[GitHub] [arrow] pitrou commented on a diff in pull request #33925: GH-33923: [Docs] Tensor canonical extension type specification

["pitrou (via GitHub)" <gi...@apache.org>]

pitrou commented on code in PR #33925:
URL: https://github.com/apache/arrow/pull/33925#discussion_r1094354347


##########
docs/source/format/CanonicalExtensions.rst:
##########
@@ -72,4 +72,30 @@ same rules as laid out above, and provide backwards compatibility guarantees.
 Official List
 =============
 
-No canonical extension types have been standardized yet.
+Fixed shape tensor
+==================
+
+* Extension name: `arrow.fixed_shape_tensor`.
+
+* The storage type of the extension: ``FixedSizeList`` where:
+
+  * **value_type** is the data type of individual tensors and
+    is an instance of ``pyarrow.DataType`` or ``pyarrow.Field``.
+  * **list_size** is the product of all the elements in tensor shape.
+
+* Extension type parameters:
+
+  * **value_type** = Arrow DataType of the tensor elements
+  * **shape** = shape of the contained tensors as a tuple
+  * **is_row_major** = boolean indicating the order of elements

Review Comment:
   Note that "contiguous" can be ambiguous. There are two possible meanings:
   * physical contiguity: all values span a single contiguous area in memory (there are no "holes" without values in them), regardless of how they are ordered
   * logical contiguity: values can be walked in either "C" or "F" order
   
   Numpy uses the latter meaning. Note that logical contiguity implies physical contiguity, but the converse is not true, for example if you swap some dimensions in a more than 3+D array.
   
   For now, it seems this spec and discussion is talking about _physical_ contiguity, as it doesn't care how the values are logically accessed (the interpretation of data is not part of the spec).
   



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