Implementing double in C

The previous two sections described how to define a double Type and arithmetic operations on that Type, but all of them were implemented in pure Python. In this section we will see how to define the double type in such a way that it can be used by operations implemented in C (which we will define in the section after that).

How does it work?

In order to be C-compatible, a Type must provide a C interface to the Python data that satisfy the constraints it puts forward. In other words, it must define C code that can convert a Python reference into some type suitable for manipulation in C and it must define C code that can convert some C structure in which the C implementation of an operation stores its variables into a reference to an object that can be used from Python and is a valid value for the Type.

For example, in the current example, we have a Type which represents a Python float. First, we will choose a corresponding C type. The natural choice would be the primitive double type. Then, we need to write code that will take a PyObject*, check that it is a Python float and extract its value as a double. Finally, we need to write code that will take a C double and will build a PyObject* of Python type float that we can work with from Python. We will be using CPython and thus special care must be given to making sure reference counts are updated properly!

The C code we will write makes use of CPython’s C API which you can find here.

What needs to be defined

In order to be C-compatible, the Type subclass interface CType must be used. It defines several additional methods, which all start with the c_ prefix. The complete list can be found in the documentation for graph.type.CType. Here, we’ll focus on the most important ones:

class CLinkerType
c_declare(name, sub, check_input=True)

This must return C code which declares variables. These variables will be available to operations defined in C. You may also write typedefs.

c_init(name, sub)

This must return C code which initializes the variables declared in c_declare. Either this or c_extract will be called.

c_extract(name, sub, check_input=True, **kwargs)

This must return C code which takes a reference to a Python object and initializes the variables declared in c_declare to match the Python object’s data. Either this or c_init will be called.

c_sync(name, sub)

When the computations are done, transfer the variables from the C structure we put them in to the destination Python object. This will only be called for the outputs.

c_cleanup(name, sub)

When we are done using the data, clean up whatever we allocated and decrease the appropriate reference counts.

c_headers([c_compiler])
c_libraries([c_compiler])
c_header_dirs([c_compiler])
c_lib_dirs([c_compiler])

Allows you to specify headers, libraries and associated directories.

These methods have two versions, one with a c_compiler argument and one without. The version with c_compiler is tried first and if it doesn’t work, the one without is.

The c_compiler argument is the C compiler that will be used to compile the C code for the node that uses this type.

c_compile_args([c_compiler])
c_no_compile_args([c_compiler])

Allows to specify special compiler arguments to add/exclude.

These methods have two versions, one with a c_compiler argument and one without. The version with c_compiler is tried first and if it doesn’t work, the one without is.

The c_compiler argument is the C compiler that will be used to compile the C code for the node that uses this type.

c_init_code()

Allows you to specify code that will be executed once when the module is initialized, before anything else is executed. For instance, if a type depends on NumPy’s C API, then 'import_array();' has to be among the snippets returned by c_init_code().

c_support_code()

Allows to add helper functions/structs (in a string or a list of strings) that the Type needs.

c_compiler()

Allows to specify a special compiler. This will force this compiler for the current compilation block (a particular op or the full graph). This is used for the GPU code.

c_code_cache_version()

Should return a tuple of hashable objects like integers. This specifies the version of the code. It is used to cache the compiled code. You MUST change the returned tuple for each change in the code. If you don’t want to cache the compiled code return an empty tuple or don’t implement it.

c_element_type()

Optional: should return the name of the primitive C type of items into variables handled by this Theano type. For example, for a matrix of 32-bit signed NumPy integers, it should return "npy_int32". If C type may change from an instance to another (e.g. Scalar('int32') vs Scalar('int64')), consider implementing this method. If C type is fixed accross instances, this method may be useless (as you already know the C type when you work with the C code).

Each of these functions take two arguments, name and sub which must be used to parameterize the C code they return. name is a string which is chosen by the compiler to represent a Variable of the CType in such a way that there are no name conflicts between different pieces of data. Therefore, all variables declared in c_declare should have a name which includes name. Furthermore, the name of the variable containing a pointer to the Python object associated to the Variable is py_<name>.

sub, on the other hand, is a dictionary containing bits of C code suitable for use in certain situations. For instance, sub['fail'] contains code that should be inserted wherever an error is identified.

c_declare and c_extract also accept a third check_input optional argument. If you want your type to validate its inputs, it must only do it when check_input is True.

The example code below should help you understand how everything plays out:

Warning

If some error condition occurs and you want to fail and/or raise an Exception, you must use the fail code contained in sub['fail'] (there is an example in the definition of c_extract below). You must NOT use the return statement anywhere, ever, nor break outside of your own loops or goto to strange places or anything like that. Failure to comply with this restriction could lead to erratic behavior, segfaults and/or memory leaks because Theano defines its own cleanup system and assumes that you are not meddling with it. Furthermore, advanced operations or types might do code transformations on your code such as inserting it in a loop – in that case they can call your code-generating methods with custom failure code that takes into account what they are doing!

Defining the methods

c_declare

from theano.graph.type import Generic


class double(Generic):
    def c_declare(self, name, sub, check_input=True):
        return """
        double %(name)s;
        """ % dict(name = name)

Very straightforward. All we need to do is write C code to declare a double. That double will be named whatever is passed to our function in the name argument. That will usually be some mangled name like “V0”, “V2” or “V92” depending on how many nodes there are in the computation graph and what rank the current node has. This function will be called for all Variables whose type is double.

You can declare as many variables as you want there and you can also do typedefs. Make sure that the name of each variable contains the name argument in order to avoid name collisions (collisions will happen if you don’t parameterize the variable names as indicated here). Also note that you cannot declare a variable called py_<name> or storage_<name> because Theano already defines them.

What you declare there is basically the C interface you are giving to your CType. If you wish people to develop operations that make use of it, it’s best to publish it somewhere.

c_init

def c_init(self, name, sub):
    return """
    %(name)s = 0.0;
    """ % dict(name = name)

This function has to initialize the double we declared previously to a suitable value. This is useful if we want to avoid dealing with garbage values, especially if our data type is a pointer. This is not going to be called for all Variables with the double type. Indeed, if a Variable is an input that we pass from Python, we will want to extract that input from a Python object, therefore it is the c_extract method that will be called instead of c_init. You can therefore not assume, when writing c_extract, that the initialization has been done (in fact you can assume that it hasn’t been done).

c_init will typically be called on output Variables, but in general you should only assume that either c_init or c_extract has been called, without knowing for sure which of the two.

c_extract

def c_extract(self, name, sub, check_input=True, **kwargs):
    return """
    if (!PyFloat_Check(py_%(name)s)) {
        PyErr_SetString(PyExc_TypeError, "expected a float");
        %(fail)s
    }
    %(name)s = PyFloat_AsDouble(py_%(name)s);
    """ % dict(name = name, fail = sub['fail'])

This method is slightly more sophisticated. What happens here is that we have a reference to a Python object which Theano has placed in py_%(name)s where %(name)s must be substituted for the name given in the inputs. This special variable is declared by Theano as PyObject* py_%(name)s where PyObject* is a pointer to a Python object as defined by CPython’s C API. This is the reference that corresponds, on the Python side of things, to a Variable with the double type. It is what the end user will give and what he or she expects to get back.

In this example, the user will give a Python float. The first thing we should do is verify that what we got is indeed a Python float. The PyFloat_Check function is provided by CPython’s C API and does this for us. If the check fails, we set an exception and then we insert code for failure. The code for failure is in sub["fail"] and it basically does a goto to cleanup code.

If the check passes then we convert the Python float into a double using the PyFloat_AsDouble function (yet again provided by CPython’s C API) and we put it in our double variable that we declared previously.

c_sync

def c_sync(name, sub):
    return """
    Py_XDECREF(py_%(name)s);
    py_%(name)s = PyFloat_FromDouble(%(name)s);
    if (!py_%(name)s) {
        printf("PyFloat_FromDouble failed on: %%f\\n", %(name)s);
        Py_XINCREF(Py_None);
        py_%(name)s = Py_None;
    }
    """ % dict(name = name)
double.c_sync = c_sync

This function is probably the trickiest. What happens here is that we have computed some operation on doubles and we have put the variable into the double variable %(name)s. Now, we need to put this data into a Python object that we can manipulate on the Python side of things. This Python object must be put into the py_%(name)s variable which Theano recognizes (this is the same pointer we get in c_extract).

Now, that pointer is already a pointer to a valid Python object (unless you or a careless implementer did terribly wrong things with it). If we want to point to another object, we need to tell Python that we don’t need the old one anymore, meaning that we need to decrease the previous object’s reference count. The first line, Py_XDECREF(py_%(name)s) does exactly this. If it is forgotten, Python will not be able to reclaim the data even if it is not used anymore and there will be memory leaks! This is especially important if the data you work on is large.

Now that we have decreased the reference count, we call PyFloat_FromDouble on our double variable in order to convert it to a Python float. This returns a new reference which we assign to py_%(name)s. From there Theano will do the rest and the end user will happily see a Python float come out of his computations.

The rest of the code is not absolutely necessary and it is basically “good practice”. PyFloat_FromDouble can return NULL on failure. NULL is a pretty bad reference to have and neither Python nor Theano like it. If this happens, we change the NULL pointer (which will cause us problems) to a pointer to None (which is not a NULL pointer). Since None is an object like the others, we need to increase its reference count before we can set a new pointer to it. This situation is unlikely to ever happen, but if it ever does, better safe than sorry.

Warning

I said this already but it really needs to be emphasized that if you are going to change the py_%(name)s pointer to point to a new reference, you must decrease the reference count of whatever it was pointing to before you do the change. This is only valid if you change the pointer, if you are not going to change the pointer, do NOT decrease its reference count!

c_cleanup

def c_cleanup(name, sub):
    return ""
double.c_cleanup = c_cleanup

We actually have nothing to do here. We declared a double on the stack so the C language will reclaim it for us when its scope ends. We didn’t malloc() anything so there’s nothing to free(). Furthermore, the py_%(name)s pointer hasn’t changed so we don’t need to do anything with it. Therefore, we have nothing to cleanup. Sweet!

There are however two important things to keep in mind:

First, note that c_sync and c_cleanup might be called in sequence, so they need to play nice together. In particular, let’s say that you allocate memory in c_init or c_extract for some reason. You might want to either embed what you allocated to some Python object in c_sync or to free it in c_cleanup. If you do the former, you don’t want to free the allocated storage so you should set the pointer to it to NULL to avoid that c_cleanup mistakenly frees it. Another option is to declare a variable in c_declare that you set to true in c_sync to notify c_cleanup that c_sync was called.

Second, whenever you use %(fail)s in c_extract or in the code of an operation, you can count on c_cleanup being called right after that. Therefore, it’s important to make sure that c_cleanup doesn’t depend on any code placed after a reference to %(fail)s. Furthermore, because of the way Theano blocks code together, only the variables declared in c_declare will be visible in c_cleanup!

What the generated C will look like

c_init and c_extract will only be called if there is a Python object on which we want to apply computations using C code. Conversely, c_sync will only be called if we want to communicate the values we have computed to Python, and c_cleanup will only be called when we don’t need to process the data with C anymore. In other words, the use of these functions for a given Variable depends on the the relationship between Python and C with respect to that Variable. For instance, imagine you define the following function and call it:

x, y, z = double('x'), double('y'), double('z')
a = add(x, y)
b = mul(a, z)
f = function([x, y, z], b)
f(1.0, 2.0, 3.0)

Using the CLinker, the code that will be produced will look roughly like this:

// BEGIN defined by Theano
PyObject* py_x = ...;
PyObject* py_y = ...;
PyObject* py_z = ...;
PyObject* py_a = ...; // note: this reference won't actually be used for anything
PyObject* py_b = ...;
// END defined by Theano

{
  double x; //c_declare for x
  x = ...; //c_extract for x
  {
    double y; //c_declare for y
    y = ...; //c_extract for y
    {
      double z; //c_declare for z
      z = ...; //c_extract for z
      {
        double a; //c_declare for a
        a = 0; //c_init for a
        {
          double b; //c_declare for b
          b = 0; //c_init for b
          {
            a = x + y; //c_code for add
            {
              b = a * z; //c_code for mul
            labelmul:
              //c_cleanup for mul
            }
          labeladd:
            //c_cleanup for add
          }
        labelb:
          py_b = ...; //c_sync for b
          //c_cleanup for b
        }
      labela:
        //c_cleanup for a
      }
    labelz:
      //c_cleanup for z
    }
  labely:
    //c_cleanup for y
  }
labelx:
  //c_cleanup for x
}

It’s not pretty, but it gives you an idea of how things work (note that the variable names won’t be x, y, z, etc. - they will get a unique mangled name). The fail code runs a goto to the appropriate label in order to run all cleanup that needs to be done. Note which variables get extracted (the three inputs x, y and z), which ones only get initialized (the temporary variable a and the output b) and which one is synced (the final output b).

The C code above is a single C block for the whole graph. Depending on which linker is used to process the computation graph, it is possible that one such block is generated for each operation and that we transit through Python after each operation. In that situation, a would be synced by the addition block and extracted by the multiplication block.

Final version

from theano.graph.type import

class Double(Type):

    def filter(self, x, strict=False, allow_downcast=None):
        if strict and not isinstance(x, float):
            raise TypeError('Expected a float!')
        return float(x)

    def values_eq_approx(self, x, y, tolerance=1e-4):
        return abs(x - y) / (x + y) < tolerance

    def __str__(self):
        return "double"

    def c_declare(self, name, sub):
        return """
        double %(name)s;
        """ % dict(name = name)

    def c_init(self, name, sub):
        return """
        %(name)s = 0.0;
        """ % dict(name = name)

    def c_extract(self, name, sub, **kwargs):
        return """
        if (!PyFloat_Check(py_%(name)s)) {
            PyErr_SetString(PyExc_TypeError, "expected a float");
            %(fail)s
        }
        %(name)s = PyFloat_AsDouble(py_%(name)s);
        """ % dict(sub, name = name)

    def c_sync(self, name, sub):
        return """
        Py_XDECREF(py_%(name)s);
        py_%(name)s = PyFloat_FromDouble(%(name)s);
        if (!py_%(name)s) {
            printf("PyFloat_FromDouble failed on: %%f\\n", %(name)s);
            Py_XINCREF(Py_None);
            py_%(name)s = Py_None;
        }
        """ % dict(name = name)

    def c_cleanup(self, name, sub):
        return ""

double = Double()

DeepCopyOp

We have an internal Op called DeepCopyOp. It is used to make sure we respect the user vs Theano memory region as described in the tutorial. Theano has a Python implementation that calls the object’s copy() or deepcopy() method for Theano types for which it does not know how to generate C code.

You can implement c_code for this op. You register it like this:

theano.compile.ops.register_deep_copy_op_c_code(YOUR_TYPE_CLASS, THE_C_CODE, version=())

In your C code, you should use %(iname)s and %(oname)s to represent the C variable names of the DeepCopyOp input and output respectively. See an example for the type GpuArrayType (GPU array) in the file theano/gpuarray/type.py. The version parameter is what is returned by DeepCopyOp.c_code_cache_version(). By default, it will recompile the c code for each process.

ViewOp

We have an internal Op called ViewOp. It is used for some verification of inplace/view Ops. Its C implementation increments and decrements Python reference counts, and thus only works with Python objects. If your new type represents Python objects, you should tell ViewOp to generate C code when working with this type, as otherwise it will use Python code instead. This is achieved by calling:

theano.compile.ops.register_view_op_c_code(YOUR_TYPE_CLASS, THE_C_CODE, version=())

In your C code, you should use %(iname)s and %(oname)s to represent the C variable names of the ViewOp input and output respectively. See an example for the type GpuArrayType (GPU array) in the file thean/gpuarray/type.py. The version parameter is what is returned by ViewOp.c_code_cache_version(). By default, it will recompile the c code for each process.

Shape and Shape_i

We have 2 generic Op`s, `Shape and Shape_i, that return the shape of any Theano Variable that has a shape attribute (Shape_i returns only one of the elements of the shape).

from theano.theano.shape import register_shape_c_code, register_shape_i_c_code

register_shape_c_code(YOUR_TYPE_CLASS, THE_C_CODE, version=())
register_shape_i_c_code(YOUR_TYPE_CLASS, THE_C_CODE, CHECK_INPUT, version=())

The C code works as the ViewOp. Shape_i has the additional i parameter that you can use with %(i)s.

In your CHECK_INPUT, you must check that the input has enough dimensions to be able to access the i-th one.