Hey All, We have 4 binding +1 votes, no non-binding +1 votes, and no -1 votes, so the vote passes.
Thanks everyone for your work and participation on this! As a follow up we will: [ ] merge changes to the format ( https://github.com/apache/arrow/pull/37166/files) [ ] merge C++ and Python implementation ( https://github.com/apache/arrow/pull/38008) Rok On Mon, Oct 2, 2023 at 4:25 PM Rok Mihevc <rok.mih...@gmail.com> wrote: > +1 > Thanks everyone for voting! > > I'd like to leave the vote open until Wednesday, > > Rok > > On Fri, Sep 29, 2023 at 8:58 PM Matt Topol <zotthewiz...@gmail.com> wrote: > >> +1 >> >> Thanks for all the work here! >> >> On Fri, Sep 29, 2023 at 11:04 AM Dewey Dunnington >> <de...@voltrondata.com.invalid> wrote: >> >> > +1! Thank you for iterating on this with all of us! >> > >> > On Fri, Sep 29, 2023 at 11:28 AM Alenka Frim >> > <ale...@voltrondata.com.invalid> wrote: >> > > >> > > +1 >> > > Thanks for pushing this through! >> > > >> > > On Wed, Sep 27, 2023 at 2:44 PM Rok Mihevc <rok.mih...@gmail.com> >> wrote: >> > > >> > > > Hi all, >> > > > >> > > > Following the discussion [1][2] I would like to propose a vote to >> add >> > > > variable shape tensor canonical extension type language to >> > > > CanonicalExtensions.rst [3] as written below. >> > > > A draft C++ implementation and a Python wrapper can be seen here >> [2]. >> > > > >> > > > The vote will be open for at least 72 hours. >> > > > >> > > > [ ] +1 Accept this proposal >> > > > [ ] +0 >> > > > [ ] -1 Do not accept this proposal because... >> > > > >> > > > >> > > > [1] >> https://lists.apache.org/thread/qc9qho0fg5ph1dns4hjq56hp4tj7rk1k >> > > > [2] https://github.com/apache/arrow/pull/37166 >> > > > [3] >> > > > >> > > > >> > >> https://github.com/apache/arrow/blob/main/docs/source/format/CanonicalExtensions.rst >> > > > >> > > > >> > > > Variable shape tensor >> > > > ===================== >> > > > >> > > > * Extension name: `arrow.variable_shape_tensor`. >> > > > >> > > > * The storage type of the extension is: ``StructArray`` where struct >> > > > is composed of **data** and **shape** fields describing a single >> > > > tensor per row: >> > > > >> > > > * **data** is a ``List`` holding tensor elements of a single >> tensor. >> > > > Data type of the list elements is uniform across the entire >> column. >> > > > * **shape** is a ``FixedSizeList<uint32>[ndim]`` of the tensor >> shape >> > > > where >> > > > the size of the list ``ndim`` is equal to the number of >> dimensions >> > of >> > > > the >> > > > tensor. >> > > > >> > > > * Extension type parameters: >> > > > >> > > > * **value_type** = the Arrow data type of individual tensor >> elements. >> > > > >> > > > Optional parameters describing the logical layout: >> > > > >> > > > * **dim_names** = explicit names of tensor dimensions >> > > > as an array. The length of it should be equal to the shape >> > > > length and equal to the number of dimensions. >> > > > >> > > > ``dim_names`` can be used if the dimensions have well-known >> > > > names and they map to the physical layout (row-major). >> > > > >> > > > * **permutation** = indices of the desired ordering of the >> > > > original dimensions, defined as an array. >> > > > >> > > > The indices contain a permutation of the values [0, 1, .., N-1] >> > where >> > > > N is the number of dimensions. The permutation indicates which >> > > > dimension of the logical layout corresponds to which dimension >> of >> > the >> > > > physical tensor (the i-th dimension of the logical view >> corresponds >> > > > to the dimension with number ``permutations[i]`` of the physical >> > > > tensor). >> > > > >> > > > Permutation can be useful in case the logical order of >> > > > the tensor is a permutation of the physical order (row-major). >> > > > >> > > > When logical and physical layout are equal, the permutation will >> > always >> > > > be ([0, 1, .., N-1]) and can therefore be left out. >> > > > >> > > > * **uniform_dimensions** = indices of dimensions whose sizes are >> > > > guaranteed to remain constant. Indices are a subset of all >> possible >> > > > dimension indices ([0, 1, .., N-1]). >> > > > The uniform dimensions must still be represented in the >> ``shape`` >> > > > field, >> > > > and must always be the same value for all tensors in the array >> -- >> > this >> > > > allows code to interpret the tensor correctly without accounting >> > for >> > > > uniform dimensions while still permitting optional optimizations >> > that >> > > > take advantage of the uniformity. ``uniform_dimensions`` can be >> > left >> > > > out, >> > > > in which case it is assumed that all dimensions might be >> variable. >> > > > >> > > > * **uniform_shape** = shape of the dimensions that are guaranteed >> to >> > stay >> > > > constant over all tensors in the array, with the shape of the >> > ragged >> > > > dimensions >> > > > set to 0. >> > > > An array containing a tensor with shape (2, 3, 4) and >> > > > ``uniform_dimensions`` >> > > > (0, 2) would have ``uniform_shape`` (2, 0, 4). >> > > > >> > > > * Description of the serialization: >> > > > >> > > > The metadata must be a valid JSON object, that optionally includes >> > > > dimension names with keys **"dim_names"**, ordering of >> > > > dimensions with key **"permutation"**, indices of dimensions whose >> > sizes >> > > > are guaranteed to remain constant with key >> **"uniform_dimensions"** >> > and >> > > > shape of those dimensions with key **"uniform_shape"**. >> > > > Minimal metadata is an empty JSON object. >> > > > >> > > > - Example of minimal metadata is: >> > > > >> > > > ``{}`` >> > > > >> > > > - Example with ``dim_names`` metadata for NCHW ordered data: >> > > > >> > > > ``{ "dim_names": ["C", "H", "W"] }`` >> > > > >> > > > - Example with ``uniform_dimensions`` metadata for a set of color >> > images >> > > > with variable width: >> > > > >> > > > ``{ "dim_names": ["H", "W", "C"], "uniform_dimensions": [1] }`` >> > > > >> > > > - Example of permuted 3-dimensional tensor: >> > > > >> > > > ``{ "permutation": [2, 0, 1] }`` >> > > > >> > > > This is the physical layout shape and the shape of the logical >> > > > layout given an individual tensor of shape [100, 200, 500] would >> > > > be ``[500, 100, 200]``. >> > > > >> > > > .. note:: >> > > > >> > > > With the exception of permutation all other parameters and storage >> > > > of VariableShapeTensor define the *physical* storage of the >> tensor. >> > > > >> > > > For example, consider a tensor with: >> > > > shape = [10, 20, 30] >> > > > dim_names = [x, y, z] >> > > > permutations = [2, 0, 1] >> > > > >> > > > This means the logical tensor has names [z, x, y] and shape [30, >> 10, >> > 20]. >> > > > >> > > > Elements in a variable shape tensor extension array are stored >> > > > in row-major/C-contiguous order. >> > > > >> > > > >> > > > >> > > > Rok >> > > > >> > >> >
