Theano defines a class, COp, from which new C ops can inherit. The class COp aims to simplify the process of implementing C ops by doing the following :

  • It allows you to define the C implementation of your op in a distinct C code file. This makes it easier to keep your Python and C code readable and well indented.
  • It can automatically handle all the methods that return C code, in addition to Op.c_code_cache_version() based on the provided external C implementation.

To illustrate how much simpler the class COp makes the process of defining a new op with a C implementation, let’s revisit the second example of this tutorial, the VectorTimesVector op. In that example, we implemented an op to perform the task of element-wise vector-vector multiplication. The two following blocks of code illustrate what the op would look like if it was implemented using the COp class.

The new op is defined inside a Python file with the following code :

import theano
from theano import gof

class VectorTimesVector(gof.COp):
    __props__ = ()

    func_file = "./vectorTimesVector.c"
    func_name = "APPLY_SPECIFIC(vector_times_vector)"

    def __init__(self):
        super(VectorTimesVector, self).__init__(self.func_file,
                                                self.func_name)

    def make_node(self, x, y):
        # Validate the inputs' type
        if x.type.ndim != 1:
            raise TypeError('x must be a 1-d vector')
        if y.type.ndim != 1:
            raise TypeError('y must be a 1-d vector')

        # Create an output variable of the same type as x
        output_var = theano.tensor.TensorType(
                        dtype=theano.scalar.upcast(x.dtype, y.dtype),
                        broadcastable=[False])()

        return gof.Apply(self, [x, y], [output_var])

And the following is the C implementation of the op, defined in an external C file named vectorTimesVector.c :

#section support_code

// Support code function
bool vector_same_shape(PyArrayObject* arr1, PyArrayObject* arr2)
{
    return (PyArray_DIMS(arr1)[0] == PyArray_DIMS(arr2)[0]);
}


#section support_code_apply

// Apply-specific support function
void APPLY_SPECIFIC(vector_elemwise_mult)(
    DTYPE_INPUT_0* x_ptr, int x_str,
    DTYPE_INPUT_1* y_ptr, int y_str,
    DTYPE_OUTPUT_0* z_ptr, int z_str, int nbElements)
{
    for (int i=0; i < nbElements; i++){
        z_ptr[i * z_str] = x_ptr[i * x_str] * y_ptr[i * y_str];
    }
}

// Apply-specific main function
int APPLY_SPECIFIC(vector_times_vector)(PyArrayObject* input0,
                                        PyArrayObject* input1,
                                        PyArrayObject** output0)
{
    // Validate that the inputs have the same shape
    if ( !vector_same_shape(input0, input1))
    {
        PyErr_Format(PyExc_ValueError, "Shape mismatch : "
                    "input0.shape[0] and input1.shape[0] should "
                    "match but x.shape[0] == %i and "
                    "y.shape[0] == %i",
                    PyArray_DIMS(input0)[0], PyArray_DIMS(input1)[0]);
        return 1;
    }

    // Validate that the output storage exists and has the same
    // dimension as x.
    if (NULL == *output0 || !(vector_same_shape(input0, *output0)))
    {
        /* Reference received to invalid output variable.
        Decrease received reference's ref count and allocate new
        output variable */
        Py_XDECREF(*output0);
        *output0 = (PyArrayObject*)PyArray_EMPTY(1,
                                                PyArray_DIMS(input0),
                                                TYPENUM_OUTPUT_0,
                                                0);

        if (!*output0) {
            PyErr_Format(PyExc_ValueError,
                        "Could not allocate output storage");
            return 1;
        }
    }

    // Perform the actual vector-vector multiplication
    APPLY_SPECIFIC(vector_elemwise_mult)(
                            (DTYPE_INPUT_0*)PyArray_DATA(input0),
                            PyArray_STRIDES(input0)[0] / ITEMSIZE_INPUT_0,
                            (DTYPE_INPUT_1*)PyArray_DATA(input1),
                            PyArray_STRIDES(input1)[0] / ITEMSIZE_INPUT_1,
                            (DTYPE_OUTPUT_0*)PyArray_DATA(*output0),
                            PyArray_STRIDES(*output0)[0] / ITEMSIZE_OUTPUT_0,
                            PyArray_DIMS(input0)[0]);

    return 0;
}

As you can see from this example, the Python and C implementations are nicely decoupled which makes them much more readable than when they were intertwined in the same file and the C code contained string formatting markers.

Now that we have motivated the COp class, we can have a more precise look at what it does for us. For this, we go through the various elements that make up this new version of the VectorTimesVector op :

  • Parent class : instead of inheriting from the class Op, VectorTimesVector inherits from the class COp.
  • Constructor : in our new op, the __init__() method has an important use; to inform the constructor of the COp class of the location, on the filesystem of the C implementation of this op. To do this, it gives a list of file paths containing the C code for this op. To auto-generate the c_code method with a function call you can specify the function name as the second parameter. The paths should be given as a relative path from the folder where the descendant of the COp class is defined.
  • make_node() : the make_node() method is absolutely identical to the one in our old example. Using the COp class doesn’t change anything here.
  • External C code : the external C code implements the various functions associated with the op. Writing this C code involves a few subtleties which deserve their own respective sections.

Main function

If you pass a function name to the __init__() method of the COp class, it must respect the following constraints:

  • It must return an int. The value of that int indicates whether the op could perform its task or not. A value of 0 indicates success while any non-zero value will interrupt the execution of the Theano function. When returning non-zero the function must set a python exception indicating the details of the problem.
  • It must receive one argument for each input to the op followed by one pointer to an argument for each output of the op. The types for the argument is dependant on the Types (that is theano Types) of your inputs and outputs.
  • You can sepcify the number of inputs and outputs for your op by setting the _cop_num_inputs and _cop_num_outputs attributes on your op. The main function will always be called with that number of arguments, using NULL to fill in for missing values at the end. This can be used if your op has a variable number of inputs or outputs, but with a fixed maximum.

For example, the main C function of an op that takes two TensorTypes (which has PyArrayObject * as its C type) as inputs and returns both their sum and the difference between them would have four parameters (two for the op’s inputs and two for its outputs) and it’s signature would look something like this :

int sumAndDiffOfScalars(PyArrayObject* in0, PyArrayObject* in1,
                        PyArrayObject** out0, PyArrayObject** out1)

Macros

For certain section tags, your C code can benefit from a number of pre-defined macros. These section tags have no macros: init_code, support_code. All other tags will have the support macros discussed below.

  • APPLY_SPECIFIC(str) which will automatically append a name unique to the Apply node that applies the Op at the end of the provided str. The use of this macro is discussed futher below.

For every input which has a dtype attribute (this means Tensors, and equivalent types on GPU), the following macros will be defined unless your Op class has an Op.check_input attribute defined to False. In these descrptions ‘i’ refers to the position (indexed from 0) in the input array.

  • DTYPE_INPUT_{i} : NumPy dtype of the data in the array. This is the variable type corresponding to the NumPy dtype, not the string representation of the NumPy dtype. For instance, if the op’s first input is a float32 ndarray, then the macro DTYPE_INPUT_0 corresponds to npy_float32 and can directly be used to declare a new variable of the same dtype as the data in the array :

    DTYPE_INPUT_0 myVar = someValue;
    
  • TYPENUM_INPUT_{i} : Typenum of the data in the array

  • ITEMSIZE_INPUT_{i} : Size, in bytes, of the elements in the array.

In the same way, the macros DTYPE_OUTPUT_{i}, ITEMSIZE_OUTPUT_{i} and TYPENUM_OUTPUT_{i} are defined for every output ‘i’ of the op.

In addition to these macros, the init_code_struct, code, and code_cleanup section tags also have the following macros:

  • FAIL : Code to insert at error points. A python exception should be set prior to this code. An invocation look like this:

    if (error) {
      // Set python exception
      FAIL
    }
    

    You can add a semicolon after the macro if it makes your editor happy.

  • PARAMS : Name of the params variable for this node. (only for Ops which have params, which is discussed elsewhere)

Finally the tag code and code_cleanup have macros to pass the inputs and output names. These are name INPUT_{i} and OUTPUT_{i} where i is the 0-based index position in the input and output arrays respectively.

Support code

Certain section are limited in what you can place in them due to semantic and syntactic restrictions of the C++ language. Most of these restrictions apply to the tags that end in _struct.

When we defined the VectorTimesVector op without using the COp class, we had to make a distinction between two types of support_code : the support code that was apply-specific and the support code that wasn’t. The apply-specific code was defined in the c_support_code_apply() method and the elements defined in that code (global variables and functions) had to include the name of the Apply node in their own names to avoid conflicts between the different versions of the apply-specific code. The code that wasn’t apply-specific was simply defined in the c_support_code() method.

To make indentifiers that include the Apply node name use the APPLY_SPECIFIC(str) macro. In the above example, this macro is used when defining the functions vector_elemwise_mult() and vector_times_vector() as well as when calling function vector_elemwise_mult() from inside vector_times_vector().

When using the COp class, we still have to make the distinction between C code for each of the methods of a C class. These sections of code are separated by #section <tag> markers. The tag determines the name of the method this C code applies to with the rule that <tag> applies to c_<tag>. Unknown tags are an error and will be reported. Duplicate tags will be merged together in the order the appear in the C files.

The rules for knowing if where a piece of code should be put can be sometimes tricky. The key thing to remember is that things that can be shared between instances of the op should be apply-agnostic and go into a section which does not end in _apply or _struct. The distinction of _apply and _struct mostly hinghes on how you want to manange the lifetime of the object. Note that to use an apply-specific object, you have to be in a apply-specific section, so some portions of the code that might seem apply-agnostic may still be apply-specific because of the data they use (this does not include arguments).

In the above example, the function vector_same_shape() is apply-agnostic because it uses none of the macros defined by the class COp and it doesn’t rely on any apply-specific code. The function vector_elemwise_mult() is apply-specific because it uses the macros defined by COp. Finally, the function vector_times_vector() is apply-specific because it uses those same macros and also because it calls vector_elemwise_mult() which is an apply-specific function.

Using GDB to debug Op’s C code

When debugging C code, it can be useful to use GDB for code compiled by Theano.

For this, you must enable this Theano: cmodule.remove_gxx_opt=True. For the GPU, you must add in this second flag nvcc.flags=-g (it slow down computation on the GPU, but it is enabled by default on the CPU).

Then you must start Python inside GDB and in it start your Python process (e.g. theano-nose):

$gdb python
(gdb)r bin/theano-nose theano/

Quick guide to GDB.

Final Note

This tutorial focuses on providing C implementations to ops that manipulate Theano tensors. For more information about other Theano types, you can refer to the section Alternate Theano Types.