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# A wrapper for NumPy and PyTorch arrays¶

KeOps is all about bringing **semi-symbolic** calculus
to modern computing libraries,
alleviating the need for **huge intermediate variables**
such as *kernel* or *distance* matrices in machine
learning and computational geometry.

## First steps¶

A simple high-level interface to the KeOps inner routines is provided by
the `pykeops.numpy.LazyTensor`

or `pykeops.torch.LazyTensor`

**symbolic wrapper**, which can be used with **NumPy arrays** or **PyTorch
tensors** respectively.

To illustrate its main features on a **simple example**, let’s generate two point
clouds \((x_i)_{i\in[1,M]}\) and \((y_j)_{j\in[1,N]}\) in the unit square:

```
import numpy as np
M, N = 1000, 2000
x = np.random.rand(M, 2)
y = np.random.rand(N, 2)
```

With NumPy, an efficient way of computing the index of the **nearest y-neighbor**

for all points \(x_i\) is to perform a `numpy.argmin()`

reduction on the **M-by-N matrix** of squared distances

computed using **tensorized**, broadcasted operators:

```
x_i = x[:, None, :] # (M, 1, 2) numpy array
y_j = y[None, :, :] # (1, N, 2) numpy array
D_ij = ((x_i - y_j) ** 2).sum(-1) # (M, N) array of squared distances |x_i-y_j|^2
s_i = np.argmin(D_ij, axis=1) # (M,) array of integer indices
print(s_i[:10])
```

Out:

```
[1968 460 563 840 599 351 1585 1660 664 70]
```

That’s good! Going further, we may speed-up these computations
using the **CUDA routines** of the PyTorch library:

```
import torch
use_cuda = torch.cuda.is_available()
tensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
x_i = tensor(x[:, None, :]) # (M, 1, 2) torch tensor
y_j = tensor(y[None, :, :]) # (1, N, 2) torch tensor
D_ij = ((x_i - y_j) ** 2).sum(-1) # (M, N) tensor of squared distances |x_i-y_j|^2
s_i = D_ij.argmin(dim=1) # (M,) tensor of integer indices
print(s_i[:10])
```

Out:

```
tensor([1968, 460, 563, 840, 599, 351, 1585, 1660, 664, 70],
device='cuda:0')
```

But **can we scale to larger point clouds?**
Unfortunately, tensorized codes will throw an exception
as soon as the **M-by-N matrix** \((D_{i,j})\) stops fitting
contiguously on the device memory, which happens
when \(\sqrt{MN}\) goes past a hardware-dependent threshold
in the [5,000; 50,000] range:

```
M, N = (100000, 200000) if use_cuda else (1000, 2000)
x = np.random.rand(M, 2)
y = np.random.rand(N, 2)
x_i = tensor(x[:, None, :]) # (M, 1, 2) torch tensor
y_j = tensor(y[None, :, :]) # (1, N, 2) torch tensor
try:
D_ij = ((x_i - y_j) ** 2).sum(-1) # (M, N) tensor of squared distances |x_i-y_j|^2
except RuntimeError as err:
print(err)
```

Out:

```
CUDA out of memory. Tried to allocate 149.01 GiB (GPU 0; 10.76 GiB total capacity; 9.93 MiB already allocated; 9.88 GiB free; 18.00 MiB reserved in total by PyTorch)
```

**That’s unfortunate…** And unexpected!
After all, modern GPUs routinely handle
the real-time rendering of scenes with millions of triangles moving around.
So how do SIGGRAPH programmers achieve such
a level of performance?

The key to efficient numerical schemes is to remark that
even though the distance matrix \((D_{i,j})\) is not **sparse** in the
traditional sense, it definitely is **from a computational point of view**.
As its coefficients are fully described by two lists of points
and a **symbolic formula**, sensible implementations will always
compute required values on-the-fly…
Bypassing, **lazily**, the cumbersome pre-computation and storage
of all pairwise distances \(\|x_i-y_j\|^2\).

```
from pykeops.numpy import LazyTensor as LazyTensor_np
x_i = LazyTensor_np(x[:, None, :]) # (M, 1, 2) KeOps LazyTensor, wrapped around the numpy array x
y_j = LazyTensor_np(y[None, :, :]) # (1, N, 2) KeOps LazyTensor, wrapped around the numpy array y
D_ij = ((x_i - y_j) ** 2).sum(-1) # **Symbolic** (M, N) matrix of squared distances
print(D_ij)
```

Out:

```
KeOps LazyTensor
formula: Sum(Square((Var(0,2,0) - Var(1,2,1))))
shape: (100000, 200000)
```

With KeOps, implementing **lazy** numerical schemes really
is **that simple**!
Our `LazyTensor`

variables
are encoded by a list of data arrays plus an arbitrary
symbolic formula, written with a custom mathematical syntax
that is modified after each “pythonic” operation such as `-`

, `**2`

or `.exp()`

.

We may now perform our `pykeops.torch.LazyTensor.argmin()`

reduction using
an efficient Map-Reduce scheme, implemented
as a highly templated CUDA kernel around
our custom symbolic formula.
As evidenced by our benchmarks,
the KeOps routines have a **linear memory footprint**
and generally **outperform tensorized GPU implementations by two orders of magnitude**.

```
s_i = D_ij.argmin(dim=1).ravel() # genuine (M,) array of integer indices
print("s_i is now a {} of shape {}.".format(type(s_i), s_i.shape))
print(s_i[:10])
```

Out:

```
s_i is now a <class 'numpy.ndarray'> of shape (100000,).
[ 46465 85844 155588 316 142286 58386 35086 146843 155873 182062]
```

Going further, you may combine `LazyTensors`

using a **wide range of mathematical operations**.
For instance, with data arrays stored directly on the GPU,
a Laplacian kernel dot product

in dimension D=10 can be performed with:

```
from pykeops.torch import LazyTensor
D = 10
x = torch.randn(M, D).type(tensor) # M target points in dimension D, stored on the GPU
y = torch.randn(N, D).type(tensor) # N source points in dimension D, stored on the GPU
b = torch.randn(N, 4).type(tensor) # N values of the 4D source signal, stored on the GPU
x.requires_grad = True # In the next section, we'll compute gradients wrt. x!
x_i = LazyTensor(x[:, None, :]) # (M, 1, D) LazyTensor
y_j = LazyTensor(y[None, :, :]) # (1, N, D) LazyTensor
D_ij = ((x_i - y_j) ** 2).sum(-1).sqrt() # Symbolic (M, N) matrix of distances
K_ij = (- D_ij).exp() # Symbolic (M, N) Laplacian (aka. exponential) kernel matrix
a_i = K_ij @ b # The matrix-vector product "@" can be used on "raw" PyTorch tensors!
print("a_i is now a {} of shape {}.".format(type(a_i), a_i.shape))
```

Out:

```
a_i is now a <class 'torch.Tensor'> of shape torch.Size([100000, 4]).
```

## Automatic differentiation¶

Crucially, `LazyTensors`

**fully support** the `torch.autograd`

engine:
you may backprop through KeOps reductions as easily as through
vanilla PyTorch operations.
For instance, coming back to the kernel dot product above,
we may compute the gradient

with:

```
[g_i] = torch.autograd.grad((a_i ** 2).sum(), [x], create_graph=True)
print("g_i is now a {} of shape {}.".format(type(g_i), g_i.shape))
```

Out:

```
g_i is now a <class 'torch.Tensor'> of shape torch.Size([100000, 10]).
```

As usual with PyTorch, having set the `create_graph=True`

option
allows us to compute higher-order derivatives as needed:

```
[h_i] = torch.autograd.grad(g_i.exp().sum(), [x], create_graph=True)
print("h_i is now a {} of shape {}.".format(type(h_i), h_i.shape))
```

Out:

```
h_i is now a <class 'torch.Tensor'> of shape torch.Size([100000, 10]).
```

Warning

As of today, backpropagation is **not supported** through
the `pykeops.torch.LazyTensor.min()`

, `pykeops.torch.LazyTensor.max()`

or `pykeops.torch.LazyTensor.Kmin()`

reductions:
we’re working on it, but are not there just yet.
Until then, a simple workaround is to use
the indices computed by the
`pykeops.torch.LazyTensor.argmin()`

, `pykeops.torch.LazyTensor.argmax()`

or `pykeops.torch.LazyTensor.argKmin()`

reductions to define a fully differentiable PyTorch tensor as we now explain.

Coming back to our example about nearest neighbors in the unit cube:

```
x = torch.randn(M, 3).type(tensor)
y = torch.randn(N, 3).type(tensor)
x.requires_grad = True
x_i = LazyTensor(x[:, None, :]) # (M, 1, 3) LazyTensor
y_j = LazyTensor(y[None, :, :]) # (1, N, 3) LazyTensor
D_ij = ((x_i - y_j) ** 2).sum(-1) # Symbolic (M, N) matrix of squared distances
```

We could compute the `(M,)`

vector of squared distances to the **nearest y-neighbor** with:

```
to_nn = D_ij.min(dim=1).view(-1)
```

But instead, using:

```
s_i = D_ij.argmin(dim=1).view(-1) # (M,) integer Torch tensor
to_nn_alt = ((x - y[s_i, :]) ** 2).sum(-1)
```

outputs the same result, while also allowing us to **compute arbitrary gradients**:

```
print("Difference between the two vectors: {:.2e}".format(
(to_nn - to_nn_alt).abs().max()))
[g_i] = torch.autograd.grad(to_nn_alt.sum(), [x])
print("g_i is now a {} of shape {}.".format(type(g_i), g_i.shape))
```

Out:

```
Difference between the two vectors: 5.96e-08
g_i is now a <class 'torch.Tensor'> of shape torch.Size([100000, 3]).
```

The only real downside here is that we had to write **twice** the
“squared distance” formula that specifies our computation.
We hope to fix this (minor) inconvenience sooner rather than later!

## Batch processing¶

As should be expected, `LazyTensors`

also provide a simple support of **batch processing**,
with broadcasting over dummy (=1) batch dimensions:

```
A, B = 7, 3 # Batch dimensions
x_i = LazyTensor(torch.randn(A, B, M, 1, D))
l_i = LazyTensor(torch.randn(1, 1, M, 1, D))
y_j = LazyTensor(torch.randn(1, B, 1, N, D))
s = LazyTensor(torch.rand(A, 1, 1, 1, 1))
D_ij = ((l_i * x_i - y_j) ** 2).sum(-1) # Symbolic (A, B, M, N, 1) LazyTensor
K_ij = (- 1.6 * D_ij / (1 + s ** 2)) # Some arbitrary (A, B, M, N, 1) Kernel matrix
a_i = K_ij.sum(dim=3)
print("a_i is now a {} of shape {}.".format(type(a_i), a_i.shape))
```

Out:

```
a_i is now a <class 'torch.Tensor'> of shape torch.Size([7, 3, 100000, 1]).
```

Everything works just fine, with two major caveats:

The structure of KeOps computations is still a little bit

**rigid**, and`LazyTensors`

should only be used in situations where the**large**dimensions M and N over which the main reduction will be performed are in positions -3 and -2 (respectively), with**vector**variables in position -1 and an arbitrary number of batch dimensions beforehand. We’re working towards a full support of**tensor**variables, but this will probably take a few more weeks to implement and test properly…KeOps

`LazyTensors`

never collapse their last “dimension”, even after a`.sum(-1)`

reduction whose**keepdim**argument is implicitely set to**True**.

```
print("Convenient, numpy-friendly shape: ", K_ij.shape)
print("Actual shape, used internally by KeOps: ", K_ij._shape)
```

Out:

```
Convenient, numpy-friendly shape: (7, 3, 100000, 200000)
Actual shape, used internally by KeOps: (7, 3, 100000, 200000, 1)
```

This is the reason why in the example above,
**a_i** is a 4D Tensor of shape `(7, 3, 1000, 1)`

and **not**
a 3D Tensor of shape `(7, 3, 1000)`

.

## Supported formulas¶

The full range of mathematical operations supported by
`LazyTensors`

is described
in our API documentation.
Let’s just mention that the lines below define valid computations:

```
x_i = LazyTensor(torch.randn(A, B, M, 1, D))
l_i = LazyTensor(torch.randn(1, 1, M, 1, D))
y_j = LazyTensor(torch.randn(1, B, 1, N, D))
s = LazyTensor(torch.rand(A, 1, 1, 1, 1))
F_ij = (x_i ** 1.5 + y_j / l_i).cos() - (x_i | y_j) + (x_i[:, :, :, :, 2] * s.relu() * y_j)
print(F_ij)
a_j = F_ij.sum(dim=2)
print("a_j is now a {} of shape {}.".format(type(a_j), a_j.shape))
```

Out:

```
KeOps LazyTensor
formula: ((Cos((Powf(Var(0,10,0), Var(1,1,2)) + (Var(2,10,1) / Var(3,10,0)))) - (Var(0,10,0) | Var(2,10,1))) + ((Elem(Var(0,10,0),2) * ReLU(Var(4,1,2))) * Var(2,10,1)))
shape: (7, 3, 100000, 200000, 10)
a_j is now a <class 'torch.Tensor'> of shape torch.Size([7, 3, 200000, 10]).
```

Enjoy! And feel free to check the next tutorial for a discussion
of the varied reduction operations that can be applied to
KeOps `LazyTensors`

.

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