RFC: add API to enforce a specified dimensionality

Comienza leyendo la discusión previa en el issue #97 y después revisa la firma propuesta de enforce_shape y los ejemplos de este RFC. Determina si existe consenso sobre la API y el manejo de la dimensionalidad desconocida, los símbolos y los puntos suspensivos; se considera terminado cuando hay una especificación acordada, no un plan de implementación.

Previous discussion: https://github.com/data-apis/array-api/issues/97
Resolution was to ignore the case of data-dependent (unknown) dimensionalities.

I have a suggestion how to implement it by adding a new function to the API with a specific interface.

This allows to support variable dimensionality in a large number of practical cases.
Additionally, it allows to partially control dimension sizes that are currently unknown.

Example 1:

x, [d1, d2, d3] = xp.enforce_shape(x, [None, None, None])

enforces dimensionality to be 3, will fail for inputs of other dimensionalities.
If dimensionality is not yet known - will fail in runtime at this operation.
Elements d1, d2 and d3 are integers or symbols, not Nones.

Returned x is either the same tensor, or tensor with attached information about shape.
Main reason for returning a tensor is that operation could not be cut out of the graph if resulting shape is not used to obtain result.

Example 2:

x, [d1, d2, d3] = xp.enforce_shape(x, [d1_input, None, 4])

expects input to have 3 dimensions, first axis matches to (maybe) symbol d1_input, last axis of length 4.

Example 3:

x, [d1, d2, (axes, n_elements), d3] = xp.enforce_shape(x, [1, None, ..., 3])

expects input to have 3 or more dimensions. First axis is of length 1, second of any length, last of length 3.

Note here, that for ellipsis a tuple with two objects is returned: axes corresponds to x.shape[2:-1], and n_elements is a product of elements in axes.

axes is either a tuple of Union[int, Symbol] (if dimensionality is known)
or a Symbol for a tuple of unknown size (if dimensionality is unknown), same type as used to represent unknown shape.

n_elements is either int (if size of all axes is known) or symbol for axis.

Signature

Tensor, List[Union[int, None, AxisSymbol, ellipsis]] -> Tuple[Tensor, List[Union[int, AxisSymbol, EllipsisTuple]]] 
EllipsisTuple = Tuple[TupleSymbol, AxisSymbol] 
              | Tuple[Tuple[SizeOrSymbol, ...], SizeOrSymbol] 
SizeOrSymbol = Union[int, AxisSymbol]

I see this as a good intermediate point: sufficient to deal with multiple practical cases while requiring framework developers to maintain very little.

Downstream package dev perspective

• there are cases when a dimensionality should be enforced (e.g. 1d/2d). This is covered by proposed function
• there are cases when axes can be split into operation-important and non-important.
  There is a generic path to deal with these cases by first reducing to a fixed dimensionality, and converting back afterwards.
  Example: last dimension should be rgb, should convert to hsl. Assuming we have a function that implements conversion for 2d array.

x_rgb, [(axes, n_elements), last_axis] = xp.enforce_shape(x_rgb, [..., None])
x_rgb_2d = xp.reshape(x_rgb, (n_elements, last_axis))
x_hsv_2d = convert_rgb_to_hsv(x_rgb_2d) # or any other function that works with 'fixed' dimensionality
result = xp.reshape(x_hsv_2d, axes + (last_axis,))

• if there is a need to e.g. specify last or first dimension, now it is possible with graph-based frameworks too. Standard way is to add validations like tf.assert(...) and bind them in the graph, which can't be done in a cross-framework manner

Frameworks perspective

While the function is novel, it does not introduce any new entities: symbols for axes and symbols for shapes (like TensorShape) should exist anyway.
Function behavior overlaps with and extends tf.set_shape by supporting ellipsis.

For frameworks without data-dependent shapes, this is straightforward to implement based on already exposed shape property.