[mlir][sparse] improve sparse attribute documentation

[aartbik]

Summary

Moved some parts from comments (not user facing) to the actual description
(user facing). Rephrased a bit as well.

[vmrajas]

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td

38

Adding "(unique)" after compressed could end up confusing users. It could either mean that:

1. "unique" can be used interchangeably with "compressed".
2. only unique elements are stored.
3. The can be used only when elements in the tensor are unique.

Can we remove "(unique)" here and may be add more explanation at the end of this note. (after "no duplicates.")

42

support --> supported

[aartbik]

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td

38

But this is TACO terminology. They use compressed (U) and compressed (not U) for this, even though compressed is the user facing keyword. But I changed it as you requested.

[aartbik]

addressed comments

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorAttrDefs.td

def SparseTensorEncodingAttr : SparseTensor_Attr<"SparseTensorEncoding",
   let description = [{
     An attribute to encode TACO-style information on sparsity properties
     of tensors. The encoding is eventually used by a **sparse compiler**
     pass to generate sparse code fully automatically for all tensor
     expressions that involve tensors with a sparse encoding. Compiler
     passes that run before this sparse compiler pass need to be
     aware of the semantics of tensor types with such an encoding.
 
+    The attribute consists of the following fields.
+    - Dimension level type for each dimension of a tensor type:
+        - **dense** : dimension is dense, all entries along this dimension
+    are stored.
+        - **compressed** (unique) : dimension is sparse, only nonzeros along
+    this dimensions are stored, no duplicates.
+    - Dimension ordering on the indices of this tensor type. Unlike dense
+      storage, most sparse storage schemes do not provide fast random access.
+      This affine map specifies the order of dimensions that should be support
+      by the sparse storage scheme. For example, for a 2-d tensor, "(i,j) -> (i,j)"
+      requests row-wise storage and "(i,j) -> (j,i)" requests column-wise storage.
+    - The required bit width for "pointer" storage (integral offsets into
+      the sparse storage scheme). A narrow width reduces the memory footprint
+      of overhead storage, as long as the width suffices to define the total
+      required range (viz. the maximum number of stored entries over all indirection
+      dimensions). The choices are `8`, `16`, `32`, `64`, or `0` for a native width.
+    - The required bit width for "index" storage (elements of the coordinates of
+      stored entries). A narrow width reduces the memory footprint of overhead
+      storage, as long as the width suffices to define the total required range
+      (viz. the maximum value of each tensor index over all dimensions). The
+      choices are `8`, `16`, `32`, `64`, or `0` for a native width.
+
     Example:
 
     ```mlir
     #DCSC = #sparse_tensor.encoding<{
       dimLevelType = [ "compressed", "compressed" ],
       dimOrdering = affine_map<(i,j) -> (j,i)>,
       pointerBitWidth = 32,
       indexBitWidth = 8
     }>
 
-
     ... tensor<8x8xf64, #DCSC> ...
     ```
   }];
 
   // Data in sparse tensor encoding.
   let parameters = (
     ins
     // A dimension level type for each dimension of a tensor type.
-    // The choices are `dense` (dimension should be stored in its entirety),
-    // `compressed` (only non-zero regions or elements should be stored),
-    // or `singleton` (no sibling elements for parent).
     ArrayRefParameter<
       "SparseTensorEncodingAttr::DimLevelType",
-      "Per-dimension level type"
+      "Per-dimension level type (dense or compressed)"
       >: $dimLevelType,
     // A dimension order on the indices of this tensor type.
-    // Unlike dense storage, most sparse storage schemes do not provide
-    // fast random access. This affine map specifies the order of
-    // dimensions that should be support by the sparse storage scheme
-    // (e.g. (i,j) -> (i,j) requests 2-d row-wise and (i,j) -> (j,i)
-    // requests 2-d column-wise storage).
     // TODO: block structure with higher-dim inputs
     "AffineMap":$dimOrdering,
-    // The required bit width for pointer storage. A narrow width reduces
-    // the memory footprint of overhead storage, as long as the width
-    // suffices to define the total required range (viz. the maximum
-    // number of stored entries over all indirection dimensions). The choices
-    // are `8`, `16`, `32`, `64`, or `0` for a native width.
+    // The required bit width for pointer storage.
     "unsigned":$pointerBitWidth,
-    // The required bit width for index storage. A narrow width reduces
-    // the memory footprint of overhead storage, as long as the width
-    // suffices to define the total required range (viz. the maximum
-    // value of each tensor index over all dimensions). The choices are `8`,
-    // `16`, `32`, `64`, or `0` for a native width.
+    // The required bit width for index storage.
     "unsigned":$indexBitWidth
   );
 
   let genVerifyDecl = 1;
   let hasCustomAssemblyFormat = 1;
 
   let extraClassDeclaration = [{
-    // Dimension level types that define sparse tensors:
-    //   Dense               - dimension is dense, every entry is stored
-    //   Compressed (unique) - dimension is sparse, only nonzeros are stored (no duplicates)
+    // Dimension level types.
     enum class DimLevelType {
       Dense, Compressed
     };
   }];
 }
 
 def IsSparseTensorPred
   : CPred<"!!::mlir::sparse_tensor::getSparseTensorEncoding($_self)">;