Custom C extensions for pytorch

Author: Soumith Chintala

Step 1. prepare your C code

First, you have to write your C functions.

Below you can find an example implementation of forward and backward functions of a module that adds its both inputs.

In your .c files you can include TH using an #include <TH/TH.h> directive, and THC using #include <THC/THC.h>.

ffi utils will make sure a compiler can find them during the build.

/* src/my_lib.c */
#include <TH/TH.h>

int my_lib_add_forward(THFloatTensor *input1, THFloatTensor *input2,
THFloatTensor *output)
{
    if (!THFloatTensor_isSameSizeAs(input1, input2))
        return 0;
    THFloatTensor_resizeAs(output, input1);
    THFloatTensor_add(output, input1, input2);
    return 1;
}

int my_lib_add_backward(THFloatTensor *grad_output, THFloatTensor *grad_input)
{
    THFloatTensor_resizeAs(grad_input, grad_output);
    THFloatTensor_fill(grad_input, 1);
    return 1;
}

There are no constraints on the code, except that you will have to prepare a single header, which will list all functions want to call from python.

It will be used by the ffi utils to generate appropriate wrappers.

/* src/my_lib.h */
int my_lib_add_forward(THFloatTensor *input1, THFloatTensor *input2, THFloatTensor *output);
int my_lib_add_backward(THFloatTensor *grad_output, THFloatTensor *grad_input);

Now, you’ll need a super short file, that will build your custom extension:

# build.py
from torch.utils.ffi import create_extension
ffi = create_extension(
name='_ext.my_lib',
headers='src/my_lib.h',
sources=['src/my_lib.c'],
with_cuda=False
)
ffi.build()

Step 2: Include it in your Python code

After you run it, pytorch will create an _ext directory and put my_lib inside.

Package name can have an arbitrary number of packages preceding the final module name (including none). If the build succeeded you can import your extension just like a regular python file.

# functions/add.py
import torch
from torch.autograd import Function
from _ext import my_lib


class MyAddFunction(Function):
    def forward(self, input1, input2):
        output = torch.FloatTensor()
        my_lib.my_lib_add_forward(input1, input2, output)
        return output

    def backward(self, grad_output):
        grad_input = torch.FloatTensor()
        my_lib.my_lib_add_backward(grad_output, grad_input)
        return grad_input
# modules/add.py
from torch.nn import Module
from functions.add import MyAddFunction

class MyAddModule(Module):
    def forward(self, input1, input2):
        return MyAddFunction()(input1, input2)
# main.py
import torch.nn as nn
from torch.autograd import Variable
from modules.add import MyAddModule

class MyNetwork(nn.Module):
    def __init__(self):
        super(MyNetwork, self).__init__(
            add=MyAddModule(),
        )

    def forward(self, input1, input2):
        return self.add(input1, input2)

model = MyNetwork()
input1, input2 = Variable(torch.randn(5, 5)), Variable(torch.randn(5, 5))
print(model(input1, input2))
print(input1 + input2)