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F# RFC FS-1077 - Tolerant Slicing

The design suggestion Tolerant Slicing has been approved for RFC. This RFC covers the detailed proposal for this suggestion.


This RFC proposes loosening up the out-of-bounds checking behavior on slicing for lists, strings, arrays (including multidimensional variants). Out-of-bounds indices specified during slicing will be "snapped to the ends of the array" instead of throwing out of range exception. This will improve usability for certain use cases.


In our current implementation of slicing, when an out-of-bounds index is given for the beginning or end of the slice, the resultant behavior can differ depending on the case.

For example, l.[(-1)..0] throws an error, because -1 is not a valid index in the list. Based on this, one may expect l.[0..(-1)] to also throw an error. However, the latter slice completes successfully and returns [].

To illustrate this, we provide a more detailed examination of F#, C#, and Python slicing behaviors:

Assuming we have a list L with lower bound a and upper bound b, then: L = { L[a], L[a+1] ... L[b-1], L[b] }

Our current slicing behavior in F# looks like:

L.[x..y] x < a a <= x <= b x > b
y < a if x > y [] else Error [] []
a <= y <= b Error if x > y [] else { L[x] .. L[y] } []
y > b Error Error if x > y [] else Error

The current behavior in C# 8 is:

L[x..y] x < a a <= x <= b+1 x > b+1
y < a Error Error Error
a <= y <= b+1 Error { L[x] .. L[y-1] } Error
y > b+1 Error Error Error

The current behavior in Python is:

L[x:y] a <= x <= b+1 x > b+1
a <= y <= b+1 if x > y [] else { L[x] .. L[y-1] } []
y > b+1 { L[x] .. L[b] } []

(Note: x < a and y < a cases aren't shown here because Python negative indices mean something else)

As in the example above the slicing behavior in F# is a lot more confusing than C# or Python. Sometimes when indexes are out of bounds you get [], sometimes you get Error, and sometimes you get either.

C# disallows any out of bound indexes and Python just takes L[x:y] = L[x:min(y, b)] and [] if the bounds don't make sense. The out-of-bounds slicing behavior in C# and Python are much easier to create a mental model of, especially to new users.

Sample use case

One common technique used in machine learning models used to classify images is max pooling. Typically, this involves "pooling" together a 2D slice of a 2D matrix representation of an image, and extracting a single value from this pool. We then slide this pool around the entire image, obtaining a new representation of the image that yields better results.

This may look like:


For example, if we want to use max pooling with a size of 3x3, we could write code that looks like

for i <- 0..height-3
    for j <- 0..width-3
        result[i][j] = max (image[i..i+2, j..j+2])

However, notice that the result after pooling is smaller. We are losing resolution, and we ideally want our input size to equal our output.

One solution commonly used in the machine learning world is padding, where we pad the edges of the input with zeros, like


This way, after padding, when we apply the sliding window to mean pool the image, we obtain a result image of the same dimensions.

This could look like:

image2 = new [image.height+2][image.width+2]

for i <- 1..image.height-1
    for j <- image.width-1
        image2[i+1][j+1] = image[i][j]

for i <- 0..image2.height-3
    for j <- 0..image2.width-3
        result[i][j] = max (image2[i..i+2, j..j+2])

However, this can be simplified greatly with different slicing semantics. With the new slicing behavior, we could write:

for i <- 0..height
    for j <- 0..width
        result[i][j] = max (image[i-1..i+1, j-1..j+1])

Because the new slicing behavior ignores values that are out of bounds, this effectively accomplishes the same result as padding the edges of the input image with zeroes.

And this would accomplish the same mean pooling behavior, but with:

  • Less code
  • Better performance
  • Uses less memory
  • Easier to understand
  • Harder to mess up bounds

Detailed design

l.[(-1)..0] vs l.[0..(-1)]

This is the proposed out-of-bound slicing behavior for F#:

L.[x..y] x < a a <= x <= b x > b
y < a [] [] []
a <= y <= b { L[a] .. L[y] } if x > y [] else { L[x] .. L[y] } []
y > b { L[a] .. L[b] } { L[x] .. L[b] } []

This is pretty much equivalent to L.[x..y] = L.[max(x, a)..min(y, b)] and [] otherwise. The slicing behavior would be consistent with Python.

Using the example above, we'd end up with:

`j' l.[0..j] l.[..j] l.[j..2] l.[j..]
≤-1 [] [] [0;1;2] [0;1;2]
0 [0] [0] [0;1;2] [0;1;2]
1 [0;1] [0;1] [1;2] [1;2]
2 [0;1;2] [0;1;2] [2] [2]
≥3 [0;1;2] [0;1;2] [] []


  • The legacy behavior has been L.[0..(-1)] = [], so we don't want to change that to Error all of a sudden.
  • This would work with concatenation and recursive cases where we need a base case of l.[0..(-1)] = []. This behavior lends itself well to elegant, functional code based on composition with little explicit bounds checking (if any).
  • For people who are doing calculations on arrays of numbers (perhaps ML workloads) your life would be easier as you wouldn't have to explicitly check for bounds.
  • This would bring F# more in line with python and make it more accessible for python users, which is a growth opportunity for F# that does not rely on C# bleeding functional programmers


  • We'd change the behavior for cases like L.[0..99999] and L.[-1..0] where they would become valid. If users are catching that exception and performing logic based on that we'd break their code (they shouldn't be doing so anyways).
  • If you accidentally access an out-of-bounds slice the compiler's not going to warn you about it.


  • Keep existing behavior for out-of-bounds slicing
  • Follow the C# behavior and throw errors, which would be a runtime-breaking change.

Unresolved questions


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