[Mlir-commits] [mlir] [mlir][tensor] Document `dest` operand (PR #71726)

[Matthias Springer]

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@@ -18,31 +18,32 @@ def Tensor_Dialect : Dialect {
   let description = [{
     The `tensor` dialect is intended to hold core tensor creation and
     manipulation ops, which are not strongly associated with any particular
-    other dialect or domain abstraction. The primary smoke test of this is ops
-    that make sense for any tensor element type.
-
-    We leave it to other dialects to hold the vast swath of possible
-    computations one might want to do on a tensor.
-
-    The `tensor` type is (for better or for worse) used to represent all kinds
-    of things, and supports an open-ended set of element types. Examples:
+    other dialect or domain abstraction. The primary inclusion criteria for ops
+    in this dialect is that they make sense for any tensor element type. When
+    this is not the case, the op is left to live in other dialects. Examples of
+    element types that could be supported by the `tensor` dialect include:
 
     - representing large, dense aggregations of primitive types, suitable for
       high-performance numerical computing.
-    - representing shapes in the `shape` dialect, which consist of small
-      1D tensors of `index` data type.
+    - representing shapes in the `shape` dialect, which consist of small 1D
+      tensors of `index` data type.
     - representing aggregations of strings or “variant” types.
-    - representing large, sparse aggregations of primitive types, suitable
-      for high-performance numerical computing.
+    - representing large, sparse aggregations of primitive types, suitable for
+      high-performance numerical computing.
 
-    Thus, for the `tensor` dialect, we prefer for now to constrain the
-    scope as much as possible. The expectation is that at some point
+    Because of this broad element type support, we prefer for now to keep the
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matthias-springer wrote:

I would emphasize less the "broad element type support" here (in line 34). Imo, the main reason to keep the `tensor` dialect small is because we have dedicated dialects for various MLIR concepts/functionality such as sparsity (`sparse_tensor`) or structured computations (`linalg`). And the `tensor` dialect contains only the most basic operations around tensors that do not conceptually belong into any of the other dialects.


https://github.com/llvm/llvm-project/pull/71726