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[mlir][sparse] fixed doc formatting 

Indentation seems to have an impact on website layout.

Reviewed By: grosul1

Differential Revision: https://reviews.llvm.org/D107403
This commit is contained in:
Aart Bik 2021-08-03 14:36:03 -07:00
parent cb2a2ba8d6
commit 75baf6285e
2 changed files with 38 additions and 38 deletions
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2
mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorBase.td
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@ -29,7 +29,7 @@ def SparseTensor_Dialect : Dialect {
sparse code automatically was pioneered for dense linear algebra by
[Bik96] in MT1 (see https://www.aartbik.com/sparse.php) and formalized
to tensor algebra by [Kjolstad17,Kjolstad20] in the Sparse Tensor
Algebra Compiler (TACO) project (see http://tensor-compiler.org/).
Algebra Compiler (TACO) project (see http://tensor-compiler.org).
The MLIR implementation closely follows the "sparse iteration theory"
that forms the foundation of TACO. A rewriting rule is applied to each

74
mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorOps.td
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@ -56,20 +56,20 @@ def SparseTensor_ConvertOp : SparseTensor_Op<"convert", [SameOperandsAndResultTy
Results<(outs AnyTensor:$dest)> {
string summary = "Converts between different tensor types";
string description = [{
Converts one sparse or dense tensor type to another tensor type. The rank
and dimensions of the source and destination types must match exactly,
only the sparse encoding of these types may be different. The name `convert`
was preferred over `cast`, since the operation may incur a non-trivial cost.
Converts one sparse or dense tensor type to another tensor type. The rank
and dimensions of the source and destination types must match exactly,
only the sparse encoding of these types may be different. The name `convert`
was preferred over `cast`, since the operation may incur a non-trivial cost.
When converting between two different sparse tensor types, only explicitly
stored values are moved from one underlying sparse storage format to
the other. When converting from an unannotated dense tensor type to a
sparse tensor type, an explicit test for nonzero values is used. When
converting to an unannotated dense tensor type, implicit zeroes in the
sparse storage format are made explicit. Note that the conversions can have
non-trivial costs associated with them, since they may involve elaborate
data structure transformations. Also, conversions from sparse tensor types
into dense tensor types may be infeasible in terms of storage requirements.
When converting between two different sparse tensor types, only explicitly
stored values are moved from one underlying sparse storage format to
the other. When converting from an unannotated dense tensor type to a
sparse tensor type, an explicit test for nonzero values is used. When
converting to an unannotated dense tensor type, implicit zeroes in the
sparse storage format are made explicit. Note that the conversions can have
non-trivial costs associated with them, since they may involve elaborate
data structure transformations. Also, conversions from sparse tensor types
into dense tensor types may be infeasible in terms of storage requirements.
Examples:
@ -88,15 +88,15 @@ def SparseTensor_ToPointersOp : SparseTensor_Op<"pointers", [NoSideEffect]>,
Results<(outs AnyStridedMemRefOfRank<1>:$result)> {
let summary = "Extract pointers array at given dimension from a tensor";
let description = [{
Returns the pointers array of the sparse storage scheme at the
given dimension for the given sparse tensor. This is similar to the
`memref.buffer_cast` operation in the sense that it provides a bridge
between a tensor world view and a bufferized world view. Unlike the
`memref.buffer_cast` operation, however, this sparse operation actually
lowers into a call into a support library to obtain access to the
pointers array.
Returns the pointers array of the sparse storage scheme at the
given dimension for the given sparse tensor. This is similar to the
`memref.buffer_cast` operation in the sense that it provides a bridge
between a tensor world view and a bufferized world view. Unlike the
`memref.buffer_cast` operation, however, this sparse operation actually
lowers into a call into a support library to obtain access to the
pointers array.
Example:
Example:
```mlir
%1 = sparse_tensor.pointers %0, %c1
@ -112,15 +112,15 @@ def SparseTensor_ToIndicesOp : SparseTensor_Op<"indices", [NoSideEffect]>,
Results<(outs AnyStridedMemRefOfRank<1>:$result)> {
let summary = "Extract indices array at given dimension from a tensor";
let description = [{
Returns the indices array of the sparse storage scheme at the
given dimension for the given sparse tensor. This is similar to the
`memref.buffer_cast` operation in the sense that it provides a bridge
between a tensor world view and a bufferized world view. Unlike the
`memref.buffer_cast` operation, however, this sparse operation actually
lowers into a call into a support library to obtain access to the
indices array.
Returns the indices array of the sparse storage scheme at the
given dimension for the given sparse tensor. This is similar to the
`memref.buffer_cast` operation in the sense that it provides a bridge
between a tensor world view and a bufferized world view. Unlike the
`memref.buffer_cast` operation, however, this sparse operation actually
lowers into a call into a support library to obtain access to the
indices array.
Example:
Example:
```mlir
%1 = sparse_tensor.indices %0, %c1
@ -136,15 +136,15 @@ def SparseTensor_ToValuesOp : SparseTensor_Op<"values", [NoSideEffect]>,
Results<(outs AnyStridedMemRefOfRank<1>:$result)> {
let summary = "Extract numerical values array from a tensor";
let description = [{
Returns the values array of the sparse storage scheme for the given
sparse tensor, independent of the actual dimension. This is similar to
the `memref.buffer_cast` operation in the sense that it provides a bridge
between a tensor world view and a bufferized world view. Unlike the
`memref.buffer_cast` operation, however, this sparse operation actually
lowers into a call into a support library to obtain access to the
values array.
Returns the values array of the sparse storage scheme for the given
sparse tensor, independent of the actual dimension. This is similar to
the `memref.buffer_cast` operation in the sense that it provides a bridge
between a tensor world view and a bufferized world view. Unlike the
`memref.buffer_cast` operation, however, this sparse operation actually
lowers into a call into a support library to obtain access to the
values array.
Example:
Example:
```mlir
%1 = sparse_tensor.values %0 : tensor<64x64xf64, #CSR> to memref<?xf64>