# High-level frameworks¶

To provide a transparent interface for the backpropagation algorithm, deep learning libraries rely on three core modules:

1. A comprehensive list of operations, with forward and backward routines implemented next to each other.

2. Efficient GPU and CPU backends for those routines, allowing users to take advantage of their hardware without having to write a single line of C++.

3. A high-level graph manipulation engine for symbolic computations, which executes the backpropagation’s “backward pass” whenever a gradient value is required.

## A minimal working example¶

Let us illustrate the underlying mechanics of PyTorch – the most popular framework among academics – in a simple case: the computation of the Gaussian kernel norm:

\begin{split}\begin{aligned} H(q,p)~&=~ \frac{1}{2} \sum_{i,j=1}^\mathrm{N} k( q_i - q_j ) \, \langle\,p_i,p_j\,\rangle_{\mathbb{R}^\mathrm{D}} & \text{where} & & k(x) ~=~& \exp(-\|x\|^2\,/\,2\sigma^2)\label{eq:hamiltonien_kernel2}\\ &=~ \frac{1}{2} \langle\,p,\, K_{q,q}\, p\,\rangle_{\mathbb{R}^{\mathrm{N}\times\mathrm{D}}} & \text{where} & & (K_{q,q})_{i,j} ~&=~k( q_i - q_j ),\label{eq:hamiltonien_kernel3}\end{aligned}\end{split}

and of its gradients with respect to the input arrays $$(q_i) \in \mathbb{R}^{\mathrm{N}\times \mathrm{D}}$$ and $$(p_i) \in \mathbb{R}^{\mathrm{N}\times \mathrm{D}}$$. Using the standard (tensorized) PyTorch interface, programmers may write:

import torch                   # GPU + autodiff library
from torchviz import make_dot  # See github.com/szagoruyko/pytorchviz

# With PyTorch, using the GPU is that simple:
use_gpu = torch.cuda.is_available()
dtype   = torch.cuda.FloatTensor if use_gpu else torch.FloatTensor
# Under the hood, this flag determines the backend that is to be
# used for forward and backward operations, which have all been
# implemented both in pure CPU and GPU (CUDA) code.

# Step 1: Define numerical tensors (from scratch or numpy) --------------------
N, D = 1000, 3  # Work with clouds of 1,000 points in 3D
# Generate arbitrary arrays on the host (CPU) or device (GPU):
q = torch.rand(N, D).type(dtype)  # random tensor of shape (N,D)
p = torch.rand(N, D).type(dtype)  # random tensor of shape (N,D)
s = torch.Tensor([2.5]).type(dtype)  # deviation "sigma" of our kernel

# Step 2: Ask PyTorch to keep track of q and p's children ---------------------
# In this demo, we won't try to fine tune the kernel and
# do not need any derivative with respect to sigma:

# Step 3: Actual computations -------------------------------------------------
# Every PyTorch instruction is executed on-the-fly, but the graph API
# 'torch.autograd' keeps track of the operations and stores in memory
# the intermediate results that are needed for the backward pass.
q_i  = q[:,None,:]  # shape (N,D) -> (N,1,D)
q_j  = q[None,:,:]  # shape (N,D) -> (1,N,D)
D_ij  = ((q_i - q_j) ** 2).sum(dim=2)  # squared distances |q_i-q_j|^2
K_ij = (- D_ij / (2 * s**2) ).exp()    # Gaussian kernel matrix
v    = K_ij@p  # matrix multiplication. (N,N) @ (N,D) = (N,D)

# Finally, compute the kernel norm H(q,p):
H = .5 * torch.dot( p.view(-1), v.view(-1) ) # .5 * <p,v>

# Display the computational graph in the figure below, annotated by hand:
make_dot(H, {'q':q, 'p':p}).render(view=True)


## Encoding formulas as tree-like objects¶

With PyTorch, tensor variables are much more than plain numerical arrays. Any tensor that descends from a differentiable variable (marked with the flag requires_grad = True) possesses two essential attributes:

1. A data pointer which refers to a C++ array that may be stored in either Host (CPU) or Device (GPU) memories.

2. A grad_fn recursive tree-like object, which records the computational history of the tensor and can be used whenever a backward pass is required by the grad() operator.

In the picture above, we displayed the H.grad_fn attribute of our kernel norm using the GraphViz Dot program. This acyclic graph is the exact equivalent of the second “backward” line of the backpropagation diagram that we presented in the previous section:

• White nodes stand for backward operators $$\partial_x F_{i+1} : (x_i,x_{i+1}^*) \mapsto x_i^*$$.

• The green leave is the first covariable $$x_p^*\in \mathbb{R}$$, the “gradient with respect to the output” that is initialized to 1 by default.

• Red leaves are the covariables “$$x_0^*$$”, the gradients that we are about to compute.

• Blue leaves are the stored values $$x_i$$ that were computed during the forward pass.

## A well-packaged backropagation engine¶

Thanks to the groundwork done by the PyTorch symbolic engine, computing gradients is now as simple as writing:

grad_q, grad_p = torch.autograd.grad( H, [q, p] )  # pair of (N,D) tensors


That’s it – and it goes pretty fast! As should be evident by now, the blend of semi-symbolic calculus and parallel performances that deep learning frameworks provide is a game changer for applied mathematicians. Before going any further, we thus strongly advise readers to try out these scripts on their machines and go through the main Matlab/NumPy to PyTorch migration guide.

## Custom operators, higher-order differentiation¶

As explained in this tutorial, creating new pairs of (forward, backward) PyTorch operators is easy. Allowing users to inject their own C++ code in a PyTorch program, the torch.autograd.Function module is a convenient interface for the developers of PyTorch_Geometric, GPytorch and other contributed extensions to the vanilla framework.

Please note that the PyTorch engine also supports the computation of high-order gradients through the create_graph = True optional argument of the grad() operator. Even though full Hessian matrices may not be computed efficiently using backprop-like strategies – they’re typically way too large anyway – formulas that involve gradients may themselves be understood as “vector computer programs” and differentiated accordingly. In practice, developers of contributed libraries just have to make sure that their backward operators rely on well-defined forward routines, thus allowing the autograd engine to bootstrap the computation of high-order derivatives.