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A Tour of PyTorch Internals (Part I)

The fundamental unit in PyTorch is the Tensor. This post will serve as an overview for how we implement Tensors in PyTorch, such that the user can interact with it from the Python shell. In particular, we want to answer four main questions:

How does PyTorch extend the Python interpreter to define a Tensor type that can be manipulated from Python code?
How does PyTorch wrap the C libraries that actually define the Tensor's properties and methods?
How does PyTorch cwrap work to generate code for Tensor methods?
How does PyTorch's build system take all of these components to compile and generate a workable application?

Extending the Python Interpreter

PyTorch defines a new package torch. In this post we will consider the ._C module. This module is known as an "extension module" - a Python module written in C. Such modules allow us to define new built-in object types (e.g. the Tensor) and to call C/C++ functions.

Hello, world!

Please explain in detail what will happen if the following program is executed:

#include <iostream>

int main() {
    std::cout << "Hello, world!" << std::endl;
}

Cython

Cython has two major benefits:

Making python code faster, particularly things that can't be done in scipy/numpy
Wrapping/interfacing with C/C++ code

Cython gains most of it's benefit from statically typing arguments. However, statically typing is not required, in fact, regular python code is valid cython (but don't expect much of a speed up). By incrementally adding more type information, the code can speed up by several factors. This gist just provides a very basic usage of cython.

Mathematics: What do grad students in math do all day?

by Yasha Berchenko-Kogan

A lot of math grad school is reading books and papers and trying to understand what's going on. The difficulty is that reading math is not like reading a mystery thriller, and it's not even like reading a history book or a New York Times article.

The main issue is that, by the time you get to the frontiers of math, the words to describe the concepts don't really exist yet. Communicating these ideas is a bit like trying to explain a vacuum cleaner to someone who has never seen one, except you're only allowed to use words that are four letters long or shorter.

What can you say?

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	import numpy as np
	import time
	import cv2
	from direct.showbase.ShowBase import ShowBase
	from panda3d.core import FrameBufferProperties, WindowProperties
	from panda3d.core import GraphicsPipe, GraphicsOutput
	from panda3d.core import Texture
	from panda3d.core import loadPrcFileData

	loadPrcFileData('', 'show-frame-rate-meter true')

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	#define let(name,value) const __typeof__ (value) name = value;
	#define var(name,value) __typeof__ (value) name = value;

	int main(int argc, char *argv[]) {
	let(x,3); // const int x = 3;
	var(y,5); // int y = 5;

	printf("x:%i y:%i",x,y); // -> x:3 y:5
	}

	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):