Cython Architecture

This section documents spaCy's C-level data structures and interfaces, intended for use from Cython. Some of the attributes are primarily for internal use, and all C-level functions and methods are designed for speed over safety – if you make a mistake and access an array out-of-bounds, the program may crash abruptly.

With Cython there are four ways of declaring complex data types. Unfortunately we use all four in different places, as they all have different utility:

classA normal Python class.Language
cdef class A Python extension type. Differs from a normal Python class in that its attributes can be defined on the underlying struct. Can have C-level objects as attributes (notably structs and pointers), and can have methods which have C-level objects as arguments or return types.Lexeme
cdef struct A struct is just a collection of variables, sort of like a named tuple, except the memory is contiguous. Structs can't have methods, only attributes.LexemeC
cdef cppclass A C++ class. Like a struct, this can be allocated on the stack, but can have methods, a constructor and a destructor. Differs from `cdef class` in that it can be created and destroyed without acquiring the Python global interpreter lock. This style is the most obscure.StateC

The most important classes in spaCy are defined as cdef class objects. The underlying data for these objects is usually gathered into a struct, which is usually named c. For instance, the Lexeme class holds a LexemeC struct, at Lexeme.c. This lets you shed the Python container, and pass a pointer to the underlying data into C-level functions.


spaCy's core data structures are implemented as Cython cdef classes. Memory is managed through the cymem cymem.Pool class, which allows you to allocate memory which will be freed when the Pool object is garbage collected. This means you usually don't have to worry about freeing memory. You just have to decide which Python object owns the memory, and make it own the Pool. When that object goes out of scope, the memory will be freed. You do have to take care that no pointers outlive the object that owns them — but this is generally quite easy.

All Cython modules should have the # cython: infer_types=True compiler directive at the top of the file. This makes the code much cleaner, as it avoids the need for many type declarations. If possible, you should prefer to declare your functions nogil, even if you don't especially care about multi-threading. The reason is that nogil functions help the Cython compiler reason about your code quite a lot — you're telling the compiler that no Python dynamics are possible. This lets many errors be raised, and ensures your function will run at C speed.

Cython gives you many choices of sequences: you could have a Python list, a numpy array, a memory view, a C++ vector, or a pointer. Pointers are preferred, because they are fastest, have the most explicit semantics, and let the compiler check your code more strictly. C++ vectors are also great — but you should only use them internally in functions. It's less friendly to accept a vector as an argument, because that asks the user to do much more work. Here's how to get a pointer from a numpy array, memory view or vector:

cdef void get_pointers(np.ndarray[int, mode='c'] numpy_array, vector[int] cpp_vector, int[::1] memory_view) nogil:
pointer1 = <int*>
pointer2 =
pointer3 = &memory_view[0]

Both C arrays and C++ vectors reassure the compiler that no Python operations are possible on your variable. This is a big advantage: it lets the Cython compiler raise many more errors for you.

When getting a pointer from a numpy array or memoryview, take care that the data is actually stored in C-contiguous order — otherwise you'll get a pointer to nonsense. The type-declarations in the code above should generate runtime errors if buffers with incorrect memory layouts are passed in. To iterate over the array, the following style is preferred:

cdef int c_total(const int* int_array, int length) nogil:
    total = 0
    for item in int_array[:length]:
        total += item
    return total

If this is confusing, consider that the compiler couldn't deal with for item in int_array: — there's no length attached to a raw pointer, so how could we figure out where to stop? The length is provided in the slice notation as a solution to this. Note that we don't have to declare the type of item in the code above — the compiler can easily infer it. This gives us tidy code that looks quite like Python, but is exactly as fast as C — because we've made sure the compilation to C is trivial.

Your functions cannot be declared nogil if they need to create Python objects or call Python functions. This is perfectly okay — you shouldn't torture your code just to get nogil functions. However, if your function isn't nogil, you should compile your module with cython -a --cplus my_module.pyx and open the resulting my_module.html file in a browser. This will let you see how Cython is compiling your code. Calls into the Python run-time will be in bright yellow. This lets you easily see whether Cython is able to correctly type your code, or whether there are unexpected problems.

Working in Cython is very rewarding once you're over the initial learning curve. As with C and C++, the first way you write something in Cython will often be the performance-optimal approach. In contrast, Python optimisation generally requires a lot of experimentation. Is it faster to have an if item in my_dict check, or to use .get()? What about try/except? Does this numpy operation create a copy? There's no way to guess the answers to these questions, and you'll usually be dissatisfied with your results — so there's no way to know when to stop this process. In the worst case, you'll make a mess that invites the next reader to try their luck too. This is like one of those volcanic gas-traps, where the rescuers keep passing out from low oxygen, causing another rescuer to follow — only to succumb themselves. In short, just say no to optimizing your Python. If it's not fast enough the first time, just switch to Cython.