asem000 / kernex Goto Github PK

How does Kernex handle borders and out-of-bounds indices, especially with padding="same"? I skimmed the source code but did not find it. Very superficially it seems to me that it performs 0-padding. Is this the case?

If yes, it would be useful to support different schemes like "mirror", "edge", or ideally dropping the out-of-bounds indices.

Repro:

from pylab import *
import jax.numpy as jnp
import kernex

@kernex.kmap(kernel_size=(11, 11), padding="same")
def kernel(patch):
    return jnp.mean(patch)

data = ones((100, 100))
out = kernel(data)

figure()
plot(out[50], "+-", label="Kernex")
plot(ones_like(out[50]), "+-", label="Ideal")
legend()

Does kernex support Pytrees? I did not find an example. It would be very useful to support moving-window filters with "global" weights or simply multiple inputs, such as a cross-channel bilateral filter in my case.

Repro:

import jax.numpy as jnp
import kernex

@kernex.kmap(kernel_size=(3, 3))
def kernel(tree):
    x, y = tree
    return jnp.sum(x * jnp.square(y))

data = jnp.arange(20 * 30).reshape((20, 30))
out = kernel((data, data))

raises

Traceback (most recent call last):
  File "/home/clemisch/kernex_tree.py", line 52, in <module>
    out = kernel((data, data))
          ^^^^^^^^^^^^^^^^^^^^
  File "/home/clemisch/venvs/11/lib64/python3.11/site-packages/kernex/interface/kernel_interface.py", line 131, in call
    self.shape = array.shape
                 ^^^^^^^^^^^
AttributeError: 'tuple' object has no attribute 'shape'

This project looks really cool!

I would love to understand at a high level how this package works -- how do you actually implement stencil computations in JAX? Do you reuse jax.lax.scan or something else? Does it support auto-diff? How does performance compare on CPU/GPU/TPU (or whichever configs you've tried)?

Enable the kernel map backend.
The default now is vmap, however it is beneficial to enable the user to choose among other variants like pmap and lax.map

Docstring and some example in readme.

• Dilation can be added by changing the view indices generation step value.

kernex operate on views of the first function array argument, it is useful to add argnum/argname to select which argument to operate on in a similar fashion in jax transformation

I think mode="reflect" for padding_kwargs is incorrect:

import jax.numpy as jnp
import kernex

@kernex.kmap(
    kernel_size=(3,),
    padding=("same"),
    relative=False,
    padding_kwargs=dict(mode="reflect"),
)
def f(x):
    return x

x = jnp.array([1, 2, 3, 4, 5])
y = f(x)
z = jnp.pad(x, 1, mode="reflect")

print("x: ", x)
print("y: ", y)
print("z: ", z)

gives

x:  [1 2 3 4 5]
y:  [[3 1 2]     # <-- the `3` is incorrect, should be `2`
     [1 2 3]
     [2 3 4]
     [3 4 5]
     [4 5 4]]
z:  [2 1 2 3 4 5 4]   # <-- here, the first element is `2`

The Kernex output reflects incorrectly: the first element is 3 instead of 2.