[mlir][sparse] Renaming "pointer/index" to "position/coordinate"

[wrengr]

Summary

The old "pointer/index" names often cause confusion since these names clash with names of unrelated things in MLIR; so this change rectifies this by changing everything to use "position/coordinate" terminology instead.

In addition to the basic terminology, there have also been various conventions for making certain distinctions like: (1) the overall storage for coordinates in the sparse-tensor, vs the particular collection of coordinates of a given element; and (2) particular coordinates given as a Value or TypedValue<MemRefType>, vs particular coordinates given as ValueRange or similar. I have striven to maintain these distinctions
as follows:

• "p/c" are used for individual position/coordinate values, when there is no risk of confusion. (Just like we use "d/l" to abbreviate "dim/lvl".)
• "pos/crd" are used for individual position/coordinate values, when a longer name is helpful to avoid ambiguity or to form compound names (e.g., "parentPos"). (Just like we use "dim/lvl" when we need a longer form of "d/l".)
  
  I have also used these forms for a handful of compound names where the old name had been using a three-letter form previously, even though a longer form would be more appropriate. I've avoided renaming these to use a longer form purely for expediency sake, since changing them would require a cascade of other renamings. They should be updated to follow the new naming scheme, but that can be done in future patches.
• "coords" is used for the complete collection of crd values associated with a single element. In the runtime library this includes both std::vector and raw pointer representations. In the compiler, this is used specifically for buffer variables with C++ type Value, TypedValue<MemRefType>, etc.
  
  The bare form "coords" is discouraged, since it fails to make the dim/lvl distinction; so the compound names "dimCoords/lvlCoords" should be used instead. (Though there may exist a rare few cases where is is appropriate to be intentionally ambiguous about what coordinate-space the coords live in; in which case the bare "coords" is appropriate.)
  
  There is seldom the need for the pos variant of this notion. In most circumstances we use the term "cursor", since the same buffer is reused for a 'moving' pos-collection.
• "dcvs/lcvs" is used in the compiler as the ValueRange analogue of "dimCoords/lvlCoords". (The "vs" stands for "Values".) I haven't found the need for it, but "pvs" would be the obvious name for a pos-ValueRange.
  
  The old "ind"-vs-"ivs" naming scheme does not seem to have been sustained in more recent code, which instead prefers other mnemonics (e.g., adding "Buf" to the end of the names for TypeValue<MemRefType>). I have cleaned up a lot of these to follow the "coords"-vs-"cvs" naming scheme, though haven't done an exhaustive cleanup.
• "positions/coordinates" are used for larger collections of pos/crd values; in particular, these are used when referring to the complete sparse-tensor storage components.
  
  I also prefer to use these unabbreviated names in the documentation, unless there is some specific reason why using the abbreviated forms helps resolve ambiguity.

In addition to making this terminology change, this change also does some cleanup along the way:

• correcting the dim/lvl terminology in certain places.
• adding const when it requires no other code changes.
• miscellaneous cleanup that was entailed in order to make the proper distinctions. Most of these are in CodegenUtils.{h,cpp}

[wrengr]

The one remaining concern I have with our new naming convention, is what exactly to name the bitwidth fields of the encoding attribute. Originally (and as currently uploaded) I went with the long-form names positionWidth/coordinateWidth, because I was worried about the UX of leaking our coding convention into the dialect syntax itself. However, when doing my final editing pass I noticed that there are other places where our naming convention leaks into the dialect syntax already (e.g., ops which take lvlCoords vs coordinates). So now I'm wondering if we should use the names posWidth/crdWidth afterall, to be consistent with the naming convention within the compiler itself.

Thoughts?

[Peiming]

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

150–154

So, the convention here is we apply dim ordering transformation *before* high order mapping to translate lvlCrds to dimCrds?

231

To answer your question, I would vote for posBitWidth and crdBitWidth, but this looks good to me too.

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

122

Same here, but probably need to make it clear that we returns lvlCrds instead of dimCrds array

486

This is not related to this patch, but @aartbik do you think it is better for us to use dimCoords here to make it more similar to tensor.insert operation?

Let's say, if users wants to insert a elements into block sparsity tensor, do we want the whole list of lvlCrds to be provided, or just the dimCrds?

Both way works, but just want to make it clear.

An ideal world is that we can provide seamless APIs to sparse tensors as if they are dense ones (i.e., any operation that uses lvl are internal in sparse compiler, otherwise they can be used by other dialects with little risk).

[Peiming]

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

62–63

Though I have to admit that I did not think about this when introducing the operation, I think there should be no such restriction.

If user requests a result tensor with dimOrdering, all they have to do this to ensure that the coordinates they provided are indeed in the provided ordering.

We just to make it clear that we will not permute the coordinates in the provided array but simply takes it .

[aartbik]

In D144773#4151916, @wrengr wrote:

So now I'm wondering if we should use the names posWidth/crdWidth afterall, to be consistent with the naming convention within the compiler itself.

Answering this "inline", I think we should use posWidth/crdWidth indeed to have a single convention, *and* because it is just shorter...

[aartbik]

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

23

period at end for pedantic consistency

25

can we link to def of IndexAttr?

(I am not a proponent of linking too much, but since this is not a sparse tensor type specific thing, it may be worthwhile)

26

maybe summary string or something like that, since in the code below, the field name is not immediately apperent?

134

perhaps add "no sibling at this level" to make the distinction Stephen explained to us very clear?

147

Perhaps even make this a FIXME, or TODO since your upcoming syntax changes will fix that?

174

FIXME/TODO?

231

+1 on the pos/crd syntax? perhaps also dependent on how you plan to represent this in the upcoming change?

301–302

Is adding "MLIR" really useful. It feels like we need to do that at (too) many other places too then?

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

113

number-of-stored-entries?

138

nice catch! ;-)

mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h

18

I somehow find the PCV notation a lot more readable ;-)

mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp

320

changes here are a bit more than just mechanical....

mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.h

51

also pos/crd?

[wrengr]

Addressing comments

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

25

I really wish tablegen allowed deriving from def (instead of only from class), since that'd obviate the need for most of this commentary explanation (let alone duplicating the code for the predicate etc)

134

This is one of those examples I mentioned where I feel like the wording used in the StableHLO spec is very good, whereas the wording here is a bit outdated / less clear. (I was hoping to get away with relatively cursory documentation cleanup for this CL; and was planning to copy over the StableHLO description in the CL that implements the new "hybrid" syntax.)

150–154

Yep. (I'm just documenting the currently implemented behavior/semantics; this CL doesn't change the intended semantics nor the implementation thereof.)

231

Cool cool, I'll switch to "pos/crd" to make it more consistent

Re future changes, my thoughts are: (1) so long as we only support "one size fits all" bitwidths, I'm planning to leave these as top-level fields. (2) once we support per-level bitwidths, I'm planning to add fields of the same name to the struct/dict for each level-type where the bitwidths are relevant. (3) once we have per-level bitwidths, we may want to keep the top-level fields around to specify a default value shared by all levels (which individual levels can then override), or we may want to remove the top-level fields in order to simplify the roundtripping problem.

301–302

I forget why I added that here...

I do think there are places where we should be more explicit about the "MLIR Type" vs "C++ type" distinction (especially when having team discussions). Though I agree that it's not necessary/useful to do so everywhere in the code (since it's usually obvious).

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

62–63

I agree that it'd be nice to lift the restriction (so long as it's clearly documented what coordinate-space the $coordinates are in); however, I'm not sure whether the current implementation supports it (feel free to let me know :). Fwiw, this CL isn't trying to change anything, it's just trying to clarify the (currently) intended semantics. I'll reword this to try making it clearer that the restriction is with the current implementation rather than with the intended semantics.

Supposing that $coordinates remain level-coordinates that are un/packed directly, the main issue I foresee re supporting dimOrdering and higherOrdering is re type inference/checking. In particular, if we want to ensure safety then I see two options: (1) the $coordinates needs to come equipped with the lvlSizes the coordinates are valid for, so that we can check those lvlSizes against the ones inferred from the dimSizes+orderings; (2) we'd need to scan through $coordinates to check that all the coords are in-bounds for the lvlSizes inferred from the dimSizes+orderings. The first option is clearly preferable from a performance perspective, though for it to actually ensure safety we need to trust whoever provides the lvlSizes.

486

I think we really want two different ops here:

• For the sake of offering a drop-in replacement for dense tensors, it makes sense to have an op that takes dimCoords. Though since the goal is to be a drop-in replacement, why not just have our passes rewrite tensor.insert itself (like we do for other tensor-dialect ops)?

• However, the current sparse_tensor::InsertOp serves as an IR for the sparse-compiler itself and isn't intended for end-users. In particular, the op serves as a proxy for the runtime library's function —which takes lvlCoords, since it expects the compiler to have handled all the dim<->lvl stuff beforehand. (And for the codegen pass, the op is lowered to analogous code.)

We have a whole bunch of these internal-only ops. It would probably be a good idea to move all these off to a "subdialect" sparse_tensor.internal (or whatever) to keep them distinct from the user-facing ops. That would be especially nice as we start picking up users, since it helps clarify how stable the API is/isn't. (It would be more ideal to split these off into a proper Dialect, since that'd clean up the code for declaring which ops are/aren't legal after a pass finishes. However that'd require a whole lot of extra boilerplate.)

mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h

18

:)

mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp

320

Yeah. I considered splitting this (and other changes to this file) off into a separate CL, but it didn't feel right to rename everything else but then leave these in an inconsistent state. But I can split them off to a separate CL if you'd prefer

[Peiming]

mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp

624

I feel that you probably do not need (void)vsize, since assert are also guarded by ifndef NDEBUG.

But maybe it is better to keep it to make it more robust.

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp

404–405

I think you can safely rename all dim in LoopEmitter.cpp to lvl, it does not take care of dimOrdering (but already handled by Sparsification.cpp)

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.h

263

I think pos is the right name here, it is a value *loaded* from posBuffer, rather than a index to the buffer. (See L970 in LoopEmitter.cpp)

[wrengr]

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp

404–405

Yep. As per my previous comment though, cleaning up the LoopEmitter and Utils/Merger stuff snowballed into another huge CL. Thankfully those files are fairly well insulated from the rest of the compiler, so it's easy enough to fork that off into a separate patch

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.h

263

Yeah, I was pretty sure it's the position analogue of "coords". (We still don't have a good name for that though...)

I'll leave it as a todo for now though. I've avoided doing much renaming in this file, since when I tried, it snowballed into another huge CL (since all the stuff in LoopEmitter and Utils/Merger also needs to switch to using "lvl" instead of "dim"). That'll prolly come out next week or so

[wrengr]

rebase

[aartbik]

mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp

63–65

interesting cleanup, yes. I really meant pointer here due to the way I view memref as the base address at this point, but it is good to avoid the confusion of all different meanings here, and it is indeed a memref ;-)

[wrengr]

mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp

63–65

fwiw, I was following the precedent set by some of our other code for similar sorts of MemRef helper functions :)

mlir/include/mlir-c/Dialect/SparseTensor.h

enum MlirSparseTensorDimLevelType {
   MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON_NO = 18,     // 0b100_10
   MLIR_SPARSE_TENSOR_DIM_LEVEL_SINGLETON_NU_NO = 19,  // 0b100_11
 };
 
 //===----------------------------------------------------------------------===//
 // SparseTensorEncodingAttr
 //===----------------------------------------------------------------------===//
 
-/// Checks whether the given attribute is a sparse_tensor.encoding attribute.
+/// Checks whether the given attribute is a `sparse_tensor.encoding` attribute.
 MLIR_CAPI_EXPORTED bool
 mlirAttributeIsASparseTensorEncodingAttr(MlirAttribute attr);
 
-/// Creates a sparse_tensor.encoding attribute with the given parameters.
+/// Creates a `sparse_tensor.encoding` attribute with the given parameters.
 MLIR_CAPI_EXPORTED MlirAttribute mlirSparseTensorEncodingAttrGet(
-    MlirContext ctx, intptr_t numDimLevelTypes,
+    MlirContext ctx, intptr_t lvlRank,
     enum MlirSparseTensorDimLevelType const *dimLevelTypes,
-    MlirAffineMap dimOrdering, MlirAffineMap higherOrdering,
-    int pointerBitWidth, int indexBitWidth);
+    MlirAffineMap dimOrdering, MlirAffineMap higherOrdering, int posWidth,
+    int crdWidth);
 
-/// Returns the number of dim level types in a sparse_tensor.encoding attribute.
+/// Returns the level-rank of the `sparse_tensor.encoding` attribute.
 MLIR_CAPI_EXPORTED intptr_t
-mlirSparseTensorEncodingGetNumDimLevelTypes(MlirAttribute attr);
+mlirSparseTensorEncodingGetLvlRank(MlirAttribute attr);
 
-/// Returns a specified dim level type in a sparse_tensor.encoding attribute.
+/// Returns a specified level-type of the `sparse_tensor.encoding` attribute.
 MLIR_CAPI_EXPORTED enum MlirSparseTensorDimLevelType
-mlirSparseTensorEncodingAttrGetDimLevelType(MlirAttribute attr, intptr_t pos);
+mlirSparseTensorEncodingAttrGetDimLevelType(MlirAttribute attr, intptr_t lvl);
 
-/// Returns the dimension ordering in a sparse_tensor.encoding attribute.
+/// Returns the dimension-ordering of the `sparse_tensor.encoding` attribute.
 MLIR_CAPI_EXPORTED MlirAffineMap
 mlirSparseTensorEncodingAttrGetDimOrdering(MlirAttribute attr);
 
-/// Returns the higher ordering in a sparse_tensor.encoding attribute.
+/// Returns the higher-ordering of the `sparse_tensor.encoding` attribute.
 MLIR_CAPI_EXPORTED MlirAffineMap
 mlirSparseTensorEncodingAttrGetHigherOrdering(MlirAttribute attr);
 
-/// Returns the pointer bit width in a sparse_tensor.encoding attribute.
+/// Returns the position bitwidth of the `sparse_tensor.encoding` attribute.
 MLIR_CAPI_EXPORTED int
-mlirSparseTensorEncodingAttrGetPointerBitWidth(MlirAttribute attr);
+mlirSparseTensorEncodingAttrGetPosWidth(MlirAttribute attr);
 
-/// Returns the index bit width in a sparse_tensor.encoding attribute.
+/// Returns the coordinate bitwidth of the `sparse_tensor.encoding` attribute.
 MLIR_CAPI_EXPORTED int
-mlirSparseTensorEncodingAttrGetIndexBitWidth(MlirAttribute attr);
+mlirSparseTensorEncodingAttrGetCrdWidth(MlirAttribute attr);
 
 #ifdef __cplusplus
 }
 #endif
 
 #include "mlir/Dialect/SparseTensor/Transforms/Passes.capi.h.inc"
 
 #endif // MLIR_C_DIALECT_SPARSETENSOR_H

mlir/include/mlir/Dialect/SparseTensor/IR/Enums.h

 #include <complex>
 #include <optional>
 
 namespace mlir {
 namespace sparse_tensor {
 
 /// This type is used in the public API at all places where MLIR expects
 /// values with the built-in type "index".  For now, we simply assume that
-/// type is 64-bit, but targets with different "index" bit widths should
+/// type is 64-bit, but targets with different "index" bitwidths should
 /// link with an alternatively built runtime support library.
 // TODO: support such targets?
 using index_type = uint64_t;
 
-/// Encoding of overhead types (both pointer overhead and indices
+/// Encoding of overhead types (both position overhead and coordinate
 /// overhead), for "overloading" @newSparseTensor.
 enum class OverheadType : uint32_t {
   kIndex = 0,
   kU64 = 1,
   kU32 = 2,
   kU16 = 3,
   kU8 = 4
 };

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

 include "mlir/IR/TensorEncoding.td"
 
 // All of the Tensor attributes will extend this class.
 class SparseTensor_Attr<string name,
                         list<Trait> traits = []>
     : AttrDef<SparseTensor_Dialect, name, traits>;
 
 //===----------------------------------------------------------------------===//
+// Type aliases.
+//
+// These attributes are just like `IndexAttr` (include/mlir/IR/OpBase.td),
+// except that:
+// (1) the `summary` is more specific (i.e., the fourth parameter to
+//     `TypedAttrBase`), which helps tablegen provide better error messages.
+// (2) tablegen-generated getters will have the given `returnType`, in
+//     lieu of the `APInt` that `IndexAttr` uses.  This avoids the boilerplate
+//     of needing to say `get{FOO}().getZExtValue()`, as well as using
+//     C++ types which better document intent.
+//===----------------------------------------------------------------------===//
+
+def DimensionAttr :
+    TypedAttrBase<
+      Index, "IntegerAttr",
+      And<[CPred<"$_self.isa<::mlir::IntegerAttr>()">,
+           CPred<"$_self.cast<::mlir::IntegerAttr>().getType()"
+                 ".isa<::mlir::IndexType>()">]>,
+      "dimension attribute"> {
+  let returnType = [{::mlir::sparse_tensor::Dimension}];
+  let convertFromStorage = [{$_self.getValue().getZExtValue()}];
+}
+
+def LevelAttr :
+    TypedAttrBase<
+      Index, "IntegerAttr",
+      And<[CPred<"$_self.isa<::mlir::IntegerAttr>()">,
+           CPred<"$_self.cast<::mlir::IntegerAttr>().getType()"
+                 ".isa<::mlir::IndexType>()">]>,
+      "level attribute"> {
+  let returnType = [{::mlir::sparse_tensor::Level}];
+  let convertFromStorage = [{$_self.getValue().getZExtValue()}];
+}
+
+//===----------------------------------------------------------------------===//
 // Sparse Tensor Dimension Slice Attribute.
 //===----------------------------------------------------------------------===//
 
 def SparseTensorDimSliceAttr : SparseTensor_Attr<"SparseTensorDimSlice", []> {
   let mnemonic = "slice";
 
   let description = [{
     An attribute to encode slice information of a sparse tensor on a particular
let description = [{
     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** : dimension is sparse, only nonzeros along this dimensions
-          are stored
-        - **singleton** : dimension stores individual indices with no siblings
-      By default, each dimension level types has the property of being unique
-      (no duplicates at that level) and ordered (indices appear sorted at that
-      level). The following two suffixes can be used to make the last two
-      dimension level types not-unique (duplicates may appear) and not-ordered
-      (indices may appear unsorted).
+    - Level-type for each level of a tensor type:
+        - **dense** : all entries along this level are stored.
+        - **compressed** : only nonzeros along this level are stored.
+        - **singleton** : a variant of the compressed level-format,
+          for when coordinates are guaranteed to have no siblings at this level.
+      By default, each level-type has the property of being unique (no
+      duplicates at that level) and ordered (coordinates appear sorted
+      at that level).  The following two suffixes can be used to specify
+      that the level should instead be non-unique (duplicates may appear)
+      and/or non-ordered (coordinates may appear unsorted).
         - **-nu** : not unique
         - **-no** : not ordered
-      Currently, these suffixes, is present, should appear in this order.
-      In the future, we may introduce many more dimension level types and
-      properties, and separate specifying the two completely rather than
-      using this suffix mechanism.
-
-    - An optional 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
-      supported 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.  By default, an identify mapping is used,
-      which implies that the original indices directly correspond to stored
-      indices.
-
-    - An optional higher-ordering mapping from the original index space of
-      the tensor to a higher-order index space, used to define block-sparse
-      storage or ELL (jagged diagonal) storage. For example, for a 2-d tensor,
-      the mapping `(i, j) -> (i floordiv 2, j floordiv 3, i mod 2, j mod 3)`
-      imposes an higher-order partitioning into 2x3 blocks along the matrix
-      layout. A dimension ordering can be used to define a desired ordering
-      on this higher-order index space. Likewise, the dimension level types
-      define dense or compressed storage along this higher-order index space.
-      For block-sparse, blocks are typically stored with compression while
-      dense storage is used within each block (although hybrid schemes are
-      possible as well). The higher-order mapping also provides a notion of
-      "counting a dimension", where every stored element with the same index
-      is mapped to a new slice. For instance, ELL storage of a 2-d tensor can
-      be defined with the mapping `(i, j) -> (#i, i, j)` using the notation
-      of [Chou20]. Lacking the `#` symbol in MLIR's affine mapping, we use
-      a free symbol `c` to define such counting, together with a constant
-      that denotes the number of resulting slices. For example, the mapping
-      `(i, j)[c] -> (c * 3 * i, i, j)` with the first two higher-order indices
-      stored dense and the innermost compressed denotes ELL storage with
-      three jagged diagonals that count the dimension `i`.
-
-      TODO: introduce a real counting symbol to MLIR's mapping, since an
-            expression like 3*c*i has no direct interpretation?
-
-    - 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, the default, `0` to
-      indicate the native bit 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, the default, `0` to indicate a
-      native bit width.
+      Currently, these suffixes (if present) must appear in this order.
+      In the future, we may introduce additional level-types and
+      properties, and split up how the level-format and properties are
+      specified rather than using this suffix mechanism.
+
+      TODO: This field is called "dimLevelType" for historical reasons,
+      even though the types are per-level rather than per-dimension.
+      (This will be corrected in an upcoming change that completely
+      overhauls the syntax of this attribute.)
+
+    - An optional permutation which maps (higher-ordering)-coordinates
+      to level-coordinates; defaulting to the identity permutation.
+      For example, given a 2-d tensor with the default higher-ordering,
+      `(i, j) -> (i, j)` specifies row-wise storage and `(i, j) ->
+      (j, i)` specifies column-wise storage.
+
+      TODO: this field is called "dimOrdering" for historical reasons,
+      even though it actually operates on level-coordinates rather than
+      dimension-coordinates.
+      (This will be corrected in an upcoming change that completely
+      overhauls the syntax of this attribute.)
+
+    - An optional higher-order mapping from dimension-coordinates to
+      a higher-order coordinate space; defaulting to the identity map.
+      This is applied before the `dimOrdering`, thus we have the composite:
+      dimCoords --higherOrdering--> hoCoords --dimOrdering--> lvlCoords.
+      The higher-order mapping is used to define block-sparse storage,
+      jagged-diagonal (JDS/ELL/ITPACK) storage, etc.
+
+      For example, given a 2-d tensor, the mapping
+      `(i, j) -> (i floordiv 2, j floordiv 3, i mod 2, j mod 3)`
+      imposes an higher-order partitioning into 2x3 blocks along the
+      matrix layout.  For block-sparsity, blocks are typically stored
+      with compression while dense storage is used within each block
+      (although hybrid schemes are possible as well).
+
+      TODO: the following example is out-of-date and will be implemented
+      in a different manner than described here.
+      (This will be corrected in an upcoming change that completely
+      overhauls the syntax of this attribute.)
+
+      The higher-order mapping also provides a notion of "counting a
+      dimension", where every stored element with the same coordinate
+      is mapped to a new slice.  For instance, ELL storage of a 2-d
+      tensor can be defined with the mapping `(i, j) -> (#i, i, j)`
+      using the notation of [Chou20].  Lacking the `#` symbol in MLIR's
+      affine mapping, we use a free symbol `c` to define such counting,
+      together with a constant that denotes the number of resulting
+      slices.  For example, the mapping `(i, j)[c] -> (c * 3 * i, i, j)`
+      with the level-types `["dense", "dense", "compressed"]` denotes ELL
+      storage with three jagged diagonals that count the dimension `i`.
+
+    - The required bitwidth for "position" 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 levels).  The choices are `8`, `16`,
+      `32`, `64`, or, the default, `0` to indicate the native bitwidth.
+
+    - The required bitwidth for "coordinate" storage (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
+      coordinate over all levels).  The choices are `8`, `16`, `32`,
+      `64`, or, the default, `0` to indicate a native bitwidth.
 
-    - An optional array of SparseTensorDimSliceAttr, which specifies how the sparse
-      tensor is partitioned on each level.
+    - An optional array of `SparseTensorDimSliceAttr`, which specifies
+      how the sparse tensor is partitioned on each dimension.
 
     Examples:
 
     ```mlir
     // Sparse vector.
     #SparseVector = #sparse_tensor.encoding<{
       dimLevelType = [ "compressed" ]
     }>
     ... tensor<?xf32, #SparseVector> ...
 
     // Sorted Coordinate Scheme.
     #SortedCOO = #sparse_tensor.encoding<{
       dimLevelType = [ "compressed-nu", "singleton" ]
     }>
     ... tensor<?x?xf64, #SortedCOO> ...
 
     // Doubly compressed sparse column storage with specific bitwidths.
     #DCSC = #sparse_tensor.encoding<{
       dimLevelType = [ "compressed", "compressed" ],
       dimOrdering = affine_map<(i, j) -> (j, i)>,
-      pointerBitWidth = 32,
-      indexBitWidth = 8
+      posWidth = 32,
+      crdWidth = 8
     }>
     ... tensor<8x8xf64, #DCSC> ...
 
     // Block sparse row storage (2x3 blocks).
     #BCSR = #sparse_tensor.encoding<{
       dimLevelType = [ "compressed", "compressed", "dense", "dense" ],
       dimOrdering  = affine_map<(ii, jj, i, j) -> (ii, jj, i, j)>,
       higherOrdering = affine_map<(i, j) -> (i floordiv 2, j floordiv 3, i mod 2, j mod 3)>
let description = [{
     ... tensor<?x?xf64, #CSC_SLICE> ...
 
     ```
   }];
 
   // Data in sparse tensor encoding.
   let parameters = (
     ins
-    // A dimension level type for each dimension of the tensor type.
+    // A level-type for each level of the sparse storage.
     ArrayRefParameter<
       "::mlir::sparse_tensor::DimLevelType",
-      "per dimension level type"
+      "level-types"
       >: $dimLevelType,
-    // A dimension order on the indices of this tensor type.
+    // A permutation from (higher-ordering)-coordinates to level-coordinates.
     "AffineMap":$dimOrdering,
-    // A mapping between the original and higher-ordering index space.
+    // A mapping from dimension-coordinates to (higher-ordering)-coordinates.
     "AffineMap":$higherOrdering,
-    // The required bit width for pointer storage.
-    "unsigned":$pointerBitWidth,
-    // The required bit width for index storage.
-    "unsigned":$indexBitWidth,
-    // A dimension level type for each dimension of the tensor type.
+    // The required bitwidth for position storage.
+    "unsigned":$posWidth,
+    // The required bitwidth for coordinate storage.
+    "unsigned":$crdWidth,
+    // A slice attribute for each dimension of the tensor type.
     ArrayRefParameter<
       "::mlir::sparse_tensor::SparseTensorDimSliceAttr",
       "per dimension slice metadata"
       >: $dimSlices
   );
 
   let builders = [
     AttrBuilder<(ins "ArrayRef<::mlir::sparse_tensor::DimLevelType>":$dimLevelType,
                      "AffineMap":$dimOrdering,
                      "AffineMap":$higherOrdering,
-                     "unsigned":$pointerBitWidth,
-                     "unsigned":$indexBitWidth), [{
+                     "unsigned":$posWidth,
+                     "unsigned":$crdWidth), [{
       return $_get($_ctxt, dimLevelType,
                          dimOrdering,
                          higherOrdering,
-                         pointerBitWidth,
-                         indexBitWidth,
+                         posWidth,
+                         crdWidth,
                          ArrayRef<::mlir::sparse_tensor::SparseTensorDimSliceAttr>{});
     }]>
   ];
 
   let extraClassDeclaration = [{
-    /// Returns the type for pointer storage based on pointerBitWidth
-    Type getPointerType() const;
+    /// Returns the type for position storage based on posWidth
+    Type getPosType() const;
 
-    /// Returns the type for index storage based on indexBitWidth
-    Type getIndexType() const;
+    /// Returns the type for coordinate storage based on crdWidth
+    Type getCrdType() const;
 
     /// Constructs a new encoding with the dimOrdering and higherOrdering
     /// reset to the default/identity.
     SparseTensorEncodingAttr withoutOrdering() const;
 
     /// Constructs a new encoding with the pointer and index bitwidth
     /// reset to the default.
     SparseTensorEncodingAttr withoutBitWidths() const;

 
 //===----------------------------------------------------------------------===//
 // Sparse Tensor Storage Specifier Enum Attribute.
 //===----------------------------------------------------------------------===//
 
 // The C++ enum for Storage Specifier kind.
 def SparseTensorStorageSpecifierKindEnum
     : I32EnumAttr<"StorageSpecifierKind", "sparse tensor storage specifier kind", [
-        I32EnumAttrCase<"DimSize",    0, "dim_sz">,
-        I32EnumAttrCase<"PtrMemSize", 1, "ptr_mem_sz">,
-        I32EnumAttrCase<"IdxMemSize", 2, "idx_mem_sz">,
+        I32EnumAttrCase<"LvlSize",    0, "lvl_sz">,
+        I32EnumAttrCase<"PosMemSize", 1, "pos_mem_sz">,
+        I32EnumAttrCase<"CrdMemSize", 2, "crd_mem_sz">,
         I32EnumAttrCase<"ValMemSize", 3, "val_mem_sz">,
       ]> {
   let genSpecializedAttr = 0;
   let cppNamespace = SparseTensor_Dialect.cppNamespace;
 }
 
 // Define the enum StorageSpecifier kind attribute.
 def SparseTensorStorageSpecifierKindAttr

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

def SparseTensor_Dialect : Dialect {
   let name = "sparse_tensor";
   let cppNamespace = "::mlir::sparse_tensor";
   let description = [{
     The `SparseTensor` dialect supports all the attributes, types,
     operations, and passes that are required to make sparse tensor
     types first class citizens within the MLIR compiler infrastructure.
     The dialect forms a bridge between high-level operations on sparse
     tensors types and lower-level operations on the actual sparse storage
-    schemes consisting of pointers, indices, and values. Lower-level
+    schemes consisting of positions, coordinates, and values. Lower-level
     support may consist of fully generated code or may be provided by
     means of a small sparse runtime support library.
 
     The concept of **treating sparsity as a property, not a tedious
     implementation detail**, by letting a **sparse compiler** generate
     sparse code automatically was pioneered for 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).
 
-    The MLIR implementation [Biketal22] closely follows the "sparse iteration
-    theory" that forms the foundation of TACO. A rewriting rule is applied to
-    each tensor expression in the Linalg dialect (MLIR's tensor index notation)
-    where the sparsity of tensors is indicated using the per-dimension level
-    types dense/compressed together with a specification of the order on the
-    dimensions (see [Chou18] for an in-depth discussions and possible
-    extensions to these level types). Subsequently, a topologically sorted
-    iteration graph, reflecting the required order on indices with respect
-    to the dimensions of each tensor, is constructed to ensure that all tensors
-    are visited in natural index order. Next, iteration lattices are
-    constructed for the tensor expression for every index in topological
-    order. Each iteration lattice point consists of a conjunction of tensor
-    indices together with a tensor (sub)expression that needs to be evaluated
-    for that conjunction.  Within the lattice, iteration points are ordered
-    according to the way indices are exhausted. As such these iteration
-    lattices drive actual sparse code generation, which consists of a
-    relatively straightforward one-to-one mapping from iteration lattices
-    to combinations of for-loops, while-loops, and if-statements. Sparse
-    tensor outputs that materialize uninitialized are handled with direct
-    insertions if all parallel loops are outermost or insertions that
-    indirectly go through a 1-dimensional access pattern expansion
+    The MLIR implementation [Biketal22] closely follows the "sparse
+    iteration theory" that forms the foundation of TACO.  A rewriting
+    rule is applied to each tensor expression in the Linalg dialect
+    (MLIR's tensor index notation) where the sparsity of tensors is
+    indicated using the per-level level-types (e.g., dense, compressed,
+    singleton) together with a specification of the order on the levels
+    (see [Chou18] for an in-depth discussions and possible extensions
+    to these level-types).  Subsequently, a topologically sorted
+    iteration graph, reflecting the required order on coordinates with
+    respect to the levels of each tensor, is constructed to ensure
+    that all tensors are visited in natural level-coordinate order.
+    Next, iteration lattices are constructed for the tensor expression
+    for every index in topological order.  Each iteration lattice point
+    consists of a conjunction of tensor coordinates together with a tensor
+    (sub)expression that needs to be evaluated for that conjunction.
+    Within the lattice, iteration points are ordered according to
+    the way coordinates are exhausted.  As such these iteration
+    lattices drive actual sparse code generation, which consists of
+    a relatively straightforward one-to-one mapping from iteration
+    lattices to combinations of for-loops, while-loops, and if-statements.
+    Sparse tensor outputs that materialize uninitialized are handled with
+    direct insertions if all parallel loops are outermost or insertions
+    that indirectly go through a 1-dimensional access pattern expansion
     (a.k.a. workspace) where feasible [Gustavson72,Bik96,Kjolstad19].
 
     * [Bik96] Aart J.C. Bik. Compiler Support for Sparse Matrix Computations.
     PhD thesis, Leiden University, May 1996.
     * [Biketal22] Aart J.C. Bik, Penporn Koanantakool, Tatiana Shpeisman,
     Nicolas Vasilache, Bixia Zheng, and Fredrik Kjolstad. Compiler Support
     for Sparse Tensor Computations in MLIR. ACM Transactions on Architecture
     and Code Optimization, June, 2022. See: https://dl.acm.org/doi/10.1145/3544559

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

string description = [{
     ```mlir
     sparse_tensor.new %source : !Source to tensor<1024x1024xf64, #CSR>
     ```
   }];
   let assemblyFormat = "$source attr-dict `:` type($source) `to` type($result)";
 }
 
 def SparseTensor_PackOp : SparseTensor_Op<"pack", [Pure]>,
-    Arguments<(ins 1DTensorOf<[AnyType]>:$data,
-                   2DTensorOf<[AnySignlessIntegerOrIndex]>:$indices)>,
+    Arguments<(ins 1DTensorOf<[AnyType]>:$values,
+                   2DTensorOf<[AnySignlessIntegerOrIndex]>:$coordinates)>,
     Results<(outs AnySparseTensor: $result)> {
-  let summary = "Returns a sparse tensor from the given (data, indices) pair";
+  let summary = "Returns a sparse tensor from the given (values, coordinates) pair";
 
   let description = [{
-    Packs the data/indices into a COO sparse tensor. The coordinates in `indices`
-    shall not exceed the dimension sizes of the returned sparse tensor. Note
-    that the returned tensor must be statically shaped because it is impossible
-    to infer the shape from sparse coordinates.
+    Packs the values/coordinates into a COO sparse tensor.  The length
+    of `values` must match the outer-length of `coordinates`, since these
+    two tensors are "zipped" together.  The `coordinates` argument provides
+    level-coords for each value, therefore, the inner-length of `coordinates`
+    must match the level-rank of the returned tensor, and each level-coords
+    must be valid for the level-sizes of the returned tensor.  Note that
+    the returned tensor must be statically shaped because it is impossible
+    to infer the dimension-shape from level-coordinates alone.
+
+    TODO: The returned tensor is allowed (in principle) to have non-identity
+    dimOrdering/higherOrdering mappings.  However, the current implementation
+    does not yet support them.
+
+    - `coordinates : tensor<NSE x lvlRank x iType>`
+      supplies the level-coords for each element in `values`.
+    - `values : tensor<NSE x V>`
+      supplies the corresponding values for each entry in `coordinates`.
 
-    `$indices`: stored via a 2-D tensor of integer elements with shape [N, ndims],
-    which specifies the indices of the elements in the sparse tensor that contains
-    non-zero values.
-
-    `$data`: stored via a 1-D tensor with shape [N], that supplies the corresponding
-    values for the indices.
-
-    The operation can be used to materialize a sparse tensor from external sources.
-    E.g., when passing from Python as two numpy arrays for data and indices.
+    This operation can be used to materialize a sparse tensor from external
+    sources; e.g., when passing two numpy arrays from Python.
 
     Example:
-    ```mlir
-    %data    = arith.constant dense<[ 1.1,   2.2,   3.3 ]> : tensor<3xf64>
-    %indices = arith.constant dense<[[0,0], [1,2], [1,3]]> : tensor<3x2xindex>
 
-    %st = sparse_tensor.pack %data, %indices : tensor<3xf64>, tensor<3x2xindex
-                                            to tensor<3x4xf64, #COO>
+    ```mlir
+    %values      = arith.constant dense<[ 1.1,   2.2,   3.3 ]> : tensor<3xf64>
+    %coordinates = arith.constant dense<[[0,0], [1,2], [1,3]]> : tensor<3x2xindex>
+    %st = sparse_tensor.pack %values, %coordinates
+        : tensor<3xf64>, tensor<3x2xindex> to tensor<3x4xf64, #COO>
     // yields COO format |1.1, 0.0, 0.0, 0.0|
     //     of 3x4 matrix |0.0, 0.0, 2.2, 3.3|
     //                   |0.0, 0.0, 0.0, 0.0|
     ```
   }];
 
-  let assemblyFormat = "$data `,` $indices attr-dict `:` type($data) `,` type($indices)"
-                                                   "`to` type($result)";
+  let assemblyFormat =
+    "$values `,` $coordinates attr-dict"
+    "`:` type($values) `,` type($coordinates) `to` type($result)";
 
   let hasVerifier = 1;
 }
 
 def SparseTensor_UnpackOp : SparseTensor_Op<"unpack", [Pure]>,
     Arguments<(ins AnySparseTensor:$tensor)>,
-    Results<(outs 1DTensorOf<[AnyType]>:$data,
-                  2DTensorOf<[AnySignlessIntegerOrIndex]>:$indices,
-                  AnySignlessIntegerOrIndex:$nnz)> {
-  let summary = "Returns the (data, indices) pair unpacked from the input tensor";
+    Results<(outs 1DTensorOf<[AnyType]>:$values,
+                  2DTensorOf<[AnySignlessIntegerOrIndex]>:$coordinates,
+                  AnySignlessIntegerOrIndex:$nse)> {
+  let summary = "Returns the (values, coordinates) pair unpacked from the input tensor";
+
+  let description = [{
+    The unpack operation is the inverse of `sparse_tensor::pack`.  It returns
+    the values, level-coordinates, and number-of-stored-entries extracted
+    from the sparse tensor.  The source tensor is allowed (in principle)
+    to have non-identity dimOrdering/higherOrdering mappings.  Regardless
+    of the mappings, the returned `coordinates` are always level-coordinates,
+    because this is what we mean by "unpacking" as opposed to other forms
+    of exposing sparse tensors to external clients.  This operation can be
+    used for returning an unpacked MLIR sparse tensor to frontend; e.g.,
+    returning two numpy arrays to Python.
 
-  let description = [{
-    Unpack is the inverse operation of `sparse_tensor::pack`. It returns the data/indices
-    extracted from a COO sparse tensor. Additionally, it also returns an integer value
-    indicating the number of entries in the source tensor.
-
-    The operation can be used to return an unpacked MLIR sparse tensor to frontend.
-    E.g., returning two numpy arrays for data and indices.
+    TODO: the current implementation does not yet support non-identity mappings.
 
-    The unpack operation ends the life time of the sparse tensor, and using this
-    after the unpack is undefined behavior.
+    This operation ends the lifetime of the sparse tensor, and using
+    the tensor after the unpack is undefined behavior.
 
     Example:
+
     ```mlir
     // input COO format |1.1, 0.0, 0.0, 0.0|
     //    of 3x4 matrix |0.0, 0.0, 2.2, 3.3|
     //                  |0.0, 0.0, 0.0, 0.0|
-    %data, %indices, %nnz = sparse_tensor.unpack %st
-                          : tensor<3x4xf64, #COO>
-                         to tensor<2xf64>, tensor<2x2xindex>, index
-
-    // %data    = arith.constant dense<[ 1.1,   2.2,   3.3 ]> : tensor<3xf64>
-    // %indices = arith.constant dense<[[0,0], [1,2], [1,3]]> : tensor<3x2xindex>
-    // %nnz = 2
-
+    %values, %coordinates, %nse
+      = sparse_tensor.unpack %st
+      : tensor<3x4xf64, #COO> to tensor<2xf64>, tensor<2x2xindex>, index
+    // %values      = arith.constant dense<[ 1.1,   2.2,   3.3 ]> : tensor<3xf64>
+    // %coordinates = arith.constant dense<[[0,0], [1,2], [1,3]]> : tensor<3x2xindex>
+    // %nse = 3
     ```
   }];
 
-  let assemblyFormat = "$tensor attr-dict `:` type($tensor) "
-                                        "`to` type($data) `,` type($indices)`,` type($nnz)";
+  let assemblyFormat =
+    "$tensor attr-dict `:` type($tensor)"
+    "`to` type($values) `,` type($coordinates) `,` type($nse)";
 
   let hasVerifier = 1;
 }
 
 def SparseTensor_ConvertOp : SparseTensor_Op<"convert",
   [Pure]>,
     Arguments<(ins AnyTensor:$source)>,
     Results<(outs AnyTensor:$dest)> {
string description = [{
     ```
 
   }];
   let assemblyFormat = "$source attr-dict `:` type($source) `to` type($dest)";
   let hasFolder = 1;
   let hasVerifier = 1;
 }
 
-def SparseTensor_ToPointersOp : SparseTensor_Op<"pointers", [Pure]>,
-    Arguments<(ins AnySparseTensor:$tensor, IndexAttr:$dimension)>,
+def SparseTensor_ToPositionsOp : SparseTensor_Op<"positions", [Pure]>,
+    Arguments<(ins AnySparseTensor:$tensor, LevelAttr:$level)>,
     Results<(outs AnyStridedMemRefOfRank<1>:$result)> {
-  let summary = "Extracts pointers array at given dimension from a tensor";
+  let summary = "Extracts the `level`-th positions array of the `tensor`";
   let description = [{
-    Returns the pointers array of the sparse storage format at the
-    given dimension for the given sparse tensor. This is similar to the
-    `bufferization.to_memref` operation in the sense that it provides a bridge
-    between a tensor world view and a bufferized world view. Unlike the
-    `bufferization.to_memref` operation, however, this sparse operation actually
-    lowers into code that extracts the pointers array from the sparse storage
-    scheme (either by calling a support library or through direct code).
+    Returns the positions array of the tensor's storage at the given
+    level.  This is similar to the `bufferization.to_memref` operation
+    in the sense that it provides a bridge between a tensor world view
+    and a bufferized world view.  Unlike the `bufferization.to_memref`
+    operation, however, this sparse operation actually lowers into code
+    that extracts the positions array from the sparse storage itself
+    (either by calling a support library or through direct code).
 
     Writing into the result of this operation is undefined behavior.
 
     Example:
 
     ```mlir
-    %1 = sparse_tensor.pointers %0 { dimension = 1 : index }
+    %1 = sparse_tensor.positions %0 { level = 1 : index }
        : tensor<64x64xf64, #CSR> to memref<?xindex>
     ```
   }];
   let assemblyFormat = "$tensor attr-dict `:` type($tensor) `to` type($result)";
   let hasVerifier = 1;
 }
 
-def SparseTensor_ToIndicesOp : SparseTensor_Op<"indices", [Pure]>,
-    Arguments<(ins AnySparseTensor:$tensor, IndexAttr:$dimension)>,
+def SparseTensor_ToCoordinatesOp : SparseTensor_Op<"coordinates", [Pure]>,
+    Arguments<(ins AnySparseTensor:$tensor, LevelAttr:$level)>,
     Results<(outs AnyStridedMemRefOfRank<1>:$result)> {
-  let summary = "Extracts indices array at given dimension from a tensor";
+  let summary = "Extracts the `level`-th coordinates array of the `tensor`";
   let description = [{
-    Returns the indices array of the sparse storage format at the
-    given dimension for the given sparse tensor. This is similar to the
-    `bufferization.to_memref` operation in the sense that it provides a bridge
-    between a tensor world view and a bufferized world view. Unlike the
-    `bufferization.to_memref` operation, however, this sparse operation actually
-    lowers into code that extracts the indices array from the sparse storage
-    scheme (either by calling a support library or through direct code).
+    Returns the coordinates array of the tensor's storage at the given
+    level.  This is similar to the `bufferization.to_memref` operation
+    in the sense that it provides a bridge between a tensor world view
+    and a bufferized world view.  Unlike the `bufferization.to_memref`
+    operation, however, this sparse operation actually lowers into code
+    that extracts the coordinates array from the sparse storage itself
+    (either by calling a support library or through direct code).
 
     Writing into the result of this operation is undefined behavior.
 
     Example:
 
     ```mlir
-    %1 = sparse_tensor.indices %0 { dimension = 1 : index }
+    %1 = sparse_tensor.coordinates %0 { level = 1 : index }
        : tensor<64x64xf64, #CSR> to memref<?xindex>
     ```
   }];
   let assemblyFormat = "$tensor attr-dict `:` type($tensor) `to` type($result)";
   let hasVerifier = 1;
 }
 
-def SparseTensor_ToIndicesBufferOp : SparseTensor_Op<"indices_buffer", [Pure]>,
+def SparseTensor_ToCoordinatesBufferOp : SparseTensor_Op<"coordinates_buffer", [Pure]>,
     Arguments<(ins AnySparseTensor:$tensor)>,
     Results<(outs AnyStridedMemRefOfRank<1>:$result)> {
-  let summary = "Extracts the linear indices array from a tensor";
+  let summary = "Extracts the linear coordinates array from a tensor";
   let description = [{
-    Returns the linear indices array for a sparse tensor with a trailing COO
-    region with at least two dimensions. It is an error if the tensor doesn't
-    contain such a COO region. This is similar to the `bufferization.to_memref`
-    operation in the sense that it provides a bridge between a tensor world view
-    and a bufferized world view. Unlike the `bufferization.to_memref` operation,
-    however, this sparse operation actually lowers into code that extracts the
-    linear indices array from the sparse storage scheme that stores the indices
-    for the COO region as an array of structures. For example, a 2D COO sparse
-    tensor with two non-zero elements at coordinates (1, 3) and (4, 6) are
-    stored in a linear buffer as (1, 4, 3, 6) instead of two buffer as (1, 4)
-    and (3, 6).
+    Returns the linear coordinates array for a sparse tensor with
+    a trailing COO region with at least two levels.  It is an error
+    if the tensor doesn't contain such a COO region.  This is similar
+    to the `bufferization.to_memref` operation in the sense that it
+    provides a bridge between a tensor world view and a bufferized
+    world view.  Unlike the `bufferization.to_memref` operation,
+    however, this operation actually lowers into code that extracts
+    the linear coordinates array from the sparse storage scheme that
+    stores the coordinates for the COO region as an array of structures.
+    For example, a 2D COO sparse tensor with two non-zero elements at
+    coordinates (1, 3) and (4, 6) are stored in a linear buffer as
+    (1, 4, 3, 6) instead of two buffer as (1, 4) and (3, 6).
 
     Writing into the result of this operation is undefined behavior.
 
     Example:
 
     ```mlir
-    %1 = sparse_tensor.indices_buffer %0
+    %1 = sparse_tensor.coordinates_buffer %0
        : tensor<64x64xf64, #COO> to memref<?xindex>
     ```
   }];
   let assemblyFormat = "$tensor attr-dict `:` type($tensor) `to` type($result)";
   let hasVerifier = 1;
 }
 
 def SparseTensor_ToValuesOp : SparseTensor_Op<"values", [Pure]>,
     Arguments<(ins AnySparseTensor:$tensor)>,
def SparseTensor_ToSliceStrideOp : SparseTensor_Op<"slice.stride", [Pure]>,
   let assemblyFormat = "$slice `at` $dim attr-dict `:` type($slice)";
   let hasVerifier = 1;
 }
 
 def SparseTensor_StorageSpecifierInitOp : SparseTensor_Op<"storage_specifier.init", [Pure]>,
     Results<(outs SparseTensorStorageSpecifier:$result)> {
   let summary = "";
   let description = [{
-    Returns an initial storage specifier value. A storage specifier value holds
-    the sizes for tensor dimensions, pointer arrays, index arrays, and the value array.
+    Returns an initial storage specifier value.  A storage specifier
+    value holds the level-sizes, position arrays, coordinate arrays,
+    and the value array.
 
     Example:
 
     ```mlir
     %0 = sparse_tensor.storage_specifier.init :  !sparse_tensor.storage_specifier<#CSR>
     ```
   }];
 
   let assemblyFormat = "attr-dict `:` qualified(type($result))";
 }
 
 def SparseTensor_GetStorageSpecifierOp : SparseTensor_Op<"storage_specifier.get", [Pure]>,
     Arguments<(ins SparseTensorStorageSpecifier:$specifier,
                    SparseTensorStorageSpecifierKindAttr:$specifierKind,
-                   OptionalAttr<IndexAttr>:$dim)>,
+                   OptionalAttr<LevelAttr>:$level)>,
     Results<(outs Index:$result)> {
   let summary = "";
   let description = [{
     Returns the requested field of the given storage_specifier.
 
-    Example of querying the size of the index array for level 0:
+    Example of querying the size of the coordinates array for level 0:
 
     ```mlir
-    %0 = sparse_tensor.storage_specifier.get %arg0 idx_mem_sz at 0
+    %0 = sparse_tensor.storage_specifier.get %arg0 crd_mem_sz at 0
          : !sparse_tensor.storage_specifier<#COO>
     ```
   }];
 
-  let assemblyFormat = "$specifier $specifierKind (`at` $dim^)? attr-dict `:` "
-                       "qualified(type($specifier))";
+  let assemblyFormat = "$specifier $specifierKind (`at` $level^)? attr-dict"
+                       "`:` qualified(type($specifier))";
   let hasVerifier = 1;
   let hasFolder = 1;
 }
 
 def SparseTensor_SetStorageSpecifierOp : SparseTensor_Op<"storage_specifier.set",
     [Pure, AllTypesMatch<["result", "specifier"]>]>,
     Arguments<(ins SparseTensorStorageSpecifier:$specifier,
                    SparseTensorStorageSpecifierKindAttr:$specifierKind,
-                   OptionalAttr<IndexAttr>:$dim,
+                   OptionalAttr<LevelAttr>:$level,
                    Index:$value)>,
     Results<(outs SparseTensorStorageSpecifier:$result)> {
   let summary = "";
   let description = [{
     Set the field of the storage specifier to the given input value. Returns
     the updated storage_specifier as a new SSA value.
 
-    Example of updating the sizes of the index array for level 0:
+    Example of updating the sizes of the coordinates array for level 0:
 
     ```mlir
-    %0 = sparse_tensor.storage_specifier.set %arg0 idx_mem_sz at 0 with %new_sz
+    %0 = sparse_tensor.storage_specifier.set %arg0 crd_mem_sz at 0 with %new_sz
        : !sparse_tensor.storage_specifier<#COO>
-
     ```
   }];
-  let assemblyFormat = "$specifier $specifierKind (`at` $dim^)? `with` $value attr-dict `:` "
-                       "qualified(type($result))";
+  let assemblyFormat = "$specifier $specifierKind (`at` $level^)? `with` $value"
+                       " attr-dict `:` qualified(type($result))";
   let hasVerifier = 1;
 }
 
 def SparseTensor_NumberOfEntriesOp : SparseTensor_Op<"number_of_entries", [Pure]>,
     Arguments<(ins AnySparseTensor:$tensor)>,
     Results<(outs Index:$result)> {
   let summary = "Returns the number of entries that are stored in the tensor.";
   let description = [{
     Returns the number of entries that are stored in the given sparse tensor.
     Note that this is typically the number of nonzero elements in the tensor,
     but since explicit zeros may appear in the storage formats, the more
     accurate nomenclature is used.
 
     Example:
 
     ```mlir
     %noe = sparse_tensor.number_of_entries %tensor : tensor<64x64xf64, #CSR>
     ```
   }];
   let assemblyFormat = "$tensor attr-dict `:` type($tensor)";
 }
 
 def SparseTensor_ConcatenateOp : SparseTensor_Op<"concatenate", [Pure]>,
-    Arguments<(ins Variadic<AnyRankedTensor>:$inputs, IndexAttr:$dimension)>,
+    Arguments<(ins Variadic<AnyRankedTensor>:$inputs, DimensionAttr:$dimension)>,
     Results<(outs AnyRankedTensor:$result)> {
 
   let summary = "Concatenates a list of tensors into a single tensor.";
   let description = [{
-     Concatenates a list input tensors and the output tensor with the same rank.
-     The concatenation happens on the specified `dimension` (0<= dimension < rank).
-     The resulting `dimension` size is the sum of all the input dimension sizes,
-     while all the other dimensions should have the same size in the input and
-     output tensors.
+     Concatenates a list input tensors and the output tensor with the same
+     dimension-rank.  The concatenation happens on the specified `dimension`
+     (0 <= dimension < dimRank).  The resulting `dimension` size is the
+     sum of all the input sizes for that dimension, while all the other
+     dimensions should have the same size in the input and output tensors.
 
      Only statically-sized input tensors are accepted, while the output tensor
      can be dynamically-sized.
 
      Example:
 
      ```mlir
      %0 = sparse_tensor.concatenate %1, %2 { dimension = 0 : index }

 
 def SparseTensor_InsertOp : SparseTensor_Op<"insert",
     [TypesMatchWith<"value type matches element type of tensor",
                     "tensor", "value",
                     "$_self.cast<ShapedType>().getElementType()">,
      AllTypesMatch<["tensor", "result"]>]>,
     Arguments<(ins AnyType:$value,
                AnySparseTensor:$tensor,
-               Variadic<Index>:$indices)>,
+               Variadic<Index>:$lvlCoords)>,
     Results<(outs AnySparseTensor:$result)> {
-  string summary = "Inserts a value into given sparse tensor";
+  string summary = "Inserts a value into the sparse tensor";
   string description = [{
-    Inserts the given value at given indices into the underlying
-    sparse storage format of the given tensor with the given indices.
-    The arity of indices must match the rank of the tensor. This
-    operation can only be applied when a tensor materializes unintialized
-    with a `bufferization.alloc_tensor` operation and the final tensor
-    is constructed with a `load` operation that has the `hasInserts`
-    attribute set.
+    Inserts the value into the underlying storage of the tensor at the
+    given level-coordinates.  The arity of `lvlCoords` must match the
+    level-rank of the tensor.  This operation can only be applied when
+    the tensor materializes unintialized from a `bufferization.alloc_tensor`
+    operation and the final tensor is constructed with a `load` operation
+    which has the `hasInserts` attribute set.
 
-    Properties in the sparse tensor type fully describe what kind
-    of insertion order is allowed. When all dimensions have "unique"
+    The level-properties of the sparse tensor type fully describe what
+    kind of insertion order is allowed.  When all levels have "unique"
     and "ordered" properties, for example, insertions should occur in
-    strict lexicographical index order. Other properties define
-    different insertion regimens. Inserting in a way contrary to
-    these properties results in undefined behavior.
+    strict lexicographical level-coordinate order.  Other properties
+    define different insertion regimens.  Inserting in a way contrary
+    to these properties results in undefined behavior.
 
     Note that this operation is "impure" in the sense that even though
     the result is modeled through an SSA value, the insertion is eventually
     done "in place", and referencing the old SSA value is undefined behavior.
     This operation is scheduled to be unified with the dense counterpart
     `tensor.insert` that has pure SSA semantics.
 
     Example:
 
     ```mlir
     %result = sparse_tensor.insert %val into %tensor[%i,%j] : tensor<1024x1024xf64, #CSR>
     ```
   }];
-  let assemblyFormat = "$value `into` $tensor `[` $indices `]` attr-dict `:` type($tensor)";
+  let assemblyFormat = "$value `into` $tensor `[` $lvlCoords `]` attr-dict"
+                       "`:` type($tensor)";
   let hasVerifier = 1;
 }
 
 def SparseTensor_PushBackOp : SparseTensor_Op<"push_back",
     [TypesMatchWith<"value type matches element type of inBuffer",
                     "inBuffer", "value",
                     "$_self.cast<ShapedType>().getElementType()">,
      AllTypesMatch<["inBuffer", "outBuffer"]>]>,

 def SparseTensor_ExpandOp : SparseTensor_Op<"expand", []>,
     Arguments<(ins AnySparseTensor:$tensor)>,
     Results<(outs AnyStridedMemRefOfRank<1>:$values,
                   StridedMemRefRankOf<[I1],[1]>:$filled,
                   StridedMemRefRankOf<[Index],[1]>:$added,
                   Index:$count)> {
   string summary = "Expands an access pattern for insertion";
   string description = [{
-    Performs an access pattern expansion for the innermost dimensions of the
+    Performs an access pattern expansion for the innermost levels of the
     given tensor. This operation is useful to implement kernels in which a
     sparse tensor appears as output. This technique is known under several
     different names and using several alternative implementations,
     for example, phase counter [Gustavson72], expanded or switch array
     [Pissanetzky84], in phase scan [Duff90], access pattern expansion [Bik96],
     and workspaces [Kjolstad19].
 
-    The values and filled array have sizes that suffice for a *dense* innermost
-    dimension (e.g. a full row for matrices). The added array and count are used
-    to store new indices when a false value is encountered in the filled array.
-    All arrays should be allocated before the loop (possibly even shared between
-    loops in a future optimization) so that their *dense* initialization can be
-    amortized over many iterations. Setting and resetting the dense arrays in
-    the loop nest itself is kept *sparse* by only iterating over set elements
-    through an indirection using the added array, so that the operations are
-    kept proportional to the number of nonzeros.
+    The `values` and `filled` arrays must have lengths equal to the
+    level-size of the innermost level (i.e., as if the innermost level
+    were *dense*).  The `added` array and `count` are used to store new
+    level-coordinates when a false value is encountered in the `filled`
+    array.  All arrays should be allocated before the loop (possibly even
+    shared between loops in a future optimization) so that their *dense*
+    initialization can be amortized over many iterations.  Setting and
+    resetting the dense arrays in the loop nest itself is kept *sparse*
+    by only iterating over set elements through an indirection using
+    the added array, so that the operations are kept proportional to
+    the number of nonzeros.
 
     Note that this operation is "impure" in the sense that even though the
     results are modeled through SSA values, the operation relies on a proper
     side-effecting context that sets and resets the expanded arrays.
 
     Example:
 
     ```mlir
     %values, %filled, %added, %count = sparse_tensor.expand %tensor
       : tensor<4x4xf64, #CSR> to memref<?xf64>, memref<?xi1>, memref<?xindex>
     ```
   }];
   let assemblyFormat = "$tensor attr-dict `:` type($tensor) `to` type($values)"
                        " `,` type($filled) `,` type($added)";
 }
 
 def SparseTensor_CompressOp : SparseTensor_Op<"compress",
     [AllTypesMatch<["tensor", "result"]>]>,
     Arguments<(ins AnyStridedMemRefOfRank<1>:$values,
                    StridedMemRefRankOf<[I1],[1]>:$filled,
                    StridedMemRefRankOf<[Index],[1]>:$added,
                    Index:$count,
                    AnySparseTensor:$tensor,
-                   Variadic<Index>:$indices)>,
+                   Variadic<Index>:$lvlCoords)>,
     Results<(outs AnySparseTensor:$result)> {
   string summary = "Compressed an access pattern for insertion";
   string description = [{
     Finishes a single access pattern expansion by moving inserted elements
     into the sparse storage scheme of the given tensor with the given
-    indices. The arity of indices is one less than the rank of the tensor,
-    with the remainder innermost indices defined through the added array.
-    The values and filled array are reset in a *sparse* fashion by only
-    iterating over set elements through an indirection using the added
-    array, so that the operations are kept proportional to the number of
-    nonzeros. See the `sparse_tensor.expand` operation for more details.
+    level-coordinates.  The arity of `lvlCoords` is one less than the
+    level-rank of the tensor, with the coordinate of the innermost
+    level defined through the `added` array.  The `values` and `filled`
+    arrays are reset in a *sparse* fashion by only iterating over set
+    elements through an indirection using the `added` array, so that
+    the operations are kept proportional to the number of nonzeros.
+    See the `sparse_tensor.expand` operation for more details.
 
     Note that this operation is "impure" in the sense that even though
     the result is modeled through an SSA value, the insertion is eventually
     done "in place", and referencing the old SSA value is undefined behavior.
 
     Example:
 
     ```mlir
     %result = sparse_tensor.compress %values, %filled, %added, %count into %tensor[%i]
       : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<4x4xf64, #CSR>
     ```
   }];
   let assemblyFormat = "$values `,` $filled `,` $added `,` $count"
-                       " `into` $tensor `[` $indices `]` attr-dict"
+                       " `into` $tensor `[` $lvlCoords `]` attr-dict"
                        " `:` type($values) `,` type($filled) `,` type($added)"
                        " `,` type($tensor)";
   let hasVerifier = 1;
 }
 
 def SparseTensor_LoadOp : SparseTensor_Op<"load", [SameOperandsAndResultType]>,
     Arguments<(ins AnySparseTensor:$tensor, UnitAttr:$hasInserts)>,
     Results<(outs AnyTensor:$result)> {
let description = [{
      non-zero for sparse tensors) and executes the block.
 
      `tensor`: the input tensor to iterate over.
      `initArgs`: the initial loop argument to carry and update during each iteration.
      `order`: an optional permutation affine map that specifies the order in which
      the dimensions are visited (e.g., row first or column first). This is only
      applicable when the input tensor is a non-annotated dense tensor.
 
-     For an input tensor with rank n, the block must take n + 1 (and additional loop
-     carried variables as described below) arguments. The first n arguments must be
-     Index type, together indicating the current coordinates of the element being visited.
-     The last argument must have the same type as the
-     tensor's element type, representing the actual value loaded from the input
-     tensor at the given coordinates.
+     For an input tensor with dim-rank `n`, the block must take `n + 1`
+     arguments (plus additional loop-carried variables as described below).
+     The first `n` arguments provide the dimension-coordinates of the element
+     being visited, and must all have `index` type.  The `(n+1)`-th argument
+     provides the element's value, and must have the tensor's element type.
 
      `sparse_tensor.foreach` can also operate on loop-carried variables and returns
      the final values after loop termination. The initial values of the variables are
      passed as additional SSA operands to the "sparse_tensor.foreach" following the n + 1
-     SSA values mentioned above (n coordinate and 1 value).
+     SSA values mentioned above (n coordinates and 1 value).
 
      The region must terminate with a "sparse_tensor.yield" that passes the current
      values of all loop-carried variables to the next iteration, or to the
      result, if at the last iteration. The number and static types of loop-carried
      variables may not change with iterations.
 
      For example:
      ```mlir
      %c0 = arith.constant 0 : i32
      %ret = sparse_tensor.foreach in %0 init(%c0): tensor<?x?xi32, #DCSR>, i32 -> i32 do {
       ^bb0(%arg1: index, %arg2: index, %arg3: i32, %iter: i32):
         %sum = arith.add %iter, %arg3
         sparse_tensor.yield %sum
      }
      ```
 
-     It is important to note that foreach generated loop iterates over the stored elements
-     in the storage order. However, no matter what storage order is used, the indices passed
-     to the block always obey the original dimension order.
+     It is important to note that the generated loop iterates over
+     elements in their storage order.  However, regardless of the
+     storage scheme used by the tensor, the block is always given
+     the dimension-coordinates.
 
      For example:
      ```mlir
      #COL_MAJOR = #sparse_tensor.encoding<{
        dimLevelType = [ "compressed", "compressed" ],
        dimOrdering = affine_map<(i,j) -> (j,i)>
      }>
 

mlir/include/mlir/Dialect/SparseTensor/IR/SparseTensorType.h

public:
   // We can't just delegate these, since we want to use this class's
   // `getLvlType` method instead of STEA's.
   bool isDenseLvl(Level l) const { return isDenseDLT(getLvlType(l)); }
   bool isCompressedLvl(Level l) const { return isCompressedDLT(getLvlType(l)); }
   bool isSingletonLvl(Level l) const { return isSingletonDLT(getLvlType(l)); }
   bool isOrderedLvl(Level l) const { return isOrderedDLT(getLvlType(l)); }
   bool isUniqueLvl(Level l) const { return isUniqueDLT(getLvlType(l)); }
 
-  /// Returns the index-overhead bitwidth, defaulting to zero.
-  unsigned getIndexBitWidth() const { return enc ? enc.getIndexBitWidth() : 0; }
+  /// Returns the coordinate-overhead bitwidth, defaulting to zero.
+  unsigned getCrdWidth() const { return enc ? enc.getCrdWidth() : 0; }
 
-  /// Returns the pointer-overhead bitwidth, defaulting to zero.
-  unsigned getPointerBitWidth() const {
-    return enc ? enc.getPointerBitWidth() : 0;
-  }
+  /// Returns the position-overhead bitwidth, defaulting to zero.
+  unsigned getPosWidth() const { return enc ? enc.getPosWidth() : 0; }
 
-  /// Returns the index-overhead MLIR type, defaulting to `IndexType`.
-  Type getIndexType() const {
-    return detail::getIntegerOrIndexType(getContext(), getIndexBitWidth());
+  /// Returns the coordinate-overhead MLIR type, defaulting to `IndexType`.
+  Type getCrdType() const {
+    return detail::getIntegerOrIndexType(getContext(), getCrdWidth());
   }
 
-  /// Returns the pointer-overhead MLIR type, defaulting to `IndexType`.
-  Type getPointerType() const {
-    return detail::getIntegerOrIndexType(getContext(), getPointerBitWidth());
+  /// Returns the position-overhead MLIR type, defaulting to `IndexType`.
+  Type getPosType() const {
+    return detail::getIntegerOrIndexType(getContext(), getPosWidth());
   }
 
 private:
   // These two must be const, to ensure coherence of the memoized fields.
   const RankedTensorType rtp;
   const SparseTensorEncodingAttr enc;
   // Memoized to avoid frequent redundant conditionals.
   const Level lvlRank;

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

def SparseTensor_StorageSpecifier : SparseTensor_Type<"StorageSpecifier"> {
   let description = [{
     Syntax:
 
     ```
     storage_specifier-type ::= `!storage_specifier` `<` encoding `>`
     encoding ::= attribute-value
     ```
 
-    Values with storage_specifier types represent aggregated storage scheme metadata
-    for the given sparse tensor encoding.
-    It currently holds a set of values for sizes of sparse tensor dimension, index array,
-    pointer array and value array.
-    Note that the type is not yet stable and subject to change in the near future.
+    Values with storage_specifier types represent aggregated storage scheme
+    metadata for the given sparse tensor encoding.  It currently holds
+    a set of values for level-sizes, coordinate arrays, position arrays,
+    and value array.  Note that the type is not yet stable and subject to
+    change in the near future.
 
     Examples:
 
     ```mlir
     // A storage specifier that can be used to store storage scheme metadata from CSR matrix.
     !storage_specifier<#CSR>
     ```
   }];

mlir/include/mlir/ExecutionEngine/SparseTensor/COO.h

 #include <cinttypes>
 #include <functional>
 #include <vector>
 
 namespace mlir {
 namespace sparse_tensor {
 
 /// An element of a sparse tensor in coordinate-scheme representation
-/// (i.e., a pair of indices and value).  For example, a rank-1 vector
-/// element would look like
+/// (i.e., a pair of coordinates and value).  For example, a rank-1
+/// vector element would look like
 ///   ({i}, a[i])
 /// and a rank-5 tensor element would look like
 ///   ({i,j,k,l,m}, a[i,j,k,l,m])
 ///
-/// The indices are represented as a (non-owning) pointer into a shared
-/// pool of indices, rather than being stored directly in this object.
-/// This significantly improves performance because it: (1) reduces
-/// the per-element memory footprint, and (2) centralizes the memory
-/// management for indices.  The only downside is that the indices
-/// themselves cannot be retrieved without knowing the rank of the
-/// tensor to which this element belongs (and that rank is not stored
-/// in this object).
+/// The coordinates are represented as a (non-owning) pointer into
+/// a shared pool of coordinates, rather than being stored directly in
+/// this object.  This significantly improves performance because it:
+/// (1) reduces the per-element memory footprint, and (2) centralizes
+/// the memory management for coordinates.  The only downside is that
+/// the coordinates themselves cannot be retrieved without knowing the
+/// rank of the tensor to which this element belongs (and that rank is
+/// not stored in this object).
 template <typename V>
 struct Element final {
-  Element(const uint64_t *ind, V val) : indices(ind), value(val){};
-  const uint64_t *indices; // pointer into shared index pool
+  Element(const uint64_t *coords, V val) : coords(coords), value(val){};
+  const uint64_t *coords; // pointer into shared coordinates pool
   V value;
 };
 
 /// Closure object for `operator<` on `Element` with a given rank.
 template <typename V>
 struct ElementLT final {
   ElementLT(uint64_t rank) : rank(rank) {}
 
   /// Compares two elements a la `operator<`.
   ///
   /// Precondition: the elements must both be valid for `rank`.
   bool operator()(const Element<V> &e1, const Element<V> &e2) const {
     for (uint64_t d = 0; d < rank; ++d) {
-      if (e1.indices[d] == e2.indices[d])
+      if (e1.coords[d] == e2.coords[d])
         continue;
-      return e1.indices[d] < e2.indices[d];
+      return e1.coords[d] < e2.coords[d];
     }
     return false;
   }
 
   const uint64_t rank;
 };
 
 /// The type of callback functions which receive an element.  We avoid
public:
   explicit SparseTensorCOO(uint64_t dimRank, const uint64_t *dimSizes,
                            uint64_t capacity = 0)
       : dimSizes(dimSizes, dimSizes + dimRank), isSorted(true) {
     assert(dimRank > 0 && "Trivial shape is not supported");
     for (uint64_t d = 0; d < dimRank; ++d)
       assert(dimSizes[d] > 0 && "Dimension size zero has trivial storage");
     if (capacity) {
       elements.reserve(capacity);
-      indices.reserve(capacity * dimRank);
+      coordinates.reserve(capacity * dimRank);
     }
   }
 
-  /// Gets the rank of the tensor.
+  /// Gets the dimension-rank of the tensor.
   uint64_t getRank() const { return dimSizes.size(); }
 
   /// Gets the dimension-sizes array.
   const std::vector<uint64_t> &getDimSizes() const { return dimSizes; }
 
   /// Gets the elements array.
   const std::vector<Element<V>> &getElements() const { return elements; }
 
   /// Returns the `operator<` closure object for the COO's element type.
   ElementLT<V> getElementLT() const { return ElementLT<V>(getRank()); }
 
   /// Adds an element to the tensor.  This method does not check whether
-  /// `ind` is already associated with a value, it adds it regardless.
+  /// `dimCoords` is already associated with a value, it adds it regardless.
   /// Resolving such conflicts is left up to clients of the iterator
   /// interface.
   ///
   /// This method invalidates all iterators.
   ///
   /// Asserts:
-  /// * the `ind` is valid for `rank`
-  /// * the elements of `ind` are valid for `dimSizes`.
-  void add(const std::vector<uint64_t> &ind, V val) {
-    const uint64_t *base = indices.data();
-    uint64_t size = indices.size();
-    uint64_t rank = getRank();
-    assert(ind.size() == rank && "Element rank mismatch");
-    for (uint64_t r = 0; r < rank; ++r) {
-      assert(ind[r] < dimSizes[r] && "Index is too large for the dimension");
-      indices.push_back(ind[r]);
-    }
-    // This base only changes if indices were reallocated. In that case, we
-    // need to correct all previous pointers into the vector. Note that this
-    // only happens if we did not set the initial capacity right, and then only
-    // for every internal vector reallocation (which with the doubling rule
-    // should only incur an amortized linear overhead).
-    const uint64_t *newBase = indices.data();
+  /// * the `dimCoords` is valid for `getRank`.
+  /// * the components of `dimCoords` are valid for `getDimSizes`.
+  void add(const std::vector<uint64_t> &dimCoords, V val) {
+    const uint64_t *base = coordinates.data();
+    const uint64_t size = coordinates.size();
+    const uint64_t dimRank = getRank();
+    assert(dimCoords.size() == dimRank && "Element rank mismatch");
+    for (uint64_t d = 0; d < dimRank; ++d) {
+      assert(dimCoords[d] < dimSizes[d] &&
+             "Coordinate is too large for the dimension");
+      coordinates.push_back(dimCoords[d]);
+    }
+    // This base only changes if `coordinates` was reallocated.  In which
+    // case, we need to correct all previous pointers into the vector.
+    // Note that this only happens if we did not set the initial capacity
+    // right, and then only for every internal vector reallocation (which
+    // with the doubling rule should only incur an amortized linear overhead).
+    const uint64_t *const newBase = coordinates.data();
     if (newBase != base) {
       for (uint64_t i = 0, n = elements.size(); i < n; ++i)
-        elements[i].indices = newBase + (elements[i].indices - base);
+        elements[i].coords = newBase + (elements[i].coords - base);
       base = newBase;
     }
     // Add the new element and update the sorted bit.
-    Element<V> addedElem(base + size, val);
+    const Element<V> addedElem(base + size, val);
     if (!elements.empty() && isSorted)
       isSorted = getElementLT()(elements.back(), addedElem);
     elements.push_back(addedElem);
   }
 
   const_iterator begin() const { return elements.cbegin(); }
   const_iterator end() const { return elements.cend(); }
 
-  /// Sorts elements lexicographically by index.  If an index is mapped to
-  /// multiple values, then the relative order of those values is unspecified.
+  /// Sorts elements lexicographically by coordinates.  If a coordinate
+  /// is mapped to multiple values, then the relative order of those
+  /// values is unspecified.
   ///
   /// This method invalidates all iterators.
   void sort() {
     if (isSorted)
       return;
     std::sort(elements.begin(), elements.end(), getElementLT());
     isSorted = true;
   }
 
 private:
   const std::vector<uint64_t> dimSizes; // per-dimension sizes
   std::vector<Element<V>> elements;     // all COO elements
-  std::vector<uint64_t> indices;        // shared index pool
+  std::vector<uint64_t> coordinates;    // shared coordinate pool
   bool isSorted;
 };
 
 } // namespace sparse_tensor
 } // namespace mlir
 
 #endif // MLIR_EXECUTIONENGINE_SPARSETENSOR_COO_H

mlir/include/mlir/ExecutionEngine/SparseTensor/File.h

 
 template <typename T>
 struct is_complex<std::complex<T>> final : public std::true_type {};
 
 /// Returns an element-value of non-complex type.  If `IsPattern` is true,
 /// then returns an arbitrary value.  If `IsPattern` is false, then
 /// reads the value from the current line buffer beginning at `linePtr`.
 template <typename V, bool IsPattern>
-inline std::enable_if_t<!is_complex<V>::value, V> readCOOValue(char **linePtr) {
+inline std::enable_if_t<!is_complex<V>::value, V> readValue(char **linePtr) {
   // The external formats always store these numerical values with the type
   // double, but we cast these values to the sparse tensor object type.
   // For a pattern tensor, we arbitrarily pick the value 1 for all entries.
   if constexpr (IsPattern)
     return 1.0;
   return strtod(*linePtr, linePtr);
 }
 
 /// Returns an element-value of complex type.  If `IsPattern` is true,
 /// then returns an arbitrary value.  If `IsPattern` is false, then reads
 /// the value from the current line buffer beginning at `linePtr`.
 template <typename V, bool IsPattern>
-inline std::enable_if_t<is_complex<V>::value, V> readCOOValue(char **linePtr) {
+inline std::enable_if_t<is_complex<V>::value, V> readValue(char **linePtr) {
   // Read two values to make a complex. The external formats always store
   // numerical values with the type double, but we cast these values to the
   // sparse tensor object type. For a pattern tensor, we arbitrarily pick the
   // value 1 for all entries.
   if constexpr (IsPattern)
     return V(1.0, 1.0);
   double re = strtod(*linePtr, linePtr);
   double im = strtod(*linePtr, linePtr);
   // Avoiding brace-notation since that forbids narrowing to `float`.
   return V(re, im);
 }
 
-/// Returns an element-value.  If `is_pattern` is true, then returns an
-/// arbitrary value.  If `is_pattern` is false, then reads the value from
+/// Returns an element-value.  If `isPattern` is true, then returns an
+/// arbitrary value.  If `isPattern` is false, then reads the value from
 /// the current line buffer beginning at `linePtr`.
 template <typename V>
-inline V readCOOValue(char **linePtr, bool is_pattern) {
-  if (is_pattern)
-    return readCOOValue<V, true>(linePtr);
-  return readCOOValue<V, false>(linePtr);
+inline V readValue(char **linePtr, bool isPattern) {
+  return isPattern ? readValue<V, true>(linePtr) : readValue<V, false>(linePtr);
 }
 
 } // namespace detail
 
 //===----------------------------------------------------------------------===//
 
 // TODO: benchmark whether to keep various methods inline vs moving them
 // off to the cpp file.
public:
 
   /// Gets the MME "symmetric" property setting.  Is only valid after
   /// parsing the header.
   bool isSymmetric() const {
     assert(isValid() && "Attempt to isSymmetric() before readHeader()");
     return isSymmetric_;
   }
 
-  /// Gets the rank of the tensor.  Is only valid after parsing the header.
+  /// Gets the dimension-rank of the tensor.  Is only valid after parsing
+  /// the header.
   uint64_t getRank() const {
     assert(isValid() && "Attempt to getRank() before readHeader()");
     return idata[0];
   }
 
-  /// Gets the number of non-zeros.  Is only valid after parsing the header.
-  uint64_t getNNZ() const {
-    assert(isValid() && "Attempt to getNNZ() before readHeader()");
+  /// Gets the number of stored elements.  Is only valid after parsing
+  /// the header.
+  uint64_t getNSE() const {
+    assert(isValid() && "Attempt to getNSE() before readHeader()");
     return idata[1];
   }
 
   /// Gets the dimension-sizes array.  The pointer itself is always
   /// valid; however, the values stored therein are only valid after
   /// parsing the header.
   const uint64_t *getDimSizes() const { return idata + 2; }
 
   /// Safely gets the size of the given dimension.  Is only valid
   /// after parsing the header.
   uint64_t getDimSize(uint64_t d) const {
     assert(d < getRank() && "Dimension out of bounds");
     return idata[2 + d];
   }
 
   /// Asserts the shape subsumes the actual dimension sizes.  Is only
   /// valid after parsing the header.
   void assertMatchesShape(uint64_t rank, const uint64_t *shape) const;
 
   /// Reads a sparse tensor element from the next line in the input file and
   /// returns the value of the element. Stores the coordinates of the element
-  /// to the `indices` array.
+  /// to the `dimCoords` array.
   template <typename V>
-  V readCOOElement(uint64_t rank, uint64_t *indices) {
-    assert(rank == getRank() && "rank mismatch");
-    char *linePtr = readCOOIndices(indices);
-    return detail::readCOOValue<V>(&linePtr, isPattern());
+  V readElement(uint64_t dimRank, uint64_t *dimCoords) {
+    assert(dimRank == getRank() && "rank mismatch");
+    char *linePtr = readCoords(dimCoords);
+    return detail::readValue<V>(&linePtr, isPattern());
   }
 
   /// Allocates a new COO object for `lvlSizes`, initializes it by reading
-  /// all the elements from the file and applying `dim2lvl` to their indices,
-  /// and then closes the file.
+  /// all the elements from the file and applying `dim2lvl` to their
+  /// dim-coordinates, and then closes the file.
   ///
   /// Preconditions:
   /// * `lvlSizes` must be valid for `lvlRank`.
   /// * `dim2lvl` must be valid for `getRank()`.
-  /// * `dim2lvl` maps indices valid for `getDimSizes()` to indices
-  ///   valid for `lvlSizes`.
+  /// * `dim2lvl` maps `getDimSizes()`-coordinates to `lvlSizes`-coordinates.
   /// * the file's actual value type can be read as `V`.
   ///
   /// Asserts:
   /// * `isValid()`
   /// * `dim2lvl` is a permutation, and therefore also `lvlRank == getRank()`.
   ///   (This requirement will be lifted once we functionalize `dim2lvl`.)
   //
   // NOTE: This method is factored out of `readSparseTensor` primarily to
bool readToBuffers(uint64_t lvlRank, const uint64_t *dim2lvl,
                      C *lvlCoordinates, V *values);
 
 private:
   /// Attempts to read a line from the file.  Is private because there's
   /// no reason for client code to call it.
   void readLine();
 
   /// Reads the next line of the input file and parses the coordinates
-  /// into the `indices` argument.  Returns the position in the `line`
+  /// into the `dimCoords` argument.  Returns the position in the `line`
   /// buffer where the element's value should be parsed from.  This method
-  /// has been factored out from `readCOOElement` to minimize code bloat
+  /// has been factored out from `readElement` to minimize code bloat
   /// for the generated library.
   ///
-  /// Precondition: `indices` is valid for `getRank()`.
-  template <typename I>
-  char *readCOOIndices(I *indices) {
+  /// Precondition: `dimCoords` is valid for `getRank()`.
+  template <typename C>
+  char *readCoords(C *dimCoords) {
     readLine();
     // Local variable for tracking the parser's position in the `line` buffer.
     char *linePtr = line;
     for (uint64_t dimRank = getRank(), d = 0; d < dimRank; ++d) {
-      // Parse the 1-based index.
-      uint64_t idx = strtoul(linePtr, &linePtr, 10);
-      // Store the 0-based index.
-      indices[d] = static_cast<I>(idx - 1);
+      // Parse the 1-based coordinate.
+      uint64_t c = strtoul(linePtr, &linePtr, 10);
+      // Store the 0-based coordinate.
+      dimCoords[d] = static_cast<C>(c - 1);
     }
     return linePtr;
   }
 
   /// The internal implementation of `readCOO`.  We template over
   /// `IsPattern` in order to perform LICM without needing to duplicate the
   /// source code.
   //
SparseTensorCOO<V> *SparseTensorReader::readCOO(uint64_t lvlRank,
   assert(isValid() && "Attempt to readCOO() before readHeader()");
   // Construct a `PermutationRef` for the `pushforward` below.
   // TODO: This specific implementation does not generalize to arbitrary
   // mappings, but once we functionalize the `dim2lvl` argument we can
   // simply use that function instead.
   const uint64_t dimRank = getRank();
   assert(lvlRank == dimRank && "Rank mismatch");
   detail::PermutationRef d2l(dimRank, dim2lvl);
-  // Prepare a COO object with the number of nonzeros as initial capacity.
-  auto *lvlCOO = new SparseTensorCOO<V>(lvlRank, lvlSizes, getNNZ());
+  // Prepare a COO object with the number of stored elems as initial capacity.
+  auto *lvlCOO = new SparseTensorCOO<V>(lvlRank, lvlSizes, getNSE());
   // Do some manual LICM, to avoid assertions in the for-loop.
   const bool IsPattern = isPattern();
   if (IsPattern)
     readCOOLoop<V, true>(lvlRank, d2l, lvlCOO);
   else
     readCOOLoop<V, false>(lvlRank, d2l, lvlCOO);
   // Close the file and return the COO.
   closeFile();
   return lvlCOO;
 }
 
 template <typename V, bool IsPattern>
 void SparseTensorReader::readCOOLoop(uint64_t lvlRank,
                                      detail::PermutationRef dim2lvl,
                                      SparseTensorCOO<V> *lvlCOO) {
   const uint64_t dimRank = getRank();
-  std::vector<uint64_t> dimInd(dimRank);
-  std::vector<uint64_t> lvlInd(lvlRank);
-  for (uint64_t nnz = getNNZ(), k = 0; k < nnz; ++k) {
-    // We inline `readCOOElement` here in order to avoid redundant
+  std::vector<uint64_t> dimCoords(dimRank);
+  std::vector<uint64_t> lvlCoords(lvlRank);
+  for (uint64_t nse = getNSE(), k = 0; k < nse; ++k) {
+    // We inline `readElement` here in order to avoid redundant
     // assertions, since they're guaranteed by the call to `isValid()`
-    // and the construction of `dimInd` above.
-    char *linePtr = readCOOIndices(dimInd.data());
-    const V value = detail::readCOOValue<V, IsPattern>(&linePtr);
-    dim2lvl.pushforward(dimRank, dimInd.data(), lvlInd.data());
+    // and the construction of `dimCoords` above.
+    char *linePtr = readCoords(dimCoords.data());
+    const V value = detail::readValue<V, IsPattern>(&linePtr);
+    dim2lvl.pushforward(dimRank, dimCoords.data(), lvlCoords.data());
     // TODO: <https://github.com/llvm/llvm-project/issues/54179>
-    lvlCOO->add(lvlInd, value);
+    lvlCOO->add(lvlCoords, value);
   }
 }
 
 template <typename C, typename V>
 bool SparseTensorReader::readToBuffers(uint64_t lvlRank,
                                        const uint64_t *dim2lvl,
                                        C *lvlCoordinates, V *values) {
   assert(isValid() && "Attempt to readCOO() before readHeader()");
+  // Construct a `PermutationRef` for the `pushforward` below.
+  // TODO: This specific implementation does not generalize to arbitrary
+  // mappings, but once we functionalize the `dim2lvl` argument we can
+  // simply use that function instead.
   const uint64_t dimRank = getRank();
   assert(lvlRank == dimRank && "Rank mismatch");
   detail::PermutationRef d2l(dimRank, dim2lvl);
   // Do some manual LICM, to avoid assertions in the for-loop.
   bool isSorted =
       isPattern()
           ? readToBuffersLoop<C, V, true>(lvlRank, d2l, lvlCoordinates, values)
           : readToBuffersLoop<C, V, false>(lvlRank, d2l, lvlCoordinates,
                                            values);
 
   // Close the file and return isSorted.
   closeFile();
   return isSorted;
 }
 
 template <typename C, typename V, bool IsPattern>
 bool SparseTensorReader::readToBuffersLoop(uint64_t lvlRank,
                                            detail::PermutationRef dim2lvl,
                                            C *lvlCoordinates, V *values) {
   const uint64_t dimRank = getRank();
-  const uint64_t nse = getNNZ();
+  const uint64_t nse = getNSE();
   std::vector<C> dimCoords(dimRank);
   // Read the first element with isSorted=false as a way to avoid accessing its
   // previous element.
   bool isSorted = false;
   char *linePtr;
-  // We inline `readCOOElement` here in order to avoid redundant assertions,
+  // We inline `readElement` here in order to avoid redundant assertions,
   // since they're guaranteed by the call to `isValid()` and the construction
   // of `dimCoords` above.
-  auto readElement = [&]() {
-    linePtr = readCOOIndices<C>(dimCoords.data());
+  const auto readNextElement = [&]() {
+    linePtr = readCoords<C>(dimCoords.data());
     dim2lvl.pushforward(dimRank, dimCoords.data(), lvlCoordinates);
-    *values = detail::readCOOValue<V, IsPattern>(&linePtr);
+    *values = detail::readValue<V, IsPattern>(&linePtr);
     if (isSorted) {
       // Note that isSorted was set to false while reading the first element,
       // to guarantee the safeness of using prevLvlCoords.
       C *prevLvlCoords = lvlCoordinates - lvlRank;
       // TODO: define a new CoordsLT which is like ElementLT but doesn't have
       // the V parameter, and use it here.
       for (uint64_t l = 0; l < lvlRank; ++l) {
         if (prevLvlCoords[l] != lvlCoordinates[l]) {
           if (prevLvlCoords[l] > lvlCoordinates[l])
             isSorted = false;
           break;
         }
       }
     }
     lvlCoordinates += lvlRank;
     ++values;
   };
-  readElement();
+  readNextElement();
   isSorted = true;
   for (uint64_t n = 1; n < nse; ++n)
-    readElement();
+    readNextElement();
 
   return isSorted;
 }
 
 /// Writes the sparse tensor to `filename` in extended FROSTT format.
 template <typename V>
 inline void writeExtFROSTT(const SparseTensorCOO<V> &coo,
                            const char *filename) {
   assert(filename && "Got nullptr for filename");
-  auto &dimSizes = coo.getDimSizes();
-  auto &elements = coo.getElements();
-  const uint64_t rank = coo.getRank();
-  const uint64_t nnz = elements.size();
+  const auto &dimSizes = coo.getDimSizes();
+  const auto &elements = coo.getElements();
+  const uint64_t dimRank = coo.getRank();
+  const uint64_t nse = elements.size();
   std::fstream file;
   file.open(filename, std::ios_base::out | std::ios_base::trunc);
   assert(file.is_open());
-  file << "; extended FROSTT format\n" << rank << " " << nnz << std::endl;
-  for (uint64_t r = 0; r < rank - 1; ++r)
-    file << dimSizes[r] << " ";
-  file << dimSizes[rank - 1] << std::endl;
-  for (uint64_t i = 0; i < nnz; ++i) {
-    auto &idx = elements[i].indices;
-    for (uint64_t r = 0; r < rank; ++r)
-      file << (idx[r] + 1) << " ";
+  file << "; extended FROSTT format\n" << dimRank << " " << nse << std::endl;
+  for (uint64_t d = 0; d < dimRank - 1; ++d)
+    file << dimSizes[d] << " ";
+  file << dimSizes[dimRank - 1] << std::endl;
+  for (uint64_t i = 0; i < nse; ++i) {
+    const auto &coords = elements[i].coords;
+    for (uint64_t d = 0; d < dimRank; ++d)
+      file << (coords[d] + 1) << " ";
     file << elements[i].value << std::endl;
   }
   file.flush();
   file.close();
   assert(file.good());
 }
 
 } // namespace sparse_tensor
 } // namespace mlir
 
 #endif // MLIR_EXECUTIONENGINE_SPARSETENSOR_FILE_H

mlir/include/mlir/ExecutionEngine/SparseTensor/Storage.h

 // tensor manipulations.  The functionality of the support library is meant
 // to simplify benchmarking, testing, and debugging MLIR code operating on
 // sparse tensors.  However, the provided functionality is **not** part of
 // core MLIR itself.
 //
 // This file contains definitions for the following classes:
 //
 // * `SparseTensorStorageBase`
-// * `SparseTensorStorage<P, I, V>`
+// * `SparseTensorStorage<P, C, V>`
 // * `SparseTensorEnumeratorBase<V>`
-// * `SparseTensorEnumerator<P, I, V>`
+// * `SparseTensorEnumerator<P, C, V>`
 // * `SparseTensorNNZ`
 //
 // Ideally we would split the storage classes and enumerator classes
 // into separate files, to improve legibility.  But alas: because these
 // are template-classes, they must therefore provide *definitions* in the
 // header; and those definitions cause circular dependencies that make it
 // impossible to split the file up along the desired lines.  (We could
 // split the base classes from the derived classes, but that doesn't

 // This forward decl is sufficient to split `SparseTensorStorageBase` into
 // its own header, but isn't sufficient for `SparseTensorStorage` to join it.
 template <typename V>
 class SparseTensorEnumeratorBase;
 
 // These macros ensure consistent error messages, without risk of incuring
 // an additional method call to do so.
 #define ASSERT_VALID_DIM(d)                                                    \
-  assert(d < getDimRank() && "Dimension index is out of bounds");
+  assert(d < getDimRank() && "Dimension is out of bounds");
 #define ASSERT_VALID_LVL(l)                                                    \
-  assert(l < getLvlRank() && "Level index is out of bounds");
+  assert(l < getLvlRank() && "Level is out of bounds");
 #define ASSERT_COMPRESSED_LVL(l)                                               \
   assert(isCompressedLvl(l) && "Level is not compressed");
 #define ASSERT_COMPRESSED_OR_SINGLETON_LVL(l)                                  \
   do {                                                                         \
     const DimLevelType dlt = getLvlType(l);                                    \
     (void)dlt;                                                                 \
     assert((isCompressedDLT(dlt) || isSingletonDLT(dlt)) &&                    \
            "Level is neither compressed nor singleton");                       \
   } while (false)
 // Because the `SparseTensorStorageBase` ctor uses `MLIR_SPARSETENSOR_FATAL`
 // (rather than `assert`) when validating level-types, all the uses of
 // `ASSERT_DENSE_DLT` are technically unnecessary.  However, they are
 // retained for the sake of future-proofing.
 #define ASSERT_DENSE_DLT(dlt) assert(isDenseDLT(dlt) && "Level is not dense");
 
-/// Abstract base class for `SparseTensorStorage<P,I,V>`.  This class
-/// takes responsibility for all the `<P,I,V>`-independent aspects
+/// Abstract base class for `SparseTensorStorage<P,C,V>`.  This class
+/// takes responsibility for all the `<P,C,V>`-independent aspects
 /// of the tensor (e.g., shape, sparsity, permutation).  In addition,
 /// we use function overloading to implement "partial" method
 /// specialization, which the C-API relies on to catch type errors
 /// arising from our use of opaque pointers.
 ///
 /// Because this class forms a bridge between the denotational semantics
 /// of "tensors" and the operational semantics of how we store and
 /// compute with them, it also distinguishes between two different
 /// coordinate spaces (and their associated rank, shape, sizes, etc).
 /// Denotationally, we have the *dimensions* of the tensor represented
 /// by this object.  Operationally, we have the *levels* of the storage
 /// representation itself.  We use this "dimension" vs "level" terminology
 /// throughout, since alternative terminology like "tensor-dimension",
 /// "original-dimension", "storage-dimension", etc, is both more verbose
 /// and prone to introduce confusion whenever the qualifiers are dropped.
 /// Where necessary, we use "axis" as the generic term.
 ///
-/// The *size* of an axis is the cardinality of possible coordinate/index
+/// The *size* of an axis is the cardinality of possible coordinate
 /// values along that axis (regardless of which coordinates have stored
 /// element values).  As such, each size must be non-zero since if any
 /// axis has size-zero then the whole tensor would have trivial storage
 /// (since there are no possible coordinates).  Thus we use the plural
 /// term *sizes* for a collection of non-zero cardinalities, and use
 /// this term whenever referring to run-time cardinalities.  Whereas we
 /// use the term *shape* for a collection of compile-time cardinalities,
 /// where zero is used to indicate cardinalities which are dynamic (i.e.,

 
 public:
   /// Constructs a new sparse-tensor storage object with the given encoding.
   ///
   /// Preconditions:
   /// * `dimSizes`, `lvlSizes`, `lvlTypes`, and `lvl2dim` must be nonnull.
   /// * `dimSizes` must be valid for `dimRank`.
   /// * `lvlSizes`, `lvlTypes`, and `lvl2dim` must be valid for `lvlRank`.
-  /// * `lvl2dim` must map indices valid for `lvlSizes` to indices valid
-  ///   for `dimSizes`.
+  /// * `lvl2dim` must map `lvlSizes`-coordinates to `dimSizes`-coordinates.
   ///
   /// Asserts:
   /// * `dimRank` and `lvlRank` are nonzero.
   /// * `dimSizes` and `lvlSizes` contain only nonzero sizes.
   SparseTensorStorageBase(uint64_t dimRank, const uint64_t *dimSizes,
                           uint64_t lvlRank, const uint64_t *lvlSizes,
                           const DimLevelType *lvlTypes,
                           const uint64_t *lvl2dim);
public:
   /// ctor and must satisfy the preconditions/assertions thereof.
 #define DECL_NEWENUMERATOR(VNAME, V)                                           \
   virtual void newEnumerator(SparseTensorEnumeratorBase<V> **, uint64_t,       \
                              const uint64_t *, uint64_t, const uint64_t *)     \
       const;
   MLIR_SPARSETENSOR_FOREVERY_V(DECL_NEWENUMERATOR)
 #undef DECL_NEWENUMERATOR
 
-  /// Gets pointers-overhead storage.
-#define DECL_GETPOINTERS(PNAME, P)                                             \
-  virtual void getPointers(std::vector<P> **, uint64_t);
-  MLIR_SPARSETENSOR_FOREVERY_FIXED_O(DECL_GETPOINTERS)
-#undef DECL_GETPOINTERS
-
-  /// Gets indices-overhead storage.
-#define DECL_GETINDICES(INAME, I)                                              \
-  virtual void getIndices(std::vector<I> **, uint64_t);
-  MLIR_SPARSETENSOR_FOREVERY_FIXED_O(DECL_GETINDICES)
-#undef DECL_GETINDICES
-  virtual uint64_t getIndex(uint64_t l, uint64_t pos) const = 0;
+  /// Gets positions-overhead storage for the given level.
+#define DECL_GETPOSITIONS(PNAME, P)                                            \
+  virtual void getPositions(std::vector<P> **, uint64_t);
+  MLIR_SPARSETENSOR_FOREVERY_FIXED_O(DECL_GETPOSITIONS)
+#undef DECL_GETPOSITIONS
+
+  /// Gets coordinates-overhead storage for the given level.
+#define DECL_GETCOORDINATES(INAME, C)                                          \
+  virtual void getCoordinates(std::vector<C> **, uint64_t);
+  MLIR_SPARSETENSOR_FOREVERY_FIXED_O(DECL_GETCOORDINATES)
+#undef DECL_GETCOORDINATES
+  /// Gets the coordinate-value stored at the given level and position.
+  virtual uint64_t getCrd(uint64_t lvl, uint64_t pos) const = 0;
 
   /// Gets primary storage.
 #define DECL_GETVALUES(VNAME, V) virtual void getValues(std::vector<V> **);
   MLIR_SPARSETENSOR_FOREVERY_V(DECL_GETVALUES)
 #undef DECL_GETVALUES
 
-  /// Element-wise insertion in lexicographic index order.  The first
-  /// argument is the level-indices for the value being inserted.
+  /// Element-wise insertion in lexicographic coordinate order.  The first
+  /// argument is the level-coordinates for the value being inserted.
   // TODO: For better safety, this should take a parameter for the
-  // length of `lvlInd` and check that against `getLvlRank()`.
+  // length of `lvlCoords` and check that against `getLvlRank()`.
 #define DECL_LEXINSERT(VNAME, V) virtual void lexInsert(const uint64_t *, V);
   MLIR_SPARSETENSOR_FOREVERY_V(DECL_LEXINSERT)
 #undef DECL_LEXINSERT
 
   /// Expanded insertion.  Note that this method resets the
   /// values/filled-switch array back to all-zero/false while only
   /// iterating over the nonzero elements.
   ///
   /// Arguments:
-  /// * `lvlInd` the level-indices shared by the values being inserted.
+  /// * `lvlCoords` the level-coordinates shared by the values being inserted.
   /// * `values` a map from last-level coordinates to their associated value.
   /// * `filled` a map from last-level coordinates to bool, indicating
   ///   whether `values` contains a valid value to be inserted.
   /// * `added` a map from `[0..count)` to last-level coordinates for
   ///   which `filled` is true and `values` contains the assotiated value.
   /// * `count` the size of `added`.
 #define DECL_EXPINSERT(VNAME, V)                                               \
   virtual void expInsert(uint64_t *, V *, bool *, uint64_t *, uint64_t);
   MLIR_SPARSETENSOR_FOREVERY_V(DECL_EXPINSERT)
 #undef DECL_EXPINSERT
 
   /// Finishes insertion.
   virtual void endInsert() = 0;
 
 private:
   const std::vector<uint64_t> dimSizes;
   const std::vector<uint64_t> lvlSizes;
   const std::vector<DimLevelType> lvlTypes;
   const std::vector<uint64_t> lvl2dim;
 };
 
 //===----------------------------------------------------------------------===//
 // This forward decl is necessary for defining `SparseTensorStorage`,
 // but isn't sufficient for splitting it off.
-template <typename P, typename I, typename V>
+template <typename P, typename C, typename V>
 class SparseTensorEnumerator;
 
 /// A memory-resident sparse tensor using a storage scheme based on
-/// per-dimension sparse/dense annotations.  This data structure provides
+/// per-level sparse/dense annotations.  This data structure provides
 /// a bufferized form of a sparse tensor type.  In contrast to generating
 /// setup methods for each differently annotated sparse tensor, this
 /// method provides a convenient "one-size-fits-all" solution that simply
 /// takes an input tensor and annotations to implement all required setup
 /// in a general manner.
-template <typename P, typename I, typename V>
+template <typename P, typename C, typename V>
 class SparseTensorStorage final : public SparseTensorStorageBase {
   /// Private constructor to share code between the other constructors.
   /// Beware that the object is not necessarily guaranteed to be in a
   /// valid state after this constructor alone; e.g., `isCompressedLvl(l)`
-  /// doesn't entail `!(pointers[l].empty())`.
+  /// doesn't entail `!(positions[l].empty())`.
   ///
   /// Preconditions/assertions are as per the `SparseTensorStorageBase` ctor.
   SparseTensorStorage(uint64_t dimRank, const uint64_t *dimSizes,
                       uint64_t lvlRank, const uint64_t *lvlSizes,
                       const DimLevelType *lvlTypes, const uint64_t *lvl2dim)
       : SparseTensorStorageBase(dimRank, dimSizes, lvlRank, lvlSizes, lvlTypes,
                                 lvl2dim),
-        pointers(lvlRank), indices(lvlRank), lvlCursor(lvlRank) {}
+        positions(lvlRank), coordinates(lvlRank), lvlCursor(lvlRank) {}
 
 public:
   /// Constructs a sparse tensor with the given encoding, and allocates
   /// overhead storage according to some simple heuristics.  When the
   /// `bool` argument is true and `lvlTypes` are all dense, then this
   /// ctor will also initialize the values array with zeros.  That
   /// argument should be true when an empty tensor is intended; whereas
   /// it should usually be false when the ctor will be followed up by
SparseTensorStorage(uint64_t dimRank, const uint64_t *dimSizes,
                       uint64_t lvlRank, const DimLevelType *lvlTypes,
                       const uint64_t *lvl2dim,
                       SparseTensorEnumeratorBase<V> &lvlEnumerator);
 
   /// Allocates a new empty sparse tensor.  The preconditions/assertions
   /// are as per the `SparseTensorStorageBase` ctor; which is to say,
   /// the `dimSizes` and `lvlSizes` must both be "sizes" not "shapes",
   /// since there's nowhere to reconstruct dynamic sizes from.
-  static SparseTensorStorage<P, I, V> *
+  static SparseTensorStorage<P, C, V> *
   newEmpty(uint64_t dimRank, const uint64_t *dimSizes, uint64_t lvlRank,
            const uint64_t *lvlSizes, const DimLevelType *lvlTypes,
            const uint64_t *lvl2dim) {
-    return new SparseTensorStorage<P, I, V>(dimRank, dimSizes, lvlRank,
+    return new SparseTensorStorage<P, C, V>(dimRank, dimSizes, lvlRank,
                                             lvlSizes, lvlTypes, lvl2dim, true);
   }
 
   /// Allocates a new sparse tensor and initializes it from the given COO.
   /// The preconditions are as per the `SparseTensorStorageBase` ctor
   /// (where we define `lvlSizes = lvlCOO.getDimSizes().data()`), but
   /// using the following assertions in lieu of the base ctor's assertions:
   ///
   /// Asserts:
   /// * `dimRank` and `lvlRank` are nonzero.
   /// * `lvlRank == lvlCOO.getRank()`.
   /// * `lvlCOO.getDimSizes()` under the `lvl2dim` mapping is a refinement
   ///   of `dimShape`.
   //
   // TODO: The ability to reconstruct dynamic dimensions-sizes does not
   // easily generalize to arbitrary `lvl2dim` mappings.  When compiling
   // MLIR programs to use this library, we should be able to generate
   // code for effectively computing the reconstruction, but it's not clear
   // that there's a feasible way to do so from within the library itself.
   // Therefore, when we functionalize the `lvl2dim` mapping we'll have
   // to update the type/preconditions of this factory too.
-  static SparseTensorStorage<P, I, V> *
+  static SparseTensorStorage<P, C, V> *
   newFromCOO(uint64_t dimRank, const uint64_t *dimShape, uint64_t lvlRank,
              const DimLevelType *lvlTypes, const uint64_t *lvl2dim,
              SparseTensorCOO<V> &lvlCOO);
 
   /// Allocates a new sparse tensor and initializes it with the contents
   /// of another sparse tensor.
   ///
   /// Preconditions:
   /// * as per the `SparseTensorStorageBase` ctor.
-  /// * `src2lvl` must be valid for `srcRank`, must map indices valid for
-  ///   `source.getDimSizes()` to indices valid for `lvlSizes`, and therefore
-  ///   must be the inverse of `lvl2dim`.
+  /// * `src2lvl` must be valid for `srcRank`, must map coordinates valid
+  ///    for `source.getDimSizes()` to coordinates valid for `lvlSizes`,
+  ///    and therefore must be the inverse of `lvl2dim`.
   /// * `source` must have the same value type `V`.
   ///
   /// Asserts:
   /// * `dimRank` and `lvlRank` are nonzero.
   /// * `srcRank == source.getDimRank()`.
   /// * `lvlSizes` contains only nonzero sizes.
   /// * `source.getDimSizes()` is a refinement of `dimShape`.
   //
   // TODO: The `dimRank` and `dimShape` arguments are only used for
   // verifying that the source tensor has the expected shape.  So if we
   // wanted to skip that verification, then we could remove those arguments.
   // Alternatively, if we required the `dimShape` to be "sizes" instead,
   // then that would remove any constraints on `source.getDimSizes()`
   // (other than compatibility with `src2lvl`) as well as removing the
   // requirement that `src2lvl` be the inverse of `lvl2dim`.  Which would
   // enable this factory to be used for performing a much larger class of
   // transformations (which can already be handled by the `SparseTensorNNZ`
   // implementation).
-  static SparseTensorStorage<P, I, V> *
+  static SparseTensorStorage<P, C, V> *
   newFromSparseTensor(uint64_t dimRank, const uint64_t *dimShape,
                       uint64_t lvlRank, const uint64_t *lvlSizes,
                       const DimLevelType *lvlTypes, const uint64_t *lvl2dim,
                       uint64_t srcRank, const uint64_t *src2lvl,
                       const SparseTensorStorageBase &source);
 
   ~SparseTensorStorage() final = default;
 
   /// Partially specialize these getter methods based on template types.
-  void getPointers(std::vector<P> **out, uint64_t l) final {
+  void getPositions(std::vector<P> **out, uint64_t lvl) final {
     assert(out && "Received nullptr for out parameter");
-    ASSERT_VALID_LVL(l);
-    *out = &pointers[l];
+    ASSERT_VALID_LVL(lvl);
+    *out = &positions[lvl];
   }
-  void getIndices(std::vector<I> **out, uint64_t l) final {
+  void getCoordinates(std::vector<C> **out, uint64_t lvl) final {
     assert(out && "Received nullptr for out parameter");
-    ASSERT_VALID_LVL(l);
-    *out = &indices[l];
+    ASSERT_VALID_LVL(lvl);
+    *out = &coordinates[lvl];
   }
   void getValues(std::vector<V> **out) final {
     assert(out && "Received nullptr for out parameter");
     *out = &values;
   }
 
-  uint64_t getIndex(uint64_t l, uint64_t pos) const final {
-    ASSERT_COMPRESSED_OR_SINGLETON_LVL(l);
-    assert(pos < indices[l].size() && "Index position is out of bounds");
-    return indices[l][pos]; // Converts the stored `I` into `uint64_t`.
+  uint64_t getCrd(uint64_t lvl, uint64_t pos) const final {
+    ASSERT_COMPRESSED_OR_SINGLETON_LVL(lvl);
+    assert(pos < coordinates[lvl].size() && "Position is out of bounds");
+    return coordinates[lvl][pos]; // Converts the stored `C` into `uint64_t`.
   }
 
   /// Partially specialize lexicographical insertions based on template types.
-  void lexInsert(const uint64_t *lvlInd, V val) final {
-    assert(lvlInd && "Received nullptr for level-indices");
+  void lexInsert(const uint64_t *lvlCoords, V val) final {
+    assert(lvlCoords && "Received nullptr for level-coordinates");
     // First, wrap up pending insertion path.
     uint64_t diffLvl = 0;
-    uint64_t topIdx = 0;
+    uint64_t full = 0;
     if (!values.empty()) {
-      diffLvl = lexDiff(lvlInd);
+      diffLvl = lexDiff(lvlCoords);
       endPath(diffLvl + 1);
-      topIdx = lvlCursor[diffLvl] + 1;
+      full = lvlCursor[diffLvl] + 1;
     }
     // Then continue with insertion path.
-    insPath(lvlInd, diffLvl, topIdx, val);
+    insPath(lvlCoords, diffLvl, full, val);
   }
 
   /// Partially specialize expanded insertions based on template types.
-  void expInsert(uint64_t *lvlInd, V *values, bool *filled, uint64_t *added,
+  void expInsert(uint64_t *lvlCoords, V *values, bool *filled, uint64_t *added,
                  uint64_t count) final {
-    assert((lvlInd && values && filled && added) && "Received nullptr");
+    assert((lvlCoords && values && filled && added) && "Received nullptr");
     if (count == 0)
       return;
     // Sort.
     std::sort(added, added + count);
     // Restore insertion path for first insert.
     const uint64_t lastLvl = getLvlRank() - 1;
-    uint64_t index = added[0];
-    assert(filled[index] && "added index is not filled");
-    lvlInd[lastLvl] = index;
-    lexInsert(lvlInd, values[index]);
-    values[index] = 0;
-    filled[index] = false;
+    uint64_t c = added[0];
+    assert(filled[c] && "added coordinate is not filled");
+    lvlCoords[lastLvl] = c;
+    lexInsert(lvlCoords, values[c]);
+    values[c] = 0;
+    filled[c] = false;
     // Subsequent insertions are quick.
     for (uint64_t i = 1; i < count; ++i) {
-      assert(index < added[i] && "non-lexicographic insertion");
-      index = added[i];
-      assert(filled[index] && "added index is not filled");
-      lvlInd[lastLvl] = index;
-      insPath(lvlInd, lastLvl, added[i - 1] + 1, values[index]);
-      values[index] = 0;
-      filled[index] = false;
+      assert(c < added[i] && "non-lexicographic insertion");
+      c = added[i];
+      assert(filled[c] && "added coordinate is not filled");
+      lvlCoords[lastLvl] = c;
+      insPath(lvlCoords, lastLvl, added[i - 1] + 1, values[c]);
+      values[c] = 0;
+      filled[c] = false;
     }
   }
 
   /// Finalizes lexicographic insertions.
   void endInsert() final {
     if (values.empty())
       finalizeSegment(0);
     else
       endPath(0);
   }
 
-  /// Allocates a new enumerator for this class's `<P,I,V>` types and
-  /// erase the `<P,I>` parts from the type.  Callers must make sure to
+  /// Allocates a new enumerator for this class's `<P,C,V>` types and
+  /// erase the `<P,C>` parts from the type.  Callers must make sure to
   /// delete the enumerator when they're done with it.
   void newEnumerator(SparseTensorEnumeratorBase<V> **out, uint64_t trgRank,
                      const uint64_t *trgSizes, uint64_t srcRank,
                      const uint64_t *src2trg) const final {
     assert(out && "Received nullptr for out parameter");
-    *out = new SparseTensorEnumerator<P, I, V>(*this, trgRank, trgSizes,
+    *out = new SparseTensorEnumerator<P, C, V>(*this, trgRank, trgSizes,
                                                srcRank, src2trg);
   }
 
   /// Allocates a new COO object and initializes it with the contents
   /// of this tensor under the given mapping from the `getDimSizes()`
   /// coordinate-space to the `trgSizes` coordinate-space.  Callers must
   /// make sure to delete the COO when they're done with it.
   ///
   /// Preconditions/assertions are as per the `SparseTensorEnumerator` ctor.
   SparseTensorCOO<V> *toCOO(uint64_t trgRank, const uint64_t *trgSizes,
                             uint64_t srcRank, const uint64_t *src2trg) const {
     // We inline `newEnumerator` to avoid virtual dispatch and allocation.
-    SparseTensorEnumerator<P, I, V> enumerator(*this, trgRank, trgSizes,
+    SparseTensorEnumerator<P, C, V> enumerator(*this, trgRank, trgSizes,
                                                srcRank, src2trg);
     auto *coo = new SparseTensorCOO<V>(trgRank, trgSizes, values.size());
     enumerator.forallElements(
-        [&coo](const auto &trgInd, V val) { coo->add(trgInd, val); });
+        [&coo](const auto &trgCoords, V val) { coo->add(trgCoords, val); });
     // TODO: This assertion assumes there are no stored zeros,
     // or if there are then that we don't filter them out.
     // Cf., <https://github.com/llvm/llvm-project/issues/54179>
     assert(coo->getElements().size() == values.size());
     return coo;
   }
 
 private:
-  /// Appends an arbitrary new position to `pointers[l]`.  This method
+  /// Appends an arbitrary new position to `positions[lvl]`.  This method
   /// checks that `pos` is representable in the `P` type; however, it
   /// does not check that `pos` is semantically valid (i.e., larger than
-  /// the previous position and smaller than `indices[l].capacity()`).
-  void appendPointer(uint64_t l, uint64_t pos, uint64_t count = 1) {
-    ASSERT_COMPRESSED_LVL(l);
+  /// the previous position and smaller than `coordinates[lvl].capacity()`).
+  void appendPos(uint64_t lvl, uint64_t pos, uint64_t count = 1) {
+    ASSERT_COMPRESSED_LVL(lvl);
     // TODO: we'd like to recover the nicer error message:
-    // "Pointer value is too large for the P-type"
-    pointers[l].insert(pointers[l].end(), count,
+    // "Position value is too large for the P-type"
+    positions[lvl].insert(positions[lvl].end(), count,
                           detail::checkOverflowCast<P>(pos));
   }
 
-  /// Appends index `i` to level `l`, in the semantically general sense.
-  /// For non-dense levels, that means appending to the `indices[l]` array,
-  /// checking that `i` is representable in the `I` type; however, we do
-  /// not verify other semantic requirements (e.g., that `i` is in bounds
-  /// for `lvlSizes[l]`, and not previously occurring in the same segment).
-  /// For dense levels, this method instead appends the appropriate number
-  /// of zeros to the `values` array, where `full` is the number of "entries"
-  /// already written to `values` for this segment (aka one after the highest
-  /// index previously appended).
-  void appendIndex(uint64_t l, uint64_t full, uint64_t i) {
-    const auto dlt = getLvlType(l); // Avoid redundant bounds checking.
+  /// Appends coordinate `crd` to level `lvl`, in the semantically
+  /// general sense.  For non-dense levels, that means appending to the
+  /// `coordinates[lvl]` array, checking that `crd` is representable in
+  /// the `C` type; however, we do not verify other semantic requirements
+  /// (e.g., that `crd` is in bounds for `lvlSizes[lvl]`, and not previously
+  /// occurring in the same segment).  For dense levels, this method instead
+  /// appends the appropriate number of zeros to the `values` array, where
+  /// `full` is the number of "entries" already written to `values` for this
+  /// segment (aka one after the highest coordinate previously appended).
+  void appendCrd(uint64_t lvl, uint64_t full, uint64_t crd) {
+    const auto dlt = getLvlType(lvl); // Avoid redundant bounds checking.
     if (isCompressedDLT(dlt) || isSingletonDLT(dlt)) {
       // TODO: we'd like to recover the nicer error message:
-      // "Index value is too large for the I-type"
-      indices[l].push_back(detail::checkOverflowCast<I>(i));
-    } else { // Dense dimension.
+      // "Coordinate value is too large for the C-type"
+      coordinates[lvl].push_back(detail::checkOverflowCast<C>(crd));
+    } else { // Dense level.
       ASSERT_DENSE_DLT(dlt);
-      assert(i >= full && "Index was already filled");
-      if (i == full)
+      assert(crd >= full && "Coordinate was already filled");
+      if (crd == full)
         return; // Short-circuit, since it'll be a nop.
-      if (l + 1 == getLvlRank())
-        values.insert(values.end(), i - full, 0);
+      if (lvl + 1 == getLvlRank())
+        values.insert(values.end(), crd - full, 0);
       else
-        finalizeSegment(l + 1, 0, i - full);
+        finalizeSegment(lvl + 1, 0, crd - full);
     }
   }
 
-  /// Writes the given coordinate to `indices[l][pos]`.  This method
-  /// checks that `i` is representable in the `I` type; however, it
-  /// does not check that `i` is semantically valid (i.e., in bounds
-  /// for `dimSizes[l]` and not elsewhere occurring in the same segment).
-  void writeIndex(uint64_t l, uint64_t pos, uint64_t i) {
-    ASSERT_COMPRESSED_OR_SINGLETON_LVL(l);
+  /// Writes the given coordinate to `coordinates[lvl][pos]`.  This method
+  /// checks that `crd` is representable in the `C` type; however, it
+  /// does not check that `crd` is semantically valid (i.e., in bounds
+  /// for `dimSizes[lvl]` and not elsewhere occurring in the same segment).
+  void writeCrd(uint64_t lvl, uint64_t pos, uint64_t crd) {
+    ASSERT_COMPRESSED_OR_SINGLETON_LVL(lvl);
     // Subscript assignment to `std::vector` requires that the `pos`-th
     // entry has been initialized; thus we must be sure to check `size()`
     // here, instead of `capacity()` as would be ideal.
-    assert(pos < indices[l].size() && "Index position is out of bounds");
+    assert(pos < coordinates[lvl].size() && "Position is out of bounds");
     // TODO: we'd like to recover the nicer error message:
-    // "Index value is too large for the I-type"
-    indices[l][pos] = detail::checkOverflowCast<I>(i);
+    // "Coordinate value is too large for the C-type"
+    coordinates[lvl][pos] = detail::checkOverflowCast<C>(crd);
   }
 
   /// Computes the assembled-size associated with the `l`-th level,
   /// given the assembled-size associated with the `(l-1)`-th level.
   /// "Assembled-sizes" correspond to the (nominal) sizes of overhead
   /// storage, as opposed to "level-sizes" which are the cardinality
   /// of possible coordinates for that level.
   ///
-  /// Precondition: the `pointers[l]` array must be fully initialized
+  /// Precondition: the `positions[l]` array must be fully initialized
   /// before calling this method.
   uint64_t assembledSize(uint64_t parentSz, uint64_t l) const {
     const auto dlt = getLvlType(l); // Avoid redundant bounds checking.
     if (isCompressedDLT(dlt))
-      return pointers[l][parentSz];
+      return positions[l][parentSz];
     if (isSingletonDLT(dlt))
       return parentSz; // New size is same as the parent.
     if (isDenseDLT(dlt))
       return parentSz * getLvlSizes()[l];
     MLIR_SPARSETENSOR_FATAL("unsupported level type: %d\n",
                             static_cast<uint8_t>(dlt));
   }
 
   /// Initializes sparse tensor storage scheme from a memory-resident sparse
-  /// tensor in coordinate scheme. This method prepares the pointers and
-  /// indices arrays under the given per-dimension dense/sparse annotations.
+  /// tensor in coordinate scheme. This method prepares the positions and
+  /// coordinates arrays under the given per-level dense/sparse annotations.
   ///
   /// Preconditions:
   /// * the `lvlElements` must be lexicographically sorted.
-  /// * the indices of every element are valid for `getLvlSizes()`
+  /// * the coordinates of every element are valid for `getLvlSizes()`
   ///   (i.e., equal rank and pointwise less-than).
   void fromCOO(const std::vector<Element<V>> &lvlElements, uint64_t lo,
                uint64_t hi, uint64_t l) {
     const uint64_t lvlRank = getLvlRank();
     assert(l <= lvlRank && hi <= lvlElements.size());
-    // Once dimensions are exhausted, insert the numerical values.
+    // Once levels are exhausted, insert the numerical values.
     if (l == lvlRank) {
       assert(lo < hi);
       values.push_back(lvlElements[lo].value);
       return;
     }
     // Visit all elements in this interval.
     uint64_t full = 0;
     while (lo < hi) { // If `hi` is unchanged, then `lo < lvlElements.size()`.
-      // Find segment in interval with same index elements in this level.
-      const uint64_t i = lvlElements[lo].indices[l];
+      // Find segment in interval with same coordinate at this level.
+      const uint64_t c = lvlElements[lo].coords[l];
       uint64_t seg = lo + 1;
       if (isUniqueLvl(l))
-        while (seg < hi && lvlElements[seg].indices[l] == i)
+        while (seg < hi && lvlElements[seg].coords[l] == c)
           ++seg;
       // Handle segment in interval for sparse or dense level.
-      appendIndex(l, full, i);
-      full = i + 1;
+      appendCrd(l, full, c);
+      full = c + 1;
       fromCOO(lvlElements, lo, seg, l + 1);
       // And move on to next segment in interval.
       lo = seg;
     }
-    // Finalize the sparse pointer structure at this level.
+    // Finalize the sparse position structure at this level.
     finalizeSegment(l, full);
   }
 
-  /// Finalizes the sparse pointer structure at this level.
+  /// Finalizes the sparse position structure at this level.
   void finalizeSegment(uint64_t l, uint64_t full = 0, uint64_t count = 1) {
     if (count == 0)
       return; // Short-circuit, since it'll be a nop.
     const auto dlt = getLvlType(l); // Avoid redundant bounds checking.
     if (isCompressedDLT(dlt)) {
-      appendPointer(l, indices[l].size(), count);
+      appendPos(l, coordinates[l].size(), count);
     } else if (isSingletonDLT(dlt)) {
       return; // Nothing to finalize.
     } else {  // Dense dimension.
       ASSERT_DENSE_DLT(dlt);
       const uint64_t sz = getLvlSizes()[l];
       assert(sz >= full && "Segment is overfull");
       count = detail::checkedMul(count, sz - full);
       // For dense storage we must enumerate all the remaining coordinates
void endPath(uint64_t diffLvl) {
     const uint64_t stop = lvlRank - diffLvl;
     for (uint64_t i = 0; i < stop; ++i) {
       const uint64_t l = lastLvl - i;
       finalizeSegment(l, lvlCursor[l] + 1);
     }
   }
 
   /// Continues a single insertion path, outer to inner.  The first
-  /// argument is the storage-level indices for the value being inserted.
-  void insPath(const uint64_t *lvlInd, uint64_t diffLvl, uint64_t topIdx,
+  /// argument is the level-coordinates for the value being inserted.
+  void insPath(const uint64_t *lvlCoords, uint64_t diffLvl, uint64_t full,
                V val) {
     const uint64_t lvlRank = getLvlRank();
     assert(diffLvl <= lvlRank && "Level-diff is out of bounds");
     for (uint64_t l = diffLvl; l < lvlRank; ++l) {
-      const uint64_t i = lvlInd[l];
-      appendIndex(l, topIdx, i);
-      topIdx = 0;
-      lvlCursor[l] = i;
+      const uint64_t c = lvlCoords[l];
+      appendCrd(l, full, c);
+      full = 0;
+      lvlCursor[l] = c;
     }
     values.push_back(val);
   }
 
-  /// Finds the lexicographically first level where the level-indices
+  /// Finds the lexicographically first level where the level-coordinates
   /// in the argument differ from those in the current cursor.
-  uint64_t lexDiff(const uint64_t *lvlInd) const {
+  uint64_t lexDiff(const uint64_t *lvlCoords) const {
     const uint64_t lvlRank = getLvlRank();
     for (uint64_t l = 0; l < lvlRank; ++l)
-      if (lvlInd[l] > lvlCursor[l])
+      if (lvlCoords[l] > lvlCursor[l])
         return l;
       else
-        assert(lvlInd[l] == lvlCursor[l] && "non-lexicographic insertion");
+        assert(lvlCoords[l] == lvlCursor[l] && "non-lexicographic insertion");
     assert(0 && "duplicate insertion");
     return -1u;
   }
 
   // Allow `SparseTensorEnumerator` to access the data-members (to avoid
   // the cost of virtual-function dispatch in inner loops), without
   // making them public to other client code.
-  friend class SparseTensorEnumerator<P, I, V>;
+  friend class SparseTensorEnumerator<P, C, V>;
 
-  std::vector<std::vector<P>> pointers;
-  std::vector<std::vector<I>> indices;
+  std::vector<std::vector<P>> positions;
+  std::vector<std::vector<C>> coordinates;
   std::vector<V> values;
   std::vector<uint64_t> lvlCursor; // cursor for lexicographic insertion.
 };
 
 #undef ASSERT_COMPRESSED_OR_SINGLETON_LVL
 #undef ASSERT_COMPRESSED_LVL
 #undef ASSERT_VALID_LVL
 #undef ASSERT_VALID_DIM
 
 //===----------------------------------------------------------------------===//
 /// A (higher-order) function object for enumerating the elements of some
 /// `SparseTensorStorage` under a permutation.  That is, the `forallElements`
 /// method encapsulates the loop-nest for enumerating the elements of
 /// the source tensor (in whatever order is best for the source tensor),
-/// and applies a permutation to the coordinates/indices before handing
+/// and applies a permutation to the coordinates before handing
 /// each element to the callback.  A single enumerator object can be
 /// freely reused for several calls to `forallElements`, just so long
 /// as each call is sequential with respect to one another.
 ///
 /// N.B., this class stores a reference to the `SparseTensorStorageBase`
 /// passed to the constructor; thus, objects of this class must not
 /// outlive the sparse tensor they depend on.
 ///
 /// Design Note: The reason we define this class instead of simply using
-/// `SparseTensorEnumerator<P,I,V>` is because we need to hide/generalize
-/// the `<P,I>` template parameters from MLIR client code (to simplify the
+/// `SparseTensorEnumerator<P,C,V>` is because we need to hide/generalize
+/// the `<P,C>` template parameters from MLIR client code (to simplify the
 /// type parameters used for direct sparse-to-sparse conversion).  And the
-/// reason we define the `SparseTensorEnumerator<P,I,V>` subclasses rather
+/// reason we define the `SparseTensorEnumerator<P,C,V>` subclasses rather
 /// than simply using this class, is to avoid the cost of virtual-method
 /// dispatch within the loop-nest.
 template <typename V>
 class MLIR_SPARSETENSOR_GSL_POINTER [[nodiscard]] SparseTensorEnumeratorBase {
 public:
   /// Constructs an enumerator which automatically applies the given
   /// mapping from the source tensor's dimensions to the desired
   /// target tensor dimensions.
   ///
   /// Preconditions:
   /// * the `src` must have the same `V` value type.
   /// * `trgSizes` must be valid for `trgRank`.
-  /// * `src2trg` must be valid for `srcRank`, and must map indices
-  ///   valid for `src.getDimSizes()` to indices valid for `trgSizes`.
+  /// * `src2trg` must be valid for `srcRank`, and must map coordinates
+  ///   valid for `src.getDimSizes()` to coordinates valid for `trgSizes`.
   ///
   /// Asserts:
   /// * `trgSizes` must be nonnull and must contain only nonzero sizes.
   /// * `srcRank == src.getDimRank()`.
   /// * `src2trg` must be nonnull.
   SparseTensorEnumeratorBase(const SparseTensorStorageBase &src,
                              uint64_t trgRank, const uint64_t *trgSizes,
                              uint64_t srcRank, const uint64_t *src2trg)
public:
   uint64_t getTrgRank() const { return trgSizes.size(); }
 
   /// Gets the target's dimension-/level-sizes.  (These are usually
   /// "dimensions", though that may coincide with "level-rank" depending
   /// on usage.)
   const std::vector<uint64_t> &getTrgSizes() const { return trgSizes; }
 
   /// Enumerates all elements of the source tensor, permutes their
-  /// indices, and passes the permuted element to the callback.
+  /// coordinates, and passes the permuted element to the callback.
   /// The callback must not store the cursor reference directly,
   /// since this function reuses the storage.  Instead, the callback
   /// must copy it if they want to keep it.
   virtual void forallElements(ElementConsumer<V> yield) = 0;
 
 protected:
   const SparseTensorStorageBase &src;
   std::vector<uint64_t> trgSizes;  // in target order.
   std::vector<uint64_t> lvl2trg;   // source-levels -> target-dims/lvls.
   std::vector<uint64_t> trgCursor; // in target order.
 };
 
 //===----------------------------------------------------------------------===//
-template <typename P, typename I, typename V>
+template <typename P, typename C, typename V>
 class MLIR_SPARSETENSOR_GSL_POINTER [[nodiscard]] SparseTensorEnumerator final
     : public SparseTensorEnumeratorBase<V> {
   using Base = SparseTensorEnumeratorBase<V>;
-  using StorageImpl = SparseTensorStorage<P, I, V>;
+  using StorageImpl = SparseTensorStorage<P, C, V>;
 
 public:
   /// Constructs an enumerator which automatically applies the given
   /// mapping from the source tensor's dimensions to the desired
   /// target tensor dimensions.
   ///
   /// Preconditions/assertions are as per the `SparseTensorEnumeratorBase` ctor.
   SparseTensorEnumerator(const StorageImpl &src, uint64_t trgRank,
private:
   // and `src2trg` to convert it just before yielding to the callback.
   // It's probably most efficient to just store the `srcCursor` and
   // `trgCursor` buffers in this object, but we may want to benchmark
   // that against using `std::calloc` to stack-allocate them instead.
   //
   /// The recursive component of the public `forallElements`.
   void forallElements(ElementConsumer<V> yield, uint64_t parentPos,
                       uint64_t l) {
-    // Recover the `<P,I,V>` type parameters of `src`.
+    // Recover the `<P,C,V>` type parameters of `src`.
     const auto &src = static_cast<const StorageImpl &>(this->src);
     if (l == src.getLvlRank()) {
       assert(parentPos < src.values.size() &&
              "Value position is out of bounds");
       // TODO: <https://github.com/llvm/llvm-project/issues/54179>
       yield(this->trgCursor, src.values[parentPos]);
       return;
     }
     uint64_t &cursorL = this->trgCursor[this->lvl2trg[l]];
     const auto dlt = src.getLvlType(l); // Avoid redundant bounds checking.
     if (isCompressedDLT(dlt)) {
       // Look up the bounds of the `l`-level segment determined by the
       // `(l - 1)`-level position `parentPos`.
-      const std::vector<P> &pointersL = src.pointers[l];
-      assert(parentPos + 1 < pointersL.size() &&
-             "Parent pointer position is out of bounds");
-      const uint64_t pstart = static_cast<uint64_t>(pointersL[parentPos]);
-      const uint64_t pstop = static_cast<uint64_t>(pointersL[parentPos + 1]);
-      // Loop-invariant code for looking up the `l`-level coordinates/indices.
-      const std::vector<I> &indicesL = src.indices[l];
-      assert(pstop <= indicesL.size() && "Index position is out of bounds");
+      const std::vector<P> &positionsL = src.positions[l];
+      assert(parentPos + 1 < positionsL.size() &&
+             "Parent position is out of bounds");
+      const uint64_t pstart = static_cast<uint64_t>(positionsL[parentPos]);
+      const uint64_t pstop = static_cast<uint64_t>(positionsL[parentPos + 1]);
+      // Loop-invariant code for looking up the `l`-level coordinates.
+      const std::vector<C> &coordinatesL = src.coordinates[l];
+      assert(pstop <= coordinatesL.size() && "Stop position is out of bounds");
       for (uint64_t pos = pstart; pos < pstop; ++pos) {
-        cursorL = static_cast<uint64_t>(indicesL[pos]);
+        cursorL = static_cast<uint64_t>(coordinatesL[pos]);
         forallElements(yield, pos, l + 1);
       }
     } else if (isSingletonDLT(dlt)) {
-      cursorL = src.getIndex(l, parentPos);
+      cursorL = src.getCrd(l, parentPos);
       forallElements(yield, parentPos, l + 1);
-    } else { // Dense dimension.
+    } else { // Dense level.
       ASSERT_DENSE_DLT(dlt);
       const uint64_t sz = src.getLvlSizes()[l];
       const uint64_t pstart = parentPos * sz;
-      for (uint64_t i = 0; i < sz; ++i) {
-        cursorL = i;
-        forallElements(yield, pstart + i, l + 1);
+      for (uint64_t c = 0; c < sz; ++c) {
+        cursorL = c;
+        forallElements(yield, pstart + c, l + 1);
       }
     }
   }
 };
 
 //===----------------------------------------------------------------------===//
 /// Statistics regarding the number of nonzero subtensors in
 /// a source tensor, for direct sparse=>sparse conversion a la
public:
   /// Asserts:
   /// * `enumerator.getTrgRank() == getLvlRank()`.
   /// * `enumerator.getTrgSizes() == lvlSizes`.
   template <typename V>
   void initialize(SparseTensorEnumeratorBase<V> &enumerator) {
     assert(enumerator.getTrgRank() == getLvlRank() && "Tensor rank mismatch");
     assert(enumerator.getTrgSizes() == lvlSizes && "Tensor size mismatch");
     enumerator.forallElements(
-        [this](const std::vector<uint64_t> &ind, V) { add(ind); });
+        [this](const std::vector<uint64_t> &lvlCoords, V) { add(lvlCoords); });
   }
 
   /// The type of callback functions which receive an nnz-statistic.
   using NNZConsumer = const std::function<void(uint64_t)> &;
 
-  /// Lexicographically enumerates all indicies for levels strictly
+  /// Lexicographically enumerates all coordinates for levels strictly
   /// less than `stopLvl`, and passes their nnz statistic to the callback.
   /// Since our use-case only requires the statistic not the coordinates
   /// themselves, we do not bother to construct those coordinates.
-  void forallIndices(uint64_t stopLvl, NNZConsumer yield) const;
+  void forallCoords(uint64_t stopLvl, NNZConsumer yield) const;
 
 private:
   /// Adds a new element (i.e., increment its statistics).  We use
   /// a method rather than inlining into the lambda in `initialize`,
   /// to avoid spurious templating over `V`.  And this method is private
-  /// to avoid needing to re-assert validity of `lvlInd` (which is
+  /// to avoid needing to re-assert validity of `lvlCoords` (which is
   /// guaranteed by `forallElements`).
-  void add(const std::vector<uint64_t> &lvlInd);
+  void add(const std::vector<uint64_t> &lvlCoords);
 
-  /// Recursive component of the public `forallIndices`.
-  void forallIndices(NNZConsumer yield, uint64_t stopLvl, uint64_t parentPos,
-                     uint64_t l) const;
+  /// Recursive component of the public `forallCoords`.
+  void forallCoords(NNZConsumer yield, uint64_t stopLvl, uint64_t parentPos,
 
   // All of these are in the target storage-order.
   const std::vector<uint64_t> &lvlSizes;
   const std::vector<DimLevelType> &lvlTypes;
   std::vector<std::vector<uint64_t>> nnz;
 };
 
 //===----------------------------------------------------------------------===//
-// Definitions of the ctors and factories of `SparseTensorStorage<P,I,V>`.
+// Definitions of the ctors and factories of `SparseTensorStorage<P,C,V>`.
 
-template <typename P, typename I, typename V>
-SparseTensorStorage<P, I, V> *SparseTensorStorage<P, I, V>::newFromCOO(
+template <typename P, typename C, typename V>
+SparseTensorStorage<P, C, V> *SparseTensorStorage<P, C, V>::newFromCOO(
     uint64_t dimRank, const uint64_t *dimShape, uint64_t lvlRank,
     const DimLevelType *lvlTypes, const uint64_t *lvl2dim,
     SparseTensorCOO<V> &lvlCOO) {
   assert(dimShape && "Got nullptr for dimension shape");
   assert(lvl2dim && "Got nullptr for level-to-dimension mapping");
   const auto &lvlSizes = lvlCOO.getDimSizes();
   assert(lvlRank == lvlSizes.size() && "Level-rank mismatch");
   // Must reconstruct `dimSizes` from `lvlSizes`.  While this is easy
   // enough to do when `lvl2dim` is a permutation, this approach will
   // not work for more general mappings; so we will need to move this
   // computation off to codegen.
   std::vector<uint64_t> dimSizes(dimRank);
   for (uint64_t l = 0; l < lvlRank; ++l) {
     const uint64_t d = lvl2dim[l];
     assert((dimShape[d] == 0 || dimShape[d] == lvlSizes[l]) &&
            "Dimension sizes do not match expected shape");
     dimSizes[d] = lvlSizes[l];
   }
-  return new SparseTensorStorage<P, I, V>(dimRank, dimSizes.data(), lvlRank,
+  return new SparseTensorStorage<P, C, V>(dimRank, dimSizes.data(), lvlRank,
                                           lvlTypes, lvl2dim, lvlCOO);
 }
 
-template <typename P, typename I, typename V>
-SparseTensorStorage<P, I, V> *SparseTensorStorage<P, I, V>::newFromSparseTensor(
+template <typename P, typename C, typename V>
+SparseTensorStorage<P, C, V> *SparseTensorStorage<P, C, V>::newFromSparseTensor(
     uint64_t dimRank, const uint64_t *dimShape, uint64_t lvlRank,
     const uint64_t *lvlSizes, const DimLevelType *lvlTypes,
     const uint64_t *lvl2dim, uint64_t srcRank, const uint64_t *src2lvl,
     const SparseTensorStorageBase &source) {
   // Verify that the `source` dimensions match the expected `dimShape`.
   assert(dimShape && "Got nullptr for dimension shape");
   assert(dimRank == source.getDimRank() && "Dimension-rank mismatch");
   const auto &dimSizes = source.getDimSizes();
 #ifndef NDEBUG
   for (uint64_t d = 0; d < dimRank; ++d) {
     const uint64_t sz = dimShape[d];
     assert((sz == 0 || sz == dimSizes[d]) &&
            "Dimension-sizes do not match expected shape");
   }
 #endif
   SparseTensorEnumeratorBase<V> *lvlEnumerator;
   source.newEnumerator(&lvlEnumerator, lvlRank, lvlSizes, srcRank, src2lvl);
-  auto *tensor = new SparseTensorStorage<P, I, V>(
+  auto *tensor = new SparseTensorStorage<P, C, V>(
       dimRank, dimSizes.data(), lvlRank, lvlTypes, lvl2dim, *lvlEnumerator);
   delete lvlEnumerator;
   return tensor;
 }
 
-template <typename P, typename I, typename V>
-SparseTensorStorage<P, I, V>::SparseTensorStorage(
+template <typename P, typename C, typename V>
+SparseTensorStorage<P, C, V>::SparseTensorStorage(
     uint64_t dimRank, const uint64_t *dimSizes, uint64_t lvlRank,
     const uint64_t *lvlSizes, const DimLevelType *lvlTypes,
     const uint64_t *lvl2dim, bool initializeValuesIfAllDense)
     : SparseTensorStorage(dimRank, dimSizes, lvlRank, lvlSizes, lvlTypes,
                           lvl2dim) {
-  // Provide hints on capacity of pointers and indices.
+  // Provide hints on capacity of positions and coordinates.
   // TODO: needs much fine-tuning based on actual sparsity; currently
-  //       we reserve pointer/index space based on all previous dense
-  //       dimensions, which works well up to first sparse dim; but
-  //       we should really use nnz and dense/sparse distribution.
+  // we reserve position/coordinate space based on all previous dense
+  // levels, which works well up to first sparse level; but we should
+  // really use nnz and dense/sparse distribution.
   bool allDense = true;
   uint64_t sz = 1;
   for (uint64_t l = 0; l < lvlRank; ++l) {
     const DimLevelType dlt = lvlTypes[l]; // Avoid redundant bounds checking.
     if (isCompressedDLT(dlt)) {
       // TODO: Take a parameter between 1 and `lvlSizes[l]`, and multiply
       // `sz` by that before reserving. (For now we just use 1.)
-      pointers[l].reserve(sz + 1);
-      pointers[l].push_back(0);
-      indices[l].reserve(sz);
+      positions[l].reserve(sz + 1);
+      positions[l].push_back(0);
+      coordinates[l].reserve(sz);
       sz = 1;
       allDense = false;
     } else if (isSingletonDLT(dlt)) {
-      indices[l].reserve(sz);
+      coordinates[l].reserve(sz);
       sz = 1;
       allDense = false;
-    } else { // Dense dimension.
+    } else { // Dense level.
       ASSERT_DENSE_DLT(dlt);
       sz = detail::checkedMul(sz, lvlSizes[l]);
     }
   }
   if (allDense && initializeValuesIfAllDense)
     values.resize(sz, 0);
 }
 
-template <typename P, typename I, typename V>
-SparseTensorStorage<P, I, V>::SparseTensorStorage( // NOLINT
+template <typename P, typename C, typename V>
+SparseTensorStorage<P, C, V>::SparseTensorStorage( // NOLINT
     uint64_t dimRank, const uint64_t *dimSizes, uint64_t lvlRank,
     const DimLevelType *lvlTypes, const uint64_t *lvl2dim,
     SparseTensorCOO<V> &lvlCOO)
     : SparseTensorStorage(dimRank, dimSizes, lvlRank,
                           lvlCOO.getDimSizes().data(), lvlTypes, lvl2dim,
                           false) {
   assert(lvlRank == lvlCOO.getDimSizes().size() && "Level-rank mismatch");
   // Ensure the preconditions of `fromCOO`.  (One is already ensured by
   // using `lvlSizes = lvlCOO.getDimSizes()` in the ctor above.)
   lvlCOO.sort();
   // Now actually insert the `elements`.
   const auto &elements = lvlCOO.getElements();
-  uint64_t nnz = elements.size();
-  values.reserve(nnz);
-  fromCOO(elements, 0, nnz, 0);
+  const uint64_t nse = elements.size();
+  values.reserve(nse);
+  fromCOO(elements, 0, nse, 0);
 }
 
-template <typename P, typename I, typename V>
-SparseTensorStorage<P, I, V>::SparseTensorStorage(
+template <typename P, typename C, typename V>
+SparseTensorStorage<P, C, V>::SparseTensorStorage(
     uint64_t dimRank, const uint64_t *dimSizes, uint64_t lvlRank,
     const DimLevelType *lvlTypes, const uint64_t *lvl2dim,
     SparseTensorEnumeratorBase<V> &lvlEnumerator)
     : SparseTensorStorage(dimRank, dimSizes, lvlRank,
                           lvlEnumerator.getTrgSizes().data(), lvlTypes,
                           lvl2dim) {
   assert(lvlRank == lvlEnumerator.getTrgRank() && "Level-rank mismatch");
   {
     // Initialize the statistics structure.
     SparseTensorNNZ nnz(getLvlSizes(), getLvlTypes());
     nnz.initialize(lvlEnumerator);
-    // Initialize "pointers" overhead (and allocate "indices", "values").
+    // Initialize "positions" overhead (and allocate "coordinates", "values").
     uint64_t parentSz = 1; // assembled-size of the `(l - 1)`-level.
     for (uint64_t l = 0; l < lvlRank; ++l) {
       const auto dlt = lvlTypes[l]; // Avoid redundant bounds checking.
       if (isCompressedDLT(dlt)) {
-        pointers[l].reserve(parentSz + 1);
-        pointers[l].push_back(0);
+        positions[l].reserve(parentSz + 1);
+        positions[l].push_back(0);
         uint64_t currentPos = 0;
-        nnz.forallIndices(l, [this, &currentPos, l](uint64_t n) {
+        nnz.forallCoords(l, [this, &currentPos, l](uint64_t n) {
           currentPos += n;
-          appendPointer(l, currentPos);
+          appendPos(l, currentPos);
         });
-        assert(pointers[l].size() == parentSz + 1 &&
-               "Final pointers size doesn't match allocated size");
+        assert(positions[l].size() == parentSz + 1 &&
+               "Final positions size doesn't match allocated size");
         // That assertion entails `assembledSize(parentSz, l)`
-        // is now in a valid state.  That is, `pointers[l][parentSz]`
+        // is now in a valid state.  That is, `positions[l][parentSz]`
         // equals the present value of `currentPos`, which is the
-        // correct assembled-size for `indices[l]`.
+        // correct assembled-size for `coordinates[l]`.
       }
       // Update assembled-size for the next iteration.
       parentSz = assembledSize(parentSz, l);
-      // Ideally we need only `indices[l].reserve(parentSz)`, however
+      // Ideally we need only `coordinates[l].reserve(parentSz)`, however
       // the `std::vector` implementation forces us to initialize it too.
       // That is, in the yieldPos loop we need random-access assignment
-      // to `indices[l]`; however, `std::vector`'s subscript-assignment
+      // to `coordinates[l]`; however, `std::vector`'s subscript-assignment
       // only allows assigning to already-initialized positions.
       if (isCompressedDLT(dlt) || isSingletonDLT(dlt))
-        indices[l].resize(parentSz, 0);
+        coordinates[l].resize(parentSz, 0);
       else
         ASSERT_DENSE_DLT(dlt); // Future-proofing.
     }
     values.resize(parentSz, 0); // Both allocate and zero-initialize.
   }
   // The yieldPos loop
-  lvlEnumerator.forallElements([this](const auto &lvlInd, V val) {
+  lvlEnumerator.forallElements([this](const auto &lvlCoords, V val) {
     uint64_t parentSz = 1, parentPos = 0;
     for (uint64_t lvlRank = getLvlRank(), l = 0; l < lvlRank; ++l) {
       const auto dlt = getLvlTypes()[l]; // Avoid redundant bounds checking.
       if (isCompressedDLT(dlt)) {
         // If `parentPos == parentSz` then it's valid as an array-lookup;
         // however, it's semantically invalid here since that entry
-        // does not represent a segment of `indices[l]`.  Moreover, that
+        // does not represent a segment of `coordinates[l]`.  Moreover, that
         // entry must be immutable for `assembledSize` to remain valid.
-        assert(parentPos < parentSz && "Pointers position is out of bounds");
-        const uint64_t currentPos = pointers[l][parentPos];
+        assert(parentPos < parentSz && "Parent position is out of bounds");
+        const uint64_t currentPos = positions[l][parentPos];
         // This increment won't overflow the `P` type, since it can't
-        // exceed the original value of `pointers[l][parentPos+1]`
+        // exceed the original value of `positions[l][parentPos+1]`
         // which was already verified to be within bounds for `P`
         // when it was written to the array.
-        pointers[l][parentPos]++;
-        writeIndex(l, currentPos, lvlInd[l]);
+        positions[l][parentPos]++;
+        writeCrd(l, currentPos, lvlCoords[l]);
         parentPos = currentPos;
       } else if (isSingletonDLT(dlt)) {
-        writeIndex(l, parentPos, lvlInd[l]);
+        writeCrd(l, parentPos, lvlCoords[l]);
         // the new parentPos equals the old parentPos.
-      } else { // Dense dimension.
+      } else { // Dense level.
         ASSERT_DENSE_DLT(dlt);
-        parentPos = parentPos * getLvlSizes()[l] + lvlInd[l];
+        parentPos = parentPos * getLvlSizes()[l] + lvlCoords[l];
       }
       parentSz = assembledSize(parentSz, l);
     }
     assert(parentPos < values.size() && "Value position is out of bounds");
     values[parentPos] = val;
   });
   // The finalizeYieldPos loop
   for (uint64_t parentSz = 1, l = 0; l < lvlRank; ++l) {
     const auto dlt = lvlTypes[l]; // Avoid redundant bounds checking.
     if (isCompressedDLT(dlt)) {
-      assert(parentSz == pointers[l].size() - 1 &&
-             "Actual pointers size doesn't match the expected size");
+      assert(parentSz == positions[l].size() - 1 &&
+             "Actual positions size doesn't match the expected size");
       // Can't check all of them, but at least we can check the last one.
-      assert(pointers[l][parentSz - 1] == pointers[l][parentSz] &&
-             "Pointers got corrupted");
+      assert(positions[l][parentSz - 1] == positions[l][parentSz] &&
+             "Positions got corrupted");
       // TODO: optimize this by using `memmove` or similar.
       for (uint64_t n = 0; n < parentSz; ++n) {
         const uint64_t parentPos = parentSz - n;
-        pointers[l][parentPos] = pointers[l][parentPos - 1];
+        positions[l][parentPos] = positions[l][parentPos - 1];
       }
-      pointers[l][0] = 0;
+      positions[l][0] = 0;
     } else {
       // Both dense and singleton are no-ops for the finalizeYieldPos loop.
       // This assertion is for future-proofing.
       assert((isDenseDLT(dlt) || isSingletonDLT(dlt)) &&
              "Level is neither dense nor singleton");
     }
     parentSz = assembledSize(parentSz, l);
   }
 }
 
 #undef ASSERT_DENSE_DLT
 
 } // namespace sparse_tensor
 } // namespace mlir
 
 #endif // MLIR_EXECUTIONENGINE_SPARSETENSOR_STORAGE_H

mlir/include/mlir/ExecutionEngine/SparseTensorRuntime.h

 /// kSparseToSparse STS             STS, copied from the STS source
 /// kToIterator     STS             Iterator, call @getNext to use and
 ///                                 @delSparseTensorIterator to free.
 MLIR_CRUNNERUTILS_EXPORT void *_mlir_ciface_newSparseTensor( // NOLINT
     StridedMemRefType<index_type, 1> *dimSizesRef,
     StridedMemRefType<index_type, 1> *lvlSizesRef,
     StridedMemRefType<DimLevelType, 1> *lvlTypesRef,
     StridedMemRefType<index_type, 1> *lvl2dimRef,
-    StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType ptrTp,
-    OverheadType indTp, PrimaryType valTp, Action action, void *ptr);
+    StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType posTp,
+    OverheadType crdTp, PrimaryType valTp, Action action, void *ptr);
 
 // TODO: document what all the arguments are/mean for the functions below,
 // especially with regards to "dim"-vs-"lvl" and mappings/permutations.
 
 /// Tensor-storage method to obtain direct access to the values array.
 #define DECL_SPARSEVALUES(VNAME, V)                                            \
   MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_sparseValues##VNAME(              \
       StridedMemRefType<V, 1> *out, void *tensor);
 MLIR_SPARSETENSOR_FOREVERY_V(DECL_SPARSEVALUES)
 #undef DECL_SPARSEVALUES
 
-/// Tensor-storage method to obtain direct access to the pointers array
-/// for the given dimension.
-#define DECL_SPARSEPOINTERS(PNAME, P)                                          \
-  MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_sparsePointers##PNAME(            \
-      StridedMemRefType<P, 1> *out, void *tensor, index_type d);
-MLIR_SPARSETENSOR_FOREVERY_O(DECL_SPARSEPOINTERS)
-#undef DECL_SPARSEPOINTERS
-
-/// Tensor-storage method to obtain direct access to the indices array
-/// for the given dimension.
-#define DECL_SPARSEINDICES(INAME, I)                                           \
-  MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_sparseIndices##INAME(             \
-      StridedMemRefType<I, 1> *out, void *tensor, index_type d);
-MLIR_SPARSETENSOR_FOREVERY_O(DECL_SPARSEINDICES)
-#undef DECL_SPARSEINDICES
+/// Tensor-storage method to obtain direct access to the positions array
+/// for the given level.
+#define DECL_SPARSEPOSITIONS(PNAME, P)                                         \
+  MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_sparsePositions##PNAME(           \
+      StridedMemRefType<P, 1> *out, void *tensor, index_type lvl);
+MLIR_SPARSETENSOR_FOREVERY_O(DECL_SPARSEPOSITIONS)
+#undef DECL_SPARSEPOSITIONS
+
+/// Tensor-storage method to obtain direct access to the coordinates array
+/// for the given level.
+#define DECL_SPARSECOORDINATES(CNAME, C)                                       \
+  MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_sparseCoordinates##CNAME(         \
+      StridedMemRefType<C, 1> *out, void *tensor, index_type lvl);
+MLIR_SPARSETENSOR_FOREVERY_O(DECL_SPARSECOORDINATES)
+#undef DECL_SPARSECOORDINATES
 
 /// Coordinate-scheme method for adding a new element.
 #define DECL_ADDELT(VNAME, V)                                                  \
   MLIR_CRUNNERUTILS_EXPORT void *_mlir_ciface_addElt##VNAME(                   \
-      void *coo, StridedMemRefType<V, 0> *vref,                                \
-      StridedMemRefType<index_type, 1> *iref,                                  \
-      StridedMemRefType<index_type, 1> *pref);
+      void *lvlCOO, StridedMemRefType<V, 0> *vref,                             \
+      StridedMemRefType<index_type, 1> *dimCoordsRef,                          \
+      StridedMemRefType<index_type, 1> *dim2lvlRef);
 MLIR_SPARSETENSOR_FOREVERY_V(DECL_ADDELT)
 #undef DECL_ADDELT
 
 /// Coordinate-scheme method for getting the next element while iterating.
+/// The `cref` argument uses the same coordinate-space as the `iter` (which
+/// can be either dim- or lvl-coords, depending on context).
 #define DECL_GETNEXT(VNAME, V)                                                 \
   MLIR_CRUNNERUTILS_EXPORT bool _mlir_ciface_getNext##VNAME(                   \
-      void *coo, StridedMemRefType<index_type, 1> *iref,                       \
+      void *iter, StridedMemRefType<index_type, 1> *cref,                      \
       StridedMemRefType<V, 0> *vref);
 MLIR_SPARSETENSOR_FOREVERY_V(DECL_GETNEXT)
 #undef DECL_GETNEXT
 
-/// Tensor-storage method to insert elements in lexicographical index order.
+/// Tensor-storage method to insert elements in lexicographical
+/// level-coordinate order.
 #define DECL_LEXINSERT(VNAME, V)                                               \
   MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_lexInsert##VNAME(                 \
-      void *tensor, StridedMemRefType<index_type, 1> *cref,                    \
+      void *tensor, StridedMemRefType<index_type, 1> *lvlCoordsRef,            \
       StridedMemRefType<V, 0> *vref);
 MLIR_SPARSETENSOR_FOREVERY_V(DECL_LEXINSERT)
 #undef DECL_LEXINSERT
 
 /// Tensor-storage method to insert using expansion.
 #define DECL_EXPINSERT(VNAME, V)                                               \
   MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_expInsert##VNAME(                 \
-      void *tensor, StridedMemRefType<index_type, 1> *cref,                    \
+      void *tensor, StridedMemRefType<index_type, 1> *lvlCoordsRef,            \
       StridedMemRefType<V, 1> *vref, StridedMemRefType<bool, 1> *fref,         \
       StridedMemRefType<index_type, 1> *aref, index_type count);
 MLIR_SPARSETENSOR_FOREVERY_V(DECL_EXPINSERT)
 #undef DECL_EXPINSERT
 
 //===----------------------------------------------------------------------===//
 //
 // Public functions which accept only C-style data structures to interact

 
 /// Initializes sparse tensor from a COO-flavored format expressed using
 /// C-style data structures.  The expected parameters are:
 ///
 ///   rank:    rank of tensor
 ///   nse:     number of specified elements (usually the nonzeros)
 ///   shape:   array with dimension size for each rank
 ///   values:  a "nse" array with values for all specified elements
-///   indices: a flat "nse * rank" array with indices for all specified elements
-///   perm:    the permutation of the dimensions in the storage
-///   sparse:  the sparsity for the dimensions
+///   coordinates: a flat "nse * rank" array with coordinates for all
+///            specified elements
+///   perm:    the permutation of the levels in the storage
+///   sparse:  the sparsity for the levels
 ///
 /// For example, the sparse matrix
 ///     | 1.0 0.0 0.0 |
 ///     | 0.0 5.0 3.0 |
 /// can be passed as
 ///      rank    = 2
 ///      nse     = 3
 ///      shape   = [2, 3]
 ///      values  = [1.0, 5.0, 3.0]
-///      indices = [ 0, 0,  1, 1,  1, 2]
+///      coordinates = [ 0, 0,  1, 1,  1, 2]
 #define DECL_CONVERTTOMLIRSPARSETENSOR(VNAME, V)                               \
   MLIR_CRUNNERUTILS_EXPORT void *convertToMLIRSparseTensor##VNAME(             \
-      uint64_t rank, uint64_t nse, uint64_t *shape, V *values,                 \
-      uint64_t *indices, uint64_t *perm, uint8_t *sparse);
+      uint64_t rank, uint64_t nse, uint64_t *dimSizes, V *values,              \
+      uint64_t *dimCoordinates, uint64_t *dim2lvl, uint8_t *lvlTypes);
 MLIR_SPARSETENSOR_FOREVERY_V(DECL_CONVERTTOMLIRSPARSETENSOR)
 #undef DECL_CONVERTTOMLIRSPARSETENSOR
 
 /// Converts a sparse tensor to COO-flavored format expressed using
 /// C-style data structures.  The expected output parameters are pointers
 /// for these values:
 ///
 ///   rank:    rank of tensor
 ///   nse:     number of specified elements (usually the nonzeros)
-///   shape:   array with dimension size for each rank
+///   shape:   array with size for each dimension
 ///   values:  a "nse" array with values for all specified elements
-///   indices: a flat "nse * rank" array with indices for all specified elements
+///   coordinates: a flat "nse * rank" array with coordinates for all
+///            specified elements
 ///
-/// The input is a pointer to `SparseTensorStorage<P, I, V>`, typically
+/// The input is a pointer to `SparseTensorStorage<P, C, V>`, typically
 /// returned from `convertToMLIRSparseTensor`.
 #define DECL_CONVERTFROMMLIRSPARSETENSOR(VNAME, V)                             \
   MLIR_CRUNNERUTILS_EXPORT void convertFromMLIRSparseTensor##VNAME(            \
       void *tensor, uint64_t *pRank, uint64_t *pNse, uint64_t **pShape,        \
-      V **pValues, uint64_t **pIndices);
+      V **pValues, uint64_t **pCoordinates);
 MLIR_SPARSETENSOR_FOREVERY_V(DECL_CONVERTFROMMLIRSPARSETENSOR)
 #undef DECL_CONVERTFROMMLIRSPARSETENSOR
 
 /// Creates a SparseTensorReader for reading a sparse tensor from a file with
 /// the given file name. This opens the file and read the header meta data based
 /// of the sparse tensor format derived from the suffix of the file name.
 //
 // FIXME: update `SparseTensorCodegenPass` to use

 
 /// Constructs a new sparse-tensor storage object with the given encoding,
 /// initializes it by reading all the elements from the file, and then
 /// closes the file.
 MLIR_CRUNNERUTILS_EXPORT void *_mlir_ciface_newSparseTensorFromReader(
     void *p, StridedMemRefType<index_type, 1> *lvlSizesRef,
     StridedMemRefType<DimLevelType, 1> *lvlTypesRef,
     StridedMemRefType<index_type, 1> *lvl2dimRef,
-    StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType ptrTp,
-    OverheadType indTp, PrimaryType valTp);
+    StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType posTp,
+    OverheadType crdTp, PrimaryType valTp);
 
 /// Returns the rank of the sparse tensor being read.
 MLIR_CRUNNERUTILS_EXPORT index_type getSparseTensorReaderRank(void *p);
 
 /// Returns the is_symmetric bit for the sparse tensor being read.
 MLIR_CRUNNERUTILS_EXPORT bool getSparseTensorReaderIsSymmetric(void *p);
 
-/// Returns the number of non-zero values for the sparse tensor being read.
-MLIR_CRUNNERUTILS_EXPORT index_type getSparseTensorReaderNNZ(void *p);
+/// Returns the number of stored elements for the sparse tensor being read.
+MLIR_CRUNNERUTILS_EXPORT index_type getSparseTensorReaderNSE(void *p);
 
 /// Returns the size of a dimension for the sparse tensor being read.
 MLIR_CRUNNERUTILS_EXPORT index_type getSparseTensorReaderDimSize(void *p,
                                                                  index_type d);
 
 /// SparseTensorReader method to copy the dimension-sizes into the
 /// provided memref.
 //

 
 /// Releases the SparseTensorReader. This also closes the file associated with
 /// the reader.
 MLIR_CRUNNERUTILS_EXPORT void delSparseTensorReader(void *p);
 
 /// Returns the next element for the sparse tensor being read.
 #define DECL_GETNEXT(VNAME, V)                                                 \
   MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_getSparseTensorReaderNext##VNAME( \
-      void *p, StridedMemRefType<index_type, 1> *iref,                         \
+      void *p, StridedMemRefType<index_type, 1> *dimCoordsRef,                 \
       StridedMemRefType<V, 0> *vref);
 MLIR_SPARSETENSOR_FOREVERY_V(DECL_GETNEXT)
 #undef DECL_GETNEXT
 
 /// Reads the sparse tensor, stores the coordinates and values to the given
 /// memrefs. Returns a boolean value to indicate whether the COO elements are
 /// sorted.
 #define DECL_GETNEXT(VNAME, V, CNAME, C)                                       \

 //
 // Only the extended FROSTT format is supported currently.
 MLIR_CRUNNERUTILS_EXPORT void *createSparseTensorWriter(char *filename);
 
 /// Finalizes the outputing of a sparse tensor to a file and releases the
 /// SparseTensorWriter.
 MLIR_CRUNNERUTILS_EXPORT void delSparseTensorWriter(void *p);
 
-/// Outputs the sparse tensor rank, nnz and shape.
+/// Outputs the sparse tensor dim-rank, nse, and dim-shape.
 MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_outSparseTensorWriterMetaData(
-    void *p, index_type rank, index_type nnz,
-    StridedMemRefType<index_type, 1> *dref);
+    void *p, index_type dimRank, index_type nse,
+    StridedMemRefType<index_type, 1> *dimSizesRef);
 
 /// Outputs an element for the sparse tensor.
 #define DECL_OUTNEXT(VNAME, V)                                                 \
   MLIR_CRUNNERUTILS_EXPORT void _mlir_ciface_outSparseTensorWriterNext##VNAME( \
-      void *p, index_type rank, StridedMemRefType<index_type, 1> *iref,        \
+      void *p, index_type dimRank,                                             \
+      StridedMemRefType<index_type, 1> *dimCoordsRef,                          \
       StridedMemRefType<V, 0> *vref);
 MLIR_SPARSETENSOR_FOREVERY_V(DECL_OUTNEXT)
 #undef DECL_OUTNEXT
 
 } // extern "C"
 
 #endif // MLIR_EXECUTIONENGINE_SPARSETENSORRUNTIME_H

mlir/lib/Bindings/Python/DialectSparseTensor.cpp

static void populateDialectSparseTensorSubmodule(const py::module &m) {
 
   mlir_attribute_subclass(m, "EncodingAttr",
                           mlirAttributeIsASparseTensorEncodingAttr)
       .def_classmethod(
           "get",
           [](py::object cls,
              std::vector<MlirSparseTensorDimLevelType> dimLevelTypes,
              std::optional<MlirAffineMap> dimOrdering,
-             std::optional<MlirAffineMap> higherOrdering, int pointerBitWidth,
-             int indexBitWidth, MlirContext context) {
+             std::optional<MlirAffineMap> higherOrdering, int posWidth,
+             int crdWidth, MlirContext context) {
             return cls(mlirSparseTensorEncodingAttrGet(
                 context, dimLevelTypes.size(), dimLevelTypes.data(),
                 dimOrdering ? *dimOrdering : MlirAffineMap{nullptr},
                 higherOrdering ? *higherOrdering : MlirAffineMap{nullptr},
-                pointerBitWidth, indexBitWidth));
+                posWidth, crdWidth));
           },
           py::arg("cls"), py::arg("dim_level_types"), py::arg("dim_ordering"),
-          py::arg("higher_ordering"), py::arg("pointer_bit_width"),
-          py::arg("index_bit_width"), py::arg("context") = py::none(),
+          py::arg("higher_ordering"), py::arg("pos_width"),
+          py::arg("crd_width"), py::arg("context") = py::none(),
           "Gets a sparse_tensor.encoding from parameters.")
       .def_property_readonly(
           "dim_level_types",
           [](MlirAttribute self) {
+            const int lvlRank = mlirSparseTensorEncodingGetLvlRank(self);
             std::vector<MlirSparseTensorDimLevelType> ret;
-            for (int i = 0,
-                     e = mlirSparseTensorEncodingGetNumDimLevelTypes(self);
-                 i < e; ++i)
+            ret.reserve(lvlRank);
+            for (int l = 0; l < lvlRank; ++l)
               ret.push_back(
-                  mlirSparseTensorEncodingAttrGetDimLevelType(self, i));
+                  mlirSparseTensorEncodingAttrGetDimLevelType(self, l));
             return ret;
           })
       .def_property_readonly(
           "dim_ordering",
           [](MlirAttribute self) -> std::optional<MlirAffineMap> {
             MlirAffineMap ret =
                 mlirSparseTensorEncodingAttrGetDimOrdering(self);
             if (mlirAffineMapIsNull(ret))
               return {};
             return ret;
           })
       .def_property_readonly(
           "higher_ordering",
           [](MlirAttribute self) -> std::optional<MlirAffineMap> {
             MlirAffineMap ret =
                 mlirSparseTensorEncodingAttrGetHigherOrdering(self);
             if (mlirAffineMapIsNull(ret))
               return {};
             return ret;
           })
-      .def_property_readonly(
-          "pointer_bit_width",
-          [](MlirAttribute self) {
-            return mlirSparseTensorEncodingAttrGetPointerBitWidth(self);
-          })
-      .def_property_readonly("index_bit_width", [](MlirAttribute self) {
-        return mlirSparseTensorEncodingAttrGetIndexBitWidth(self);
-      });
+      .def_property_readonly("pos_width",
+                             mlirSparseTensorEncodingAttrGetPosWidth)
+      .def_property_readonly("crd_width",
+                             mlirSparseTensorEncodingAttrGetCrdWidth);
 }
 
 PYBIND11_MODULE(_mlirDialectsSparseTensor, m) {
   m.doc() = "MLIR SparseTensor dialect.";
   populateDialectSparseTensorSubmodule(m);
 }

mlir/lib/CAPI/Dialect/SparseTensor.cpp

static_cast<int>(MLIR_SPARSE_TENSOR_DIM_LEVEL_DENSE) ==
             static_cast<int>(DimLevelType::SingletonNuNo),
     "MlirSparseTensorDimLevelType (C-API) and DimLevelType (C++) mismatch");
 
 bool mlirAttributeIsASparseTensorEncodingAttr(MlirAttribute attr) {
   return unwrap(attr).isa<SparseTensorEncodingAttr>();
 }
 
 MlirAttribute mlirSparseTensorEncodingAttrGet(
-    MlirContext ctx, intptr_t numDimLevelTypes,
+    MlirContext ctx, intptr_t lvlRank,
     MlirSparseTensorDimLevelType const *dimLevelTypes,
-    MlirAffineMap dimOrdering, MlirAffineMap higherOrdering,
-    int pointerBitWidth, int indexBitWidth) {
+    MlirAffineMap dimOrdering, MlirAffineMap higherOrdering, int posWidth,
+    int crdWidth) {
   SmallVector<DimLevelType> cppDimLevelTypes;
-  cppDimLevelTypes.resize(numDimLevelTypes);
-  for (intptr_t i = 0; i < numDimLevelTypes; ++i)
-    cppDimLevelTypes[i] = static_cast<DimLevelType>(dimLevelTypes[i]);
+  cppDimLevelTypes.reserve(lvlRank);
+  for (intptr_t l = 0; l < lvlRank; ++l)
+    cppDimLevelTypes.push_back(static_cast<DimLevelType>(dimLevelTypes[l]));
   return wrap(SparseTensorEncodingAttr::get(
       unwrap(ctx), cppDimLevelTypes, unwrap(dimOrdering),
-      unwrap(higherOrdering), pointerBitWidth, indexBitWidth));
+      unwrap(higherOrdering), posWidth, crdWidth));
 }
 
 MlirAffineMap mlirSparseTensorEncodingAttrGetDimOrdering(MlirAttribute attr) {
   return wrap(unwrap(attr).cast<SparseTensorEncodingAttr>().getDimOrdering());
 }
 
 MlirAffineMap
 mlirSparseTensorEncodingAttrGetHigherOrdering(MlirAttribute attr) {
   return wrap(
       unwrap(attr).cast<SparseTensorEncodingAttr>().getHigherOrdering());
 }
 
-intptr_t mlirSparseTensorEncodingGetNumDimLevelTypes(MlirAttribute attr) {
+intptr_t mlirSparseTensorEncodingGetLvlRank(MlirAttribute attr) {
   return unwrap(attr).cast<SparseTensorEncodingAttr>().getLvlRank();
 }
 
 MlirSparseTensorDimLevelType
 mlirSparseTensorEncodingAttrGetDimLevelType(MlirAttribute attr, intptr_t lvl) {
   return static_cast<MlirSparseTensorDimLevelType>(
       unwrap(attr).cast<SparseTensorEncodingAttr>().getLvlType(lvl));
 }
 
-int mlirSparseTensorEncodingAttrGetPointerBitWidth(MlirAttribute attr) {
-  return unwrap(attr).cast<SparseTensorEncodingAttr>().getPointerBitWidth();
+int mlirSparseTensorEncodingAttrGetPosWidth(MlirAttribute attr) {
+  return unwrap(attr).cast<SparseTensorEncodingAttr>().getPosWidth();
 }
 
-int mlirSparseTensorEncodingAttrGetIndexBitWidth(MlirAttribute attr) {
-  return unwrap(attr).cast<SparseTensorEncodingAttr>().getIndexBitWidth();
+int mlirSparseTensorEncodingAttrGetCrdWidth(MlirAttribute attr) {
+  return unwrap(attr).cast<SparseTensorEncodingAttr>().getCrdWidth();
 }

mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp

 
 Type mlir::sparse_tensor::detail::getIntegerOrIndexType(MLIRContext *ctx,
                                                         unsigned bitwidth) {
   if (bitwidth)
     return IntegerType::get(ctx, bitwidth);
   return IndexType::get(ctx);
 }
 
-Type SparseTensorEncodingAttr::getPointerType() const {
-  return detail::getIntegerOrIndexType(getContext(), getPointerBitWidth());
+Type SparseTensorEncodingAttr::getPosType() const {
+  return detail::getIntegerOrIndexType(getContext(), getPosWidth());
 }
 
-Type SparseTensorEncodingAttr::getIndexType() const {
-  return detail::getIntegerOrIndexType(getContext(), getIndexBitWidth());
+Type SparseTensorEncodingAttr::getCrdType() const {
+  return detail::getIntegerOrIndexType(getContext(), getCrdWidth());
 }
 
 SparseTensorEncodingAttr SparseTensorEncodingAttr::withoutOrdering() const {
-  return SparseTensorEncodingAttr::get(
-      getContext(), getDimLevelType(), AffineMap(), AffineMap(),
-      getPointerBitWidth(), getIndexBitWidth());
+  return SparseTensorEncodingAttr::get(getContext(), getDimLevelType(),
+                                       AffineMap(), AffineMap(), getPosWidth(),
+                                       getCrdWidth());
 }
 
 SparseTensorEncodingAttr SparseTensorEncodingAttr::withoutBitWidths() const {
   return SparseTensorEncodingAttr::get(getContext(), getDimLevelType(),
                                        getDimOrdering(), getHigherOrdering(), 0,
                                        0);
 }
 
if (COND) {                                                                  \
     parser.emitError(parser.getNameLoc(), MSG);                                \
     return {};                                                                 \
   }
 
   RETURN_ON_FAIL(parser.parseLess())
   RETURN_ON_FAIL(parser.parseLBrace())
 
   // Process the data from the parsed dictionary value into struct-like data.
-  SmallVector<DimLevelType> dlt;
+  SmallVector<DimLevelType> lvlTypes;
   SmallVector<SparseTensorDimSliceAttr> slices;
   AffineMap dimOrd = {};
   AffineMap higherOrd = {};
-  unsigned ptr = 0;
-  unsigned ind = 0;
+  unsigned posWidth = 0;
+  unsigned crdWidth = 0;
 
   StringRef attrName;
   // Exactly 6 keys.
   SmallVector<StringRef, 6> keys = {"dimLevelType",   "dimOrdering",
-                                    "higherOrdering", "pointerBitWidth",
-                                    "indexBitWidth",  "slice"};
+                                    "higherOrdering", "posWidth",
+                                    "crdWidth",       "slice"};
   while (succeeded(parser.parseOptionalKeyword(&attrName))) {
     if (!llvm::is_contained(keys, attrName)) {
       parser.emitError(parser.getNameLoc(), "unexpected key: ") << attrName;
       return {};
     }
 
     // Consume the `=` after keys
     RETURN_ON_FAIL(parser.parseEqual())
+    // FIXME: using `operator==` below duplicates the string comparison
+    // cost of the `is_contained` check above. Should instead use some
+    // "find" function that returns the index into `keys` so that we can
+    // dispatch on that instead.
     if (attrName == "dimLevelType") {
       Attribute attr;
       RETURN_ON_FAIL(parser.parseAttribute(attr));
       auto arrayAttr = attr.dyn_cast<ArrayAttr>();
       ERROR_IF(!arrayAttr, "expected an array for dimension level types")
       for (auto i : arrayAttr) {
         auto strAttr = i.dyn_cast<StringAttr>();
         ERROR_IF(!strAttr, "expected a string value in dimension level types")
         auto strVal = strAttr.getValue();
         if (auto optDLT = parseDLT(strVal)) {
-          dlt.push_back(optDLT.value());
+          lvlTypes.push_back(optDLT.value());
         } else {
           parser.emitError(parser.getNameLoc(),
                            "unexpected dimension level type: ")
               << strVal;
           return {};
         }
       }
     } else if (attrName == "dimOrdering") {
       Attribute attr;
       RETURN_ON_FAIL(parser.parseAttribute(attr))
       auto affineAttr = attr.dyn_cast<AffineMapAttr>();
       ERROR_IF(!affineAttr, "expected an affine map for dimension ordering")
       dimOrd = affineAttr.getValue();
     } else if (attrName == "higherOrdering") {
       Attribute attr;
       RETURN_ON_FAIL(parser.parseAttribute(attr))
       auto affineAttr = attr.dyn_cast<AffineMapAttr>();
       ERROR_IF(!affineAttr, "expected an affine map for higher ordering")
       higherOrd = affineAttr.getValue();
-    } else if (attrName == "pointerBitWidth") {
+    } else if (attrName == "posWidth") {
       Attribute attr;
       RETURN_ON_FAIL(parser.parseAttribute(attr))
       auto intAttr = attr.dyn_cast<IntegerAttr>();
-      ERROR_IF(!intAttr, "expected an integral pointer bitwidth")
-      ptr = intAttr.getInt();
-    } else if (attrName == "indexBitWidth") {
+      ERROR_IF(!intAttr, "expected an integral position bitwidth")
+      posWidth = intAttr.getInt();
+    } else if (attrName == "crdWidth") {
       Attribute attr;
       RETURN_ON_FAIL(parser.parseAttribute(attr))
       auto intAttr = attr.dyn_cast<IntegerAttr>();
       ERROR_IF(!intAttr, "expected an integral index bitwidth")
-      ind = intAttr.getInt();
+      crdWidth = intAttr.getInt();
     } else if (attrName == "slice") {
       RETURN_ON_FAIL(parser.parseLSquare())
       // Dispatches to DimSliceAttr to skip mnemonic
       bool finished = false;
       while (auto attr = SparseTensorDimSliceAttr::parse(parser, nullptr)) {
         auto sliceAttr = attr.cast<SparseTensorDimSliceAttr>();
         slices.push_back(sliceAttr);
         if (parser.parseOptionalComma().failed()) {
#define ERROR_IF(COND, MSG)                                                    \
 
   RETURN_ON_FAIL(parser.parseRBrace())
   RETURN_ON_FAIL(parser.parseGreater())
 #undef ERROR_IF
 #undef RETURN_ON_FAIL
 
   // Construct struct-like storage for attribute.
   return parser.getChecked<SparseTensorEncodingAttr>(
-      parser.getContext(), dlt, dimOrd, higherOrd, ptr, ind, slices);
+      parser.getContext(), lvlTypes, dimOrd, higherOrd, posWidth, crdWidth,
+      slices);
 }
 
 void SparseTensorEncodingAttr::print(AsmPrinter &printer) const {
   // Print the struct-like storage in dictionary fashion.
   printer << "<{ dimLevelType = [ ";
   llvm::interleaveComma(getDimLevelType(), printer, [&](DimLevelType dlt) {
     printer << "\"" << toMLIRString(dlt) << "\"";
   });
   printer << " ]";
   // Print remaining members only for non-default values.
   if (!hasIdDimOrdering())
     printer << ", dimOrdering = affine_map<" << getDimOrdering() << ">";
   if (getHigherOrdering())
     printer << ", higherOrdering = affine_map<" << getHigherOrdering() << ">";
-  if (getPointerBitWidth())
-    printer << ", pointerBitWidth = " << getPointerBitWidth();
-  if (getIndexBitWidth())
-    printer << ", indexBitWidth = " << getIndexBitWidth();
+  if (getPosWidth())
+    printer << ", posWidth = " << getPosWidth();
+  if (getCrdWidth())
+    printer << ", crdWidth = " << getCrdWidth();
   if (!getDimSlices().empty()) {
     printer << ", slice = [ ";
     llvm::interleaveComma(getDimSlices(), printer,
                           [&](SparseTensorDimSliceAttr attr) {
                             // Calls SparseTensorDimSliceAttr::print directly to
                             // skip mnemonic.
                             attr.print(printer);
                           });
     printer << " ]";
   }
 
   printer << " }>";
 }
 
 LogicalResult SparseTensorEncodingAttr::verify(
     function_ref<InFlightDiagnostic()> emitError,
     ArrayRef<DimLevelType> dimLevelType, AffineMap dimOrdering,
-    AffineMap higherOrdering, unsigned pointerBitWidth, unsigned indexBitWidth,
+    AffineMap higherOrdering, unsigned posWidth, unsigned crdWidth,
     ArrayRef<SparseTensorDimSliceAttr> dimSlices) {
-  if (!acceptBitWidth(pointerBitWidth))
-    return emitError() << "unexpected pointer bitwidth: " << pointerBitWidth;
-  if (!acceptBitWidth(indexBitWidth))
-    return emitError() << "unexpected index bitwidth: " << indexBitWidth;
+  if (!acceptBitWidth(posWidth))
+    return emitError() << "unexpected position bitwidth: " << posWidth;
+  if (!acceptBitWidth(crdWidth))
+    return emitError() << "unexpected coordinate bitwidth: " << crdWidth;
   // Before we can check that the level-rank is consistent/coherent
   // across all fields, we need to define it.  The source-of-truth for
   // the `getLvlRank` method is the length of the level-types array,
   // since it must always be provided and have full rank; therefore we
   // use that same source-of-truth here.
   const Level lvlRank = dimLevelType.size();
   if (lvlRank == 0)
     return emitError() << "expected a non-empty array for level types";
if (failed(X)) {                                                             \
     return failure();                                                          \
   }
 
 LogicalResult SparseTensorEncodingAttr::verifyEncoding(
     ArrayRef<DynSize> dimShape, Type elementType,
     function_ref<InFlightDiagnostic()> emitError) const {
   // Check structural integrity.  In particular, this ensures that the
   // level-rank is coherent across all the fields.
-  RETURN_FAILURE_IF_FAILED(verify(
-      emitError, getDimLevelType(), getDimOrdering(), getHigherOrdering(),
-      getPointerBitWidth(), getIndexBitWidth(), getDimSlices()))
+  RETURN_FAILURE_IF_FAILED(verify(emitError, getDimLevelType(),
+                                  getDimOrdering(), getHigherOrdering(),
+                                  getPosWidth(), getCrdWidth(), getDimSlices()))
   // Check integrity with tensor type specifics.  In particular, we
   // need only check that the dimension-rank of the tensor agrees with
   // the dimension-rank of the encoding.
   const Dimension dimRank = dimShape.size();
   if (dimRank == 0)
     return emitError() << "expected non-scalar sparse tensor";
   if (const auto higherOrdering = getHigherOrdering()) {
     if (higherOrdering.getNumDims() != dimRank)
std::fill_n(std::back_inserter(lvlTypes), lvlRank - 2,
                 *getDimLevelType(LevelFormat::Singleton, ordered, false));
     // Ends by a unique singleton level unless the lvlRank is 1.
     lvlTypes.push_back(*getDimLevelType(LevelFormat::Singleton, ordered, true));
   }
 
   // TODO: Maybe pick the bitwidth based on input/output tensors (probably the
   // largest one among them) in the original operation instead of using the
   // default value.
-  unsigned pointerBitWidth = src.getPointerBitWidth();
-  unsigned indexBitWidth = src.getIndexBitWidth();
+  unsigned posWidth = src.getPosWidth();
+  unsigned crdWidth = src.getCrdWidth();
   auto enc = SparseTensorEncodingAttr::get(src.getContext(), lvlTypes, lvlPerm,
-                                           AffineMap(), pointerBitWidth,
-                                           indexBitWidth);
+                                           AffineMap(), posWidth, crdWidth);
   return RankedTensorType::get(src.getDimShape(), src.getElementType(), enc);
 }
 
 RankedTensorType sparse_tensor::getCOOFromType(RankedTensorType src,
                                                bool ordered) {
   return getCOOFromTypeWithOrdering(
       src, AffineMap::getMultiDimIdentityMap(src.getRank(), src.getContext()),
       ordered);
getNormalizedEncodingForSpecifier(SparseTensorEncodingAttr enc) {
   SmallVector<DimLevelType> dlts;
   for (auto dlt : enc.getDimLevelType())
     dlts.push_back(*getDimLevelType(*getLevelFormat(dlt), true, true));
 
   return SparseTensorEncodingAttr::get(
       enc.getContext(), dlts,
       AffineMap(), // dimOrdering (irrelavant to storage speicifer)
       AffineMap(), // highLvlOrdering (irrelavant to storage specifer)
-      // Always use index for memSize, dimSize instead of reusing
-      // getBitwidth from pointers/indices.
+      // Always use `index` for memSize and lvlSize instead of reusing
+      // `getPosWidth`/`getCrdWidth`.
       // It allows us to reuse the same SSA value for different bitwidth,
       // It also avoids casting between index/integer (returned by DimOp)
       0, 0,
       // FIXME: we should keep the slice information, for now it is okay as only
       // constant can be used for slice
       ArrayRef<SparseTensorDimSliceAttr>{} /*enc.getDimSlices()*/);
 }
 
 StorageSpecifierType
 StorageSpecifierType::get(MLIRContext *ctx, SparseTensorEncodingAttr encoding) {
   return Base::get(ctx, getNormalizedEncodingForSpecifier(encoding));
 }
 
 //===----------------------------------------------------------------------===//
 // SparseTensorDialect Operations.
 //===----------------------------------------------------------------------===//
 
-static LogicalResult dimIsInBounds(Dimension dim, Value tensor) {
-  return success(dim < getDimRank(tensor));
+static LogicalResult lvlIsInBounds(Level lvl, Value tensor) {
+  return success(lvl < getSparseTensorType(tensor).getLvlRank());
 }
 
-static LogicalResult isMatchingWidth(Value result, unsigned width) {
-  const Type etp = getMemRefType(result).getElementType();
+static LogicalResult isMatchingWidth(Value mem, unsigned width) {
+  const Type etp = getMemRefType(mem).getElementType();
   return success(width == 0 ? etp.isIndex() : etp.isInteger(width));
 }
 
 static LogicalResult verifySparsifierGetterSetter(
-    StorageSpecifierKind mdKind, std::optional<APInt> lvl,
+    StorageSpecifierKind mdKind, std::optional<Level> lvl,
     TypedValue<StorageSpecifierType> md, Operation *op) {
   if (mdKind == StorageSpecifierKind::ValMemSize && lvl) {
     return op->emitError(
         "redundant level argument for querying value memory size");
   }
 
   const auto enc = md.getType().getEncoding();
   const Level lvlRank = enc.getLvlRank();
 
   if (mdKind != StorageSpecifierKind::ValMemSize) {
     if (!lvl)
       return op->emitError("missing level argument");
 
-    const Level l = lvl.value().getZExtValue();
+    const Level l = lvl.value();
     if (l >= lvlRank)
-      return op->emitError("requested level out of bound");
+      return op->emitError("requested level is out of bounds");
 
-    if (mdKind == StorageSpecifierKind::PtrMemSize && enc.isSingletonLvl(l))
+    if (mdKind == StorageSpecifierKind::PosMemSize && enc.isSingletonLvl(l))
       return op->emitError(
-          "requested pointer memory size on a singleton level");
+          "requested position memory size on a singleton level");
   }
   return success();
 }
 
-static LogicalResult verifyPackUnPack(Operation *op, TensorType cooTp,
-                                      TensorType dataTp, TensorType idxTp) {
-  if (!isUniqueCOOType(cooTp))
-    return op->emitError("must operate on a COO tensor");
-
-  auto enc = getSparseTensorEncoding(cooTp);
-  if (idxTp.getElementType() != enc.getIndexType() ||
-      dataTp.getElementType() != cooTp.getElementType())
-    return op->emitError("unmatched type between input and output");
-
-  auto dNOE = dataTp.getShape()[0];
-  auto iNOE = idxTp.getShape()[0];
-  if (!ShapedType::isDynamic(dNOE) && !ShapedType::isDynamic(iNOE) &&
-      dNOE != iNOE)
-    return op->emitError("unmatched number of elements in data and indices");
-
-  // A tensor<?xNxi32> for indices means the input COO is rank N
-  auto inRank = idxTp.getShape()[1];
-  auto ouRank = cooTp.getRank();
-  if (!ShapedType::isDynamic(inRank) && inRank != ouRank)
-    return op->emitError("unmatched rank between input and output");
+static LogicalResult verifyPackUnPack(Operation *op, bool requiresStaticShape,
+                                      SparseTensorType tensorTp,
+                                      RankedTensorType valuesTp,
+                                      RankedTensorType coordinatesTp) {
+  if (requiresStaticShape && !tensorTp.hasStaticDimShape())
+    return op->emitError("the sparse-tensor must have static shape");
+  if (!tensorTp.hasEncoding())
+    return op->emitError("the sparse-tensor must have an encoding attribute");
+  if (!tensorTp.isIdentity())
+    return op->emitError("the sparse-tensor must have the identity mapping");
+  if (!isUniqueCOOType(tensorTp))
+    return op->emitError("the sparse-tensor must have a COO type");
+
+  if (coordinatesTp.getRank() != 2)
+    return op->emitError("coordinates must have rank 2");
+  if (requiresStaticShape && !coordinatesTp.hasStaticShape())
+    return op->emitError("coordinates must have static shape");
+  if (coordinatesTp.getElementType() != tensorTp.getCrdType())
+    return op->emitError("input/output coordinate-types don't match");
+
+  if (valuesTp.getRank() != 1)
+    return op->emitError("values must have rank 1");
+  if (requiresStaticShape && !valuesTp.hasStaticShape())
+    return op->emitError("values must have static shape");
+  if (valuesTp.getElementType() != tensorTp.getElementType())
+    return op->emitError("input/output element-types don't match");
+
+  const auto valuesNSE = valuesTp.getShape()[0];
+  const auto coordsNSE = coordinatesTp.getShape()[0];
+  if (!ShapedType::isDynamic(valuesNSE) && !ShapedType::isDynamic(coordsNSE) &&
+      valuesNSE != coordsNSE)
+    return op->emitError("values/coordinates number-of-elements don't match");
+
+  // NOTE: We use `getLvlRank` because the `coordinatesTp` is for
+  // level-coordinates (cf., the op documentation).
+  const auto coordsRank = coordinatesTp.getShape()[1];
+  const auto tensorRank = tensorTp.getLvlRank();
+  if (!ShapedType::isDynamic(coordsRank) && coordsRank != tensorRank)
+    return op->emitError("input/output level-ranks don't match");
 
   return success();
 }
 
 LogicalResult PackOp::verify() {
-  TensorType dataTp = getData().getType(), idxTp = getIndices().getType();
-  TensorType retTp = getResult().getType();
-
-  if (!retTp.hasStaticShape() || !dataTp.hasStaticShape() ||
-      !idxTp.hasStaticShape())
-    return emitError("all input types must be statically shaped");
-
-  return verifyPackUnPack(*this, retTp, dataTp, idxTp);
+  const auto valuesTp = getRankedTensorType(getValues());
+  const auto coordinatesTp = getRankedTensorType(getCoordinates());
+  const auto resTp = getSparseTensorType(getResult());
+  return verifyPackUnPack(*this, true, resTp, valuesTp, coordinatesTp);
 }
 
 LogicalResult UnpackOp::verify() {
-  TensorType dataTp = getData().getType(), idxTp = getIndices().getType();
-  TensorType srcTp = getTensor().getType();
-
-  return verifyPackUnPack(*this, srcTp, dataTp, idxTp);
+  const auto valuesTp = getRankedTensorType(getValues());
+  const auto coordinatesTp = getRankedTensorType(getCoordinates());
+  const auto srcTp = getSparseTensorType(getTensor());
+  return verifyPackUnPack(*this, false, srcTp, valuesTp, coordinatesTp);
 }
 
 LogicalResult ConvertOp::verify() {
   if (auto tp1 = getSource().getType().dyn_cast<RankedTensorType>()) {
     if (auto tp2 = getDest().getType().dyn_cast<RankedTensorType>()) {
       if (tp1.getRank() != tp2.getRank())
         return emitError("unexpected conversion mismatch in rank");
       auto shape1 = tp1.getShape();
OpFoldResult ConvertOp::fold(FoldAdaptor adaptor) {
   // convert for codegen to remove. This is because we use trivial
   // sparse-to-sparse convert to tell bufferization that the sparse codegen
   // will expand the tensor buffer into sparse tensor storage.
   if (!getSparseTensorEncoding(dstType) && dstType == getSource().getType())
     return getSource();
   return {};
 }
 
-LogicalResult ToPointersOp::verify() {
+LogicalResult ToPositionsOp::verify() {
   auto e = getSparseTensorEncoding(getTensor().getType());
-  // FIXME: there seems to be some dim/lvl confusion here.
-  if (failed(dimIsInBounds(getDimension().getZExtValue(), getTensor())))
-    return emitError("requested pointers dimension out of bounds");
-  if (failed(isMatchingWidth(getResult(), e.getPointerBitWidth())))
-    return emitError("unexpected type for pointers");
+  if (failed(lvlIsInBounds(getLevel(), getTensor())))
+    return emitError("requested level is out of bounds");
+  if (failed(isMatchingWidth(getResult(), e.getPosWidth())))
+    return emitError("unexpected type for positions");
   return success();
 }
 
-LogicalResult ToIndicesOp::verify() {
+LogicalResult ToCoordinatesOp::verify() {
   auto e = getSparseTensorEncoding(getTensor().getType());
-  // FIXME: there seems to be some dim/lvl confusion here.
-  if (failed(dimIsInBounds(getDimension().getZExtValue(), getTensor())))
-    return emitError("requested indices dimension out of bounds");
-  if (failed(isMatchingWidth(getResult(), e.getIndexBitWidth())))
-    return emitError("unexpected type for indices");
+  if (failed(lvlIsInBounds(getLevel(), getTensor())))
+    return emitError("requested level is out of bounds");
+  if (failed(isMatchingWidth(getResult(), e.getCrdWidth())))
+    return emitError("unexpected type for coordinates");
   return success();
 }
 
-LogicalResult ToIndicesBufferOp::verify() {
+LogicalResult ToCoordinatesBufferOp::verify() {
   auto e = getSparseTensorEncoding(getTensor().getType());
   if (getCOOStart(e) >= e.getLvlRank())
     return emitError("expected sparse tensor with a COO region");
   return success();
 }
 
 LogicalResult ToValuesOp::verify() {
   auto ttp = getRankedTensorType(getTensor());
LogicalResult ToSliceStrideOp::verify() {
   auto rank = getRankedTensorType(getSlice()).getRank();
   if (rank <= getDim().getSExtValue() || getDim().getSExtValue() < 0)
     return emitError("requested dimension out of bound");
   return success();
 }
 
 LogicalResult GetStorageSpecifierOp::verify() {
   RETURN_FAILURE_IF_FAILED(verifySparsifierGetterSetter(
-      getSpecifierKind(), getDim(), getSpecifier(), getOperation()))
+      getSpecifierKind(), getLevel(), getSpecifier(), getOperation()))
   return success();
 }
 
 template <typename SpecifierOp>
 static SetStorageSpecifierOp getSpecifierSetDef(SpecifierOp op) {
   return op.getSpecifier().template getDefiningOp<SetStorageSpecifierOp>();
 }
 
 OpFoldResult GetStorageSpecifierOp::fold(FoldAdaptor adaptor) {
-  StorageSpecifierKind kind = getSpecifierKind();
-  std::optional<APInt> dim = getDim();
+  const StorageSpecifierKind kind = getSpecifierKind();
+  const auto lvl = getLevel();
   for (auto op = getSpecifierSetDef(*this); op; op = getSpecifierSetDef(op))
-    if (kind == op.getSpecifierKind() && dim == op.getDim())
+    if (kind == op.getSpecifierKind() && lvl == op.getLevel())
       return op.getValue();
   return {};
 }
 
 LogicalResult SetStorageSpecifierOp::verify() {
   RETURN_FAILURE_IF_FAILED(verifySparsifierGetterSetter(
-      getSpecifierKind(), getDim(), getSpecifier(), getOperation()))
+      getSpecifierKind(), getLevel(), getSpecifier(), getOperation()))
   return success();
 }
 
 //===----------------------------------------------------------------------===//
 // TensorDialect Linalg.Generic Operations.
 //===----------------------------------------------------------------------===//
 
 template <class T>
if (!absent.empty()) {
     RETURN_FAILURE_IF_FAILED(
         verifyNumBlockArgs(this, absent, "absent", TypeRange{}, outputType))
   }
   return success();
 }
 
 LogicalResult ConcatenateOp::verify() {
   const auto dstTp = getSparseTensorType(*this);
-  const Dimension concatDim = getDimension().getZExtValue();
+  const Dimension concatDim = getDimension();
   const Dimension dimRank = dstTp.getDimRank();
 
   if (getInputs().size() <= 1)
     return emitError("Need at least two tensors to concatenate.");
 
   if (concatDim >= dimRank)
     return emitError(llvm::formatv(
         "Concat-dimension is out of bounds for dimension-rank ({0} >= {1})",
if (d == concatDim) {
       }
     }
   }
 
   return success();
 }
 
 LogicalResult InsertOp::verify() {
-  if (getDimRank(getTensor()) != static_cast<Dimension>(getIndices().size()))
-    return emitOpError("incorrect number of indices");
+  const auto stt = getSparseTensorType(getTensor());
+  if (stt.getLvlRank() != static_cast<Level>(getLvlCoords().size()))
+    return emitOpError("incorrect number of coordinates");
   return success();
 }
 
 void PushBackOp::build(OpBuilder &builder, OperationState &result,
                        Value curSize, Value inBuffer, Value value) {
   build(builder, result, curSize, inBuffer, value, Value());
 }
 
 LogicalResult PushBackOp::verify() {
   if (Value n = getN()) {
     auto nValue = dyn_cast_or_null<arith::ConstantIndexOp>(n.getDefiningOp());
     if (nValue && nValue.value() < 1)
       return emitOpError("n must be not less than 1");
   }
   return success();
 }
 
 LogicalResult CompressOp::verify() {
-  if (getDimRank(getTensor()) !=
-      1 + static_cast<Dimension>(getIndices().size()))
-    return emitOpError("incorrect number of indices");
+  const auto stt = getSparseTensorType(getTensor());
+  if (stt.getLvlRank() != 1 + static_cast<Level>(getLvlCoords().size()))
+    return emitOpError("incorrect number of coordinates");
   return success();
 }
 
 void ForeachOp::build(
     OpBuilder &builder, OperationState &result, Value tensor,
     ValueRange initArgs, AffineMapAttr order,
     function_ref<void(OpBuilder &, Location, ValueRange, Value, ValueRange)>
         bodyBuilder) {
   build(builder, result, initArgs.getTypes(), tensor, initArgs, order);
   // Builds foreach body.
   if (!bodyBuilder)
     return;
   const auto stt = getSparseTensorType(tensor);
   const Dimension dimRank = stt.getDimRank();
 
-  // Starts with `dimRank`-many indices.
+  // Starts with `dimRank`-many coordinates.
   SmallVector<Type> blockArgTypes(dimRank, builder.getIndexType());
   // Followed by one value.
   blockArgTypes.push_back(stt.getElementType());
   // Followed by the reduction variables.
   blockArgTypes.append(initArgs.getTypes().begin(), initArgs.getTypes().end());
 
   SmallVector<Location> blockArgLocs(blockArgTypes.size(), tensor.getLoc());
 

mlir/lib/Dialect/SparseTensor/Transforms/BufferizableOpInterfaceImpl.cpp

bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
                                const AnalysisState &state) const {
     return false;
   }
 
   AliasingOpResultList getAliasingOpResults(Operation *op, OpOperand &opOperand,
                                             const AnalysisState &state) const {
     assert(op->getNumResults() == 1);
     assert(isUniqueCOOType(op->getResultTypes()[0].cast<RankedTensorType>()));
-    // PackOp reuses the input tensors as data/indices instead of creating new
-    // ones when packing into a COO format.
+    // PackOp reuses the input tensors as values/coordinates instead of
+    // creating new ones when packing into a COO format.
     return {{op->getOpResult(0), BufferRelation::Equivalent}};
   }
 
   BufferRelation bufferRelation(Operation *oo, OpResult opResult,
                                 const AnalysisState &state) const {
     return BufferRelation::Unknown;
   }
 };
struct UnpackOpInterface
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
                                const AnalysisState &state) const {
     return false;
   }
 
   AliasingOpResultList getAliasingOpResults(Operation *op, OpOperand &opOperand,
                                             const AnalysisState &state) const {
-    // Conceptually, UnpackOp equals to a list of toIndices/toValueOp
+    // Conceptually, UnpackOp equals to a list of toCoordinates/toValueOp
     return {};
   }
 };
 
 struct InsertOpInterface
     : public BufferizableOpInterface::ExternalModel<InsertOpInterface,
                                                     sparse_tensor::InsertOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
struct NumberOfEntriesOpInterface
   }
 
   AliasingOpResultList getAliasingOpResults(Operation *op, OpOperand &opOperand,
                                             const AnalysisState &state) const {
     return {};
   }
 };
 
-struct ToIndicesBufferOpInterface
+struct ToCoordinatesBufferOpInterface
     : public BufferizableOpInterface::ExternalModel<
-          ToIndicesBufferOpInterface, sparse_tensor::ToIndicesBufferOp> {
+          ToCoordinatesBufferOpInterface,
+          sparse_tensor::ToCoordinatesBufferOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
                               const AnalysisState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
                                const AnalysisState &state) const {
-    // Potential writes into memory through the result of sparse_tensor.indices
-    // are not considered.
+    // Potential writes into memory through the result of
+    // `sparse_tensor.coordinates` are not considered.
     return false;
   }
 
   AliasingOpResultList getAliasingOpResults(Operation *op, OpOperand &opOperand,
                                             const AnalysisState &state) const {
     return {};
   }
 };
 
-struct ToIndicesOpInterface
+struct ToCoordinatesOpInterface
     : public BufferizableOpInterface::ExternalModel<
-          ToIndicesOpInterface, sparse_tensor::ToIndicesOp> {
+          ToCoordinatesOpInterface, sparse_tensor::ToCoordinatesOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
                               const AnalysisState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
                                const AnalysisState &state) const {
-    // Potential writes into memory through the result of sparse_tensor.indices
-    // are not considered.
+    // Potential writes into memory through the result of
+    // `sparse_tensor.coordinates` are not considered.
     return false;
   }
 
   AliasingOpResultList getAliasingOpResults(Operation *op, OpOperand &opOperand,
                                             const AnalysisState &state) const {
     return {};
   }
 };
 
-struct ToPointersOpInterface
+struct ToPositionsOpInterface
     : public BufferizableOpInterface::ExternalModel<
-          ToPointersOpInterface, sparse_tensor::ToPointersOp> {
+          ToPositionsOpInterface, sparse_tensor::ToPositionsOp> {
   bool bufferizesToMemoryRead(Operation *op, OpOperand &opOperand,
                               const AnalysisState &state) const {
     return true;
   }
 
   bool bufferizesToMemoryWrite(Operation *op, OpOperand &opOperand,
                                const AnalysisState &state) const {
-    // Potential writes into memory through the result of sparse_tensor.pointers
-    // are not considered.
+    // Potential writes into memory through the result of
+    // `sparse_tensor.positions` are not considered.
     return false;
   }
 
   AliasingOpResultList getAliasingOpResults(Operation *op, OpOperand &opOperand,
                                             const AnalysisState &state) const {
     return {};
   }
 };
registry.addExtension(+[](MLIRContext *ctx,
     sparse_tensor::ConvertOp::attachInterface<ConvertOpInterface>(*ctx);
     sparse_tensor::LoadOp::attachInterface<LoadOpInterface>(*ctx);
     sparse_tensor::NewOp::attachInterface<NewOpInterface>(*ctx);
     sparse_tensor::InsertOp::attachInterface<InsertOpInterface>(*ctx);
     sparse_tensor::NumberOfEntriesOp::attachInterface<
         NumberOfEntriesOpInterface>(*ctx);
     sparse_tensor::PackOp::attachInterface<PackOpInterface>(*ctx);
     sparse_tensor::UnpackOp::attachInterface<UnpackOpInterface>(*ctx);
-    sparse_tensor::ToIndicesBufferOp::attachInterface<
-        ToIndicesBufferOpInterface>(*ctx);
-    sparse_tensor::ToIndicesOp::attachInterface<ToIndicesOpInterface>(*ctx);
-    sparse_tensor::ToPointersOp::attachInterface<ToPointersOpInterface>(*ctx);
+    sparse_tensor::ToCoordinatesBufferOp::attachInterface<
+        ToCoordinatesBufferOpInterface>(*ctx);
+    sparse_tensor::ToCoordinatesOp::attachInterface<ToCoordinatesOpInterface>(
+        *ctx);
+    sparse_tensor::ToPositionsOp::attachInterface<ToPositionsOpInterface>(*ctx);
     sparse_tensor::ToValuesOp::attachInterface<ToValuesOpInterface>(*ctx);
   });
 }

mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.h

 OverheadType overheadTypeEncoding(unsigned width);
 
 /// Converts an overhead storage type to its internal type-encoding.
 OverheadType overheadTypeEncoding(Type tp);
 
 /// Converts the internal type-encoding for overhead storage to an mlir::Type.
 Type getOverheadType(Builder &builder, OverheadType ot);
 
-/// Returns the OverheadType for pointer overhead storage.
-OverheadType pointerOverheadTypeEncoding(SparseTensorEncodingAttr enc);
+/// Returns the OverheadType for position overhead storage.
+OverheadType posTypeEncoding(SparseTensorEncodingAttr enc);
 
-/// Returns the OverheadType for index overhead storage.
-OverheadType indexOverheadTypeEncoding(SparseTensorEncodingAttr enc);
-
-/// Returns the mlir::Type for pointer overhead storage.
-Type getPointerOverheadType(Builder &builder, SparseTensorEncodingAttr enc);
-
-/// Returns the mlir::Type for index overhead storage.
-Type getIndexOverheadType(Builder &builder, SparseTensorEncodingAttr enc);
+/// Returns the OverheadType for coordinate overhead storage.
+OverheadType crdTypeEncoding(SparseTensorEncodingAttr enc);
 
 /// Convert OverheadType to its function-name suffix.
 StringRef overheadTypeFunctionSuffix(OverheadType ot);
 
 /// Converts an overhead storage type to its function-name suffix.
 StringRef overheadTypeFunctionSuffix(Type overheadTp);
 
 /// Converts a primary storage type to its internal type-encoding.

 Value genIsNonzero(OpBuilder &builder, Location loc, Value v);
 
 /// Computes the shape of destination tensor of a reshape operator. This is only
 /// used when operands have dynamic shape. The shape of the destination is
 /// stored into dstShape.
 void genReshapeDstShape(Location loc, PatternRewriter &rewriter,
                         SmallVectorImpl<Value> &dstShape,
                         ArrayRef<Value> srcShape,
-                        ArrayRef<int64_t> staticDstShape,
+                        ArrayRef<StaticSize> staticDstShape,
                         ArrayRef<ReassociationIndices> reassociation);
 
-/// Translate indices during a reshaping operation.
-void translateIndicesArray(OpBuilder &builder, Location loc,
-                           ArrayRef<ReassociationIndices> reassociation,
-                           ValueRange srcIndices, ArrayRef<Value> srcShape,
-                           ArrayRef<Value> dstShape,
-                           SmallVectorImpl<Value> &dstIndices);
+/// Reshape coordinates during a reshaping operation.
+void reshapeCvs(OpBuilder &builder, Location loc,
+                ValueRange srcSizes, ValueRange srcCvs, // NOLINT
+                ValueRange dstSizes, SmallVectorImpl<Value> &dstCvs);
 
 /// Returns a function reference (first hit also inserts into module). Sets
 /// the "_emit_c_interface" on the function declaration when requested,
 /// so that LLVM lowering generates a wrapper function that takes care
 /// of ABI complications with passing in and returning MemRefs to C functions.
 FlatSymbolRefAttr getFunc(ModuleOp module, StringRef name, TypeRange resultType,
                           ValueRange operands, EmitCInterface emitCInterface);
 

 /// `sizes` parameter should be as filled by sizesFromPtr(); that way
 /// we can reuse the genDimSizeCall() results generated by sizesFromPtr().
 Value allocDenseTensor(OpBuilder &builder, Location loc,
                        RankedTensorType tensorTp, ValueRange sizes);
 
 /// Generates code to deallocate a dense buffer.
 void deallocDenseTensor(OpBuilder &builder, Location loc, Value buffer);
 
-/// Generates the code to read the value from tensor[ivs]. The generated code
+/// Generates code to read the value from `tensor[ivs]`. The generated code
 /// looks like the following and the insertion point after this routine is
-/// inside the if-then branch behind the assignment to ind.
+/// inside the then-branch.
 ///    if (tensor[ivs] != 0)
 ///      insert_point
 Value genValueForDense(OpBuilder &builder, Location loc, Value tensor,
                        ValueRange ivs);
 
 /// Generates the loop structure to iterate over a dense tensor or a sparse
 /// tensor constant to support the lowering of dense-to-sparse convert operator.
 //
 // The loop to iterate a dense tensor:
 //   for i1 in dim1
 //    ..
 //     for ik in dimk
 //       val = a[i1,..,ik]
 //       if val != 0
 //         loop-body
 //
 // The loop to iterate a sparse tensor constant:
 //   for i in range(NNZ)
 //     val = values[i]
-//     [i1,..,ik] = indices[i]
+//     [i1,..,ik] = coordinates[i]
 //     loop-body
 void genDenseTensorOrSparseConstantIterLoop(
     OpBuilder &builder, Location loc, Value src, unsigned rank,
     function_ref<void(OpBuilder &, Location, Value, ValueRange)> bodyBuilder);
 
 /// Populates given sizes array from dense tensor or sparse tensor constant.
 void sizesFromSrc(OpBuilder &builder, SmallVectorImpl<Value> &sizes,
                   Location loc, Value src);
 
 /// Generates a 1D MemRefType with a dynamic size. When withLayout is set, the
 /// returned memref has a layout has unknown strides and offsets. Otherwise,
 /// a memref with a standard unit stride zero offset layout is returned.
 inline MemRefType get1DMemRefType(Type etp, bool withLayout) {
   auto layout = withLayout ? StridedLayoutAttr::StridedLayoutAttr::get(
                                  etp.getContext(), ShapedType::kDynamic,
                                  {ShapedType::kDynamic})
                            : StridedLayoutAttr();
   return MemRefType::get(ShapedType::kDynamic, etp, layout);
 }
 
 /// Scans to top of generated loop.
 Operation *getTop(Operation *op);
 
-/// Iterate over a sparse constant, generates constantOp for value and indices.
-/// E.g.,
+/// Iterate over a sparse constant, generates constantOp for value
+/// and coordinates.  E.g.,
 /// sparse<[ [0], [28], [31] ],
 ///          [ (-5.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] >
 /// =>
 /// %c1 = arith.constant 0
 /// %v1 = complex.constant (5.13, 2.0)
 /// callback({%c1}, %v1)
 ///
 /// %c2 = arith.constant 28
 /// %v2 = complex.constant (3.0, 4.0)
 /// callback({%c2}, %v2)
 ///
 /// %c3 = arith.constant 31
 /// %v3 = complex.constant (5.0, 6.0)
 /// callback({%c3}, %v3)
 void foreachInSparseConstant(
     Location loc, RewriterBase &rewriter, SparseElementsAttr attr,
     AffineMap order, function_ref<void(ArrayRef<Value>, Value)> callback);
 
-/// Converts the vector indices and store it into the memory pointed by
-/// `ind`, apply (optional) `offset` on `offsetDim`.
-void storeIndices(OpBuilder &builder, Location loc, unsigned rank, Value ind,
-                  ValueRange ivs, unsigned offsetDim = 0,
-                  Value offset = Value());
+/// Loads `size`-many values from the memref, which must have rank-1 and
+/// size greater-or-equal to `size`.  If the optional `(offsetIdx,offsetVal)`
+/// arguments are provided, then the `offsetVal` will be added to the
+/// `offsetIdx`-th value after loading.
+SmallVector<Value> loadAll(OpBuilder &builder, Location loc, size_t size,
+                           Value mem, size_t offsetIdx = 0,
+                           Value offsetVal = Value());
+
+/// Stores all the values of `vs` into the memref `mem`, which must have
+/// rank-1 and size greater-or-equal to `vs.size()`.  If the optional
+/// `(offsetIdx,offsetVal)` arguments are provided, then the `offsetVal`
+/// will be added to the `offsetIdx`-th value before storing.
+void storeAll(OpBuilder &builder, Location loc, Value mem, ValueRange vs,
+              size_t offsetIdx = 0, Value offsetVal = Value());
 
 /// Reshapes the linear values buffer for an annotated all dense sparse tensor
 /// to match the shape of the corresponding dense tensor to support direct
-/// access of the buffer through indices.
+/// access of the buffer through `lvlCoords`.
 Value reshapeValuesToLevels(OpBuilder &builder, Location loc,
                             SparseTensorEncodingAttr enc, ValueRange dimSizes,
-                            Value valuesBuffer, Value idxBuffer);
+                            Value valuesBuffer, Value lvlCoords);
 
 //===----------------------------------------------------------------------===//
 // Inlined constant generators.
 //
 // All these functions are just wrappers to improve code legibility;
 // therefore, we mark them as `inline` to avoid introducing any additional
 // overhead due to the legibility.
 //
 // TODO: Ideally these should move upstream, so that we don't
 // develop a design island.  However, doing so will involve
 // substantial design work.  For related prior discussion, see
 // <https://llvm.discourse.group/t/evolving-builder-apis-based-on-lessons-learned-from-edsc/879>
 //===----------------------------------------------------------------------===//
 
 /// Generates a 0-valued constant of the given type.  In addition to
-/// the scalar types (`ComplexType`, ``FloatType`, `IndexType`,
+/// the scalar types (`ComplexType`, `FloatType`, `IndexType`,
 /// `IntegerType`), this also works for `RankedTensorType` and `VectorType`
 /// (for which it generates a constant `DenseElementsAttr` of zeros).
 inline Value constantZero(OpBuilder &builder, Location loc, Type tp) {
   if (auto ctp = tp.dyn_cast<ComplexType>()) {
     auto zeroe = builder.getZeroAttr(ctp.getElementType());
     auto zeroa = builder.getArrayAttr({zeroe, zeroe});
     return builder.create<complex::ConstantOp>(loc, tp, zeroa);
   }

 
 /// Generates a constant of the internal type-encoding for overhead storage.
 inline Value constantOverheadTypeEncoding(OpBuilder &builder, Location loc,
                                           unsigned width) {
   return constantI32(builder, loc,
                      static_cast<uint32_t>(overheadTypeEncoding(width)));
 }
 
-/// Generates a constant of the internal type-encoding for pointer
+/// Generates a constant of the internal type-encoding for position
 /// overhead storage.
-inline Value constantPointerTypeEncoding(OpBuilder &builder, Location loc,
-                                         SparseTensorEncodingAttr enc) {
-  return constantOverheadTypeEncoding(builder, loc, enc.getPointerBitWidth());
+inline Value constantPosTypeEncoding(OpBuilder &builder, Location loc,
+  return constantOverheadTypeEncoding(builder, loc, enc.getPosWidth());
 }
 
-/// Generates a constant of the internal type-encoding for index overhead
-/// storage.
-inline Value constantIndexTypeEncoding(OpBuilder &builder, Location loc,
-                                       SparseTensorEncodingAttr enc) {
-  return constantOverheadTypeEncoding(builder, loc, enc.getIndexBitWidth());
+/// Generates a constant of the internal type-encoding for coordinate
+/// overhead storage.
+inline Value constantCrdTypeEncoding(OpBuilder &builder, Location loc,
+  return constantOverheadTypeEncoding(builder, loc, enc.getCrdWidth());
 }
 
 /// Generates a constant of the internal type-encoding for primary storage.
 inline Value constantPrimaryTypeEncoding(OpBuilder &builder, Location loc,
                                          Type elemTp) {
   return constantI32(builder, loc,
                      static_cast<uint32_t>(primaryTypeEncoding(elemTp)));
 }
 
 /// Generates a constant of the internal dimension level type encoding.
 inline Value constantDimLevelTypeEncoding(OpBuilder &builder, Location loc,
                                           DimLevelType dlt) {
   return constantI8(builder, loc, static_cast<uint8_t>(dlt));
 }
 
 inline bool isZeroRankedTensorOrScalar(Type type) {
   auto rtp = type.dyn_cast<RankedTensorType>();
   return !rtp || rtp.getRank() == 0;
 }
 
-/// Infers the result type and generates ToPointersOp.
-Value genToPointers(OpBuilder &builder, Location loc, Value tensor, Level lvl);
+/// Infers the result type and generates `ToPositionsOp`.
+Value genToPositions(OpBuilder &builder, Location loc, Value tensor, Level lvl);
 
-/// Infers the result type and generates ToIndicesOp. If the lvl is within a COO
-/// region, the result type is a memref with unknown stride and offset.
-/// Otherwise, the result type is a memref without any specified layout.
-Value genToIndices(OpBuilder &builder, Location loc, Value tensor, Level lvl,
-                   Level cooStart);
+/// Infers the result type and generates `ToCoordinatesOp`.  If the
+/// level is within a COO region, the result type is a memref with unknown
+/// stride and offset.  Otherwise, the result type is a memref without
+/// any specified layout.
+Value genToCoordinates(OpBuilder &builder, Location loc, Value tensor,
+                       Level lvl, Level cooStart);
 
-/// Infers the result type and generates ToValuesOp.
+/// Infers the result type and generates `ToValuesOp`.
 Value genToValues(OpBuilder &builder, Location loc, Value tensor);
 
 /// Generates code to retrieve the values size for the sparse tensor.
 Value genValMemSize(OpBuilder &builder, Location loc, Value tensor);
 
 } // namespace sparse_tensor
 } // namespace mlir
 
 #endif // MLIR_DIALECT_SPARSETENSOR_TRANSFORMS_CODEGENUTILS_H_

mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp

 #include "mlir/IR/Types.h"
 #include "mlir/IR/Value.h"
 #include <optional>
 
 using namespace mlir;
 using namespace mlir::sparse_tensor;
 
 /// If the tensor is a sparse constant, generates and returns the pair of
-/// the constants for the indices and the values.
+/// the constants for the coordinates and the values.
 static std::optional<std::pair<Value, Value>>
 genSplitSparseConstant(OpBuilder &builder, Location loc, Value tensor) {
   if (auto constOp = tensor.getDefiningOp<arith::ConstantOp>()) {
-    if (auto attr = constOp.getValue().dyn_cast<SparseElementsAttr>()) {
-      DenseElementsAttr indicesAttr = attr.getIndices();
-      Value indices = builder.create<arith::ConstantOp>(loc, indicesAttr);
-      DenseElementsAttr valuesAttr = attr.getValues();
-      Value values = builder.create<arith::ConstantOp>(loc, valuesAttr);
-      return std::make_pair(indices, values);
+    if (auto a = constOp.getValue().dyn_cast<SparseElementsAttr>()) {
+      auto coordinates = builder.create<arith::ConstantOp>(loc, a.getIndices());
+      auto values = builder.create<arith::ConstantOp>(loc, a.getValues());
+      return std::make_pair(coordinates, values);
     }
   }
   return {};
 }
 
-/// Generates the code to copy the index at indices[ivs] to ind, and return
-/// the value at value[ivs].
-static Value genIndexAndValueForSparse(OpBuilder &builder, Location loc,
-                                       Value indices, Value values,
-                                       SmallVectorImpl<Value> &indicesArray,
-                                       ValueRange ivs, unsigned rank) {
-  for (unsigned i = 0; i < rank; i++) {
-    Value idx = constantIndex(builder, loc, i);
-    Value val = builder.create<tensor::ExtractOp>(loc, indices,
-                                                  ValueRange{ivs[0], idx});
-    val = builder.create<arith::IndexCastOp>(loc, builder.getIndexType(), val);
-    // builder.create<memref::StoreOp>(loc, val, ind, idx);
-    indicesArray.push_back(val);
-  }
-  return builder.create<tensor::ExtractOp>(loc, values, ivs[0]);
+/// Reads `coordinates[k][0..rank-1]` and `value[k]`, appending the
+/// former onto `cvs` and returning the latter.
+// FIXME: Change the `rank` argument to `Dimension dimRank` or `Level lvlRank`,
+// to clarify its intended meaning.
+static Value genCoordsAndValueForSparse(OpBuilder &builder, Location loc,
+                                        Value coordinates, Value values,
+                                        SmallVectorImpl<Value> &cvs, Value k,
+                                        unsigned rank) {
+  for (unsigned d = 0; d < rank; d++) {
+    Value dim = constantIndex(builder, loc, d);
+    Value crd =
+        builder.create<tensor::ExtractOp>(loc, coordinates, ValueRange{k, dim});
+    crd = builder.create<arith::IndexCastOp>(loc, builder.getIndexType(), crd);
+    // builder.create<memref::StoreOp>(loc, crd, cvs, dim);
+    cvs.push_back(crd);
   }
-
-/// Generates the code to read the value from tensor[ivs], and conditionally
-/// stores the indices ivs to the memory in ind. The generated code looks like
-/// the following and the insertion point after this routine is inside the
-/// if-then branch behind the assignment to ind. This is to ensure that the
-/// code that uses the ind, such as an addEltX call generated after, is inside
-/// the if-then branch.
+  return builder.create<tensor::ExtractOp>(loc, values, k);
+}
+
+/// Generates code to read the value from `tensor[ivs]`, and open
+/// a conditional for whether the value is non-zero.  The generated code
+/// looks like the following and the insertion point after this routine
+/// is inside the then-branch.
 ///    if (tensor[ivs] != 0)
-///      ind = ivs
-static Value genIndexAndValueForDense(OpBuilder &builder, Location loc,
+///      insert_point
+static Value genCoordsAndValueForDense(OpBuilder &builder, Location loc,
                                        Value tensor,
-                                      SmallVectorImpl<Value> &indicesArray,
+                                       SmallVectorImpl<Value> &cvs,
                                        ValueRange ivs) {
   Value val = genValueForDense(builder, loc, tensor, ivs);
-  indicesArray.append(ivs.begin(), ivs.end());
+  cvs.append(ivs.begin(), ivs.end());
   return val;
 }
 
 //===----------------------------------------------------------------------===//
 // ExecutionEngine/SparseTensorUtils helper functions.
 //===----------------------------------------------------------------------===//
 
 OverheadType mlir::sparse_tensor::overheadTypeEncoding(unsigned width) {

 OverheadType mlir::sparse_tensor::overheadTypeEncoding(Type tp) {
   if (tp.isIndex())
     return OverheadType::kIndex;
   if (auto intTp = tp.dyn_cast<IntegerType>())
     return overheadTypeEncoding(intTp.getWidth());
   llvm_unreachable("Unknown overhead type");
 }
 
+// TODO: should offer an overload of this that takes a `MLIRContext*`
+// instead of the builder, similar to `detail::getIntegerOrIndexType`.
 Type mlir::sparse_tensor::getOverheadType(Builder &builder, OverheadType ot) {
   switch (ot) {
   case OverheadType::kIndex:
     return builder.getIndexType();
   case OverheadType::kU64:
     return builder.getIntegerType(64);
   case OverheadType::kU32:
     return builder.getIntegerType(32);
   case OverheadType::kU16:
     return builder.getIntegerType(16);
   case OverheadType::kU8:
     return builder.getIntegerType(8);
   }
   llvm_unreachable("Unknown OverheadType");
 }
 
 OverheadType
-mlir::sparse_tensor::pointerOverheadTypeEncoding(SparseTensorEncodingAttr enc) {
-  return overheadTypeEncoding(enc.getPointerBitWidth());
+mlir::sparse_tensor::posTypeEncoding(SparseTensorEncodingAttr enc) {
+  return overheadTypeEncoding(enc.getPosWidth());
 }
 
 OverheadType
-mlir::sparse_tensor::indexOverheadTypeEncoding(SparseTensorEncodingAttr enc) {
-  return overheadTypeEncoding(enc.getIndexBitWidth());
+mlir::sparse_tensor::crdTypeEncoding(SparseTensorEncodingAttr enc) {
+  return overheadTypeEncoding(enc.getCrdWidth());
 }
 
-Type mlir::sparse_tensor::getPointerOverheadType(Builder &builder,
-                                                 SparseTensorEncodingAttr enc) {
-  return getOverheadType(builder, pointerOverheadTypeEncoding(enc));
-}
-
-Type mlir::sparse_tensor::getIndexOverheadType(Builder &builder,
-                                               SparseTensorEncodingAttr enc) {
-  return getOverheadType(builder, indexOverheadTypeEncoding(enc));
-}
+// TODO: we ought to add some `static_assert` tests to ensure that the
+// `STEA::get{Pos,Crd}Type` methods agree with `getOverheadType(builder,
+// {pos,crd}OverheadTypeEncoding(enc))`
 
 // TODO: Adjust the naming convention for the constructors of
 // `OverheadType` so we can use the `MLIR_SPARSETENSOR_FOREVERY_O` x-macro
 // here instead of `MLIR_SPARSETENSOR_FOREVERY_FIXED_O`; to further reduce
 // the possibility of typo bugs or things getting out of sync.
 StringRef mlir::sparse_tensor::overheadTypeFunctionSuffix(OverheadType ot) {
   switch (ot) {
   case OverheadType::kIndex:
for (unsigned j = start; j < start + map.size(); j++) {
         dstShape.push_back(constantIndex(rewriter, loc, staticDstShape[j]));
       }
     }
     start = start + map.size();
   }
   assert(start == staticDstShape.size());
 }
 
-void mlir::sparse_tensor::translateIndicesArray(
+void mlir::sparse_tensor::reshapeCvs(
     OpBuilder &builder, Location loc,
-    ArrayRef<ReassociationIndices> reassociation, ValueRange srcIndices,
-    ArrayRef<Value> srcShape, ArrayRef<Value> dstShape,
-    SmallVectorImpl<Value> &dstIndices) {
+    ArrayRef<ReassociationIndices> reassociation, // NOLINT
+    ValueRange srcSizes, ValueRange srcCvs,       // NOLINT
+    ValueRange dstSizes, SmallVectorImpl<Value> &dstCvs) {
+  const unsigned srcRank = srcSizes.size();
+  const unsigned dstRank = dstSizes.size();
+  assert(srcRank == srcCvs.size() && "Source rank mismatch");
+  const bool isCollapse = srcRank > dstRank;
+  const ValueRange sizes = isCollapse ? srcSizes : dstSizes;
+  // Iterate over reassociation map.
   unsigned i = 0;
   unsigned start = 0;
-  unsigned dstRank = dstShape.size();
-  unsigned srcRank = srcShape.size();
-  assert(srcRank == srcIndices.size());
-  bool isCollapse = srcRank > dstRank;
-  ArrayRef<Value> shape = isCollapse ? srcShape : dstShape;
-  // Iterate over reassociation map.
   for (const auto &map : llvm::enumerate(reassociation)) {
     // Prepare strides information in dimension slice.
     Value linear = constantIndex(builder, loc, 1);
     for (unsigned j = start, end = start + map.value().size(); j < end; j++) {
-      linear = builder.create<arith::MulIOp>(loc, linear, shape[j]);
+      linear = builder.create<arith::MulIOp>(loc, linear, sizes[j]);
     }
     // Start expansion.
     Value val;
     if (!isCollapse)
-      val = srcIndices[i];
+      val = srcCvs[i];
     // Iterate over dimension slice.
     for (unsigned j = start, end = start + map.value().size(); j < end; j++) {
-      linear = builder.create<arith::DivUIOp>(loc, linear, shape[j]);
+      linear = builder.create<arith::DivUIOp>(loc, linear, sizes[j]);
       if (isCollapse) {
-        Value old = srcIndices[j];
-        Value mul = builder.create<arith::MulIOp>(loc, old, linear);
+        const Value mul = builder.create<arith::MulIOp>(loc, srcCvs[j], linear);
         val = val ? builder.create<arith::AddIOp>(loc, val, mul) : mul;
       } else {
-        Value old = val;
+        const Value old = val;
         val = builder.create<arith::DivUIOp>(loc, val, linear);
-        assert(dstIndices.size() == j);
-        dstIndices.push_back(val);
+        assert(dstCvs.size() == j);
+        dstCvs.push_back(val);
         val = builder.create<arith::RemUIOp>(loc, old, linear);
       }
     }
     // Finalize collapse.
     if (isCollapse) {
-      assert(dstIndices.size() == i);
-      dstIndices.push_back(val);
+      assert(dstCvs.size() == i);
+      dstCvs.push_back(val);
     }
     start += map.value().size();
     i++;
   }
-  assert(dstIndices.size() == dstRank);
+  assert(dstCvs.size() == dstRank);
 }
 
 FlatSymbolRefAttr mlir::sparse_tensor::getFunc(ModuleOp module, StringRef name,
                                                TypeRange resultType,
                                                ValueRange operands,
                                                EmitCInterface emitCInterface) {
   MLIRContext *context = module.getContext();
   auto result = SymbolRefAttr::get(context, name);
Value mlir::sparse_tensor::genValueForDense(OpBuilder &builder, Location loc,
   scf::IfOp ifOp = builder.create<scf::IfOp>(loc, cond, /*else*/ false);
   builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
   return val;
 }
 
 // FIXME:
 // 1. Dense tensors loop should be generated by loop emitter.
 // 2. Support reduction variables to propagate SSA chains properly.
+// 3. Change the `rank` argument to `Dimension dimRank` or `Level lvlRank`,
+//    to clarify its meaning.
 void mlir::sparse_tensor::genDenseTensorOrSparseConstantIterLoop(
     OpBuilder &builder, Location loc, Value src, unsigned rank,
     function_ref<void(OpBuilder &, Location, Value, ValueRange)> bodyBuilder) {
-  SmallVector<Value> indicesArray;
+  // `cvs` is actually the flattened coordinates array for all elements,
+  // not just for one element (since we do not `SmallVector::clear` after
+  // each iteration of the body of the loopnest.
+  SmallVector<Value> cvs;
   SmallVector<Value> lo;
   SmallVector<Value> hi;
   SmallVector<Value> st;
-  Value zero = constantIndex(builder, loc, 0);
-  Value one = constantIndex(builder, loc, 1);
-  auto indicesValues = genSplitSparseConstant(builder, loc, src);
-  bool isCOOConstant = indicesValues.has_value();
-  Value indices;
-  Value values;
-  if (isCOOConstant) {
-    indices = indicesValues->first;
-    values = indicesValues->second;
+  const Value zero = constantIndex(builder, loc, 0);
+  const Value one = constantIndex(builder, loc, 1);
+  const auto splitSrc = genSplitSparseConstant(builder, loc, src);
+  if (splitSrc.has_value()) {
+    const Value srcCoordinates = splitSrc->first;
+    const Value srcValues = splitSrc->second;
     lo.push_back(zero);
-    hi.push_back(linalg::createOrFoldDimOp(builder, loc, values, 0));
+    hi.push_back(linalg::createOrFoldDimOp(builder, loc, srcValues, 0));
     st.push_back(one);
+    scf::buildLoopNest(builder, loc, lo, hi, st, {},
+                       [&](OpBuilder &builder, Location loc, ValueRange ivs,
+                           ValueRange /*args*/) -> scf::ValueVector {
+                         Value val = genCoordsAndValueForSparse(
+                             builder, loc, srcCoordinates, srcValues, cvs,
+                             ivs[0], rank);
+                         bodyBuilder(builder, loc, val, cvs);
+                         return {};
+                       });
   } else {
     for (unsigned i = 0; i < rank; i++) {
       lo.push_back(zero);
       hi.push_back(linalg::createOrFoldDimOp(builder, loc, src, i));
       st.push_back(one);
     }
-  }
-
-  scf::buildLoopNest(
-      builder, loc, lo, hi, st, {},
+    scf::buildLoopNest(builder, loc, lo, hi, st, {},
                        [&](OpBuilder &builder, Location loc, ValueRange ivs,
-          ValueRange args) -> scf::ValueVector {
-        Value val;
-        if (isCOOConstant)
-          val = genIndexAndValueForSparse(builder, loc, indices, values,
-                                          indicesArray, ivs, rank);
-        else
-          val = genIndexAndValueForDense(builder, loc, src, indicesArray, ivs);
-        bodyBuilder(builder, loc, val, indicesArray);
+                           ValueRange /*args*/) -> scf::ValueVector {
+                         Value val = genCoordsAndValueForDense(builder, loc,
+                                                               src, cvs, ivs);
+                         bodyBuilder(builder, loc, val, cvs);
                          return {};
                        });
   }
+}
 
 void mlir::sparse_tensor::sizesFromSrc(OpBuilder &builder,
                                        SmallVectorImpl<Value> &sizes,
                                        Location loc, Value src) {
   const Dimension dimRank = getSparseTensorType(src).getDimRank();
   for (Dimension d = 0; d < dimRank; d++)
     sizes.push_back(linalg::createOrFoldDimOp(builder, loc, src, d));
 }
 
 Operation *mlir::sparse_tensor::getTop(Operation *op) {
   for (; isa<scf::ForOp>(op->getParentOp()) ||
          isa<scf::WhileOp>(op->getParentOp()) ||
          isa<scf::ParallelOp>(op->getParentOp()) ||
          isa<scf::IfOp>(op->getParentOp());
        op = op->getParentOp())
     ;
   return op;
 }
 
 void sparse_tensor::foreachInSparseConstant(
     Location loc, RewriterBase &rewriter, SparseElementsAttr attr,
     AffineMap order, function_ref<void(ArrayRef<Value>, Value)> callback) {
-  Dimension dimRank = getSparseTensorType(attr).getDimRank();
-  // Foreach on constant.
-  DenseElementsAttr indicesAttr = attr.getIndices();
-  DenseElementsAttr valuesAttr = attr.getValues();
-
-  using CooValuePair = std::pair<SmallVector<IntegerAttr>, Attribute>;
-  SmallVector<CooValuePair> cooV;
-  for (size_t i = 0, nse = valuesAttr.size(); i < nse; i++) {
-    cooV.emplace_back();
-    for (Dimension j = 0; j < dimRank; j++) {
-      auto coordAttr = indicesAttr.getValues<IntegerAttr>()[i * dimRank + j];
-      cooV.back().first.push_back(coordAttr);
-    }
-    auto valAttr = valuesAttr.getValues<Attribute>()[i];
-    cooV.back().second = valAttr;
+  const Dimension dimRank = getSparseTensorType(attr).getDimRank();
+  const auto coordinates = attr.getIndices().getValues<IntegerAttr>();
+  const auto values = attr.getValues().getValues<Attribute>();
+
+  // This is like the `Element<V>` class in the runtime library, but for
+  // MLIR attributes.  In the future we may want to move this out into
+  // a proper class definition to help improve code legibility (e.g.,
+  // `first` -> `coords`, `second` -> `value`) as well as being able
+  // to factor out analogues of `ElementLT<V>` for the sort below, etc.
+  using ElementAttr = std::pair<SmallVector<IntegerAttr>, Attribute>;
+
+  // Construct the COO from the SparseElementsAttr.
+  SmallVector<ElementAttr> elems;
+  for (size_t i = 0, nse = values.size(); i < nse; i++) {
+    elems.emplace_back();
+    elems.back().second = values[i];
+    auto &coords = elems.back().first;
+    coords.reserve(dimRank);
+    for (Dimension d = 0; d < dimRank; d++)
+      coords.push_back(coordinates[i * dimRank + d]);
   }
 
   // Sorts the sparse element attribute based on coordinates.
-  std::sort(cooV.begin(), cooV.end(),
-            [order](const CooValuePair &lhs, const CooValuePair &rhs) {
-              const SmallVectorImpl<IntegerAttr> &lc = lhs.first;
-              const SmallVectorImpl<IntegerAttr> &rc = rhs.first;
-              for (size_t i = 0, e = lc.size(); i < e; i++) {
-                auto l =
-                    order
-                        ? order.getResult(i).cast<AffineDimExpr>().getPosition()
-                        : i;
-                if (lc[l].getInt() == rc[l].getInt())
+  std::sort(elems.begin(), elems.end(),
+            [order, dimRank](const ElementAttr &lhs, const ElementAttr &rhs) {
+              const auto &lhsCoords = lhs.first;
+              const auto &rhsCoords = rhs.first;
+              for (Dimension d = 0; d < dimRank; d++) {
+                // FIXME: This only makes sense for permutations.
+                // And since we don't check that `order` is a permutation,
+                // it can also cause OOB errors when we use `l`.
+                const Level l = order ? order.getDimPosition(d) : d;
+                if (lhsCoords[l].getInt() == rhsCoords[l].getInt())
                   continue;
-                return lc[l].getInt() < rc[l].getInt();
+                return lhsCoords[l].getInt() < rhsCoords[l].getInt();
               }
               llvm_unreachable("no equal coordinate in sparse element attr");
             });
 
-  SmallVector<Value> coords;
-  for (size_t i = 0, nse = valuesAttr.size(); i < nse; i++) {
-    coords.clear();
-    for (Dimension j = 0; j < dimRank; j++) {
-      auto coordAttr = cooV[i].first[j];
-      auto coord =
-          rewriter.create<arith::ConstantIndexOp>(loc, coordAttr.getInt());
-      // Remaps coordinates.
-      coords.push_back(coord);
+  SmallVector<Value> cvs;
+  cvs.reserve(dimRank);
+  for (size_t i = 0, nse = values.size(); i < nse; i++) {
+    // Remap coordinates.
+    cvs.clear();
+    for (Dimension d = 0; d < dimRank; d++) {
+      auto crd = elems[i].first[d].getInt();
+      cvs.push_back(rewriter.create<arith::ConstantIndexOp>(loc, crd));
     }
+    // Remap value.
     Value val;
     if (attr.getElementType().isa<ComplexType>()) {
-      auto valAttr = cooV[i].second.cast<ArrayAttr>();
+      auto valAttr = elems[i].second.cast<ArrayAttr>();
       val = rewriter.create<complex::ConstantOp>(loc, attr.getElementType(),
                                                  valAttr);
     } else {
-      auto valAttr = cooV[i].second.cast<TypedAttr>();
-      // Remaps value.
+      auto valAttr = elems[i].second.cast<TypedAttr>();
       val = rewriter.create<arith::ConstantOp>(loc, valAttr);
     }
     assert(val);
-    callback(coords, val);
+    callback(cvs, val);
   }
 }
 
-void sparse_tensor::storeIndices(OpBuilder &builder, Location loc,
-                                 unsigned size, Value ind, ValueRange ivs,
-                                 unsigned offsetDim, Value offset) {
+SmallVector<Value> sparse_tensor::loadAll(OpBuilder &builder, Location loc,
+                                          size_t size, Value mem,
+                                          size_t offsetIdx, Value offsetVal) {
 #ifndef NDEBUG
-  const auto memTp = ind.getType().cast<MemRefType>();
-  (void)memTp;
+  const auto memTp = mem.getType().cast<MemRefType>();
   assert(memTp.getRank() == 1);
   const DynSize memSh = memTp.getDimSize(0);
-  (void)memSh;
-  assert(ShapedType::isDynamic(memSh) || memSh == static_cast<DynSize>(size));
-  assert(ivs.size() == static_cast<size_t>(size));
-  assert(offsetDim < size);
+  assert(ShapedType::isDynamic(memSh) || memSh >= static_cast<DynSize>(size));
+  assert(offsetIdx == 0 || offsetIdx < size);
 #endif // NDEBUG
-
+  SmallVector<Value> vs;
+  vs.reserve(size);
   for (unsigned i = 0; i < size; i++) {
-    Value idx = ivs[i];
-    if (offsetDim == i && offset)
-      idx = builder.create<arith::AddIOp>(loc, idx, offset);
-    builder.create<memref::StoreOp>(loc, idx, ind,
+    Value v = builder.create<memref::LoadOp>(loc, mem,
                                              constantIndex(builder, loc, i));
+    if (i == offsetIdx && offsetVal)
+      v = builder.create<arith::AddIOp>(loc, v, offsetVal);
+    vs.push_back(v);
+  }
+  return vs;
+}
+
+void sparse_tensor::storeAll(OpBuilder &builder, Location loc, Value mem,
+                             ValueRange vs, size_t offsetIdx, Value offsetVal) {
+#ifndef NDEBUG
+  const size_t vsize = vs.size();
+  const auto memTp = mem.getType().cast<MemRefType>();
+  assert(memTp.getRank() == 1);
+  const DynSize memSh = memTp.getDimSize(0);
+  assert(ShapedType::isDynamic(memSh) || memSh >= static_cast<DynSize>(vsize));
+  assert(offsetIdx == 0 || offsetIdx < vsize);
+#endif // NDEBUG
+  for (const auto &v : llvm::enumerate(vs)) {
+    const Value w =
+        (offsetIdx == v.index() && offsetVal)
+            ? builder.create<arith::AddIOp>(loc, v.value(), offsetVal)
+            : v.value();
+    builder.create<memref::StoreOp>(loc, w, mem,
+                                    constantIndex(builder, loc, v.index()));
   }
 }
 
 Value sparse_tensor::reshapeValuesToLevels(OpBuilder &builder, Location loc,
                                            SparseTensorEncodingAttr enc,
                                            ValueRange dimSizes,
                                            Value valuesBuffer,
-                                           Value idxBuffer) {
-  // Use the `idxBuffer` to store the level sizes.
+                                           Value lvlCoords) {
+  // Reuse the `lvlCoords` buffer to store the level-sizes.
   const Level lvlRank = enc.getLvlRank();
   SmallVector<Value> lvlSizes;
   lvlSizes.reserve(lvlRank);
   for (Level l = 0; l < lvlRank; l++)
     // FIXME: `toOrigDim` is deprecated.
     lvlSizes.push_back(dimSizes[toOrigDim(enc, l)]);
-  storeIndices(builder, loc, lvlRank, idxBuffer, lvlSizes);
+  storeAll(builder, loc, lvlCoords, lvlSizes);
   // The memref ReshapeOp requires the sizes buffer to have a static
   // shape.
   const auto iTp = builder.getIndexType();
-  const SmallVector<DynSize, 1> idxBufferShape{static_cast<DynSize>(lvlRank)};
-  const auto idxBufferTp = MemRefType::get(idxBufferShape, iTp);
-  idxBuffer = builder.create<memref::CastOp>(loc, idxBufferTp, idxBuffer);
+  const SmallVector<DynSize, 1> lvlSizesShape{static_cast<DynSize>(lvlRank)};
+  const auto lvlSizesTp = MemRefType::get(lvlSizesShape, iTp);
+  lvlCoords = builder.create<memref::CastOp>(loc, lvlSizesTp, lvlCoords);
+  // Finally, create the ReshapeOp.
   const SmallVector<DynSize> resShape(lvlRank, ShapedType::kDynamic);
   const Type elemTp = getMemRefType(valuesBuffer).getElementType();
   const auto resTp = MemRefType::get(resShape, elemTp);
-  return builder.create<memref::ReshapeOp>(loc, resTp, valuesBuffer, idxBuffer);
+  return builder.create<memref::ReshapeOp>(loc, resTp, valuesBuffer, lvlCoords);
 }
 
-Value sparse_tensor::genToPointers(OpBuilder &builder, Location loc,
+Value sparse_tensor::genToPositions(OpBuilder &builder, Location loc,
                                     Value tensor, Level lvl) {
   const auto srcTp = getSparseTensorType(tensor);
-  const Type ptrTp = getPointerOverheadType(builder, srcTp.getEncoding());
-  const Type memTp = get1DMemRefType(ptrTp, /*withLayout=*/false);
-  return builder.create<ToPointersOp>(loc, memTp, tensor,
+  const Type posTp = srcTp.getEncoding().getPosType();
+  const Type memTp = get1DMemRefType(posTp, /*withLayout=*/false);
+  return builder.create<ToPositionsOp>(loc, memTp, tensor,
                                        builder.getIndexAttr(lvl));
 }
 
-Value sparse_tensor::genToIndices(OpBuilder &builder, Location loc,
+Value sparse_tensor::genToCoordinates(OpBuilder &builder, Location loc,
                                       Value tensor, Level lvl, Level cooStart) {
   const auto srcTp = getSparseTensorType(tensor);
-  const Type idxTp = getIndexOverheadType(builder, srcTp.getEncoding());
-  const Type memTp = get1DMemRefType(idxTp, /*withLayout=*/lvl >= cooStart);
-  return builder.create<ToIndicesOp>(loc, memTp, tensor,
+  const Type crdTp = srcTp.getEncoding().getCrdType();
+  const Type memTp = get1DMemRefType(crdTp, /*withLayout=*/lvl >= cooStart);
+  return builder.create<ToCoordinatesOp>(loc, memTp, tensor,
                                          builder.getIndexAttr(lvl));
 }
 
 Value sparse_tensor::genToValues(OpBuilder &builder, Location loc,
                                  Value tensor) {
   RankedTensorType srcTp = getRankedTensorType(tensor);
   Type valTp = get1DMemRefType(srcTp.getElementType(),
                                /*withLayout=*/false);
   return builder.create<ToValuesOp>(loc, valTp, tensor);
 }
 
 Value sparse_tensor::genValMemSize(OpBuilder &builder, Location loc,
                                    Value tensor) {
   return getDescriptorFromTensorTuple(tensor).getValMemSize(builder, loc);
 }

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.h

public:
   }
 
   ///
   /// Getters.
   ///
   const std::vector<std::vector<Value>> &getPidxs() const { return pidxs; };
   const std::vector<std::vector<Value>> &getCoord() const { return coord; };
   const std::vector<std::vector<Value>> &getHighs() const { return highs; };
-  const std::vector<std::vector<Value>> &getPtrBuffer() const {
-    return ptrBuffer;
+  const std::vector<std::vector<Value>> &getPosBuffer() const {
+    return posBuffer;
   };
-  const std::vector<std::vector<Value>> &getIdxBuffer() const {
-    return idxBuffer;
+  const std::vector<std::vector<Value>> &getCrdBuffer() const {
+    return crdBuffer;
   };
   const std::vector<Value> &getValBuffer() const { return valBuffer; };
 
   constexpr static llvm::StringLiteral getLoopEmitterLoopAttrName() {
     return llvm::StringLiteral("Emitted from");
   }
 
 private:
struct LoopLevelInfo {
     Block *const userCodeBlock; // the block holding users' generated code.
     const Value iv;             // the induction variable for the loop
   };
 
   /// Linearizes address for dense dimension (i.e., p = (i * d0) + j).
   Value genAddress(OpBuilder &builder, Location loc, size_t tid, size_t dim,
                    Value iv);
 
-  /// Generates instructions to compute the coordinate of tesnors[tid] on `l`
-  /// under the current loop context.
-  Value genSparseCoord(OpBuilder &builder, Location loc, size_t tid, size_t l);
+  /// Generates instructions to compute the coordinate of tensors[tid][lvl]
+  /// under the current loop context.  The final argument is the
+  /// collapsed-output level, whereas this function handles converting
+  /// that to the uncollapsed-input level
+  Value genSparseCrd(OpBuilder &builder, Location loc, size_t tid,
+                     size_t dstLvl);
 
   bool isOutputTensor(size_t tid) {
     return hasOutput && tid == tensors.size() - 1;
   }
 
   bool isSparseOutput(size_t tid) { return isOutputTensor(tid) && isSparseOut; }
 
   /// Setups [lo, hi] for iterating tensor[dim], it assumes that tensor[0
private:
   bool hasOutput;
   bool isSparseOut;
   /// Input and (optional) output tensors.
   std::vector<Value> tensors;
   /// The dim type array for each tensor.
   std::vector<std::vector<DimLevelType>> dimTypes;
   /// Sparse iteration information (by tensor and dim). These arrays
   /// are updated to remain current within the current loop.
+  // TODO: we may want to rename "pidx(s)" to `posCursor(s)` or similar.
   std::vector<std::vector<Value>> pidxs;
   std::vector<std::vector<Value>> coord;
   std::vector<std::vector<Value>> highs;
   std::vector<std::vector<Value>> lvlSizes;
-  std::vector<std::vector<Value>> ptrBuffer; // to_pointers
-  std::vector<std::vector<Value>> idxBuffer; // to_indices
+  std::vector<std::vector<Value>> posBuffer; // to_positions
+  std::vector<std::vector<Value>> crdBuffer; // to_coordinates
   std::vector<Value> valBuffer;              // to_value
 
   /// Whether the sparse input is a slice.
   std::vector<bool> isSparseSlices;
 
   /// Loop Stack, stores the information of all the nested loops that are
   /// alive.
   std::vector<LoopLevelInfo> loopStack;

mlir/lib/Dialect/SparseTensor/Transforms/LoopEmitter.cpp

 
 using namespace mlir;
 using namespace mlir::sparse_tensor;
 
 //===----------------------------------------------------------------------===//
 // File local helper functions.
 //===----------------------------------------------------------------------===//
 
-/// Generates a pointer/index load from the sparse storage scheme. Narrower
-/// data types need to be zero extended before casting the value into the
-/// index type used for looping and indexing.
-static Value genIndexLoad(OpBuilder &builder, Location loc, Value ptr,
+/// Generates a position/coordinate load from the sparse storage scheme.
+/// Narrower data types need to be zero extended before casting the
+/// value into the `Index` type used for looping and indexing.
+static Value genIndexLoad(OpBuilder &builder, Location loc, Value mem,
                           Value s) {
   // For the scalar case, we simply zero extend narrower indices into 64-bit
   // values before casting to index without a performance penalty. Here too,
   // however, indices that already are 64-bit, in theory, cannot express the
   // full range as explained above.
-  Value load = builder.create<memref::LoadOp>(loc, ptr, s);
+  Value load = builder.create<memref::LoadOp>(loc, mem, s);
   if (!load.getType().isa<IndexType>()) {
     if (load.getType().getIntOrFloatBitWidth() < 64)
       load = builder.create<arith::ExtUIOp>(loc, builder.getI64Type(), load);
     load =
         builder.create<arith::IndexCastOp>(loc, builder.getIndexType(), load);
   }
   return load;
 }
static Value toSliceCoord(OpBuilder &builder, Location loc, Value v,
   // iv = iv * stride + offset
   v = builder.create<arith::MulIOp>(loc, v, stride);
   v = builder.create<arith::AddIOp>(loc, v, offset);
   return v;
 }
 
 // Converts a coordinate relative to the underlying tensor to the coordinate
 // relative to the slice, returns a extra reminder value
-static std::pair<Value, Value> fromSliceCoord(OpBuilder &builder, Location loc,
-                                              Value v,
-                                              SparseTensorEncodingAttr enc,
-                                              unsigned lvl) {
+static std::pair<Value, Value> fromSliceCrd(OpBuilder &builder, Location loc,
   Value stride = getSliceStride(builder, loc, enc, lvl);
   Value offset = getSliceOffset(builder, loc, enc, lvl);
   // iv = (iv - offset) / stride
   v = builder.create<arith::SubIOp>(loc, v, offset);
   Value rem = builder.create<arith::RemUIOp>(loc, v, stride);
   v = builder.create<arith::DivUIOp>(loc, v, stride);
   return std::make_pair(v, rem);
 }
 
 static std::pair<Value, Value>
-genSliceLegitPredicate(OpBuilder &builder, Location loc, Value coord,
+genSliceLegitPredicate(OpBuilder &builder, Location loc, Value crd,
                        SparseTensorEncodingAttr enc, unsigned lvl) {
-  std::pair<Value, Value> trans = fromSliceCoord(builder, loc, coord, enc, lvl);
-  // First, coord >= offset (TODO: seems unsigned >= 0 won't be folded, skip
+  std::pair<Value, Value> trans = fromSliceCrd(builder, loc, crd, enc, lvl);
+  // First, crd >= offset (TODO: seems unsigned >= 0 won't be folded, skip
   // the check if the offset is zero).
   auto geOffset =
-      builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::uge, coord,
+      builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::uge, crd,
                                     getSliceOffset(builder, loc, enc, lvl));
   // Second, coord_in_slice < length
   auto ltLength =
       builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ult, trans.first,
                                     getSliceSize(builder, loc, enc, lvl));
 
   // Third, rem == 0; confirmed that (a % 1) will be folded to 0
   auto fitStride =
Value LoopEmitter::genAddress(OpBuilder &builder, Location loc, size_t tid,
   if (isSparseSlices[tid]) {
     auto enc = getSparseTensorEncoding(tensors[tid].getType());
     iv = toSliceCoord(builder, loc, iv, enc, dim);
   }
   Value add = builder.create<arith::AddIOp>(loc, mul, iv);
   return add;
 }
 
-Value LoopEmitter::genSparseCoord(OpBuilder &builder, Location loc, size_t tid,
-                                  size_t l) {
-  Value c = constantIndex(builder, loc, 0);
-  auto reass = getCollapseReassociation(tid, l);
-  for (unsigned i = 0; i < reass.size(); i++) {
-    auto lvl = reass[i];
-    // A load on the indices array yields the coordinate.
-    Value ptr = idxBuffer[tid][lvl];
-    Value off = genIndexLoad(builder, loc, ptr, pidxs[tid][l]);
+Value LoopEmitter::genSparseCrd(OpBuilder &builder, Location loc, size_t tid,
+                                size_t dstLvl) {
+  Value crd = constantIndex(builder, loc, 0);
+  const auto reassoc = getCollapseReassociation(tid, dstLvl);
+  for (unsigned i = 0; i < reassoc.size(); i++) {
+    const auto srcLvl = reassoc[i];
+    // A load on the coordinates array yields the coordinate.
+    const Value mem = crdBuffer[tid][srcLvl];
+    const Value pos = pidxs[tid][dstLvl];
+    const Value off = genIndexLoad(builder, loc, mem, pos);
     // Linearized the coordinates within the same collapse reassociation.
-    c = builder.create<arith::AddIOp>(loc, c, off);
-    if (i != reass.size() - 1) {
-      c = builder.create<arith::MulIOp>(loc, c,
-                                        this->lvlSizes[tid][reass[i + 1]]);
+    crd = builder.create<arith::AddIOp>(loc, crd, off);
+    if (i != reassoc.size() - 1) {
+      crd = builder.create<arith::MulIOp>(loc, crd,
+                                          this->lvlSizes[tid][reassoc[i + 1]]);
     }
   }
-  return c;
+  return crd;
 }
 
 LoopEmitter::LoopEmitter(ValueRange tensors, StringAttr loopTag, bool hasOutput,
                          bool isSparseOut, ArrayRef<unsigned> topSort) {
   initialize(tensors, loopTag, hasOutput, isSparseOut, topSort);
 }
 
 void LoopEmitter::initialize(ValueRange ts, StringAttr loopTag, bool hasOutput,
                              bool isSparseOut, ArrayRef<unsigned> topSort) {
   // First initializes fields.
   this->loopTag = loopTag;
   this->hasOutput = hasOutput;
   this->isSparseOut = isSparseOut;
   this->tensors.assign(ts.begin(), ts.end());
   this->isSparseSlices.assign(tensors.size(), false);
   this->dimTypes.assign(tensors.size(), std::vector<DimLevelType>());
   this->pidxs.assign(tensors.size(), std::vector<Value>());
   this->coord.assign(tensors.size(), std::vector<Value>());
   this->highs.assign(tensors.size(), std::vector<Value>());
   this->lvlSizes.assign(tensors.size(), std::vector<Value>());
-  this->ptrBuffer.assign(tensors.size(), std::vector<Value>());
-  this->idxBuffer.assign(tensors.size(), std::vector<Value>());
+  this->posBuffer.assign(tensors.size(), std::vector<Value>());
+  this->crdBuffer.assign(tensors.size(), std::vector<Value>());
   this->valBuffer.assign(tensors.size(), nullptr);
   this->loopStack.reserve(topSort.size());
   this->sparsiferLoopLvlMap.assign(topSort.size(), 0);
   this->collapseReassoc.assign(tensors.size(), nullptr);
 
   for (size_t tid = 0, e = tensors.size(); tid < e; tid++) {
     auto t = tensors[tid];
     // a scalar or 0-dimension tensors
for (size_t tid = 0, e = tensors.size(); tid < e; tid++) {
     } else
       dimTypes[tid].assign(rank, DimLevelType::Dense);
 
     // Initialize using empty value.
     pidxs[tid].assign(rank, Value());
     coord[tid].assign(rank, Value());
     highs[tid].assign(rank, Value());
     lvlSizes[tid].assign(rank, Value());
-    ptrBuffer[tid].assign(rank, Value());
-    idxBuffer[tid].assign(rank, Value());
+    posBuffer[tid].assign(rank, Value());
+    crdBuffer[tid].assign(rank, Value());
   }
 
   // FIXME: This map should be maintained outside loop emitter.
   for (unsigned i = 0, e = topSort.size(); i < e; i++) {
     // This is an inverse map of the topologically sorted loop index from
     // sparsifier. This is needed to map the AffineDimExpr back to the loopStack
     // index used in loop emitter.
     sparsiferLoopLvlMap[topSort[i]] = i;
for (size_t t = 0, e = tensors.size(); t < e; t++) {
     // suggests there may be some dim/lvl confusion going on here.
     const Level lvlRank = rtp.getRank();
     const auto shape = rtp.getShape();
     const auto enc = getSparseTensorEncoding(rtp);
     const Level cooStart = enc ? getCOOStart(enc) : lvlRank;
     // Scan all levels of current tensor.
     for (Level l = 0; l < lvlRank; l++) {
       // This should be called only once at beginning.
-      assert(!ptrBuffer[t][l] && !idxBuffer[t][l] && !highs[t][l]);
+      assert(!posBuffer[t][l] && !crdBuffer[t][l] && !highs[t][l]);
       const auto dlt = dimTypes[t][l];
       // Handle sparse storage schemes.
       if (isCompressedDLT(dlt)) {
-        // Generate sparse primitives to obtains pointer and indices.
-        ptrBuffer[t][l] = genToPointers(builder, loc, tensor, l);
-        idxBuffer[t][l] = genToIndices(builder, loc, tensor, l, cooStart);
+        // Generate sparse primitives to obtains positions and coordinates.
+        posBuffer[t][l] = genToPositions(builder, loc, tensor, l);
+        crdBuffer[t][l] = genToCoordinates(builder, loc, tensor, l, cooStart);
       } else if (isSingletonDLT(dlt)) {
-        // Singleton dimension, fetch indices.
-        idxBuffer[t][l] = genToIndices(builder, loc, tensor, l, cooStart);
+        // Singleton level, fetch coordinates.
+        crdBuffer[t][l] = genToCoordinates(builder, loc, tensor, l, cooStart);
       } else {
-        // Dense dimension, nothing to fetch.
+        // Dense level, nothing to fetch.
         assert(isDenseDLT(dlt));
       }
 
       // Find upper bound in current dimension.
       // FIXME: `toOrigDim` is deprecated
       const Dimension d = toOrigDim(enc, l);
       lvlSizes[t][l] = highs[t][l] =
           mlir::linalg::createOrFoldDimOp(builder, loc, tensor, d);
for (auto [t, d] : llvm::zip(tids, dims)) {
     if (isSparse) {
       tid = t;
       dim = d;
     }
     isSparseInput = isSparseInput || isSparse;
   }
 
   auto enc = getSparseTensorEncoding(tensors[tid].getType());
-  auto reass = getCollapseReassociation(tid, dim);
-  dim = reass.front();
+  const auto reassoc = getCollapseReassociation(tid, dim);
+  dim = reassoc.front();
   // TODO: support dynamic slices.
   Value step = constantIndex(builder, loc, 1);
   Value lo = isSparseInput ? pidxs[tid][dim]      // current offset
                            : loopSeqStack.back(); // universal index
   Value hi = highs[tid][dim];
 
   Operation *loop = nullptr;
   Value iv;
if (isParallel) {
     // In-place update on the reduction variable vector.
     assert(forOp.getNumRegionIterArgs() == reduc.size());
     for (int i = 0, e = reduc.size(); i < e; i++)
       reduc[i] = forOp.getRegionIterArg(i);
     loop = forOp;
   }
   assert(loop && iv);
 
-  Value c;
+  Value crd;
   if (isSparseInput) {
-    assert(reass.size() == 1 || isUniqueCOOType(tensors[tid].getType()));
+    assert(reassoc.size() == 1 || isUniqueCOOType(tensors[tid].getType()));
     // For COO, the position is the same across consecutive levels.
-    llvm::for_each(reass, [this, tid, iv](int lvl) { pidxs[tid][lvl] = iv; });
-    c = genSparseCoord(builder, loc, tid, dim);
+    llvm::for_each(reassoc,
+                   [this, tid, iv](Level lvl) { pidxs[tid][lvl] = iv; });
+    crd = genSparseCrd(builder, loc, tid, dim);
   } else {
-    // Dense tensor, the coordinates is the inducation variable.
-    c = iv;
+    // Dense tensor, the coordinate is the inducation variable.
+    crd = iv;
   }
 
   if (isSparseSlices[tid] && isSparseInput) {
     // For sparse level slices, we need to filter out invalid coordinates that
     // are not included in the slice.
     SmallVector<Type> types;
     for (Value red : reduc)
       types.push_back(red.getType());
 
-    auto [trans, pred] = genSliceLegitPredicate(builder, loc, c, enc, dim);
+    auto [trans, pred] = genSliceLegitPredicate(builder, loc, crd, enc, dim);
     bool hasReduc = !types.empty();
     scf::IfOp ifOp = builder.create<scf::IfOp>(loc, types, pred,
                                                /*else*/ hasReduc);
     if (hasReduc) {
       // scf.for (a) -> v
       //  %s = scf.if (a) -> v
       //    user-generated code.
       //  else
       //    yield a
       //  yield %s
       builder.create<scf::YieldOp>(loc, ifOp.getResults());
       builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
       // On mismatch.
       builder.create<scf::YieldOp>(loc, reduc);
     }
     // Set the insertion point to matched branch.
     builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
-    c = trans;
+    crd = trans;
   }
 
-  assert(c);
-  coord[tid][dim] = c;
+  assert(crd);
+  coord[tid][dim] = crd;
   // NOTE: we can also prepare for next dim here in advance
   // Push the loop into stack
   loopStack.emplace_back(ArrayRef<size_t>(tid), ArrayRef<size_t>(dim), loop,
                          builder.getInsertionBlock(), coord[tid][dim], loopTag);
   // Emit extra locals.
   emitExtraLocalsForTensorsAtDenseDims(builder, loc, tids, dims);
 
   return loop;
Operation *LoopEmitter::enterFilterLoopOverTensorAtDim(
   Value step = constantIndex(builder, loc, 1);
 
   Value lo = pidxs[tid][dim];
   Value hi = highs[tid][dim];
 
   // TODO: We should instead use a whileOp for filter loop to allow early
   // break when exceeding (for ordered dimensions).
   // TODO: There are many other potiential opportunities that we might apply in
-  // the future. E.g., we could use binary search to located the pointer index.
+  // the future. E.g., we could use binary search to located the position index.
   scf::ForOp forOp = builder.create<scf::ForOp>(loc, lo, hi, step, reduc);
 
   // In-place update on the reduction variable vector.
   assert(forOp.getNumRegionIterArgs() == reduc.size());
   for (int i = 0, e = reduc.size(); i < e; i++)
     reduc[i] = forOp.getRegionIterArg(i);
 
   builder.setInsertionPointToStart(forOp.getBody());
   Value iv = forOp.getInductionVar();
 
   pidxs[tid][dim] = iv;
-  // Generating a load on the indices array yields the coordinate.
-  Value ptr = idxBuffer[tid][dim];
-  coord[tid][dim] = genIndexLoad(builder, loc, ptr, iv);
+  // Generating a load on the coordinates array yields the coordinate.
+  Value mem = crdBuffer[tid][dim];
+  coord[tid][dim] = genIndexLoad(builder, loc, mem, iv);
 
-  // Generate an if condition to filter out indices that is not equal to the
-  // result of the affine expression.
+  // Generate an if-condition to filter out coordinates that are not
+  // equal to the result of the affine expression.
   Value expected = genAffine(builder, affine, loc);
   auto pred = builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::eq,
                                             coord[tid][dim], expected);
   SmallVector<Type> types;
   for (Value red : reduc) {
     types.push_back(red.getType());
   }
 

 }
 
 Operation *LoopEmitter::enterCoIterationOverTensorsAtDims(
     OpBuilder &builder, Location loc, ArrayRef<size_t> tids,
     ArrayRef<size_t> dims, bool needsUniv, MutableArrayRef<Value> reduc) {
   assert(tids.size() == dims.size());
   SmallVector<Type> types;
   SmallVector<Value> operands;
-  // Construct the while-loop with a parameter for each index.
+  // Construct the while-loop with a parameter for each coordinate.
   Type indexType = builder.getIndexType();
   for (auto [tid, dim] : llvm::zip(tids, dims)) {
     if (isCompressedDLT(dimTypes[tid][dim]) ||
         isSingletonDLT(dimTypes[tid][dim])) {
       assert(pidxs[tid][dim]);
       types.push_back(indexType);
       operands.push_back(pidxs[tid][dim]);
     }
if (isCompressedDLT(dimTypes[tid][lvl]) ||
         isSingletonDLT(dimTypes[tid][lvl])) {
       Value op1 = before->getArgument(o);
       Value op2 = highs[tid][lvl];
       Value opc = builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ult,
                                                 op1, op2);
       cond = cond ? builder.create<arith::AndIOp>(loc, cond, opc) : opc;
       // Update positions
       Value pos = after->getArgument(o++);
-      auto reass = getCollapseReassociation(tid, lvl);
-      assert(reass.size() == 1 || isUniqueCOOType(tensors[tid].getType()));
+      const auto reassoc = getCollapseReassociation(tid, lvl);
+      assert(reassoc.size() == 1 || isUniqueCOOType(tensors[tid].getType()));
       // For COO, the position is the same across consecutive levels.
-      llvm::for_each(reass,
-                     [this, tid, pos](int lvl) { pidxs[tid][lvl] = pos; });
+      llvm::for_each(reassoc,
+                     [this, tid, pos](Level lvl) { pidxs[tid][lvl] = pos; });
     }
   }
   builder.create<scf::ConditionOp>(loc, cond, before->getArguments());
 
   // Generates while body.
   builder.setInsertionPointToStart(&whileOp.getAfter().front());
 
   SmallVector<std::pair<Value, unsigned>> slicesPreds;
   unsigned i = 0;
   for (auto [tid, dim] : llvm::zip(tids, dims)) {
     // Prepares for next level.
     if (isCompressedDLT(dimTypes[tid][dim]) ||
         isSingletonDLT(dimTypes[tid][dim])) {
-      coord[tid][dim] = genSparseCoord(builder, loc, tid, dim);
+      coord[tid][dim] = genSparseCrd(builder, loc, tid, dim);
       if (isSparseSlices[tid]) {
         Value load =
-            genIndexLoad(builder, loc, idxBuffer[tid][dim], pidxs[tid][dim]);
+            genIndexLoad(builder, loc, crdBuffer[tid][dim], pidxs[tid][dim]);
         auto enc = getSparseTensorEncoding(tensors[tid].getType());
         auto [trans, pred] =
             genSliceLegitPredicate(builder, loc, load, enc, dim);
         slicesPreds.emplace_back(pred, i);
         // Updates to the relative coordinate to the slice.
         coord[tid][dim] = trans;
       }
       i++;

 void LoopEmitter::prepareLoopOverTensorAtDim(OpBuilder &builder, Location loc,
                                              size_t tid, size_t dim) {
   assert(dimTypes[tid].size() > dim);
   auto dimType = dimTypes[tid][dim];
 
   if (isDenseDLT(dimType))
     return;
 
-  auto reassoc = getCollapseReassociation(tid, dim);
-  for (auto lvl : reassoc) {
-    // Either the first dimension, or the previous dimension has been set.
+  for (auto lvl : getCollapseReassociation(tid, dim)) {
+    // Either the first level, or the previous level has been set.
     assert(lvl == 0 || pidxs[tid][lvl - 1]);
     Value c0 = constantIndex(builder, loc, 0);
     Value c1 = constantIndex(builder, loc, 1);
     if (isCompressedDLT(dimType)) {
-      Value ptr = ptrBuffer[tid][lvl];
+      Value mem = posBuffer[tid][lvl];
 
       Value pLo = lvl == 0 ? c0 : pidxs[tid][lvl - 1];
-      pidxs[tid][lvl] = genIndexLoad(builder, loc, ptr, pLo);
+      pidxs[tid][lvl] = genIndexLoad(builder, loc, mem, pLo);
 
       Value pHi = builder.create<arith::AddIOp>(loc, pLo, c1);
-      highs[tid][lvl] = genIndexLoad(builder, loc, ptr, pHi);
+      highs[tid][lvl] = genIndexLoad(builder, loc, mem, pHi);
       return;
     }
     if (isSingletonDLT(dimType)) {
       Value pLo = lvl == 0 ? c0 : pidxs[tid][lvl - 1];
       Value pHi = builder.create<arith::AddIOp>(loc, pLo, c1);
 
       pidxs[tid][lvl] = pLo;
       highs[tid][lvl] = pHi;
       return;
     }
   }
 
   llvm_unreachable("Unrecognizable dimesion type!");
 }
 
 void LoopEmitter::emitExtraLocalsForTensorsAtDenseDims(OpBuilder &builder,
                                                        Location loc,
                                                        ArrayRef<size_t> tids,
                                                        ArrayRef<size_t> dims) {
-  // Initialize dense positions. Note that we generate dense indices of the
+  // Initialize dense positions. Note that we generate dense coordinates of the
   // output tensor unconditionally, since they may not appear in the lattice,
   // but may be needed for linearized codegen.
   for (auto [tid, dim] : llvm::zip(tids, dims)) {
     if (isDenseDLT(dimTypes[tid][dim])) {
       auto enc = getSparseTensorEncoding(tensors[tid].getType());
       if (enc && !isSparseOutput(tid)) {
         bool validPidx = dim == 0 || pidxs[tid][dim - 1];
         if (!validPidx) {

mlir/lib/Dialect/SparseTensor/Transforms/SparseStorageSpecifierToLLVM.cpp

 static SmallVector<Type, 2> getSpecifierFields(StorageSpecifierType tp) {
   MLIRContext *ctx = tp.getContext();
   auto enc = tp.getEncoding();
   const Level lvlRank = enc.getLvlRank();
 
   SmallVector<Type, 2> result;
   // TODO: how can we get the lowering type for index type in the later pipeline
   // to be consistent? LLVM::StructureType does not allow index fields.
-  auto indexType = IntegerType::get(tp.getContext(), 64);
-  auto dimSizes = LLVM::LLVMArrayType::get(ctx, indexType, lvlRank);
-  auto memSizes = LLVM::LLVMArrayType::get(ctx, indexType,
+  auto sizeType = IntegerType::get(tp.getContext(), 64);
+  auto lvlSizes = LLVM::LLVMArrayType::get(ctx, sizeType, lvlRank);
+  auto memSizes = LLVM::LLVMArrayType::get(ctx, sizeType,
                                            getNumDataFieldsFromEncoding(enc));
-  result.push_back(dimSizes);
+  result.push_back(lvlSizes);
   result.push_back(memSizes);
   return result;
 }
 
 static Type convertSpecifier(StorageSpecifierType tp) {
   return LLVM::LLVMStructType::getLiteral(tp.getContext(),
                                           getSpecifierFields(tp));
 }
 
 //===----------------------------------------------------------------------===//
 // Specifier struct builder.
 //===----------------------------------------------------------------------===//
 
-constexpr uint64_t kDimSizePosInSpecifier = 0;
+constexpr uint64_t kLvlSizePosInSpecifier = 0;
 constexpr uint64_t kMemSizePosInSpecifier = 1;
 
 class SpecifierStructBuilder : public StructBuilder {
 private:
   Value extractField(OpBuilder &builder, Location loc,
                      ArrayRef<int64_t> indices) {
     return genCast(builder, loc,
                    builder.create<LLVM::ExtractValueOp>(loc, value, indices),
                    builder.getIndexType());
   }
 
   void insertField(OpBuilder &builder, Location loc, ArrayRef<int64_t> indices,
                    Value v) {
     value = builder.create<LLVM::InsertValueOp>(
         loc, value, genCast(builder, loc, v, builder.getIntegerType(64)),
         indices);
   }
 
 public:
   explicit SpecifierStructBuilder(Value specifier) : StructBuilder(specifier) {
     assert(value);
   }
 
-  // Undef value for dimension sizes, all zero value for memory sizes.
+  // Undef value for level-sizes, all zero values for memory-sizes.
   static Value getInitValue(OpBuilder &builder, Location loc, Type structType);
 
-  Value dimSize(OpBuilder &builder, Location loc, unsigned dim);
-  void setDimSize(OpBuilder &builder, Location loc, unsigned dim, Value size);
+  Value lvlSize(OpBuilder &builder, Location loc, Level lvl);
+  void setLvlSize(OpBuilder &builder, Location loc, Level lvl, Value size);
 
-  Value memSize(OpBuilder &builder, Location loc, unsigned pos);
-  void setMemSize(OpBuilder &builder, Location loc, unsigned pos, Value size);
+  Value memSize(OpBuilder &builder, Location loc, FieldIndex fidx);
+  void setMemSize(OpBuilder &builder, Location loc, FieldIndex fidx,
+                  Value size);
 };
 
 Value SpecifierStructBuilder::getInitValue(OpBuilder &builder, Location loc,
                                            Type structType) {
   Value metaData = builder.create<LLVM::UndefOp>(loc, structType);
   SpecifierStructBuilder md(metaData);
   auto memSizeArrayType = structType.cast<LLVM::LLVMStructType>()
                               .getBody()[kMemSizePosInSpecifier]
                               .cast<LLVM::LLVMArrayType>();
 
   Value zero = constantZero(builder, loc, memSizeArrayType.getElementType());
   // Fill memSizes array with zero.
   for (int i = 0, e = memSizeArrayType.getNumElements(); i < e; i++)
     md.setMemSize(builder, loc, i, zero);
 
   return md;
 }
 
-/// Builds IR inserting the pos-th size into the descriptor.
-Value SpecifierStructBuilder::dimSize(OpBuilder &builder, Location loc,
-                                      unsigned dim) {
-  return extractField(builder, loc,
-                      ArrayRef<int64_t>{kDimSizePosInSpecifier, dim});
-}
-
-/// Builds IR inserting the pos-th size into the descriptor.
-void SpecifierStructBuilder::setDimSize(OpBuilder &builder, Location loc,
-                                        unsigned dim, Value size) {
-
-  insertField(builder, loc, ArrayRef<int64_t>{kDimSizePosInSpecifier, dim},
-              size);
+/// Builds IR extracting the `lvl`-th level-size from the descriptor.
+Value SpecifierStructBuilder::lvlSize(OpBuilder &builder, Location loc,
+                                      Level lvl) {
+  // This static_cast makes the narrowing of `lvl` explicit, as required
+  // by the braces notation for the ctor.
+  return extractField(
+      builder, loc,
+      ArrayRef<int64_t>{kLvlSizePosInSpecifier, static_cast<int64_t>(lvl)});
+}
+
+/// Builds IR inserting the `lvl`-th level-size into the descriptor.
+void SpecifierStructBuilder::setLvlSize(OpBuilder &builder, Location loc,
+                                        Level lvl, Value size) {
+  // This static_cast makes the narrowing of `lvl` explicit, as required
+  // by the braces notation for the ctor.
+  insertField(
+      builder, loc,
+      ArrayRef<int64_t>{kLvlSizePosInSpecifier, static_cast<int64_t>(lvl)},
 }
 
-/// Builds IR extracting the pos-th memory size into the descriptor.
+/// Builds IR extracting the `fidx`-th memory-size from the descriptor.
 Value SpecifierStructBuilder::memSize(OpBuilder &builder, Location loc,
-                                      unsigned pos) {
+                                      FieldIndex fidx) {
   return extractField(builder, loc,
-                      ArrayRef<int64_t>{kMemSizePosInSpecifier, pos});
+                      ArrayRef<int64_t>{kMemSizePosInSpecifier, fidx});
 }
 
-/// Builds IR inserting the pos-th memory size into the descriptor.
+/// Builds IR inserting the `fidx`-th memory-size into the descriptor.
 void SpecifierStructBuilder::setMemSize(OpBuilder &builder, Location loc,
-                                        unsigned pos, Value size) {
-  insertField(builder, loc, ArrayRef<int64_t>{kMemSizePosInSpecifier, pos},
+                                        FieldIndex fidx, Value size) {
+  insertField(builder, loc, ArrayRef<int64_t>{kMemSizePosInSpecifier, fidx},
               size);
 }
 
 } // namespace
 
 //===----------------------------------------------------------------------===//
 // The sparse storage specifier type converter (defined in Passes.h).
 //===----------------------------------------------------------------------===//
 
 StorageSpecifierToLLVMTypeConverter::StorageSpecifierToLLVMTypeConverter() {
   addConversion([](Type type) { return type; });
-  addConversion([](StorageSpecifierType tp) { return convertSpecifier(tp); });
+  addConversion(convertSpecifier);
 }
 
 //===----------------------------------------------------------------------===//
 // Storage specifier conversion rules.
 //===----------------------------------------------------------------------===//
 
 template <typename Base, typename SourceOp>
 class SpecifierGetterSetterOpConverter : public OpConversionPattern<SourceOp> {
 public:
   using OpAdaptor = typename SourceOp::Adaptor;
   using OpConversionPattern<SourceOp>::OpConversionPattern;
 
   LogicalResult
   matchAndRewrite(SourceOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     SpecifierStructBuilder spec(adaptor.getSpecifier());
     Value v;
-    if (op.getSpecifierKind() == StorageSpecifierKind::DimSize) {
-      v = Base::onDimSize(rewriter, op, spec,
-                          op.getDim().value().getZExtValue());
+    if (op.getSpecifierKind() == StorageSpecifierKind::LvlSize) {
+      assert(op.getLevel().has_value());
+      v = Base::onLvlSize(rewriter, op, spec, op.getLevel().value());
     } else {
       auto enc = op.getSpecifier().getType().getEncoding();
       StorageLayout layout(enc);
-      std::optional<unsigned> dim;
-      if (op.getDim())
-        dim = op.getDim().value().getZExtValue();
-      unsigned idx = layout.getMemRefFieldIndex(op.getSpecifierKind(), dim);
-      v = Base::onMemSize(rewriter, op, spec, idx);
+      FieldIndex fidx =
+          layout.getMemRefFieldIndex(op.getSpecifierKind(), op.getLevel());
+      v = Base::onMemSize(rewriter, op, spec, fidx);
     }
 
     rewriter.replaceOp(op, v);
     return success();
   }
 };
 
 struct StorageSpecifierSetOpConverter
     : public SpecifierGetterSetterOpConverter<StorageSpecifierSetOpConverter,
                                               SetStorageSpecifierOp> {
   using SpecifierGetterSetterOpConverter::SpecifierGetterSetterOpConverter;
-  static Value onDimSize(OpBuilder &builder, SetStorageSpecifierOp op,
-                         SpecifierStructBuilder &spec, unsigned d) {
-    spec.setDimSize(builder, op.getLoc(), d, op.getValue());
+  static Value onLvlSize(OpBuilder &builder, SetStorageSpecifierOp op,
+                         SpecifierStructBuilder &spec, Level lvl) {
+    spec.setLvlSize(builder, op.getLoc(), lvl, op.getValue());
     return spec;
   }
 
   static Value onMemSize(OpBuilder &builder, SetStorageSpecifierOp op,
-                         SpecifierStructBuilder &spec, unsigned i) {
-    spec.setMemSize(builder, op.getLoc(), i, op.getValue());
+                         SpecifierStructBuilder &spec, FieldIndex fidx) {
+    spec.setMemSize(builder, op.getLoc(), fidx, op.getValue());
     return spec;
   }
 };
 
 struct StorageSpecifierGetOpConverter
     : public SpecifierGetterSetterOpConverter<StorageSpecifierGetOpConverter,
                                               GetStorageSpecifierOp> {
   using SpecifierGetterSetterOpConverter::SpecifierGetterSetterOpConverter;
-  static Value onDimSize(OpBuilder &builder, GetStorageSpecifierOp op,
-                         SpecifierStructBuilder &spec, unsigned d) {
-    return spec.dimSize(builder, op.getLoc(), d);
+  static Value onLvlSize(OpBuilder &builder, GetStorageSpecifierOp op,
+                         SpecifierStructBuilder &spec, Level lvl) {
+    return spec.lvlSize(builder, op.getLoc(), lvl);
   }
   static Value onMemSize(OpBuilder &builder, GetStorageSpecifierOp op,
-                         SpecifierStructBuilder &spec, unsigned i) {
-    return spec.memSize(builder, op.getLoc(), i);
+                         SpecifierStructBuilder &spec, FieldIndex fidx) {
+    return spec.memSize(builder, op.getLoc(), fidx);
   }
 };
 
 struct StorageSpecifierInitOpConverter
     : public OpConversionPattern<StorageSpecifierInitOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult

mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp

if (getSparseTensorEncoding(operand.getType())) {
       // sparse tensor output with the flattened type array.
       flattened.append(tuple.getOperands().begin(), tuple.getOperands().end());
     } else {
       flattened.push_back(operand);
     }
   }
 }
 
-/// Generates a load with proper index typing.
+/// Generates a load with proper `index` typing.
 static Value genLoad(OpBuilder &builder, Location loc, Value mem, Value idx) {
   idx = genCast(builder, loc, idx, builder.getIndexType());
   return builder.create<memref::LoadOp>(loc, mem, idx);
 }
 
-/// Generates a store with proper index typing and (for indices) proper value.
+/// Generates a store with proper `index` typing and proper value.
 static void genStore(OpBuilder &builder, Location loc, Value val, Value mem,
                      Value idx) {
   idx = genCast(builder, loc, idx, builder.getIndexType());
   val = genCast(builder, loc, val,
                 mem.getType().cast<ShapedType>().getElementType());
   builder.create<memref::StoreOp>(loc, val, mem, idx);
 }
 
static Value sizeFromTensorAtDim(OpBuilder &builder, Location loc,
   // Note that this is typically already done by DimOp's folding.
   if (auto sz = stt.getStaticDimSize(dim))
     return constantIndex(builder, loc, *sz);
 
   // Any other query can consult the dimSizes array at field DimSizesIdx,
   // accounting for the reordering applied to the sparse storage.
   // FIXME: `toStoredDim` is deprecated.
   const Level lvl = toStoredDim(stt, dim);
-  // FIXME: this method seems to get *level* sizes, but the name is confusing
-  return desc.getDimSize(builder, loc, lvl);
+  return desc.getLvlSize(builder, loc, lvl);
 }
 
 // Gets the dimension size at the given stored level 'lvl', either as a
 // constant for a static size, or otherwise dynamically through memSizes.
 static Value sizeFromTensorAtLvl(OpBuilder &builder, Location loc,
                                  SparseTensorDescriptor desc, Level lvl) {
   // FIXME: `toOrigDim` is deprecated.
   return sizeFromTensorAtDim(builder, loc, desc,

 static void allocSchemeForRank(OpBuilder &builder, Location loc,
                                MutSparseTensorDescriptor desc, Level startLvl) {
   const SparseTensorType stt(desc.getRankedTensorType());
   Value linear = constantIndex(builder, loc, 1);
   const Level lvlRank = stt.getLvlRank();
   for (Level l = startLvl; l < lvlRank; l++) {
     const auto dlt = stt.getLvlType(l);
     if (isCompressedDLT(dlt)) {
-      // Append linear x pointers, initialized to zero. Since each compressed
+      // Append linear x positions, initialized to zero. Since each compressed
       // dimension initially already has a single zero entry, this maintains
       // the desired "linear + 1" length property at all times.
-      Type ptrType = stt.getPointerType();
-      Value ptrZero = constantZero(builder, loc, ptrType);
-      createPushback(builder, loc, desc, SparseTensorFieldKind::PtrMemRef, l,
-                     ptrZero, linear);
+      Value posZero = constantZero(builder, loc, stt.getPosType());
+      createPushback(builder, loc, desc, SparseTensorFieldKind::PosMemRef, l,
+                     posZero, linear);
       return;
     }
     if (isSingletonDLT(dlt)) {
       return; // nothing to do
     }
     // Keep compounding the size, but nothing needs to be initialized
     // at this level. We will eventually reach a compressed level or
     // otherwise the values array for the from-here "all-dense" case.
for (const DynSize sh : stt.getDimShape())
     dimSizes.push_back(ShapedType::isDynamic(sh)
                            ? dynSizes[i++]
                            : constantIndex(builder, loc, sh));
 
   // Set up some heuristic sizes. We try to set the initial
   // size based on available information. Otherwise we just
   // initialize a few elements to start the reallocation chain.
   // TODO: refine this
-  Value ptrHeuristic, idxHeuristic, valHeuristic;
+  Value posHeuristic, crdHeuristic, valHeuristic;
   if (stt.isAllDense()) {
     valHeuristic = dimSizes[0];
     for (const Value sz : ArrayRef<Value>{dimSizes}.drop_front())
       valHeuristic = builder.create<arith::MulIOp>(loc, valHeuristic, sz);
   } else if (sizeHint) {
     if (getCOOStart(stt.getEncoding()) == 0) {
-      ptrHeuristic = constantIndex(builder, loc, 2);
-      idxHeuristic = builder.create<arith::MulIOp>(
+      posHeuristic = constantIndex(builder, loc, 2);
+      crdHeuristic = builder.create<arith::MulIOp>(
           loc, constantIndex(builder, loc, dimRank), sizeHint); // AOS
     } else if (dimRank == 2 && stt.isDenseLvl(0) && stt.isCompressedLvl(1)) {
-      ptrHeuristic = builder.create<arith::AddIOp>(
+      posHeuristic = builder.create<arith::AddIOp>(
           loc, sizeHint, constantIndex(builder, loc, 1));
-      idxHeuristic = sizeHint;
+      crdHeuristic = sizeHint;
     } else {
-      ptrHeuristic = idxHeuristic = constantIndex(builder, loc, 16);
+      posHeuristic = crdHeuristic = constantIndex(builder, loc, 16);
     }
     valHeuristic = sizeHint;
   } else {
-    ptrHeuristic = idxHeuristic = valHeuristic =
+    posHeuristic = crdHeuristic = valHeuristic =
         constantIndex(builder, loc, 16);
   }
 
   foreachFieldAndTypeInSparseTensor(
       stt,
-      [&builder, &fields, stt, loc, ptrHeuristic, idxHeuristic, valHeuristic,
+      [&builder, &fields, stt, loc, posHeuristic, crdHeuristic, valHeuristic,
        enableInit](Type fType, FieldIndex fIdx, SparseTensorFieldKind fKind,
                    Level /*lvl*/, DimLevelType /*dlt*/) -> bool {
         assert(fields.size() == fIdx);
         Value field;
         switch (fKind) {
         case SparseTensorFieldKind::StorageSpec:
           field = SparseTensorSpecifier::getInitValue(builder, loc, stt);
           break;
-        case SparseTensorFieldKind::PtrMemRef:
-        case SparseTensorFieldKind::IdxMemRef:
+        case SparseTensorFieldKind::PosMemRef:
+        case SparseTensorFieldKind::CrdMemRef:
         case SparseTensorFieldKind::ValMemRef:
           field = createAllocation(
               builder, loc, fType.cast<MemRefType>(),
-              (fKind == SparseTensorFieldKind::PtrMemRef)   ? ptrHeuristic
-              : (fKind == SparseTensorFieldKind::IdxMemRef) ? idxHeuristic
+              (fKind == SparseTensorFieldKind::PosMemRef)   ? posHeuristic
+              : (fKind == SparseTensorFieldKind::CrdMemRef) ? crdHeuristic
                                                             : valHeuristic,
               enableInit);
           break;
         }
         assert(field);
         fields.push_back(field);
         // Returns true to continue the iteration.
         return true;
       });
 
   MutSparseTensorDescriptor desc(stt, fields);
 
   // Initialize the storage scheme to an empty tensor. Initialized memSizes
-  // to all zeros, sets the dimSizes to known values and gives all pointer
+  // to all zeros, sets the dimSizes to known values and gives all position
   // fields an initial zero entry, so that it is easier to maintain the
   // "linear + 1" length property.
-  Value ptrZero = constantZero(builder, loc, stt.getPointerType());
+  Value posZero = constantZero(builder, loc, stt.getPosType());
   for (Level lvlRank = stt.getLvlRank(), l = 0; l < lvlRank; l++) {
     // Fills dim sizes array.
-    // FIXME: this method seems to set *level* sizes, but the name is confusing
     // FIXME: `toOrigDim` is deprecated.
-    desc.setDimSize(builder, loc, l, dimSizes[toOrigDim(stt, l)]);
-    // Pushes a leading zero to pointers memref.
+    desc.setLvlSize(builder, loc, l, dimSizes[toOrigDim(stt, l)]);
+    // Pushes a leading zero to positions memref.
     if (stt.isCompressedLvl(l))
-      createPushback(builder, loc, desc, SparseTensorFieldKind::PtrMemRef, l,
-                     ptrZero);
+      createPushback(builder, loc, desc, SparseTensorFieldKind::PosMemRef, l,
+                     posZero);
   }
   allocSchemeForRank(builder, loc, desc, /*rank=*/0);
 }
 
 /// Helper method that generates block specific to compressed case:
 ///
-///  plo = pointers[l][pos[l-1]]
-///  phi = pointers[l][pos[l-1]+1]
-///  msz = indices[l].size()
-///  if (plo < phi) {
-///    present = indices[l][phi-1] == i[l]
+///  // given: parentPos = posCursor[lvl-1]
+///  pstart = desc.positions[lvl][parentPos]
+///  pstop = desc.positions[lvl][parentPos+1]
+///  plast = pstop - 1
+///  msz = desc.coordinates[lvl].size()
+///  if (pstart < pstop) {
+///    isPresent = (desc.coordinates[lvl][plast] == lvlCoords[lvl])
 ///  } else { // first insertion
-///    present = false
-///    pointers[l][pos[l-1]] = msz
+///    isPresent = false
+///    desc.positions[lvl][parentPos] = msz
 ///  }
-///  if (present) { // index already present
-///    next = phi-1
+///  if (isPresent) { // coordinate is already present
+///    pnext = plast
 ///  } else {
-///    indices[l].push_back(i[l])
-///    pointers[l][pos[l-1]+1] = msz+1
-///    next = msz
-///    <prepare level l + 1>
+///    desc.coordinates[lvl].push_back(lvlCoords[lvl])
+///    desc.positions[lvl][parentPos+1] = msz+1
+///    pnext = msz
+///    <prepare level lvl+1>
 ///  }
-///  pos[l] = next
+///  posCursor[lvl] = pnext
 static Value genCompressed(OpBuilder &builder, Location loc,
-                           MutSparseTensorDescriptor desc, ValueRange indices,
-                           Value value, Value pos, Level lvl) {
+                           MutSparseTensorDescriptor desc, ValueRange lvlCoords,
+                           Value /*unused*/, Value parentPos, Level lvl) {
   const SparseTensorType stt(desc.getRankedTensorType());
   const Level lvlRank = stt.getLvlRank();
   assert(lvl < lvlRank && "Level is out of bounds");
-  assert(indices.size() == static_cast<size_t>(lvlRank) &&
+  assert(lvlCoords.size() == static_cast<size_t>(lvlRank) &&
          "Level-rank mismatch");
   SmallVector<Type> types;
   Type indexType = builder.getIndexType();
   Type boolType = builder.getIntegerType(1);
-  unsigned idxIndex;
-  unsigned idxStride;
-  std::tie(idxIndex, idxStride) = desc.getIdxMemRefIndexAndStride(lvl);
-  Value one = constantIndex(builder, loc, 1);
-  Value pp1 = builder.create<arith::AddIOp>(loc, pos, one);
-  Value plo = genLoad(builder, loc, desc.getPtrMemRef(lvl), pos);
-  Value phi = genLoad(builder, loc, desc.getPtrMemRef(lvl), pp1);
-  Value msz = desc.getIdxMemSize(builder, loc, lvl);
-  Value idxStrideC;
-  if (idxStride > 1) {
-    idxStrideC = constantIndex(builder, loc, idxStride);
-    msz = builder.create<arith::DivUIOp>(loc, msz, idxStrideC);
-  }
-  Value phim1 = builder.create<arith::SubIOp>(
-      loc, genCast(builder, loc, phi, indexType), one);
+  unsigned crdFidx;
+  unsigned crdStride;
+  std::tie(crdFidx, crdStride) = desc.getCrdMemRefIndexAndStride(lvl);
+  const Value one = constantIndex(builder, loc, 1);
+  const Value pp1 = builder.create<arith::AddIOp>(loc, parentPos, one);
+  const Value positionsAtLvl = desc.getPosMemRef(lvl);
+  const Value pstart = genLoad(builder, loc, positionsAtLvl, parentPos);
+  const Value pstop = genLoad(builder, loc, positionsAtLvl, pp1);
+  const Value crdMsz = desc.getCrdMemSize(builder, loc, lvl);
+  const Value crdStrideC =
+      crdStride > 1 ? constantIndex(builder, loc, crdStride) : Value();
+  const Value msz =
+      crdStrideC ? builder.create<arith::DivUIOp>(loc, crdMsz, crdStrideC)
+                 : crdMsz;
+  const Value plast = builder.create<arith::SubIOp>(
+      loc, genCast(builder, loc, pstop, indexType), one);
   // Conditional expression.
-  Value lt =
-      builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ult, plo, phi);
+  Value lt = builder.create<arith::CmpIOp>(loc, arith::CmpIPredicate::ult,
+                                           pstart, pstop);
   types.push_back(boolType);
   scf::IfOp ifOp1 = builder.create<scf::IfOp>(loc, types, lt, /*else*/ true);
   types.pop_back();
   builder.setInsertionPointToStart(&ifOp1.getThenRegion().front());
-  Value crd = genLoad(
-      builder, loc, desc.getMemRefField(idxIndex),
-      idxStride > 1 ? builder.create<arith::MulIOp>(loc, phim1, idxStrideC)
-                    : phim1);
+  Value crd =
+      genLoad(builder, loc, desc.getMemRefField(crdFidx),
+              crdStrideC ? builder.create<arith::MulIOp>(loc, plast, crdStrideC)
+                         : plast);
   Value eq = builder.create<arith::CmpIOp>(
       loc, arith::CmpIPredicate::eq, genCast(builder, loc, crd, indexType),
-      indices[lvl]);
+      lvlCoords[lvl]);
   builder.create<scf::YieldOp>(loc, eq);
   builder.setInsertionPointToStart(&ifOp1.getElseRegion().front());
   if (lvl > 0)
-    genStore(builder, loc, msz, desc.getPtrMemRef(lvl), pos);
+    genStore(builder, loc, msz, positionsAtLvl, parentPos);
   builder.create<scf::YieldOp>(loc, constantI1(builder, loc, false));
   builder.setInsertionPointAfter(ifOp1);
   // If present construct. Note that for a non-unique dimension level, we
   // simply set the condition to false and rely on CSE/DCE to clean up the IR.
   //
   // TODO: generate less temporary IR?
   //
   for (unsigned i = 0, e = desc.getNumFields(); i < e; i++)
     types.push_back(desc.getField(i).getType());
   types.push_back(indexType);
   const Value p = stt.isUniqueLvl(lvl) ? ifOp1.getResult(0)
                                        : constantI1(builder, loc, false);
   scf::IfOp ifOp2 = builder.create<scf::IfOp>(loc, types, p, /*else*/ true);
-  // If present (fields unaffected, update next to phim1).
+  // If present (fields unaffected, update pnext to plast).
   builder.setInsertionPointToStart(&ifOp2.getThenRegion().front());
 
   // FIXME: This does not looks like a clean way, but probably the most
   // efficient way.
-  desc.getFields().push_back(phim1);
+  desc.getFields().push_back(plast);
   builder.create<scf::YieldOp>(loc, desc.getFields());
   desc.getFields().pop_back();
 
-  // If !present (changes fields, update next).
+  // If !present (changes fields, update pnext).
   builder.setInsertionPointToStart(&ifOp2.getElseRegion().front());
   Value mszp1 = builder.create<arith::AddIOp>(loc, msz, one);
-  genStore(builder, loc, mszp1, desc.getPtrMemRef(lvl), pp1);
-  createPushback(builder, loc, desc, SparseTensorFieldKind::IdxMemRef, lvl,
-                 indices[lvl]);
-  // Prepare the next dimension "as needed".
+  genStore(builder, loc, mszp1, positionsAtLvl, pp1);
+  createPushback(builder, loc, desc, SparseTensorFieldKind::CrdMemRef, lvl,
+                 lvlCoords[lvl]);
+  // Prepare the next level "as needed".
   if ((lvl + 1) < lvlRank)
     allocSchemeForRank(builder, loc, desc, lvl + 1);
 
   desc.getFields().push_back(msz);
   builder.create<scf::YieldOp>(loc, desc.getFields());
   desc.getFields().pop_back();
 
   // Update fields and return next pos.
static void genInsertBody(OpBuilder &builder, ModuleOp module,
   const OpBuilder::InsertionGuard insertionGuard(builder);
   Block *const entryBlock = func.addEntryBlock();
   builder.setInsertionPointToStart(entryBlock);
   const ValueRange args = entryBlock->getArguments();
   const Location loc = func.getLoc();
   const SparseTensorType stt(rtp);
   const Level lvlRank = stt.getLvlRank();
 
-  // Construct fields and indices arrays from parameters.
+  // Extract fields and coordinates from args.
   SmallVector<Value> fields = llvm::to_vector(args.drop_back(lvlRank + 1));
   MutSparseTensorDescriptor desc(rtp, fields);
-  const SmallVector<Value> indices =
+  const SmallVector<Value> coordinates =
       llvm::to_vector(args.take_back(lvlRank + 1).drop_back());
   Value value = args.back();
-  Value pos = constantZero(builder, loc, builder.getIndexType());
+  Value parentPos = constantZero(builder, loc, builder.getIndexType());
   // Generate code for every level.
   for (Level l = 0; l < lvlRank; l++) {
     const auto dlt = stt.getLvlType(l);
     if (isCompressedDLT(dlt)) {
       // Create:
       //   if (!present) {
-      //     indices[l].push_back(i[l])
-      //     <update pointers and prepare level l + 1>
+      //     coordinates[l].push_back(coords[l])
+      //     <update positions and prepare level l + 1>
       //   }
-      //   pos[l] = indices.size() - 1
-      //   <insert @ pos[l] at next level l + 1>
-      pos = genCompressed(builder, loc, desc, indices, value, pos, l);
+      //   positions[l] = coordinates.size() - 1
+      //   <insert @ positions[l] at next level l + 1>
+      parentPos =
+          genCompressed(builder, loc, desc, coordinates, value, parentPos, l);
     } else if (isSingletonDLT(dlt)) {
       // Create:
-      //   indices[l].push_back(i[l])
-      //   pos[l] = pos[l-1]
-      //   <insert @ pos[l] at next level l + 1>
-      createPushback(builder, loc, desc, SparseTensorFieldKind::IdxMemRef, l,
-                     indices[l]);
+      //   coordinates[l].push_back(coords[l])
+      //   positions[l] = positions[l-1]
+      //   <insert @ positions[l] at next level l + 1>
+      createPushback(builder, loc, desc, SparseTensorFieldKind::CrdMemRef, l,
+                     coordinates[l]);
     } else {
       assert(isDenseDLT(dlt));
       // Construct the new position as:
-      //   pos[l] = size * pos[l-1] + i[l]
-      //   <insert @ pos[l] at next level l + 1>
+      //   positions[l] = size * positions[l-1] + coords[l]
+      //   <insert @ positions[l] at next level l + 1>
       Value size = sizeFromTensorAtLvl(builder, loc, desc, l);
-      Value mult = builder.create<arith::MulIOp>(loc, size, pos);
-      pos = builder.create<arith::AddIOp>(loc, mult, indices[l]);
+      Value mult = builder.create<arith::MulIOp>(loc, size, parentPos);
+      parentPos = builder.create<arith::AddIOp>(loc, mult, coordinates[l]);
     }
   }
   // Reached the actual value append/insert.
   if (!stt.isDenseLvl(lvlRank - 1))
     createPushback(builder, loc, desc, SparseTensorFieldKind::ValMemRef,
                    std::nullopt, value);
   else
-    genStore(builder, loc, value, desc.getValMemRef(), pos);
+    genStore(builder, loc, value, desc.getValMemRef(), parentPos);
   builder.create<func::ReturnOp>(loc, fields);
 }
 
 /// Generates a call to a function to perform an insertion operation. If the
 /// function doesn't exist yet, call `createFunc` to generate the function.
 static void genInsertionCallHelper(OpBuilder &builder,
                                    MutSparseTensorDescriptor desc,
-                                   SmallVectorImpl<Value> &indices, Value value,
+                                   SmallVectorImpl<Value> &lcvs, Value value,
                                    func::FuncOp insertPoint,
                                    StringRef namePrefix,
                                    FuncGeneratorType createFunc) {
   // The mangled name of the function has this format:
-  //   <namePrefix>_<DLT>_<shape>_<ordering>_<eltType>
-  //     _<indexBitWidth>_<pointerBitWidth>
+  //   <namePrefix>_<DLT>_<shape>_<ordering>_<eltType>_<crdWidth>_<posWidth>
   const SparseTensorType stt(desc.getRankedTensorType());
   SmallString<32> nameBuffer;
   llvm::raw_svector_ostream nameOstream(nameBuffer);
   nameOstream << namePrefix;
-  assert(static_cast<size_t>(stt.getLvlRank()) == indices.size());
   const Level lvlRank = stt.getLvlRank();
+  assert(lcvs.size() == static_cast<size_t>(lvlRank));
   for (Level l = 0; l < lvlRank; l++)
     nameOstream << toMLIRString(stt.getLvlType(l)) << "_";
   // Static dim sizes are used in the generated code while dynamic sizes are
   // loaded from the dimSizes buffer. This is the reason for adding the shape
   // to the function name.
   for (const auto sh : stt.getDimShape())
     nameOstream << sh << "_";
   // Permutation information is also used in generating insertion.
   if (!stt.isIdentity())
     nameOstream << stt.getDimToLvlMap() << "_";
   nameOstream << stt.getElementType() << "_";
-  nameOstream << stt.getIndexBitWidth() << "_" << stt.getPointerBitWidth();
+  nameOstream << stt.getCrdWidth() << "_" << stt.getPosWidth();
 
   // Look up the function.
   ModuleOp module = insertPoint->getParentOfType<ModuleOp>();
   MLIRContext *context = module.getContext();
   auto result = SymbolRefAttr::get(context, nameOstream.str());
   auto func = module.lookupSymbol<func::FuncOp>(result.getAttr());
 
-  // Construct parameters for fields and indices.
+  // Construct operands: fields, coords, and value.
   SmallVector<Value> operands = llvm::to_vector(desc.getFields());
-  operands.append(indices);
+  operands.append(lcvs);
   operands.push_back(value);
   Location loc = insertPoint.getLoc();
 
   if (!func) {
     // Create the function.
     OpBuilder::InsertionGuard insertionGuard(builder);
     builder.setInsertionPoint(insertPoint);
 

 /// Generations insertion finalization code.
 static void genEndInsert(OpBuilder &builder, Location loc,
                          SparseTensorDescriptor desc) {
   const SparseTensorType stt(desc.getRankedTensorType());
   const Level lvlRank = stt.getLvlRank();
   for (Level l = 0; l < lvlRank; l++) {
     const auto dlt = stt.getLvlType(l);
     if (isCompressedDLT(dlt)) {
-      // Compressed dimensions need a pointer cleanup for all entries
+      // Compressed dimensions need a position cleanup for all entries
       // that were not visited during the insertion pass.
       //
       // TODO: avoid cleanup and keep compressed scheme consistent at all
       // times?
       //
       if (l > 0) {
-        Type ptrType = stt.getPointerType();
-        Value ptrMemRef = desc.getPtrMemRef(l);
-        Value hi = desc.getPtrMemSize(builder, loc, l);
+        Type posType = stt.getPosType();
+        Value posMemRef = desc.getPosMemRef(l);
+        Value hi = desc.getPosMemSize(builder, loc, l);
         Value zero = constantIndex(builder, loc, 0);
         Value one = constantIndex(builder, loc, 1);
         // Vector of only one, but needed by createFor's prototype.
-        SmallVector<Value, 1> inits{genLoad(builder, loc, ptrMemRef, zero)};
+        SmallVector<Value, 1> inits{genLoad(builder, loc, posMemRef, zero)};
         scf::ForOp loop = createFor(builder, loc, hi, inits, one);
         Value i = loop.getInductionVar();
         Value oldv = loop.getRegionIterArg(0);
-        Value newv = genLoad(builder, loc, ptrMemRef, i);
-        Value ptrZero = constantZero(builder, loc, ptrType);
+        Value newv = genLoad(builder, loc, posMemRef, i);
+        Value posZero = constantZero(builder, loc, posType);
         Value cond = builder.create<arith::CmpIOp>(
-            loc, arith::CmpIPredicate::eq, newv, ptrZero);
-        scf::IfOp ifOp = builder.create<scf::IfOp>(loc, TypeRange(ptrType),
+            loc, arith::CmpIPredicate::eq, newv, posZero);
+        scf::IfOp ifOp = builder.create<scf::IfOp>(loc, TypeRange(posType),
                                                    cond, /*else*/ true);
         builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
-        genStore(builder, loc, oldv, ptrMemRef, i);
+        genStore(builder, loc, oldv, posMemRef, i);
         builder.create<scf::YieldOp>(loc, oldv);
         builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
         builder.create<scf::YieldOp>(loc, newv);
         builder.setInsertionPointAfter(ifOp);
         builder.create<scf::YieldOp>(loc, ifOp.getResult(0));
         builder.setInsertionPointAfter(loop);
       }
     } else {

 
 /// Sparse codegen rule for dimension accesses.
 class SparseDimOpConverter : public OpConversionPattern<tensor::DimOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(tensor::DimOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
-    std::optional<int64_t> index = op.getConstantIndex();
-    if (!index || !getSparseTensorEncoding(adaptor.getSource().getType()))
+    std::optional<int64_t> dim = op.getConstantIndex();
+    if (!dim || !getSparseTensorEncoding(adaptor.getSource().getType()))
       return failure();
 
     auto desc = getDescriptorFromTensorTuple(adaptor.getSource());
-    auto sz = sizeFromTensorAtDim(rewriter, op.getLoc(), desc, *index);
+    auto sz = sizeFromTensorAtDim(rewriter, op.getLoc(), desc, *dim);
 
     rewriter.replaceOp(op, sz);
     return success();
   }
 };
 
 /// Sparse codegen rule for trivial tensor casts.
 class SparseCastConverter : public OpConversionPattern<tensor::CastOp> {
matchAndRewrite(ExpandOp op, OpAdaptor adaptor,
     // pattern expansion. As noted in the operation doc, we would like
     // to amortize this setup cost even between kernels.
     rewriter.create<linalg::FillOp>(
         loc, ValueRange{constantZero(rewriter, loc, eltType)},
         ValueRange{values});
     rewriter.create<linalg::FillOp>(
         loc, ValueRange{constantZero(rewriter, loc, boolType)},
         ValueRange{filled});
-    // Replace expansion op with these buffers and initial index.
+    // Replace expansion op with these buffers and initial coordinate.
     assert(op.getNumResults() == 4);
     rewriter.replaceOp(op, {values, filled, added, zero});
     return success();
   }
 };
 
 /// Sparse codegen rule for the compress operator.
 class SparseCompressConverter : public OpConversionPattern<CompressOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(CompressOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     Location loc = op->getLoc();
     SmallVector<Value> fields;
     auto desc = getMutDescriptorFromTensorTuple(adaptor.getTensor(), fields);
     Value values = adaptor.getValues();
     Value filled = adaptor.getFilled();
     Value added = adaptor.getAdded();
     Value count = adaptor.getCount();
     const SparseTensorType dstType(desc.getRankedTensorType());
     Type eltType = dstType.getElementType();
-    // Prepare indices.
-    SmallVector<Value> indices(adaptor.getIndices());
-    // If the innermost level is ordered, we need to sort the indices
+    // Prepare level-coords.
+    SmallVector<Value> lcvs(adaptor.getLvlCoords());
+    // If the innermost level is ordered, we need to sort the coordinates
     // in the "added" array prior to applying the compression.
     if (dstType.isOrderedLvl(dstType.getLvlRank() - 1))
       rewriter.create<SortOp>(loc, count, ValueRange{added}, ValueRange{},
                               SparseTensorSortKind::HybridQuickSort);
     // While performing the insertions, we also need to reset the elements
     // of the values/filled-switch by only iterating over the set elements,
     // to ensure that the runtime complexity remains proportional to the
     // sparsity of the expanded access pattern.
     //
     // Generate
     //    out_memrefs = for (i = 0; i < count; i++)(in_memrefs) {
-    //      index = added[i];
-    //      value = values[index];
-    //      insert({prev_indices, index}, value);
-    //      new_memrefs = insert(in_memrefs, {prev_indices, index}, value);
-    //      values[index] = 0;
-    //      filled[index] = false;
+    //      crd = added[i];
+    //      value = values[crd];
+    //      insert({lvlCoords, crd}, value);
+    //      new_memrefs = insert(in_memrefs, {lvlCoords, crd}, value);
+    //      values[crd] = 0;
+    //      filled[crd] = false;
     //      yield new_memrefs
     //    }
     scf::ForOp loop = createFor(rewriter, loc, count, desc.getFields());
     Value i = loop.getInductionVar();
-    Value index = genLoad(rewriter, loc, added, i);
-    Value value = genLoad(rewriter, loc, values, index);
-    indices.push_back(index);
+    Value crd = genLoad(rewriter, loc, added, i);
+    Value value = genLoad(rewriter, loc, values, crd);
+    lcvs.push_back(crd);
     // TODO: faster for subsequent insertions?
     auto insertPoint = op->template getParentOfType<func::FuncOp>();
-    genInsertionCallHelper(rewriter, desc, indices, value, insertPoint,
+    genInsertionCallHelper(rewriter, desc, lcvs, value, insertPoint,
                            kInsertFuncNamePrefix, genInsertBody);
-    genStore(rewriter, loc, constantZero(rewriter, loc, eltType), values,
-             index);
-    genStore(rewriter, loc, constantI1(rewriter, loc, false), filled, index);
+    genStore(rewriter, loc, constantZero(rewriter, loc, eltType), values, crd);
+    genStore(rewriter, loc, constantI1(rewriter, loc, false), filled, crd);
     rewriter.create<scf::YieldOp>(loc, desc.getFields());
     rewriter.setInsertionPointAfter(loop);
     Value result = genTuple(rewriter, loc, dstType, loop->getResults());
     // Deallocate the buffers on exit of the full loop nest.
     Operation *parent = getTop(op);
     rewriter.setInsertionPointAfter(parent);
     rewriter.create<memref::DeallocOp>(loc, values);
     rewriter.create<memref::DeallocOp>(loc, filled);
     rewriter.create<memref::DeallocOp>(loc, added);
     // Replace operation with resulting memrefs.
     rewriter.replaceOp(op, result);
     return success();
   }
 };
 
 /// Sparse codegen rule for the insert operator.
 class SparseInsertConverter : public OpConversionPattern<InsertOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(InsertOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     SmallVector<Value> fields;
     auto desc = getMutDescriptorFromTensorTuple(adaptor.getTensor(), fields);
-    // Prepare and indices.
-    SmallVector<Value> indices(adaptor.getIndices());
+    SmallVector<Value> lcvs(adaptor.getLvlCoords());
     // Generate insertion.
     Value value = adaptor.getValue();
     auto insertPoint = op->template getParentOfType<func::FuncOp>();
-    genInsertionCallHelper(rewriter, desc, indices, value, insertPoint,
+    genInsertionCallHelper(rewriter, desc, lcvs, value, insertPoint,
                            kInsertFuncNamePrefix, genInsertBody);
 
     // Replace operation with resulting memrefs.
     rewriter.replaceOp(op, genTuple(rewriter, op.getLoc(), desc));
     return success();
   }
 };
 
-/// Sparse codegen rule for pointer accesses.
-class SparseToPointersConverter : public OpConversionPattern<ToPointersOp> {
+/// Sparse codegen rule for position accesses.
+class SparseToPositionsConverter : public OpConversionPattern<ToPositionsOp> {
 public:
-  using OpAdaptor = typename ToPointersOp::Adaptor;
-  using OpConversionPattern<ToPointersOp>::OpConversionPattern;
+  using OpAdaptor = typename ToPositionsOp::Adaptor;
+  using OpConversionPattern<ToPositionsOp>::OpConversionPattern;
   LogicalResult
-  matchAndRewrite(ToPointersOp op, OpAdaptor adaptor,
+  matchAndRewrite(ToPositionsOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
-    // Replace the requested pointer access with corresponding field.
+    // Replace the requested position access with corresponding field.
     // The cast_op is inserted by type converter to intermix 1:N type
     // conversion.
     auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
-    uint64_t dim = op.getDimension().getZExtValue();
-    rewriter.replaceOp(op, desc.getPtrMemRef(dim));
+    rewriter.replaceOp(op, desc.getPosMemRef(op.getLevel()));
     return success();
   }
 };
 
-/// Sparse codegen rule for index accesses.
-class SparseToIndicesConverter : public OpConversionPattern<ToIndicesOp> {
+/// Sparse codegen rule for accessing the coordinates arrays.
+class SparseToCoordinatesConverter
+    : public OpConversionPattern<ToCoordinatesOp> {
 public:
-  using OpAdaptor = typename ToIndicesOp::Adaptor;
-  using OpConversionPattern<ToIndicesOp>::OpConversionPattern;
+  using OpAdaptor = typename ToCoordinatesOp::Adaptor;
+  using OpConversionPattern<ToCoordinatesOp>::OpConversionPattern;
   LogicalResult
-  matchAndRewrite(ToIndicesOp op, OpAdaptor adaptor,
+  matchAndRewrite(ToCoordinatesOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
-    // Replace the requested pointer access with corresponding field.
+    // Replace the requested coordinates access with corresponding field.
     // The cast_op is inserted by type converter to intermix 1:N type
     // conversion.
     Location loc = op.getLoc();
     auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
-    uint64_t dim = op.getDimension().getZExtValue();
-    Value field = desc.getIdxMemRefOrView(rewriter, loc, dim);
+    Value field = desc.getCrdMemRefOrView(rewriter, loc, op.getLevel());
 
     // Insert a cast to bridge the actual type to the user expected type. If the
     // actual type and the user expected type aren't compatible, the compiler or
     // the runtime will issue an error.
     Type resType = op.getResult().getType();
     if (resType != field.getType())
       field = rewriter.create<memref::CastOp>(loc, resType, field);
     rewriter.replaceOp(op, field);
 
     return success();
   }
 };
 
-/// Sparse codegen rule for accessing the linear indices buffer.
-class SparseToIndicesBufferConverter
-    : public OpConversionPattern<ToIndicesBufferOp> {
+/// Sparse codegen rule for accessing the linear coordinates buffer.
+class SparseToCoordinatesBufferConverter
+    : public OpConversionPattern<ToCoordinatesBufferOp> {
 public:
-  using OpAdaptor = typename ToIndicesBufferOp::Adaptor;
-  using OpConversionPattern<ToIndicesBufferOp>::OpConversionPattern;
+  using OpAdaptor = typename ToCoordinatesBufferOp::Adaptor;
+  using OpConversionPattern<ToCoordinatesBufferOp>::OpConversionPattern;
   LogicalResult
-  matchAndRewrite(ToIndicesBufferOp op, OpAdaptor adaptor,
+  matchAndRewrite(ToCoordinatesBufferOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
-    // Replace the requested pointer access with corresponding field.
+    // Replace the requested coordinates access with corresponding field.
     // The cast_op is inserted by type converter to intermix 1:N type
     // conversion.
     auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
     rewriter.replaceOp(op, desc.getAOSMemRef());
 
     return success();
   }
 };
 
 /// Sparse codegen rule for value accesses.
 class SparseToValuesConverter : public OpConversionPattern<ToValuesOp> {
 public:
   using OpAdaptor = typename ToValuesOp::Adaptor;
   using OpConversionPattern<ToValuesOp>::OpConversionPattern;
   LogicalResult
   matchAndRewrite(ToValuesOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
-    // Replace the requested pointer access with corresponding field.
+    // Replace the requested values access with corresponding field.
     // The cast_op is inserted by type converter to intermix 1:N type
     // conversion.
     auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
     rewriter.replaceOp(op, desc.getValMemRef());
     return success();
   }
 };
 
matchAndRewrite(tensor::ExtractSliceOp op, OpAdaptor adaptor,
     if (!srcEnc && !dstEnc)
       return failure();
 
     // TODO: We should check these in ExtractSliceOp::verify.
     assert(srcEnc && dstEnc && dstEnc.isSlice());
     assert(srcEnc.getDimLevelType() == dstEnc.getDimLevelType());
     assert(srcEnc.getDimOrdering() == dstEnc.getDimOrdering());
     assert(srcEnc.getHigherOrdering() == dstEnc.getHigherOrdering());
-    assert(srcEnc.getPointerBitWidth() == dstEnc.getPointerBitWidth());
-    assert(srcEnc.getIndexBitWidth() == dstEnc.getIndexBitWidth());
+    assert(srcEnc.getPosWidth() == dstEnc.getPosWidth());
+    assert(srcEnc.getCrdWidth() == dstEnc.getCrdWidth());
 
     // TODO: support dynamic slices.
     for (int i = 0, e = op.getSourceType().getRank(); i < e; i++) {
       assert(op.getStaticStrides()[i] == dstEnc.getStaticDimSliceStride(i));
       assert(op.getStaticOffsets()[i] == dstEnc.getStaticDimSliceOffset(i));
       assert(op.getStaticSizes()[i] == dstEnc.getStaticDimSliceSize(i));
     }
 
foreachFieldAndTypeInSparseTensor(
               Level /*lvl*/, DimLevelType /*dlt*/) -> bool {
           assert(fields.size() == fIdx);
           auto enc = getSparseTensorEncoding(rtp);
           Value field;
           switch (fKind) {
           case SparseTensorFieldKind::StorageSpec:
             field = SparseTensorSpecifier::getInitValue(rewriter, loc, rtp);
             break;
-          case SparseTensorFieldKind::PtrMemRef: {
-            // TACO-style COO starts with a PtrBuffer
+          case SparseTensorFieldKind::PosMemRef: {
+            // TACO-style COO starts with a PosBuffer
             // By creating a constant value for it, we avoid the complexity of
             // memory management.
-            auto tensorType = RankedTensorType::get({2}, enc.getPointerType());
+            const auto posTp = enc.getPosType();
+            auto tensorType = RankedTensorType::get({2}, posTp);
             auto memrefType = MemRefType::get(tensorType.getShape(),
                                               tensorType.getElementType());
             auto cstPtr = rewriter.create<arith::ConstantOp>(
                 loc, tensorType,
                 DenseElementsAttr::get(
                     tensorType,
                     ArrayRef<Attribute>{
-                        IntegerAttr::get(enc.getPointerType(), 0),
+                        IntegerAttr::get(posTp, 0),
                         IntegerAttr::get(
-                            enc.getPointerType(),
-                            op.getData().getType().getShape()[0])}));
+                            posTp, op.getValues().getType().getShape()[0])}));
             field = rewriter.create<bufferization::ToMemrefOp>(loc, memrefType,
                                                                cstPtr);
             break;
           }
-          case SparseTensorFieldKind::IdxMemRef: {
-            auto tensorType = op.getIndices().getType();
+          case SparseTensorFieldKind::CrdMemRef: {
+            auto tensorType = op.getCoordinates().getType();
             auto memrefType = MemRefType::get(tensorType.getShape(),
                                               tensorType.getElementType());
-            auto idxMemRef = rewriter.create<bufferization::ToMemrefOp>(
-                op->getLoc(), memrefType, op.getIndices());
+            auto crdMemRef = rewriter.create<bufferization::ToMemrefOp>(
+                op->getLoc(), memrefType, op.getCoordinates());
             ReassociationIndices reassociation;
             for (int i = 0, e = tensorType.getRank(); i < e; i++)
               reassociation.push_back(i);
 
             // Flattened the indices buffer to rank 1.
             field = rewriter.create<memref::CollapseShapeOp>(
-                loc, idxMemRef, ArrayRef<ReassociationIndices>(reassociation));
+                loc, crdMemRef, ArrayRef<ReassociationIndices>(reassociation));
             break;
           }
           case SparseTensorFieldKind::ValMemRef: {
-            auto tensorType = op.getData().getType();
+            auto tensorType = op.getValues().getType();
             auto memrefType = MemRefType::get(tensorType.getShape(),
                                               tensorType.getElementType());
             field = rewriter.create<bufferization::ToMemrefOp>(
-                op->getLoc(), memrefType, op.getData());
+                op->getLoc(), memrefType, op.getValues());
             break;
           }
           }
 
           assert(field);
           if (fType != field.getType())
             field = rewriter.create<memref::CastOp>(loc, fType, field);
           fields.push_back(field);
           // Returns true to continue the iteration.
           return true;
         });
 
     MutSparseTensorDescriptor desc(rtp, fields);
-    auto noe = linalg::createOrFoldDimOp(rewriter, loc, op.getData(), 0);
-    for (unsigned i = 0, e = rtp.getRank(); i < e; i++) {
-      int dim = rtp.getShape()[i];
-      assert(!ShapedType::isDynamic(dim));
-      desc.setDimSize(rewriter, loc, i, constantIndex(rewriter, loc, dim));
-      if (i == 0)
-        desc.setPtrMemSize(rewriter, loc, i, constantIndex(rewriter, loc, 2));
+    auto noe = linalg::createOrFoldDimOp(rewriter, loc, op.getValues(), 0);
+    // FIXME: should use `SparseTensorType::getLvlRank` in lieu of
+    // `RankedTensorType::getRank`, because the latter introduces dim/lvl
+    // ambiguity.
+    for (Level lvl = 0, lvlRank = rtp.getRank(); lvl < lvlRank; lvl++) {
+      const auto sh = rtp.getShape()[lvl];
+      assert(!ShapedType::isDynamic(sh));
+      desc.setLvlSize(rewriter, loc, lvl, constantIndex(rewriter, loc, sh));
+      if (lvl == 0)
+        desc.setPosMemSize(rewriter, loc, lvl, constantIndex(rewriter, loc, 2));
 
-      desc.setIdxMemSize(rewriter, loc, i, noe);
+      desc.setCrdMemSize(rewriter, loc, lvl, noe);
     }
     desc.setValMemSize(rewriter, loc, noe);
 
     rewriter.replaceOp(op, genTuple(rewriter, loc, desc));
     return success();
   }
 };
 
 struct SparseUnpackOpConverter : public OpConversionPattern<UnpackOp> {
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(UnpackOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     auto desc = getDescriptorFromTensorTuple(adaptor.getTensor());
     Location loc = op.getLoc();
-    int64_t rank = op.getTensor().getType().getRank();
+    const auto srcTp = getSparseTensorType(op.getTensor());
+    const Level lvlRank = srcTp.getLvlRank();
 
-    assert(isUniqueCOOType(op.getTensor().getType()) &&
-           desc.getFields().size() == 4);
+    assert(isUniqueCOOType(srcTp) && desc.getFields().size() == 4);
 
-    Value flatBuf = rank == 1 ? desc.getIdxMemRefOrView(rewriter, loc, 0)
+    Value flatBuf = lvlRank == 1 ? desc.getCrdMemRefOrView(rewriter, loc, 0)
                                  : desc.getAOSMemRef();
-    Value dataBuf = desc.getValMemRef();
+    Value valuesBuf = desc.getValMemRef();
 
-    // If frontend requests a static buffer, we reallocate the data/indices
-    // to ensure that we meet their need.
-    TensorType dataTp = op.getData().getType();
-    if (dataTp.hasStaticShape()) {
-      dataBuf = reallocOrSubView(rewriter, loc, dataTp.getShape()[0], dataBuf);
+    // If frontend requests a static buffer, we reallocate the
+    // values/coordinates to ensure that we meet their need.
+    const auto valuesTp = getRankedTensorType(op.getValues());
+    if (valuesTp.hasStaticShape()) {
+      valuesBuf =
+          reallocOrSubView(rewriter, loc, valuesTp.getShape()[0], valuesBuf);
     }
 
-    TensorType indicesTp = op.getIndices().getType();
-    if (indicesTp.hasStaticShape()) {
-      auto len = indicesTp.getShape()[0] * indicesTp.getShape()[1];
+    const auto coordinatesTp = getRankedTensorType(op.getCoordinates());
+    if (coordinatesTp.hasStaticShape()) {
+      auto len = coordinatesTp.getShape()[0] * coordinatesTp.getShape()[1];
       flatBuf = reallocOrSubView(rewriter, loc, len, flatBuf);
     }
 
-    Value idxBuf = rewriter.create<memref::ExpandShapeOp>(
-        loc, MemRefType::get(indicesTp.getShape(), indicesTp.getElementType()),
+    Value coordinatesBuf = rewriter.create<memref::ExpandShapeOp>(
+        loc,
+        MemRefType::get(coordinatesTp.getShape(),
+                        coordinatesTp.getElementType()),
         flatBuf, ArrayRef{ReassociationIndices{0, 1}});
 
     // Converts MemRefs back to Tensors.
-    Value data = rewriter.create<bufferization::ToTensorOp>(loc, dataBuf);
-    Value indices = rewriter.create<bufferization::ToTensorOp>(loc, idxBuf);
-    Value nnz = genCast(rewriter, loc, desc.getValMemSize(rewriter, loc),
-                        op.getNnz().getType());
+    Value values = rewriter.create<bufferization::ToTensorOp>(loc, valuesBuf);
+    Value coordinates =
+        rewriter.create<bufferization::ToTensorOp>(loc, coordinatesBuf);
+    Value nse = genCast(rewriter, loc, desc.getValMemSize(rewriter, loc),
+                        op.getNse().getType());
 
-    rewriter.replaceOp(op, {data, indices, nnz});
+    rewriter.replaceOp(op, {values, coordinates, nse});
     return success();
   }
 };
 
 struct SparseNewOpConverter : public OpConversionPattern<NewOp> {
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(NewOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     Location loc = op.getLoc();
     const auto dstTp = getSparseTensorType(op.getResult());
-    const auto encDst = dstTp.getEncoding();
     // Creating COO with NewOp is handled by direct IR codegen. All other cases
     // are handled by rewriting.
-    if (!dstTp.hasEncoding() || getCOOStart(encDst) != 0)
+    if (!dstTp.hasEncoding() || getCOOStart(dstTp.getEncoding()) != 0)
       return failure();
 
     // Implement the NewOp(filename) as follows:
-    //   reader = getSparseTensorReader(filename)
-    //   nse = getSparseTensorNNZ()
-    //   tmp = bufferization.alloc_tensor an ordered COO with
-    //          dst dim ordering, size_hint = nse
-    //   indices = to_indices_buffer(tmp)
-    //   values = to_values(tmp)
-    //   isSorted = getSparseTensorReaderRead(indices, values, dimOrdering)
-    //   if (!isSorted) sort_coo(nse, indices, values)
+    //   %reader = @getSparseTensorReader(%filename)
+    //   %nse = @getSparseTensorNSE(%reader)
+    //   %coo = bufferization.alloc_tensor an ordered COO with
+    //          dst dim ordering, size_hint = %nse
+    //   %coordinates = sparse_tensor.coordinates_buffer(%coo)
+    //   %values = sparse_tensor.values(%coo)
+    //   %isSorted = @sparseTensorReaderReadToBuffers(%coordinates, %values)
+    //   if (! %isSorted) sparse_tensor.sort_coo(%nse, %coordinates, %values)
     //   update storage specifier
-    //   dst = sparse_tensor.ConvertOp tmp
+    //   @delSparseTensorReader(%reader)
 
     // Create a sparse tensor reader.
-    Value fileName = op.getSource();
-    Type opaqueTp = getOpaquePointerType(rewriter);
+    const Value fileName = op.getSource();
+    const Type opaqueTp = getOpaquePointerType(rewriter);
+    // FIXME: use `createCheckedSparseTensorReader` instead, because
+    // `createSparseTensorReader` is unsafe.
     Value reader = createFuncCall(rewriter, loc, "createSparseTensorReader",
                                   {opaqueTp}, {fileName}, EmitCInterface::Off)
                        .getResult(0);
 
-    Type indexTp = rewriter.getIndexType();
+    const Type indexTp = rewriter.getIndexType();
     const Dimension dimRank = dstTp.getDimRank();
+    const Level lvlRank = dstTp.getLvlRank();
 
     // If the result tensor has dynamic dimensions, get the dynamic sizes from
     // the sparse tensor reader.
     SmallVector<Value> dynSizes;
     if (dstTp.hasDynamicDimShape()) {
+      // FIXME: call `getSparseTensorReaderDimSizes` instead, because
+      // `copySparseTensorReaderDimSizes` copies the memref over,
+      // instead of just accessing the reader's memory directly.
       Value dimSizes = genAlloca(rewriter, loc, dimRank, indexTp);
       createFuncCall(rewriter, loc, "copySparseTensorReaderDimSizes", {},
                      {reader, dimSizes}, EmitCInterface::On)
           .getResult(0);
-      ArrayRef<int64_t> dstShape = dstTp.getRankedTensorType().getShape();
-      for (auto &d : llvm::enumerate(dstShape)) {
-        if (d.value() == ShapedType::kDynamic) {
+      for (const auto &d : llvm::enumerate(dstTp.getDimShape()))
+        if (ShapedType::isDynamic(d.value()))
           dynSizes.push_back(rewriter.create<memref::LoadOp>(
               loc, dimSizes, constantIndex(rewriter, loc, d.index())));
-        }
-      }
-    }
 
-    Value nse = createFuncCall(rewriter, loc, "getSparseTensorReaderNNZ",
+    Value nse = createFuncCall(rewriter, loc, "getSparseTensorReaderNSE",
                                {indexTp}, {reader}, EmitCInterface::Off)
                     .getResult(0);
     // Construct allocation for each field.
     SmallVector<Value> fields;
     createAllocFields(rewriter, loc, dstTp, dynSizes, /*enableInit=*/false,
                       fields, nse);
     MutSparseTensorDescriptor desc(dstTp, fields);
 
-    // Read the COO tensor data.
-    Type eltTp = dstTp.getElementType();
-    Type indBufEleTp = getIndexOverheadType(rewriter, encDst);
-    SmallString<32> getReadFuncName{"getSparseTensorReaderRead",
-                                    overheadTypeFunctionSuffix(indBufEleTp),
-                                    primaryTypeFunctionSuffix(eltTp)};
-
-    Value xs = desc.getAOSMemRef();
-    Value ys = desc.getValMemRef();
-    SmallVector<Value> dim2lvlValues(dimRank, Value());
-    if (auto dimOrder = encDst.getDimOrdering()) {
+    // Construct the `dim2lvl` buffer for handing off to the runtime library.
+    // FIXME: This code is (mostly) copied from the SparseTensorConversion.cpp
+    // handling of `NewOp`, and only handles permutations.  Fixing this
+    // requires waiting for wrengr to finish redoing the CL that handles
+    // all dim<->lvl stuff more robustly.
+    SmallVector<Value> dim2lvlValues(dimRank);
+    if (!dstTp.isIdentity()) {
+      const auto dimOrder = dstTp.getDimToLvlMap();
       assert(dimOrder.isPermutation() && "Got non-permutation");
-      for (uint64_t l = 0; l < dimRank; l++) {
-        uint64_t d = dimOrder.getDimPosition(l);
+      for (Level l = 0; l < lvlRank; l++) {
+        const Dimension d = dimOrder.getDimPosition(l);
         dim2lvlValues[d] = constantIndex(rewriter, loc, l);
       }
     } else {
-      for (uint64_t l = 0; l < dimRank; l++)
-        dim2lvlValues[l] = constantIndex(rewriter, loc, l);
+      // The `SparseTensorType` ctor already ensures `dimRank == lvlRank`
+      // when `isIdentity`; so no need to re-assert it here.
+      for (Dimension d = 0; d < dimRank; d++)
+        dim2lvlValues[d] = constantIndex(rewriter, loc, d);
     }
     Value dim2lvl = allocaBuffer(rewriter, loc, dim2lvlValues);
 
-    Value f = constantI1(rewriter, loc, false);
+    // Read the COO tensor data.
+    Value xs = desc.getAOSMemRef();
+    Value ys = desc.getValMemRef();
+
+    const Type boolTp = rewriter.getIntegerType(1);
+    const Type elemTp = dstTp.getElementType();
+    const Type crdTp = dstTp.getCrdType();
+    // FIXME: This function name is weird; should rename to
+    // "sparseTensorReaderReadToBuffers".
+    SmallString<32> readToBuffersFuncName{"getSparseTensorReaderRead",
+                                          overheadTypeFunctionSuffix(crdTp),
+                                          primaryTypeFunctionSuffix(elemTp)};
     Value isSorted =
-        createFuncCall(rewriter, loc, getReadFuncName, {f.getType()},
+        createFuncCall(rewriter, loc, readToBuffersFuncName, {boolTp},
                        {reader, dim2lvl, xs, ys}, EmitCInterface::On)
             .getResult(0);
 
     // If the destination tensor is a sorted COO, we need to sort the COO tensor
     // data if the input elements aren't sorted yet.
-    if (encDst.isOrderedLvl(dimRank - 1)) {
+    if (dstTp.isOrderedLvl(lvlRank - 1)) {
+      Value kFalse = constantI1(rewriter, loc, false);
       Value notSorted = rewriter.create<arith::CmpIOp>(
-          loc, arith::CmpIPredicate::eq, isSorted, f);
+          loc, arith::CmpIPredicate::eq, isSorted, kFalse);
       scf::IfOp ifOp =
           rewriter.create<scf::IfOp>(loc, notSorted, /*else*/ false);
       rewriter.setInsertionPointToStart(&ifOp.getThenRegion().front());
       rewriter.create<SortCooOp>(
-          loc, nse, xs, ValueRange{ys}, rewriter.getIndexAttr(dimRank),
+          loc, nse, xs, ValueRange{ys}, rewriter.getIndexAttr(lvlRank),
           rewriter.getIndexAttr(0), SparseTensorSortKind::HybridQuickSort);
       rewriter.setInsertionPointAfter(ifOp);
     }
 
-    // Set PtrMemRef0[1] = nse.
-    Value c1 = constantIndex(rewriter, loc, 1);
-    Value ptrMemref0 = desc.getPtrMemRef(0);
-    Type ptrEleTy = getMemRefType(ptrMemref0).getElementType();
-    Value ptrNse =
-        ptrEleTy == nse.getType()
-            ? nse
-            : rewriter.create<arith::IndexCastOp>(loc, ptrEleTy, nse);
-    rewriter.create<memref::StoreOp>(loc, ptrNse, ptrMemref0, c1);
+    // Set PosMemRef0[1] = nse.
+    const Value c1 = constantIndex(rewriter, loc, 1);
+    const Value posMemref0 = desc.getPosMemRef(0);
+    const Type posTp = dstTp.getPosType();
+    const Value posNse = genCast(rewriter, loc, nse, posTp);
+    rewriter.create<memref::StoreOp>(loc, posNse, posMemref0, c1);
 
     // Update storage specifier.
-    Value idxSize = rewriter.create<arith::MulIOp>(
-        loc, nse, constantIndex(rewriter, loc, dimRank));
-    desc.setSpecifierField(rewriter, loc, StorageSpecifierKind::IdxMemSize, 0,
-                           idxSize);
+    Value coordinatesSize = rewriter.create<arith::MulIOp>(
+        loc, nse, constantIndex(rewriter, loc, lvlRank));
+    desc.setSpecifierField(rewriter, loc, StorageSpecifierKind::CrdMemSize, 0,
+                           coordinatesSize);
     desc.setSpecifierField(rewriter, loc, StorageSpecifierKind::ValMemSize,
                            std::nullopt, nse);
 
     // Release the sparse tensor reader.
     createFuncCall(rewriter, loc, "delSparseTensorReader", {}, {reader},
                    EmitCInterface::Off);
 
     // Replace operation with resulting memrefs.

 void mlir::populateSparseTensorCodegenPatterns(
     TypeConverter &typeConverter, RewritePatternSet &patterns,
     bool enableBufferInitialization) {
   patterns.add<SparsePackOpConverter, SparseUnpackOpConverter,
                SparseReturnConverter, SparseCallConverter, SparseDimOpConverter,
                SparseCastConverter, SparseTensorDeallocConverter,
                SparseExtractSliceCoverter, SparseTensorLoadConverter,
                SparseExpandConverter, SparseCompressConverter,
-               SparseInsertConverter, SparseToPointersConverter,
-               SparseToIndicesConverter, SparseToIndicesBufferConverter,
+               SparseInsertConverter, SparseToPositionsConverter,
+               SparseToCoordinatesConverter, SparseToCoordinatesBufferConverter,
                SparseToValuesConverter, SparseConvertConverter,
                SparseNewOpConverter, SparseNumberOfEntriesConverter>(
       typeConverter, patterns.getContext());
   patterns.add<SparseTensorAllocConverter>(typeConverter, patterns.getContext(),
                                            enableBufferInitialization);
 }

mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp

public:
   /// but is factored out so that it can also be called independently
   /// whenever subsequent `genNewCall` calls want to reuse the same
   /// buffers but different type parameters.
   //
   // TODO: This is only ever used by sparse2sparse-viaCOO `ConvertOp`;
   // is there a better way to handle that than this one-off setter method?
   NewCallParams &setTemplateTypes(SparseTensorType stt) {
     const auto enc = stt.getEncoding();
-    params[kParamPtrTp] = constantPointerTypeEncoding(builder, loc, enc);
-    params[kParamIndTp] = constantIndexTypeEncoding(builder, loc, enc);
+    params[kParamPosTp] = constantPosTypeEncoding(builder, loc, enc);
+    params[kParamCrdTp] = constantCrdTypeEncoding(builder, loc, enc);
     params[kParamValTp] =
         constantPrimaryTypeEncoding(builder, loc, stt.getElementType());
     return *this;
   }
 
   /// Checks whether all the static parameters have been initialized.
   bool isInitialized() const {
     for (unsigned i = 0; i < kNumStaticParams; ++i)
private:
   static constexpr unsigned kNumStaticParams = 8;
   static constexpr unsigned kNumDynamicParams = 2;
   static constexpr unsigned kNumParams = kNumStaticParams + kNumDynamicParams;
   static constexpr unsigned kParamDimSizes = 0;
   static constexpr unsigned kParamLvlSizes = 1;
   static constexpr unsigned kParamLvlTypes = 2;
   static constexpr unsigned kParamLvl2Dim = 3;
   static constexpr unsigned kParamDim2Lvl = 4;
-  static constexpr unsigned kParamPtrTp = 5;
-  static constexpr unsigned kParamIndTp = 6;
+  static constexpr unsigned kParamPosTp = 5;
+  static constexpr unsigned kParamCrdTp = 6;
   static constexpr unsigned kParamValTp = 7;
   static constexpr unsigned kParamAction = 8;
   static constexpr unsigned kParamPtr = 9;
 
   OpBuilder &builder;
   Location loc;
   Type pTp;
   Value params[kNumParams];

 }
 
 /// Generates a call that adds one element to a coordinate scheme.
 /// In particular, this generates code like the following:
 ///   val = a[i1,..,ik];
 ///   if val != 0
 ///     t->add(&val, [i1,..,ik], [p1,..,pk]);
 static void genAddEltCall(OpBuilder &builder, Location loc, Type eltType,
-                          Value lvlCOO, Value valPtr, Value dimInd,
+                          Value lvlCOO, Value valPtr, Value dimCoords,
                           Value dim2lvl) {
   SmallString<9> name{"addElt", primaryTypeFunctionSuffix(eltType)};
-  SmallVector<Value, 4> params{lvlCOO, valPtr, dimInd, dim2lvl};
+  SmallVector<Value, 4> params{lvlCOO, valPtr, dimCoords, dim2lvl};
   Type pTp = getOpaquePointerType(builder);
   createFuncCall(builder, loc, name, pTp, params, EmitCInterface::On);
 }
 
 /// Generates a call to `iter->getNext()`.  If there is a next element,
-/// then it is copied into the out-parameters `ind` and `elemPtr`,
+/// then it is copied into the out-parameters `coords` and `elemPtr`,
 /// and the return value is true.  If there isn't a next element, then
 /// the return value is false.
+///
+/// The `coords` argument uses the same coordinate-space as the `iter`
+/// (which can be either dim- or lvl-coords, depending on context).
 static Value genGetNextCall(OpBuilder &builder, Location loc, Value iter,
-                            Value ind, Value elemPtr) {
+                            Value coords, Value elemPtr) {
   Type elemTp = elemPtr.getType().cast<ShapedType>().getElementType();
   SmallString<10> name{"getNext", primaryTypeFunctionSuffix(elemTp)};
-  SmallVector<Value, 3> params{iter, ind, elemPtr};
+  SmallVector<Value, 3> params{iter, coords, elemPtr};
   Type i1 = builder.getI1Type();
   return createFuncCall(builder, loc, name, i1, params, EmitCInterface::On)
       .getResult(0);
 }
 
-/// Converts a pointer to COO (from calls to iter->next()) into a vector of
-/// indices, apply (optional) `offset` on `offsetDim`.
-static SmallVector<Value> loadIndices(OpBuilder &builder, Location loc,
-                                      unsigned rank, Value ind,
-                                      unsigned offsetDim = 0,
-                                      Value offset = Value()) {
-  SmallVector<Value> ivs;
-  ivs.reserve(rank);
-  for (unsigned i = 0; i < rank; i++) {
-    Value idx = constantIndex(builder, loc, i);
-    idx = builder.create<memref::LoadOp>(loc, ind, idx);
-    if (offsetDim == i && offset)
-      idx = builder.create<arith::AddIOp>(loc, idx, offset);
-    ivs.push_back(idx);
-  }
-  return ivs;
-}
-
-/// Inserts a value stored in `elemPtr` into a dense tensor created by
-/// allocDenseTensor().
+/// Loads the value stored in `elemPtr`, and stores it at the coordinates
+/// `cvs` into a dense tensor created by `allocDenseTensor`.
 static void insertScalarIntoDenseTensor(OpBuilder &builder, Location loc,
                                         Value elemPtr, Value tensor,
-                                        ValueRange ivs) {
+                                        ValueRange cvs) {
   Value elemV = builder.create<memref::LoadOp>(loc, elemPtr);
-  builder.create<memref::StoreOp>(loc, elemV, tensor, ivs);
+  builder.create<memref::StoreOp>(loc, elemV, tensor, cvs);
 }
 
 /// Determine if the runtime library supports direct conversion to the
 /// given target `dimTypes`.
 static bool canUseDirectConversion(ArrayRef<DimLevelType> dimTypes) {
   bool alreadyCompressed = false;
   for (const auto dlt : dimTypes) {
     if (isCompressedDLT(dlt)) {
       if (alreadyCompressed)
         return false; // Multiple compressed dimensions not yet supported.
       alreadyCompressed = true;
     } else if (isDenseDLT(dlt)) {
       if (alreadyCompressed)
         return false; // Dense after Compressed not yet supported.
     } else if (isSingletonDLT(dlt)) {
       // Direct conversion doesn't have any particular problems with
       // singleton after compressed.
     } else { // TODO: investigate
       return false;
     }
   }
   return true;
 }
 
-/// Helper method to translate indices during a reshaping operation.
+/// Helper method to translate coordinates during a reshaping operation.
 /// TODO: provide as general utility to MLIR at large?
-static void translateIndices(Location loc, ConversionPatternRewriter &rewriter,
-                             ArrayRef<ReassociationIndices> reassociation,
-                             TensorType dstTp, TensorType srcTp, Value dstIdx,
-                             Value srcIdx, ArrayRef<Value> dstShape,
-                             ArrayRef<Value> srcShape) {
-  const Dimension dstRank = dstTp.getRank();
-  const Dimension srcRank = srcTp.getRank();
-
-  SmallVector<Value> srcIndices;
-  srcIndices.reserve(srcRank);
-  for (Dimension d = 0; d < srcRank; d++) {
-    Value idx = rewriter.create<memref::LoadOp>(
-        loc, srcIdx, constantIndex(rewriter, loc, d));
-    srcIndices.push_back(idx);
-  }
-
-  SmallVector<Value> dstIndices;
-  translateIndicesArray(rewriter, loc, reassociation, srcIndices, srcShape,
-                        dstShape, dstIndices);
-
-  for (Dimension d = 0; d < dstRank; d++)
-    rewriter.create<memref::StoreOp>(loc, dstIndices[d], dstIdx,
-                                     constantIndex(rewriter, loc, d));
+static void reshapeCoords(Location loc, OpBuilder &builder,
+                          ValueRange srcSizes, Value srcCoords,
+                          ValueRange dstSizes, Value dstCoords) {
+  const auto srcCvs = loadAll(builder, loc, srcSizes.size(), srcCoords);
+  SmallVector<Value> dstCvs;
+  reshapeCvs(builder, loc, reassociation, srcSizes, srcCvs, dstSizes, dstCvs);
+  assert(dstCvs.size() == dstSizes.size());
+  storeAll(builder, loc, dstCoords, dstCvs);
 }
 
 /// Generate code for a general sparse to sparse reshaping operation.
 /// Note that unlike dense reshaping (which can be done with a "cheap"
 /// change of view), sparse reshaping is currently done with actual
 /// data shuffling.
 ///
 /// TODO: proportional to nnz, but still a lot of data movement
 ///       https://github.com/llvm/llvm-project/issues/56477
 ///
 ///   iter = src->toCOO();
 ///   coo = newSparseCOO()
 ///   while (elem = iter->getNext()) {
-///     coo->add(reshape(elem.indices), elem.value)
+///     coo->add(reshape(elem.coords), elem.value)
 ///   }
 ///   s = newSparseTensor(coo)
 template <typename ReshapeOp>
 static LogicalResult
 genSparse2SparseReshape(ReshapeOp op, typename ReshapeOp::Adaptor adaptor,
                         ConversionPatternRewriter &rewriter) {
   Location loc = op.getLoc();
   const auto srcTp = getSparseTensorType(op.getSrc());
   const auto dstTp = getSparseTensorType(op.getResult());
   if (!srcTp.hasEncoding() || !dstTp.hasEncoding())
     return failure();
   Type elemTp = srcTp.getElementType();
   assert(elemTp == dstTp.getElementType() &&
          "reshape should not change element type");
-  // Start an iterator over the source tensor (in original index order).
+  // Start an iterator over the source tensor (in coordinate order).
   SmallVector<Value> srcDimSizes =
       getDimSizes(rewriter, loc, srcTp, adaptor.getSrc());
   NewCallParams params(rewriter, loc);
   Value iter = params.genBuffers(srcTp.withoutOrdering(), srcDimSizes)
                    .genNewCall(Action::kToIterator, adaptor.getSrc());
   // Start a new COO for the destination tensor.
   SmallVector<Value> dstDimSizes;
   if (dstTp.hasStaticDimShape())
     // Static "shapes" are in fact "sizes".
     fillDimShape(rewriter, loc, dstTp, dstDimSizes);
   else
     genReshapeDstShape(loc, rewriter, dstDimSizes, srcDimSizes,
                        dstTp.getDimShape(), op.getReassociationIndices());
-  Value coo =
+  const Value coo =
       params.genBuffers(dstTp, dstDimSizes).genNewCall(Action::kEmptyCOO);
-  Value dstPerm = params.getDim2LvlMap();
+  const Value dstPerm = params.getDim2LvlMap();
   // Construct a while loop over the iterator.
-  Type iTp = rewriter.getIndexType();
-  Value srcIdx = genAlloca(rewriter, loc, srcTp.getDimRank(), iTp);
-  Value dstIdx = genAlloca(rewriter, loc, dstTp.getDimRank(), iTp);
-  Value elemPtr = genAllocaScalar(rewriter, loc, elemTp);
-  SmallVector<Value> noArgs;
-  SmallVector<Type> noTypes;
+  const Type iTp = rewriter.getIndexType();
+  const Value srcDimCoords = genAlloca(rewriter, loc, srcTp.getDimRank(), iTp);
+  const Value dstDimCoords = genAlloca(rewriter, loc, dstTp.getDimRank(), iTp);
+  const Value elemPtr = genAllocaScalar(rewriter, loc, elemTp);
+  const SmallVector<Value> noArgs;
+  const SmallVector<Type> noTypes;
   auto whileOp = rewriter.create<scf::WhileOp>(loc, noTypes, noArgs);
   Block *before = rewriter.createBlock(&whileOp.getBefore(), {}, noTypes);
   rewriter.setInsertionPointToEnd(before);
-  Value cond = genGetNextCall(rewriter, loc, iter, srcIdx, elemPtr);
+  Value cond = genGetNextCall(rewriter, loc, iter, srcDimCoords, elemPtr);
   rewriter.create<scf::ConditionOp>(loc, cond, before->getArguments());
-  // Translate indices from source to target and insert. Note that we do
+  // Translate coordinates from source to target and insert. Note that we do
   // not need to store the value in elemPtr, as the value is still there.
   Block *after = rewriter.createBlock(&whileOp.getAfter(), {}, noTypes);
   rewriter.setInsertionPointToStart(after);
-  translateIndices(loc, rewriter, op.getReassociationIndices(), dstTp, srcTp,
-                   dstIdx, srcIdx, dstDimSizes, srcDimSizes);
-  genAddEltCall(rewriter, loc, elemTp, coo, elemPtr, dstIdx, dstPerm);
+  // We probably don't need these assertions, but better safe than sorry.
+  assert(srcTp.getDimRank() == srcDimSizes.size());
+  assert(dstTp.getDimRank() == dstDimSizes.size());
+  reshapeCoords(loc, rewriter, op.getReassociationIndices(), srcDimSizes,
+                srcDimCoords, dstDimSizes, dstDimCoords);
+  genAddEltCall(rewriter, loc, elemTp, coo, elemPtr, dstDimCoords, dstPerm);
   rewriter.create<scf::YieldOp>(loc);
   // Final call to construct sparse tensor storage and free temporary resources.
   rewriter.setInsertionPointAfter(whileOp);
   Value dst = params.genNewCall(Action::kFromCOO, coo);
   genDelCOOCall(rewriter, loc, elemTp, coo);
   genDelIteratorCall(rewriter, loc, elemTp, iter);
   rewriter.replaceOp(op, dst);
   return success();
static void genSparseCOOIterationLoop(
     ConversionPatternRewriter &rewriter, Location loc, Value t,
     SparseTensorType stt,
     function_ref<void(OpBuilder &, Location, Value, Value)> bodyBuilder) {
   assert(stt.hasEncoding() &&
          "Generating Sparse Tensor COO Loop on a Dense Tensor!");
   const Dimension dimRank = stt.getDimRank();
   const Type elemTp = stt.getElementType();
 
-  // Start an iterator over the tensor (in original index order).
+  // Start an iterator over the tensor (in coordinate order).
   const auto noPerm = stt.withoutOrdering();
   SmallVector<Value> dimSizes = getDimSizes(rewriter, loc, noPerm, t);
   Value iter = NewCallParams(rewriter, loc)
                    .genBuffers(noPerm, dimSizes)
                    .genNewCall(Action::kToIterator, t);
 
   // Construct a while loop over the iterator.
-  Value srcIdx = genAlloca(rewriter, loc, dimRank, rewriter.getIndexType());
+  const Type iTp = rewriter.getIndexType();
+  Value srcDimCoords = genAlloca(rewriter, loc, dimRank, iTp);
   Value elemPtr = genAllocaScalar(rewriter, loc, elemTp);
-  SmallVector<Value> noArgs;
-  SmallVector<Type> noTypes;
+  const SmallVector<Value> noArgs;
+  const SmallVector<Type> noTypes;
   auto whileOp = rewriter.create<scf::WhileOp>(loc, noTypes, noArgs);
   Block *before = rewriter.createBlock(&whileOp.getBefore(), {}, noTypes);
   rewriter.setInsertionPointToEnd(before);
-  Value cond = genGetNextCall(rewriter, loc, iter, srcIdx, elemPtr);
+  Value cond = genGetNextCall(rewriter, loc, iter, srcDimCoords, elemPtr);
   rewriter.create<scf::ConditionOp>(loc, cond, before->getArguments());
   Block *after = rewriter.createBlock(&whileOp.getAfter(), {}, noTypes);
   rewriter.setInsertionPointToStart(after);
 
   const bool hasDenseDim =
       llvm::any_of(stt.getEncoding().getDimLevelType(), isDenseDLT);
   if (hasDenseDim) {
     Value elemV = rewriter.create<memref::LoadOp>(loc, elemPtr);
     Value isZero = genIsNonzero(rewriter, loc, elemV);
     scf::IfOp ifOp = rewriter.create<scf::IfOp>(loc, isZero, /*else*/ false);
     rewriter.setInsertionPointToStart(&ifOp.getThenRegion().front());
   }
   // Callback here to build loop body.
-  bodyBuilder(rewriter, loc, srcIdx, elemPtr);
+  bodyBuilder(rewriter, loc, srcDimCoords, elemPtr);
 
   // Exit the scope from the IfOp.
   if (hasDenseDim)
     rewriter.setInsertionPointToEnd(after);
 
   rewriter.create<scf::YieldOp>(loc);
   // Finish generating loop.
   rewriter.setInsertionPointAfter(whileOp);
matchAndRewrite(NewOp op, OpAdaptor adaptor,
     }
     // Use the `reader` to parse the file.
     SmallVector<Value, 8> params{
         reader,
         lvlSizesBuffer,
         genLvlTypesBuffer(rewriter, loc, stt),
         lvl2dimBuffer,
         dim2lvlBuffer,
-        constantPointerTypeEncoding(rewriter, loc, stt.getEncoding()),
-        constantIndexTypeEncoding(rewriter, loc, stt.getEncoding()),
+        constantPosTypeEncoding(rewriter, loc, stt.getEncoding()),
+        constantCrdTypeEncoding(rewriter, loc, stt.getEncoding()),
         valTp};
     Value tensor = createFuncCall(rewriter, loc, "newSparseTensorFromReader",
                                   opaqueTp, params, EmitCInterface::On)
                        .getResult(0);
     // Free the memory for `reader`.
     createFuncCall(rewriter, loc, "delSparseTensorReader", {}, {reader},
                    EmitCInterface::Off);
     rewriter.replaceOp(op, tensor);
if (srcTp.hasEncoding() && dstTp.hasEncoding()) {
             op, params.genBuffers(srcTp.withEncoding(dstEnc), dimSizes)
                     .genNewCall(Action::kSparseToSparse, src));
       } else { // use via-COO conversion.
         // Set up encoding with right mix of src and dst so that the two
         // method calls can share most parameters, while still providing
         // the correct sparsity information to either of them.
         const auto mixedEnc = SparseTensorEncodingAttr::get(
             op->getContext(), dstEnc.getDimLevelType(), dstEnc.getDimOrdering(),
-            dstEnc.getHigherOrdering(), srcEnc.getPointerBitWidth(),
-            srcEnc.getIndexBitWidth());
+            dstEnc.getHigherOrdering(), srcEnc.getPosWidth(),
+            srcEnc.getCrdWidth());
         // TODO: This is the only place where `kToCOO` (or `kToIterator`)
         // is called with a non-identity permutation.  Is there any clean
         // way to push the permutation over to the `kFromCOO` side instead?
         Value coo = params.genBuffers(srcTp.withEncoding(mixedEnc), dimSizes)
                         .genNewCall(Action::kToCOO, src);
         Value dst = params.setTemplateTypes(srcTp.withEncoding(dstEnc))
                         .genNewCall(Action::kFromCOO, coo);
         genDelCOOCall(rewriter, loc, elemTp, coo);
         rewriter.replaceOp(op, dst);
       }
       return success();
     }
     if (srcTp.hasEncoding() && !dstTp.hasEncoding()) {
       const auto srcEnc = srcTp.getEncoding();
       // This is sparse => dense conversion, which is handled as follows:
       //   dst = new Tensor(0);
       //   iter = new SparseTensorIterator(src);
       //   while (elem = iter->getNext()) {
-      //     dst[elem.indices] = elem.value;
+      //     dst[elem.coords] = elem.value;
       //   }
       //   delete iter;
       //
       // Fabricate a no-permutation encoding for NewCallParams
-      // The pointer/index types must be those of `src`.
+      // The position/coordinate types must be those of `src`.
       // The dimLevelTypes aren't actually used by Action::kToIterator.
       const auto dstEnc = SparseTensorEncodingAttr::get(
           op->getContext(),
           SmallVector<DimLevelType>(dimRank, DimLevelType::Dense), AffineMap(),
-          AffineMap(), srcEnc.getPointerBitWidth(), srcEnc.getIndexBitWidth());
+          AffineMap(), srcEnc.getPosWidth(), srcEnc.getCrdWidth());
       SmallVector<Value> dimSizes = getDimSizes(rewriter, loc, srcTp, src);
       Value iter = NewCallParams(rewriter, loc)
                        .genBuffers(dstTp.withEncoding(dstEnc), dimSizes)
                        .genNewCall(Action::kToIterator, src);
-      Value ind = genAlloca(rewriter, loc, dimRank, rewriter.getIndexType());
+      const Type iTp = rewriter.getIndexType();
+      Value dimCoords = genAlloca(rewriter, loc, dimRank, iTp);
       Value elemPtr = genAllocaScalar(rewriter, loc, elemTp);
       Block *insertionBlock = rewriter.getInsertionBlock();
       // TODO: Dense buffers should be allocated/deallocated via the callback
       // in BufferizationOptions.
       Value dst = allocDenseTensor(rewriter, loc, dstTp, dimSizes);
-      SmallVector<Value> noArgs;
-      SmallVector<Type> noTypes;
+      const SmallVector<Value> noArgs;
+      const SmallVector<Type> noTypes;
       auto whileOp = rewriter.create<scf::WhileOp>(loc, noTypes, noArgs);
       Block *before = rewriter.createBlock(&whileOp.getBefore(), {}, noTypes);
       rewriter.setInsertionPointToEnd(before);
-      Value cond = genGetNextCall(rewriter, loc, iter, ind, elemPtr);
+      Value cond = genGetNextCall(rewriter, loc, iter, dimCoords, elemPtr);
       rewriter.create<scf::ConditionOp>(loc, cond, before->getArguments());
       Block *after = rewriter.createBlock(&whileOp.getAfter(), {}, noTypes);
       rewriter.setInsertionPointToStart(after);
-      SmallVector<Value> ivs = loadIndices(rewriter, loc, dimRank, ind);
-      insertScalarIntoDenseTensor(rewriter, loc, elemPtr, dst, ivs);
+      const auto dcvs = loadAll(rewriter, loc, dimRank, dimCoords);
+      insertScalarIntoDenseTensor(rewriter, loc, elemPtr, dst, dcvs);
       rewriter.create<scf::YieldOp>(loc);
       rewriter.setInsertionPointAfter(whileOp);
       genDelIteratorCall(rewriter, loc, elemTp, iter);
       rewriter.replaceOpWithNewOp<bufferization::ToTensorOp>(
           op, dstTp.getRankedTensorType(), dst);
       // Deallocate the buffer.
       if (bufferization::allocationDoesNotEscape(op->getOpResult(0))) {
         rewriter.setInsertionPoint(insertionBlock->getTerminator());
matchAndRewrite(ConvertOp op, OpAdaptor adaptor,
     //     for ik in dimk
     //       val = a[i1,..,ik]
     //       if val != 0
     //         t->add(val, [i1,..,ik], [p1,..,pk])
     //
     // To fill the COO tensor from a sparse constant in COO format:
     //   for i in range(NNZ)
     //     val = values[i]
-    //     [i1,..,ik] = indices[i]
+    //     [i1,..,ik] = coordinates[i]
     //     t->add(val, [i1,..,ik], [p1,..,pk])
     //
     // Note that the dense tensor traversal code is actually implemented
     // using MLIR IR to avoid having to expose too much low-level
     // memref traversal details to the runtime support library.
     // Also note that the code below only generates the "new" ops and
     // the loop-nest per se; whereas the entire body of the innermost
     // loop is generated by genAddElt().
     SmallVector<Value> dimSizes;
     sizesFromSrc(rewriter, dimSizes, loc, src);
     NewCallParams params(rewriter, loc);
     Value coo =
         params.genBuffers(dstTp, dimSizes).genNewCall(Action::kEmptyCOO);
-    Value ind = genAlloca(rewriter, loc, dimRank, rewriter.getIndexType());
+    const Type iTp = rewriter.getIndexType();
+    Value dimCoords = genAlloca(rewriter, loc, dimRank, iTp);
     Value perm = params.getDim2LvlMap();
     Value elemPtr = genAllocaScalar(rewriter, loc, elemTp);
     genDenseTensorOrSparseConstantIterLoop(
         rewriter, loc, src, dimRank,
-        [&](OpBuilder &builder, Location loc, Value val, ValueRange ivs) {
-          for (Dimension d = 0; d < dimRank; d++) {
-            Value dim = constantIndex(builder, loc, d);
-            builder.create<memref::StoreOp>(loc, ivs[d], ind, dim);
-          }
+        [&](OpBuilder &builder, Location loc, Value val, ValueRange dcvs) {
+          assert(dcvs.size() == static_cast<size_t>(dimRank));
+          storeAll(builder, loc, dimCoords, dcvs);
           builder.create<memref::StoreOp>(loc, val, elemPtr);
-          genAddEltCall(builder, loc, elemTp, coo, elemPtr, ind, perm);
+          genAddEltCall(builder, loc, elemTp, coo, elemPtr, dimCoords, perm);
         });
     // Final call to construct sparse tensor storage.
     Value dst = params.genNewCall(Action::kFromCOO, coo);
     genDelCOOCall(rewriter, loc, elemTp, coo);
     rewriter.replaceOp(op, dst);
     return success();
   }
 
matchAndRewrite(bufferization::DeallocTensorOp op, OpAdaptor adaptor,
     StringRef name = "delSparseTensor";
     createFuncCall(rewriter, op->getLoc(), name, {}, adaptor.getOperands(),
                    EmitCInterface::Off);
     rewriter.eraseOp(op);
     return success();
   }
 };
 
-/// Sparse conversion rule for pointer accesses.
-class SparseTensorToPointersConverter
-    : public OpConversionPattern<ToPointersOp> {
+/// Sparse conversion rule for position accesses.
+class SparseTensorToPositionsConverter
+    : public OpConversionPattern<ToPositionsOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
-  matchAndRewrite(ToPointersOp op, OpAdaptor adaptor,
+  matchAndRewrite(ToPositionsOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
-    Type resType = op.getType();
-    Type ptrType = resType.cast<ShapedType>().getElementType();
-    SmallString<16> name{"sparsePointers", overheadTypeFunctionSuffix(ptrType)};
-    Value dim =
-        constantIndex(rewriter, op->getLoc(), op.getDimension().getZExtValue());
-    replaceOpWithFuncCall(rewriter, op, name, resType,
-                          {adaptor.getTensor(), dim}, EmitCInterface::On);
+    Type resTp = op.getType();
+    Type posTp = resTp.cast<ShapedType>().getElementType();
+    SmallString<17> name{"sparsePositions", overheadTypeFunctionSuffix(posTp)};
+    Value lvl = constantIndex(rewriter, op->getLoc(), op.getLevel());
+    replaceOpWithFuncCall(rewriter, op, name, resTp, {adaptor.getTensor(), lvl},
+                          EmitCInterface::On);
     return success();
   }
 };
 
-/// Sparse conversion rule for index accesses.
-class SparseTensorToIndicesConverter : public OpConversionPattern<ToIndicesOp> {
+/// Sparse conversion rule for coordinate accesses.
+class SparseTensorToCoordinatesConverter
+    : public OpConversionPattern<ToCoordinatesOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
-  matchAndRewrite(ToIndicesOp op, OpAdaptor adaptor,
+  matchAndRewrite(ToCoordinatesOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
+    // TODO: use `SparseTensorType::getCrdType` instead.
     Type resType = op.getType();
-    Type indType = resType.cast<ShapedType>().getElementType();
-    SmallString<15> name{"sparseIndices", overheadTypeFunctionSuffix(indType)};
+    const Type crdTp = resType.cast<ShapedType>().getElementType();
+    SmallString<19> name{"sparseCoordinates",
+                         overheadTypeFunctionSuffix(crdTp)};
     Location loc = op->getLoc();
-    Value dim = constantIndex(rewriter, loc, op.getDimension().getZExtValue());
+    Value lvl = constantIndex(rewriter, loc, op.getLevel());
 
     // The function returns a MemRef without a layout.
-    MemRefType callRetType = get1DMemRefType(indType, false);
-    SmallVector<Value> operands{adaptor.getTensor(), dim};
+    MemRefType callRetType = get1DMemRefType(crdTp, false);
+    SmallVector<Value> operands{adaptor.getTensor(), lvl};
     auto fn = getFunc(op->getParentOfType<ModuleOp>(), name, callRetType,
                       operands, EmitCInterface::On);
     Value callRet =
         rewriter.create<func::CallOp>(loc, callRetType, fn, operands)
             .getResult(0);
 
     // Cast the MemRef type to the type expected by the users, though these
     // two types should be compatible at runtime.

 /// Sparse conversion rule for the insertion operator.
 class SparseTensorInsertConverter : public OpConversionPattern<InsertOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(InsertOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     // Note that the current regime only allows for strict lexicographic
-    // index order. All values are passed by reference through stack
+    // coordinate order. All values are passed by reference through stack
     // allocated memrefs.
     Location loc = op->getLoc();
     const auto stt = getSparseTensorType(op.getTensor());
     const auto elemTp = stt.getElementType();
-    const Dimension dimRank = stt.getDimRank();
-    auto mref = genAlloca(rewriter, loc, dimRank, rewriter.getIndexType());
+    const Level lvlRank = stt.getLvlRank();
+    auto lvlCoords = genAlloca(rewriter, loc, lvlRank, rewriter.getIndexType());
     auto vref = genAllocaScalar(rewriter, loc, elemTp);
-    for (Dimension d = 0; d < dimRank; d++)
-      rewriter.create<memref::StoreOp>(loc, adaptor.getIndices()[d], mref,
-                                       constantIndex(rewriter, loc, d));
+    storeAll(rewriter, loc, lvlCoords, adaptor.getLvlCoords());
     rewriter.create<memref::StoreOp>(loc, adaptor.getValue(), vref);
     SmallString<12> name{"lexInsert", primaryTypeFunctionSuffix(elemTp)};
-    createFuncCall(rewriter, loc, name, {}, {adaptor.getTensor(), mref, vref},
-                   EmitCInterface::On);
+    createFuncCall(rewriter, loc, name, {},
+                   {adaptor.getTensor(), lvlCoords, vref}, EmitCInterface::On);
     rewriter.replaceOp(op, adaptor.getTensor());
     return success();
   }
 };
 
 /// Sparse conversion rule for the expand operator.
 class SparseTensorExpandConverter : public OpConversionPattern<ExpandOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(ExpandOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     Location loc = op->getLoc();
     const auto srcTp = getSparseTensorType(op.getTensor());
     Type eltType = srcTp.getElementType();
     Type boolType = rewriter.getIntegerType(1);
     Type idxType = rewriter.getIndexType();
     // All initialization should be done on entry of the loop nest.
     rewriter.setInsertionPointAfter(op.getTensor().getDefiningOp());
     // Get the cardinality of valid coordinates for the innermost level.
     Value sz = createOrFoldLvlCall(rewriter, loc, srcTp, adaptor.getTensor(),
                                    srcTp.getLvlRank() - 1);
-    // Allocate temporary buffers for values, filled-switch, and indices.
+    // Allocate temporary buffers for values, filled-switch, and coordinates.
     // We do not use stack buffers for this, since the expanded size may
     // be rather large (as it envelops a single expanded dense dimension).
     Value values = genAlloc(rewriter, loc, sz, eltType);
     Value filled = genAlloc(rewriter, loc, sz, boolType);
-    Value indices = genAlloc(rewriter, loc, sz, idxType);
+    Value lastLvlCoordinates = genAlloc(rewriter, loc, sz, idxType);
     Value zero = constantZero(rewriter, loc, idxType);
     // Reset the values/filled-switch to all-zero/false. Note that this
     // introduces an O(N) operation into the computation, but this reset
     // operation is amortized over the innermost loops for the access
     // pattern expansion. As noted in the operation doc, we would like
     // to amortize this setup cost even between kernels.
     rewriter.create<linalg::FillOp>(
         loc, ValueRange{constantZero(rewriter, loc, eltType)},
         ValueRange{values});
     rewriter.create<linalg::FillOp>(
         loc, ValueRange{constantZero(rewriter, loc, boolType)},
         ValueRange{filled});
-    // Replace expansion op with these buffers and initial index.
+    // Replace expansion op with these buffers and initial coordinate.
     assert(op.getNumResults() == 4);
-    rewriter.replaceOp(op, {values, filled, indices, zero});
+    rewriter.replaceOp(op, {values, filled, lastLvlCoordinates, zero});
     return success();
   }
 };
 
 /// Sparse conversion rule for the compress operator.
 class SparseTensorCompressConverter : public OpConversionPattern<CompressOp> {
 public:
   using OpConversionPattern::OpConversionPattern;
   LogicalResult
   matchAndRewrite(CompressOp op, OpAdaptor adaptor,
                   ConversionPatternRewriter &rewriter) const override {
     Location loc = op->getLoc();
     // Note that this method call resets the values/filled-switch back to
     // all-zero/false by only iterating over the set elements, so the
     // complexity remains proportional to the sparsity of the expanded
     // access pattern.
     Value values = adaptor.getValues();
     Value filled = adaptor.getFilled();
     Value added = adaptor.getAdded();
     Value count = adaptor.getCount();
     Value tensor = adaptor.getTensor();
     const auto stt = getSparseTensorType(op.getTensor());
     const Type elemTp = stt.getElementType();
-    const Dimension dimRank = stt.getDimRank();
-    auto mref = genAlloca(rewriter, loc, dimRank, rewriter.getIndexType());
-    for (Dimension d = 0; d < dimRank - 1; d++)
-      rewriter.create<memref::StoreOp>(loc, adaptor.getIndices()[d], mref,
-                                       constantIndex(rewriter, loc, d));
+    const Level lvlRank = stt.getLvlRank();
+    auto lvlCoords = genAlloca(rewriter, loc, lvlRank, rewriter.getIndexType());
+    storeAll(rewriter, loc, lvlCoords, adaptor.getLvlCoords());
     SmallString<12> name{"expInsert", primaryTypeFunctionSuffix(elemTp)};
     createFuncCall(rewriter, loc, name, {},
-                   {tensor, mref, values, filled, added, count},
+                   {tensor, lvlCoords, values, filled, added, count},
                    EmitCInterface::On);
     rewriter.replaceOp(op, adaptor.getTensor());
     // Deallocate the buffers on exit of the loop nest.
     Operation *parent = getTop(op);
     rewriter.setInsertionPointAfter(parent);
     rewriter.create<memref::DeallocOp>(loc, values);
     rewriter.create<memref::DeallocOp>(loc, filled);
     rewriter.create<memref::DeallocOp>(loc, added);
matchAndRewrite(ConcatenateOp op, OpAdaptor adaptor,
     // (1). When output is sparse and not all dims are dense, and mix of inputs:
     //    a_sparse = concat (b_dense, c_sparse, ....)
     // =>
     //    coo_for_a = newSparseCOO(shapeOf(a))
     //    for i, j, k // dense input
     //      coo->add(adjustForOffset(i,j,k), b[i,j,k])
     //
     //    for elem in sparse_input
-    //      coo->add(adjustForOffset(elem.indices), elem.value)
+    //      coo->add(adjustForOffset(elem.coords), elem.value)
     //    ...
     //    a = newSparseTensor(coo_for_a)
     //    return a
     //
     // (2). When output is dense or annotated all dense, and mix of inputs:
     //    a_dense = concat (b_dense, c_sparse, ....)
     // =>
     //    a = malloc(shapeOf(a)) or newSparseAllDense(shapeOf(a))
     //    for i, j, k // dense input
     //      a[ adjustForOffset(i,j,k) ] = b[i,j,k]
     //
     //    for elem in sparse_input
-    //      a[ adjustForOffset(elem.indices) ] = elem.value
+    //      a[ adjustForOffset(elem.coords) ] = elem.value
     //    return a
     Location loc = op.getLoc();
     const auto dstTp = getSparseTensorType(op);
     const auto dstEnc = dstTp.getEncoding();
     const Type elemTp = dstTp.getElementType();
-    const Dimension concatDim = op.getDimension().getZExtValue();
+    const Dimension concatDim = op.getDimension();
     const Dimension dimRank = dstTp.getDimRank();
 
     Value dst;     // destination tensor
     Value dstPerm; // destination tensor permutation (if sparse out)
     // A pointer to the value being inserted (if dense => sparse)
     Value elemPtr;
-    // Memory that holds the dim-indices for destination tensor (if sparse out)
-    Value dstInd;
-    // The offset applied to the dimenstion to be concated (starting from 0)
+    // Memory that holds the dim-coords for destination tensor (if sparse out)
+    Value dstDimCoords;
+    // The offset applied to the dimension to be concated (starting from 0)
     Value offset = constantIndex(rewriter, loc, 0);
 
     SmallVector<Value> dimSizes;
     concatDimSizesFromInputs(rewriter, loc, dstTp, op.getInputs(), concatDim,
                              dimSizes);
 
     NewCallParams params(rewriter, loc);
     const bool allDense = dstTp.hasEncoding() && dstTp.isAllDense();
     Value dstTensor;
     if (dstTp.hasEncoding()) {
       // Start a new COO or an initialized annotated all dense sparse tensor.
       dst = params.genBuffers(dstTp, dimSizes)
                 .genNewCall(allDense ? Action::kEmpty : Action::kEmptyCOO);
-      dstInd = genAlloca(rewriter, loc, dimRank, rewriter.getIndexType());
+      dstDimCoords = genAlloca(rewriter, loc, dimRank, rewriter.getIndexType());
       if (allDense) {
         dstTensor = dst;
         // Get the values buffer for the sparse tensor and reshape it to the
         // corresponding dense tensor shape.
         dst = genValuesCall(rewriter, loc,
                             MemRefType::get({ShapedType::kDynamic}, elemTp),
                             {dst});
-        // Use the dstInd to store the level sizes.
-        dst =
-            reshapeValuesToLevels(rewriter, loc, dstEnc, dimSizes, dst, dstInd);
+        // Pass the `dstDimCoords` buffer for `reshapeValuesToLevels`
+        // to reuse for storing level-sizes (yes, "level-sizes").
+        // This is safe to do because `dstTp` is a dense-tensor type,
+        // and therefore lvlRank == dimRank.
+        dst = reshapeValuesToLevels(rewriter, loc, dstEnc, dimSizes, dst,
+                                    dstDimCoords);
       } else {
         dstPerm = params.getDim2LvlMap();
         elemPtr = genAllocaScalar(rewriter, loc, elemTp);
       }
     } else {
       // TODO: Dense buffers should be allocated/deallocated via the callback
       // in BufferizationOptions.
       dst = allocDenseTensor(rewriter, loc, dstTp, dimSizes);
     }
     const Level lvlRank = dstTp.getLvlRank();
-    const auto dimIvs2LvlIvs = [&](ValueRange dimIvs) -> SmallVector<Value> {
-      SmallVector<Value> lvlIvs;
-      lvlIvs.reserve(lvlRank);
+    const auto dcvs2lcvs = [&](ValueRange dcvs) -> SmallVector<Value> {
+      SmallVector<Value> lcvs;
+      lcvs.reserve(lvlRank);
       for (Level l = 0; l < lvlRank; l++)
         // FIXME: `toOrigDim` is deprecated
-        lvlIvs.push_back(dimIvs[toOrigDim(dstEnc, l)]);
-      return lvlIvs;
+        lcvs.push_back(dcvs[toOrigDim(dstEnc, l)]);
+      return lcvs;
     };
     for (const auto &it : llvm::zip(op.getInputs(), adaptor.getInputs())) {
       Value orignalOp = std::get<0>(it); // Input (with encoding) from Op
       Value adaptedOp = std::get<1>(it); // Input (type converted) from adaptor
       const auto srcTp = getSparseTensorType(orignalOp);
       if (srcTp.hasEncoding()) {
         genSparseCOOIterationLoop(
             rewriter, loc, adaptedOp, srcTp,
-            [&](OpBuilder &builder, Location loc, Value idx,
+            [&](OpBuilder &builder, Location loc, Value dimCoords,
                 Value elemPtr) -> void {
-              SmallVector<Value> dimIvs =
-                  loadIndices(builder, loc, dimRank, idx, concatDim, offset);
+              const auto dcvs =
+                  loadAll(builder, loc, dimRank, dimCoords, concatDim, offset);
               if (dstTp.hasEncoding() && !allDense) {
                 // Case: sparse => sparse, except for annotated all dense.
-                storeIndices(builder, loc, dimRank, dstInd, dimIvs);
-                genAddEltCall(builder, loc, elemTp, dst, elemPtr, dstInd,
+                storeAll(builder, loc, dstDimCoords, dcvs);
+                genAddEltCall(builder, loc, elemTp, dst, elemPtr, dstDimCoords,
                               dstPerm);
               } else {
                 // Case: sparse => dense, or annotated all dense.
-                const auto lvlIvs = allDense ? dimIvs2LvlIvs(dimIvs) : dimIvs;
-                insertScalarIntoDenseTensor(builder, loc, elemPtr, dst, lvlIvs);
+                const auto lcvs = allDense ? dcvs2lcvs(dcvs) : dcvs;
+                insertScalarIntoDenseTensor(builder, loc, elemPtr, dst, lcvs);
               }
             });
       } else {
         genDenseTensorIterationLoop(
             rewriter, loc, adaptedOp, srcTp,
-            [&](OpBuilder &builder, Location loc, ValueRange dimIvs) -> void {
+            [&](OpBuilder &builder, Location loc, ValueRange dcvs) -> void {
               if (dstTp.hasEncoding() && !allDense) {
                 // Case: dense => sparse, except for annotated all dense.
-                storeIndices(builder, loc, dimRank, dstInd, dimIvs, concatDim,
-                             offset);
-                Value val = genValueForDense(builder, loc, adaptedOp, dimIvs);
+                assert(dcvs.size() == static_cast<size_t>(dimRank));
+                storeAll(builder, loc, dstDimCoords, dcvs, concatDim, offset);
+                Value val = genValueForDense(builder, loc, adaptedOp, dcvs);
                 builder.create<memref::StoreOp>(loc, val, elemPtr);
-                genAddEltCall(builder, loc, elemTp, dst, elemPtr, dstInd,
+                genAddEltCall(builder, loc, elemTp, dst, elemPtr, dstDimCoords,
                               dstPerm);
               } else {
                 // Case: dense => dense, or annotated all dense.
-                Value val = genValueForDense(builder, loc, adaptedOp, dimIvs);
-                // Despite the name, this isn't actually level-ivs until
-                // after the `dimIvs2LvlIvs` call.
-                SmallVector<Value> lvlIvs(dimIvs);
+                Value val = genValueForDense(builder, loc, adaptedOp, dcvs);
+                // Despite the name, this isn't actually level-cvs until
+                // after the `dcvs2lcvs` call.
+                SmallVector<Value> lcvs(dcvs);
                 // Apply offset.
-                lvlIvs[concatDim] = builder.create<arith::AddIOp>(
-                    loc, lvlIvs[concatDim], offset);
+                lcvs[concatDim] =
+                    builder.create<arith::AddIOp>(loc, lcvs[concatDim], offset);
                 if (allDense)
-                  lvlIvs = dimIvs2LvlIvs(lvlIvs);
-                builder.create<memref::StoreOp>(loc, val, dst, lvlIvs);
+                  lcvs = dcvs2lcvs(lcvs);
+                builder.create<memref::StoreOp>(loc, val, dst, lcvs);
               }
             });
       }
       // Accumulate offset.
       // TODO: avoid calling sparseDimSize multiple times by caching the result!
       Value curDim =
           createOrFoldDimCall(rewriter, loc, srcTp, adaptedOp, concatDim);
       offset = rewriter.create<arith::AddIOp>(loc, offset, curDim);
matchAndRewrite(OutOp op, OpAdaptor adaptor,
     const Location loc = op->getLoc();
     const auto srcTp = getSparseTensorType(op.getTensor());
     // Convert to default permuted COO.
     Value src = adaptor.getOperands()[0];
     SmallVector<Value> dimSizes = getDimSizes(rewriter, loc, srcTp, src);
     Value coo = NewCallParams(rewriter, loc)
                     .genBuffers(srcTp.withoutOrdering(), dimSizes)
                     .genNewCall(Action::kToCOO, src);
-    // Then output the tensor to external file with indices in the externally
-    // visible lexicographic index order. A sort is required if the source was
-    // not in that order yet (note that the sort can be dropped altogether if
-    // external format does not care about the order at all, but here we assume
-    // it does).
+    // Then output the tensor to external file with coordinates in the
+    // externally visible lexicographic coordinate order.  A sort is
+    // required if the source was not in that order yet (note that the
+    // sort can be dropped altogether if external format does not care
+    // about the order at all, but here we assume it does).
     const Value sort = constantI1(rewriter, loc, !srcTp.isIdentity());
     SmallVector<Value, 3> outParams{coo, adaptor.getOperands()[1], sort};
     const Type elemTp = srcTp.getElementType();
     SmallString<18> name{"outSparseTensor", primaryTypeFunctionSuffix(elemTp)};
     createFuncCall(rewriter, loc, name, {}, outParams, EmitCInterface::Off);
     genDelCOOCall(rewriter, loc, elemTp, coo);
     rewriter.eraseOp(op);
     return success();

 // Public method for populating conversion rules.
 //===----------------------------------------------------------------------===//
 
 /// Populates the given patterns list with conversion rules required for
 /// the sparsification of linear algebra operations.
 void mlir::populateSparseTensorConversionPatterns(
     TypeConverter &typeConverter, RewritePatternSet &patterns,
     const SparseTensorConversionOptions &options) {
-  patterns.add<SparseReturnConverter, SparseTensorToDimSizeConverter,
-               SparseCastConverter, SparseTensorNewConverter,
-               SparseReshapeConverter<tensor::ExpandShapeOp>,
-               SparseReshapeConverter<tensor::CollapseShapeOp>,
-               SparseTensorConcatConverter, SparseTensorAllocConverter,
-               SparseTensorDeallocConverter, SparseTensorToPointersConverter,
-               SparseTensorToIndicesConverter, SparseTensorToValuesConverter,
-               SparseNumberOfEntriesConverter, SparseTensorLoadConverter,
-               SparseTensorInsertConverter, SparseTensorExpandConverter,
-               SparseTensorCompressConverter, SparseTensorOutConverter>(
+  patterns
+      .add<SparseReturnConverter, SparseTensorToDimSizeConverter,
+           SparseTensorDeallocConverter, SparseTensorToPositionsConverter,
+           SparseTensorToCoordinatesConverter, SparseTensorToValuesConverter,
           typeConverter, patterns.getContext());
 
   patterns.add<SparseTensorConvertConverter>(typeConverter,
                                              patterns.getContext(), options);
 }

mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorRewriting.cpp

static LogicalResult genForeachOnSparseConstant(ForeachOp op,
                                                 RewriterBase &rewriter,
                                                 SparseElementsAttr attr) {
   auto loc = op.getLoc();
   SmallVector<Value> reduc = op.getInitArgs();
 
   // Foreach on constant.
   foreachInSparseConstant(
       loc, rewriter, attr, op.getOrder().value_or(AffineMap()),
-      [&reduc, &rewriter, op](ArrayRef<Value> coords, Value v) mutable {
+      [&reduc, &rewriter, op](ArrayRef<Value> cvs, Value v) mutable {
         SmallVector<Value> args;
-        args.append(coords.begin(), coords.end());
+        args.append(cvs.begin(), cvs.end());
         args.push_back(v);
         args.append(reduc);
         // Clones the foreach op to get a copy of the loop body.
         auto cloned = cast<ForeachOp>(rewriter.clone(*op.getOperation()));
         assert(args.size() == cloned.getBody()->getNumArguments());
         Operation *yield = cloned.getBody()->getTerminator();
         rewriter.inlineBlockBefore(cloned.getBody(), op, args);
         // clean up
if (dstTp.hasStaticShape()) {
         if (d.value() == ShapedType::kDynamic)
           dstDynSizes.push_back(dstSizes[d.index()]);
       }
     }
 
     // Implement the sparse2sparse reshape as follows:
     //   %tmp = bufferization.alloc_tensor : unordered COO
     //   foreach srcCoords %srcTensor
-    //     insert translateIndicesArray(srcCoords), %tmp
+    //     insert reshapeCvs(srcCoords), %tmp
     //   %t = sparse_tensor.cast %tmp
     Value nnz = rewriter.create<NumberOfEntriesOp>(loc, srcTensor);
     RankedTensorType cooTp = getUnorderedCOOFromType(dstTp);
     Value cooBuffer =
         rewriter
             .create<AllocTensorOp>(loc, cooTp, dstDynSizes, Value(),
                                    /*sizeHint=*/nnz, Attribute())
             .getResult();
 
     ForeachOp foreachOp = rewriter.create<ForeachOp>(
         loc, srcTensor, cooBuffer,
-        [&](OpBuilder &builder, Location loc, ValueRange args, Value v,
+        [&](OpBuilder &builder, Location loc, ValueRange srcLcvs, Value v,
             ValueRange reduc) {
-          SmallVector<Value> srcIndices;
-          SmallVector<Value> dstIndices;
-          for (Dimension d = 0, dimRank = srcTp.getRank(); d < dimRank; d++) {
+          const Dimension dimRank = srcTp.getRank();
+          SmallVector<Value> srcDcvs;
+          srcDcvs.reserve(dimRank);
+          for (Dimension d = 0; d < dimRank; d++) {
             // FIXME: `toStoredDim` is deprecated
             Level lvl = toStoredDim(encSrc, d);
-            srcIndices.push_back(args[lvl]);
+            srcDcvs.push_back(srcLcvs[lvl]);
           }
-          translateIndicesArray(builder, loc, op.getReassociationIndices(),
-                                srcIndices, srcSizes, dstSizes, dstIndices);
-          auto t = builder.create<InsertOp>(loc, v, reduc.front(), dstIndices);
+          SmallVector<Value> dstDcvs;
+          reshapeCvs(builder, loc, op.getReassociationIndices(), srcSizes,
+                     srcDcvs, dstSizes, dstDcvs);
+          auto t = builder.create<InsertOp>(loc, v, reduc.front(), dstDcvs);
           builder.create<sparse_tensor::YieldOp>(loc, t);
         });
     auto t = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
     auto converted = rewriter.create<ConvertOp>(loc, dstTp, t).getResult();
     rewriter.create<DeallocTensorOp>(loc, t);
     rewriter.replaceOp(op, converted);
     return success();
   }

 
 struct ConcatenateRewriter : public OpRewritePattern<ConcatenateOp> {
   using OpRewritePattern::OpRewritePattern;
   LogicalResult matchAndRewrite(ConcatenateOp op,
                                 PatternRewriter &rewriter) const override {
     const Location loc = op.getLoc();
     const auto dstTp = getSparseTensorType(op);
     const Dimension dimRank = dstTp.getDimRank();
-    const Dimension conDim = op.getDimension().getZExtValue();
+    const Dimension conDim = op.getDimension();
     SmallVector<Value> sizes;
     concatSizesFromInputs(rewriter, sizes, loc, dstTp, op.getInputs(), conDim);
 
     // %t = concatenate %s1, %s2, %s3 {dim = 1}
     // ==>
     // if (isSparseDst)
     //   if (allDense)
     //     %tmp = bufferization.alloc_tensor dstTp
if (dstTp.hasEncoding()) {
       encDst = needTmpCOO ? getSparseTensorEncoding(tp) : encDst;
       SmallVector<Value> dynSizes;
       getDynamicSizes(dstTp, sizes, dynSizes);
       dst = rewriter.create<AllocTensorOp>(loc, tp, dynSizes).getResult();
       if (allDense) {
         // Create a view of the values buffer to match the unannotated dense
         // tensor.
         Value valuesBuffer = genToValues(rewriter, loc, dst);
-        Value idxBuffer =
+        Value dimCoords =
             genAlloca(rewriter, loc, dimRank, rewriter.getIndexType(),
                       /*staticShape=*/true);
         annotatedDenseDst = dst;
         dst = reshapeValuesToLevels(rewriter, loc, encDst, sizes, valuesBuffer,
-                                    idxBuffer);
+                                    dimCoords);
       }
     } else {
       // TODO: Dense buffers should be allocated/deallocated via the callback
       // in BufferizationOptions.
       dst = allocDenseTensor(rewriter, loc, dstTp, sizes);
     }
 
     Value offset = constantIndex(rewriter, loc, 0);
     SmallVector<Value> initArgs;
     if (encDst && !allDense)
       initArgs.push_back(dst);
     ForeachOp foreachOp;
     for (Value input : op.getInputs()) {
       // Build a for op for each input tensor to append new values into the
       // output tensor.
       foreachOp = rewriter.create<ForeachOp>(
           loc, input, initArgs,
-          [&](OpBuilder &builder, Location loc, ValueRange args, Value v,
+          [&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
               ValueRange reduc) {
-            SmallVector<Value> indices(dstTp.getLvlRank());
+            SmallVector<Value> dstLcvs(dstTp.getLvlRank());
             for (Dimension d = 0; d < dimRank; d++) {
-              Value idx = args[d];
+              Value crd = dcvs[d];
               if (d == conDim)
                 // Transform coordinates for the concatenating dim.
-                idx = builder.create<arith::AddIOp>(loc, idx, offset);
+                crd = builder.create<arith::AddIOp>(loc, crd, offset);
               // FIXME: `toStoredDim` is deprecated
-              indices[toStoredDim(encDst, d)] = idx;
+              dstLcvs[toStoredDim(encDst, d)] = crd;
             }
             if (encDst && !allDense) {
               Value cond = genIsNonzero(rewriter, loc, v);
               scf::IfOp ifOp = builder.create<scf::IfOp>(
                   loc, TypeRange(reduc.front().getType()), cond, /*else*/ true);
               builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
               Value t =
-                  builder.create<InsertOp>(loc, v, reduc.front(), indices);
+                  builder.create<InsertOp>(loc, v, reduc.front(), dstLcvs);
               rewriter.create<scf::YieldOp>(loc, t);
               rewriter.setInsertionPointToStart(&ifOp.getElseRegion().front());
               rewriter.create<scf::YieldOp>(loc, reduc.front());
               rewriter.setInsertionPointAfter(ifOp);
               rewriter.create<sparse_tensor::YieldOp>(loc, ifOp.getResult(0));
             } else {
-              builder.create<memref::StoreOp>(loc, v, dst, indices);
+              builder.create<memref::StoreOp>(loc, v, dst, dstLcvs);
               builder.create<sparse_tensor::YieldOp>(loc);
             }
           });
       // Accumulates the offset. Note that only static-shaped inputs are allowed
       // by concatenate op verifier, which saves us from computing the offset
       // dynamically.
       const auto sh = getSparseTensorType(input).getStaticDimSize(conDim);
       assert(sh.has_value());
private:
   //     for ik in dimk
   //       val = a[i1,..,ik]
   //       if val != 0
   //         t->add(val, [i1,..,ik], [p1,..,pk])
   //
   // To fill the COO tensor from a sparse constant in COO format:
   //   for i in range(NNZ)
   //     val = values[i]
-  //     [i1,..,ik] = indices[i]
+  //     [i1,..,ik] = coordinates[i]
   //     t->add(val, [i1,..,ik], [p1,..,pk])
   LogicalResult dense2SparseRewrite(ConvertOp op,
                                     PatternRewriter &rewriter) const {
     Location loc = op.getLoc();
     Value src = op.getSource();
     const auto dstTp = getSparseTensorType(op);
     SmallVector<Value> sizes;
     sizesFromSrc(rewriter, sizes, loc, src);
LogicalResult dense2SparseRewrite(ConvertOp op,
     const RankedTensorType bufferTp = dstTp.isIdentity()
                                           ? dstTp.getRankedTensorType()
                                           : getUnorderedCOOFromTypeWithOrdering(
                                                 dstTp, dstTp.getDimToLvlMap());
     auto buffer =
         rewriter.create<AllocTensorOp>(loc, bufferTp, dynSizes).getResult();
     auto foreachOp = rewriter.create<ForeachOp>(
         loc, src, buffer,
-        [&](OpBuilder &builder, Location loc, ValueRange indices, Value v,
+        [&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
             ValueRange reduc) {
           Value input = reduc.front();
           const Dimension dimRank = dstTp.getDimRank();
-          SmallVector<Value> indicesArray(dimRank);
+          const Level lvlRank = dstTp.getLvlRank();
+          SmallVector<Value> lcvs(lvlRank);
           for (Dimension d = 0; d < dimRank; d++)
             // FIXME: `toStoredDim` is deprecated
-            indicesArray[toStoredDim(encDst, d)] = indices[d];
+            lcvs[toStoredDim(encDst, d)] = dcvs[d];
           if (fromSparseConst) {
-            input = builder.create<InsertOp>(loc, v, input, indicesArray);
+            input = builder.create<InsertOp>(loc, v, input, lcvs);
           } else {
             Value cond = genIsNonzero(builder, loc, v);
             auto ifOp = builder.create<scf::IfOp>(
                 loc, TypeRange(input.getType()), cond, /*else*/ true);
             builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
-            Value insert =
-                builder.create<InsertOp>(loc, v, input, indicesArray);
+            Value insert = builder.create<InsertOp>(loc, v, input, lcvs);
             builder.create<scf::YieldOp>(loc, insert);
             builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
             builder.create<scf::YieldOp>(loc, input);
             builder.setInsertionPointAfter(ifOp);
             input = ifOp.getResult(0);
           }
           builder.create<sparse_tensor::YieldOp>(loc, input);
         });
     rewriter.setInsertionPointAfter(op);
     src = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
     if (bufferTp != dstTp) {
       rewriter.replaceOpWithNewOp<ConvertOp>(op, dstTp.getRankedTensorType(),
                                              src);
       rewriter.create<DeallocTensorOp>(loc, src);
     } else {
       rewriter.replaceOp(op, src);
     }
 
     return success();
   }
 
   // Handles sparse tensor to dense tensor conversion as follows:
   //   dst = new dense tensor;
   //   foreach elemment in src
-  //     dst[elemment.indices] = element.value
+  //     dst[element.coords] = element.value
   LogicalResult sparse2DenseRewrite(ConvertOp op,
                                     PatternRewriter &rewriter) const {
     Location loc = op->getLoc();
     RankedTensorType dstTp = getRankedTensorType(op);
     Value src = op.getSource();
     RankedTensorType srcTp = getRankedTensorType(src);
 
     SmallVector<Value> sizes;
if (const SparseTensorType srcTp(srcRTT);
       // Ensure that mutating `srcRTT` didn't invalidate `dimRank`.
       assert(static_cast<Dimension>(srcRTT.getRank()) == dimRank);
       tmpCoo = rewriter
                    .create<AllocTensorOp>(loc, srcRTT, dynSrcSizes, Value(),
                                           /*sizeHint=*/nnz, Attribute())
                    .getResult();
       auto foreachOp = rewriter.create<ForeachOp>(
           loc, src, tmpCoo,
-          [&](OpBuilder &builder, Location loc, ValueRange args, Value v,
+          [&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
               ValueRange reduc) {
-            SmallVector<Value> dstIndices(dstLvlRank);
+            SmallVector<Value> dstLcvs(dstLvlRank);
             for (Dimension d = 0; d < dimRank; d++) {
               // FIXME: `toStoredDim` is deprecated
               Level l = toStoredDim(encDst, d);
-              dstIndices[l] = args[d];
+              dstLcvs[l] = dcvs[d];
             }
-            auto t =
-                builder.create<InsertOp>(loc, v, reduc.front(), dstIndices);
+            auto t = builder.create<InsertOp>(loc, v, reduc.front(), dstLcvs);
             builder.create<sparse_tensor::YieldOp>(loc, t);
           });
       src = rewriter.create<LoadOp>(loc, foreachOp.getResult(0), true);
     }
 
     // Now that the conditional is done, we can use `SparseTensorType`.
     const SparseTensorType srcTp(srcRTT);
 
     // Only need to sort if the srcTp is not already sorted (we faithfully take
     // the guarantee from the sparse tensor encoding).
     if (!srcTp.isAllOrdered()) {
       // Retrieve the values-array.
       Value y = genToValues(rewriter, loc, src);
       const auto encSrc = srcTp.getEncoding();
       // Sort the COO tensor so that its elements are ordered via increasing
-      // indices for the storage ordering of the dst tensor. Use SortCoo if the
-      // COO tensor has the same dim ordering as the dst tensor.
+      // coordinates for the storage ordering of the dst tensor.  Use SortCoo
+      // if the COO tensor has the same ordering as the dst tensor.
       if (dimRank > 1 && srcTp.hasSameDimToLvlMap(dstTp)) {
-        MemRefType indTp =
-            get1DMemRefType(getIndexOverheadType(rewriter, encSrc),
-                            /*withLayout=*/false);
-        Value xs = rewriter.create<ToIndicesBufferOp>(loc, indTp, src);
+        MemRefType coordsTp =
+            get1DMemRefType(encSrc.getCrdType(), /*withLayout=*/false);
+        Value xs = rewriter.create<ToCoordinatesBufferOp>(loc, coordsTp, src);
         rewriter.create<SortCooOp>(
             loc, nnz, xs, ValueRange{y}, rewriter.getIndexAttr(dimRank),
             rewriter.getIndexAttr(0), SparseTensorSortKind::HybridQuickSort);
       } else {
-        // Gather the indices-arrays in the dst tensor storage order.
+        // Gather the coordinates-arrays in the dst tensor storage order.
         SmallVector<Value> xs(dstLvlRank);
         const Level srcLvlRank = srcTp.getLvlRank();
         for (Level srcLvl = 0; srcLvl < srcLvlRank; srcLvl++) {
           // FIXME: `toOrigDim` is deprecated
           Dimension dim = toOrigDim(encSrc, srcLvl);
           // FIXME: `toStoredDim` is deprecated
           Level dstLvl = toStoredDim(encDst, dim);
-          xs[dstLvl] = genToIndices(rewriter, loc, src, srcLvl, /*cooStart=*/0);
+          xs[dstLvl] =
+              genToCoordinates(rewriter, loc, src, srcLvl, /*cooStart=*/0);
         }
         rewriter.create<SortOp>(loc, nnz, xs, ValueRange{y},
                                 SparseTensorSortKind::HybridQuickSort);
       }
     }
 
     // For each element in the COO tensor, insert the element to the dst tensor.
     SmallVector<Value> dynDstSizes;
     getDynamicSizes(dstTp, srcSizes, dynDstSizes);
     Value dst = rewriter
                     .create<AllocTensorOp>(loc, dstTp.getRankedTensorType(),
                                            dynDstSizes, Value(),
                                            /*sizeHint=*/nnz, Attribute())
                     .getResult();
-    SmallVector<Value> indices(dstLvlRank);
+    SmallVector<Value> dstLcvs(dstLvlRank);
     auto foreachOp = rewriter.create<ForeachOp>(
         loc, src, dst,
-        [&](OpBuilder &builder, Location loc, ValueRange args, Value v,
+        [&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
             ValueRange reduc) {
           for (Dimension d = 0; d < dimRank; d++) {
             // FIXME: `toStoredDim` is deprecated
             Level l = toStoredDim(encDst, d);
-            indices[l] = args[d];
+            dstLcvs[l] = dcvs[d];
           }
-          auto t = builder.create<InsertOp>(loc, v, reduc.front(), indices);
+          auto t = builder.create<InsertOp>(loc, v, reduc.front(), dstLcvs);
           builder.create<sparse_tensor::YieldOp>(loc, t);
         });
 
     // Release the temporary COO if it is created. Note that tmpCoo is
     // invalidated due to foreach and updated to src.
     if (tmpCoo)
       rewriter.create<DeallocTensorOp>(loc, src);
 
public:
   LogicalResult matchAndRewrite(ForeachOp op,
                                 PatternRewriter &rewriter) const override {
 
     auto loc = op.getLoc();
     Value input = op.getTensor();
     SmallVector<Value> reduc = op.getInitArgs();
     const auto stt = getSparseTensorType(input);
     const Dimension dimRank = stt.getDimRank();
+    const Level lvlRank = stt.getLvlRank();
 
     // Special-case: for each over a sparse constant uses its own rewriting
     // rule.
     if (auto constOp = input.getDefiningOp<arith::ConstantOp>()) {
       if (auto attr = constOp.getValue().dyn_cast<SparseElementsAttr>()) {
         return genForeachOnSparseConstant(op, rewriter, attr);
       }
     }
 
     // Otherwise, use loop emitter to generate loops.
     const auto enc = stt.getEncoding();
 
     // 1. Generates loop for the sparse input.
     LoopEmitter loopEmitter(
         ValueRange{input},
         StringAttr::get(getContext(), ForeachOp::getOperationName()));
     loopEmitter.initializeLoopEmit(rewriter, loc);
     for (Dimension d = 0; d < dimRank; d++) {
       // TODO: provide utility function for loop sequences that only contains
       // one for loop?
-      Dimension ld =
-          op.getOrder()
-              ? op.getOrder()->getResult(d).cast<AffineDimExpr>().getPosition()
-              : d;
-      loopEmitter.enterNewLoopSeq(rewriter, loc, 0, static_cast<size_t>(ld));
+      const Level l = op.getOrder() ? op.getOrder()->getDimPosition(d) : d;
+      loopEmitter.enterNewLoopSeq(rewriter, loc, 0, static_cast<size_t>(l));
       // Note that reduc will be taken care of by loop emitter and get updated
       // in place.
 
-      loopEmitter.enterLoopOverTensorAtDim(rewriter, loc, 0, ld, reduc);
+      loopEmitter.enterLoopOverTensorAtDim(rewriter, loc, 0, l, reduc);
     }
 
-    SmallVector<Value> coords;
-    coords.reserve(dimRank);
-    loopEmitter.getCoordinateArray(coords);
+    SmallVector<Value> lcvs;
+    lcvs.reserve(lvlRank);
+    loopEmitter.getCoordinateArray(lcvs);
 
     if (op.getOrder()) {
-      SmallVector<Value> tmp = coords; // keep a copy
+      // FIXME: There is some dim/lvl confusion here since `dimRank != lvlRank`
+      SmallVector<Value> dcvs = lcvs; // keep a copy
       for (Dimension d = 0; d < dimRank; d++) {
         auto l = op.getOrder()->getDimPosition(d);
-        coords[l] = tmp[d];
+        lcvs[l] = dcvs[d];
       }
     }
     Value vals = loopEmitter.getValBuffer()[0];
     Value pidx = loopEmitter.getPidxs()[0].back();
-    // Loads the value from sparse tensor using pointer index;
-    // loads the value from dense tensor using coordinate array.
+    // Loads the value from sparse tensor using position-index;
+    // loads the value from dense tensor using coords.
     Value val = enc ? rewriter.create<memref::LoadOp>(loc, vals, pidx)
-                    : rewriter.create<memref::LoadOp>(loc, vals, coords);
+                    : rewriter.create<memref::LoadOp>(loc, vals, lcvs);
 
     // 2. Inline the block in the foreach operator.
     Block *srcBlock = op.getBody();
 
     // Remap coordinates.
     SmallVector<Value> args;
     for (Dimension d = 0; d < dimRank; d++) {
       // FIXME: `toStoredDim` is deprecated
-      Value actual = coords[toStoredDim(enc, d)];
-      args.push_back(actual);
+      Value dimCrd = lcvs[toStoredDim(enc, d)];
+      args.push_back(dimCrd);
     }
     // Remap value.
     args.push_back(val);
     // Remap reduction variables.
     args.append(reduc);
 
     // Remove sparse_tensor.yield.
     SmallVector<Value> reducValue = srcBlock->getTerminator()->getOperands();
LogicalResult matchAndRewrite(NewOp op,
     Location loc = op.getLoc();
     const auto dstTp = getSparseTensorType(op.getResult());
     const auto encDst = dstTp.getEncoding();
     if (!dstTp.hasEncoding() || getCOOStart(encDst) == 0)
       return failure();
 
     // Implement the NewOp as follows:
     //   %orderedCoo = sparse_tensor.new %filename
-    //   %t = sparse_tensor.ConvertOp %orderedCoo
+    //   %t = sparse_tensor.convert %orderedCoo
     RankedTensorType cooTp =
         getCOOFromTypeWithOrdering(dstTp, encDst.getDimOrdering(), true);
     Value cooTensor = rewriter.create<NewOp>(loc, cooTp, op.getSource());
     Value convert = rewriter.replaceOpWithNewOp<ConvertOp>(
         op, dstTp.getRankedTensorType(), cooTensor);
 
     // Release the ordered COO tensor.
     rewriter.setInsertionPointAfterValue(convert);
     rewriter.create<DeallocTensorOp>(loc, cooTensor);
 
     return success();
   }
 };
 
 struct OutRewriter : public OpRewritePattern<OutOp> {
   using OpRewritePattern::OpRewritePattern;
   LogicalResult matchAndRewrite(OutOp op,
                                 PatternRewriter &rewriter) const override {
     Location loc = op.getLoc();
     // Calculate NNZ.
     Value src = op.getTensor();
     Value nnz = rewriter.create<NumberOfEntriesOp>(loc, src);
 
-    // Allocate a temporary buffer for storing dimension sizes and indices.
+    // Allocate a temporary buffer for storing dimension-sizes/coordinates.
     const auto srcTp = getSparseTensorType(src);
     const Dimension dimRank = srcTp.getDimRank();
     Type indexTp = rewriter.getIndexType();
     Value dimSizes = genAlloca(rewriter, loc, dimRank, indexTp);
 
     // Generate code to calculate dimension size values and store the values to
     // the buffer.
     SmallVector<Value> dims;
     sizesForTensor(rewriter, dims, loc, srcTp, src);
     for (Dimension d = 0; d < dimRank; d++) {
       rewriter.create<memref::StoreOp>(loc, dims[d], dimSizes,
                                        constantIndex(rewriter, loc, d));
     }
 
     // Create a sparse tensor writer and output meta data.
     Type opaqueTp = getOpaquePointerType(rewriter);
     Value writer =
         createFuncCall(rewriter, loc, "createSparseTensorWriter", {opaqueTp},
                        {op.getDest()}, EmitCInterface::Off)
             .getResult(0);
     Value rankValue = constantIndex(rewriter, loc, dimRank);
     createFuncCall(rewriter, loc, "outSparseTensorWriterMetaData", {},
                    {writer, rankValue, nnz, dimSizes}, EmitCInterface::On);
 
-    Value indices = dimSizes; // Reuse the dimSizes buffer for indices.
+    Value dimCoords = dimSizes; // Reuse the dimSizes buffer for dimCoords.
     Type eltTp = srcTp.getElementType();
     SmallString<29> outNextFuncName{"outSparseTensorWriterNext",
                                     primaryTypeFunctionSuffix(eltTp)};
     Value value = genAllocaScalar(rewriter, loc, eltTp);
     ModuleOp module = op->getParentOfType<ModuleOp>();
     // For each element in the source tensor, output the element.
     rewriter.create<ForeachOp>(
         loc, src, std::nullopt,
-        [&](OpBuilder &builder, Location loc, ValueRange args, Value v,
+        [&](OpBuilder &builder, Location loc, ValueRange dcvs, Value v,
             ValueRange reduc) {
           for (Dimension d = 0; d < dimRank; d++) {
-            rewriter.create<memref::StoreOp>(loc, args[d], indices,
+            rewriter.create<memref::StoreOp>(loc, dcvs[d], dimCoords,
                                              constantIndex(builder, loc, d));
           }
           rewriter.create<memref::StoreOp>(loc, v, value);
-          SmallVector<Value> operands{writer, rankValue, indices, value};
+          SmallVector<Value> operands{writer, rankValue, dimCoords, value};
           FlatSymbolRefAttr fn = getFunc(module, outNextFuncName, {}, operands,
                                          EmitCInterface::On);
           builder.create<func::CallOp>(loc, TypeRange(), fn, operands);
           builder.create<sparse_tensor::YieldOp>(loc);
         });
 
     // Release the writer.
     createFuncCall(rewriter, loc, "delSparseTensorWriter", {}, {writer},

mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorStorageLayout.h

 // with ? size actually behaves as a "vector", i.e. the stored size is the
 // capacity and the used size resides in the storage_specifier struct.
 //
 // struct {
 //   ; per-level l:
 //   ;  if dense:
 //        <nothing>
 //   ;  if compresed:
-//        memref<? x ptr>  pointers-l  ; pointers for sparse level l
-//        memref<? x idx>  indices-l   ; indices for sparse level l
+//        memref<? x pos>  positions-l   ; positions for sparse level l
+//        memref<? x crd>  coordinates-l ; coordinates for sparse level l
 //   ;  if singleton:
-//        memref<? x idx>  indices-l   ; indices for singleton level l
+//        memref<? x crd>  coordinates-l ; coordinates for singleton level l
 //
 //   memref<? x eltType> values        ; values
 //
 //   struct sparse_tensor.storage_specifier {
-//     array<rank x int> dimSizes    ; sizes for each dimension
-//     array<n x int> memSizes;      ; sizes for each data memref
+//     array<rank x int> lvlSizes    ; sizes/cardinalities for each level
+//     array<n x int> memSizes;      ; sizes/lengths for each data memref
 //   }
 // };
 //
 // In addition, for a "trailing COO region", defined as a compressed level
-// followed by one ore more singleton levels, the default SOA storage that
+// followed by one or more singleton levels, the default SOA storage that
 // is inherent to the TACO format is optimized into an AOS storage where
-// all indices of a stored element appear consecutively.  In such cases,
-// a special operation (sparse_tensor.indices_buffer) must be used to
-// access the AOS index array. In the code below, the method `getCOOStart`
+// all coordinates of a stored element appear consecutively.  In such cases,
+// a special operation (sparse_tensor.coordinates_buffer) must be used to
+// access the AOS coordinates array. In the code below, the method `getCOOStart`
 // is used to find the start of the "trailing COO region".
 //
 // Examples.
 //
 // #CSR storage of 2-dim matrix yields
-//   memref<?xindex>                           ; pointers-1
-//   memref<?xindex>                           ; indices-1
+//   memref<?xindex>                           ; positions-1
+//   memref<?xindex>                           ; coordinates-1
 //   memref<?xf64>                             ; values
 //   struct<(array<2 x i64>, array<3 x i64>)>) ; lvl0, lvl1, 3xsizes
 //
 // #COO storage of 2-dim matrix yields
-//   memref<?xindex>,                          ; pointers-0, essentially [0,sz]
-//   memref<?xindex>                           ; AOS index storage
+//   memref<?xindex>,                          ; positions-0, essentially [0,sz]
+//   memref<?xindex>                           ; AOS coordinates storage
 //   memref<?xf64>                             ; values
 //   struct<(array<2 x i64>, array<3 x i64>)>) ; lvl0, lvl1, 3xsizes
 //
 //===----------------------------------------------------------------------===//
 
 enum class SparseTensorFieldKind : uint32_t {
   StorageSpec = 0,
-  PtrMemRef = 1,
-  IdxMemRef = 2,
+  PosMemRef = 1,
+  CrdMemRef = 2,
   ValMemRef = 3
 };
 
-static_assert(static_cast<uint32_t>(SparseTensorFieldKind::PtrMemRef) ==
-              static_cast<uint32_t>(StorageSpecifierKind::PtrMemSize));
-static_assert(static_cast<uint32_t>(SparseTensorFieldKind::IdxMemRef) ==
-              static_cast<uint32_t>(StorageSpecifierKind::IdxMemSize));
+static_assert(static_cast<uint32_t>(SparseTensorFieldKind::PosMemRef) ==
+              static_cast<uint32_t>(StorageSpecifierKind::PosMemSize));
+static_assert(static_cast<uint32_t>(SparseTensorFieldKind::CrdMemRef) ==
+              static_cast<uint32_t>(StorageSpecifierKind::CrdMemSize));
 static_assert(static_cast<uint32_t>(SparseTensorFieldKind::ValMemRef) ==
               static_cast<uint32_t>(StorageSpecifierKind::ValMemSize));
 
 /// The type of field indices.  This alias is to help code be more
 /// self-documenting; unfortunately it is not type-checked, so it only
 /// provides documentation rather than doing anything to prevent mixups.
 using FieldIndex = unsigned;
 
 // TODO: Functions/methods marked with [NUMFIELDS] might should use
 // `FieldIndex` for their return type, via the same reasoning for why
 // `Dimension`/`Level` are used both for identifiers and ranks.
 
-/// For each field that will be allocated for the given sparse tensor encoding,
-/// calls the callback with the corresponding field index, field kind, dimension
-/// (for sparse tensor level memrefs) and dimlevelType.
-/// The field index always starts with zero and increments by one between two
-/// callback invocations.
-/// Ideally, all other methods should rely on this function to query a sparse
-/// tensor fields instead of relying on ad-hoc index computation.
+/// For each field that will be allocated for the given sparse tensor
+/// encoding, calls the callback with the corresponding field index,
+/// field kind, level, and level-type (the last two are only for level
+/// memrefs).  The field index always starts with zero and increments
+/// by one between each callback invocation.  Ideally, all other methods
+/// should rely on this function to query a sparse tensor fields instead
+/// of relying on ad-hoc index computation.
 void foreachFieldInSparseTensor(
     SparseTensorEncodingAttr,
     llvm::function_ref<bool(
         FieldIndex /*fieldIdx*/, SparseTensorFieldKind /*fieldKind*/,
         Level /*lvl (if applicable)*/, DimLevelType /*DLT (if applicable)*/)>);
 
 /// Same as above, except that it also builds the Type for the corresponding
 /// field.
 void foreachFieldAndTypeInSparseTensor(
     SparseTensorType,
     llvm::function_ref<bool(Type /*fieldType*/, FieldIndex /*fieldIdx*/,
                             SparseTensorFieldKind /*fieldKind*/,
                             Level /*lvl (if applicable)*/,
                             DimLevelType /*DLT (if applicable)*/)>);
 
 /// Gets the total number of fields for the given sparse tensor encoding.
 // TODO: See note [NUMFIELDS].
 unsigned getNumFieldsFromEncoding(SparseTensorEncodingAttr enc);
 
-/// Gets the total number of data fields (index arrays, pointer arrays, and a
-/// value array) for the given sparse tensor encoding.
+/// Gets the total number of data fields (coordinate arrays, position
+/// arrays, and a value array) for the given sparse tensor encoding.
 // TODO: See note [NUMFIELDS].
 unsigned getNumDataFieldsFromEncoding(SparseTensorEncodingAttr enc);
 
 inline StorageSpecifierKind toSpecifierKind(SparseTensorFieldKind kind) {
   assert(kind != SparseTensorFieldKind::StorageSpec);
   return static_cast<StorageSpecifierKind>(kind);
 }
 
 inline SparseTensorFieldKind toFieldKind(StorageSpecifierKind kind) {
-  assert(kind != StorageSpecifierKind::DimSize);
+  assert(kind != StorageSpecifierKind::LvlSize);
   return static_cast<SparseTensorFieldKind>(kind);
 }
 
 /// Provides methods to access fields of a sparse tensor with the given
 /// encoding.
 class StorageLayout {
 public:
   explicit StorageLayout(SparseTensorEncodingAttr enc) : enc(enc) {}
static void foreachFieldInSparseTensor(
     return sparse_tensor::foreachFieldInSparseTensor(enc, callback);
   }
 
   std::pair<FieldIndex, unsigned>
   getFieldIndexAndStride(SparseTensorFieldKind kind,
                          std::optional<Level> lvl) const {
     FieldIndex fieldIdx = -1u;
     unsigned stride = 1;
-    if (kind == SparseTensorFieldKind::IdxMemRef) {
+    if (kind == SparseTensorFieldKind::CrdMemRef) {
       assert(lvl.has_value());
       const Level cooStart = getCOOStart(enc);
       const Level lvlRank = enc.getLvlRank();
       if (lvl.value() >= cooStart && lvl.value() < lvlRank) {
         lvl = cooStart;
         stride = lvlRank - cooStart;
       }
     }
getFieldIndexAndStride(SparseTensorFieldKind kind,
     assert(fieldIdx != -1u);
     return std::pair<FieldIndex, unsigned>(fieldIdx, stride);
   }
 
 private:
   SparseTensorEncodingAttr enc;
 };
 
-// FIXME: Functions/methods marked with [CLARIFY_DIM_LVL] require
-// clarification on whether their "dim" argument should actually
-// be `Level` or `Dimension`.  In particular, it's unclear whether
-// `StorageSpecifierKind::DimSize` actually means to refer to dimension-sizes
-// vs level-sizes.  If it's the latter (which seems unlikely), then all the
-// noted functions should use the `Level` type alias.  If it's the former,
-// then the functions which specifically use `DimSize` should be changed
-// to use the `Dimension` type alias; however, the functions which take
-// an unknown `StorageSpecifierKind` must be adjusted to ensure that they
-// correctly interpret the "dim" argument since the interpretation depends
-// on the `StorageSpecifierKind` value.  Since wrengr couldn't figure this
-// out from context, Peiming or Bixia should review these functions and
-// update them as appropriate.
-
 class SparseTensorSpecifier {
 public:
   explicit SparseTensorSpecifier(Value specifier)
       : specifier(cast<TypedValue<StorageSpecifierType>>(specifier)) {}
 
-  // Undef value for dimension sizes, all zero value for memory sizes.
+  // Undef value for level-sizes, all zero values for memory-sizes.
   static Value getInitValue(OpBuilder &builder, Location loc,
                             SparseTensorType stt);
 
   /*implicit*/ operator Value() { return specifier; }
 
-  // FIXME: see note [CLARIFY_DIM_LVL].
   Value getSpecifierField(OpBuilder &builder, Location loc,
-                          StorageSpecifierKind kind,
-                          std::optional<unsigned> dim);
+                          StorageSpecifierKind kind, std::optional<Level> lvl);
 
-  // FIXME: see note [CLARIFY_DIM_LVL].
   void setSpecifierField(OpBuilder &builder, Location loc, Value v,
-                         StorageSpecifierKind kind,
-                         std::optional<unsigned> dim);
+                         StorageSpecifierKind kind, std::optional<Level> lvl);
 
 private:
   TypedValue<StorageSpecifierType> specifier;
 };
 
 /// A helper class around an array of values that corresponds to a sparse
 /// tensor. This class provides a set of meaningful APIs to query and update
 /// a particular field in a consistent way. Users should not make assumptions
public:
   unsigned getNumFields() const { return fields.size(); }
 
   ///
   /// Getters: get the value for required field.
   ///
 
   Value getSpecifier() const { return fields.back(); }
 
-  // FIXME: see note [CLARIFY_DIM_LVL].
   Value getSpecifierField(OpBuilder &builder, Location loc,
                           StorageSpecifierKind kind,
-                          std::optional<unsigned> dim) const {
+                          std::optional<Level> lvl) const {
     SparseTensorSpecifier md(fields.back());
-    return md.getSpecifierField(builder, loc, kind, dim);
+    return md.getSpecifierField(builder, loc, kind, lvl);
   }
 
-  // FIXME: see note [CLARIFY_DIM_LVL].
-  Value getDimSize(OpBuilder &builder, Location loc, unsigned dim) const {
-    return getSpecifierField(builder, loc, StorageSpecifierKind::DimSize, dim);
+  Value getLvlSize(OpBuilder &builder, Location loc, Level lvl) const {
+    return getSpecifierField(builder, loc, StorageSpecifierKind::LvlSize, lvl);
   }
 
-  Value getPtrMemRef(Level lvl) const {
-    return getMemRefField(SparseTensorFieldKind::PtrMemRef, lvl);
+  Value getPosMemRef(Level lvl) const {
+    return getMemRefField(SparseTensorFieldKind::PosMemRef, lvl);
   }
 
   Value getValMemRef() const {
     return getMemRefField(SparseTensorFieldKind::ValMemRef, std::nullopt);
   }
 
   Value getMemRefField(SparseTensorFieldKind kind,
                        std::optional<Level> lvl) const {
     return getField(getMemRefFieldIndex(kind, lvl));
   }
 
   Value getMemRefField(FieldIndex fidx) const {
     assert(fidx < fields.size() - 1);
     return getField(fidx);
   }
 
-  Value getPtrMemSize(OpBuilder &builder, Location loc, Level lvl) const {
-    return getSpecifierField(builder, loc, StorageSpecifierKind::PtrMemSize,
+  Value getPosMemSize(OpBuilder &builder, Location loc, Level lvl) const {
+    return getSpecifierField(builder, loc, StorageSpecifierKind::PosMemSize,
                              lvl);
   }
 
-  Value getIdxMemSize(OpBuilder &builder, Location loc, Level lvl) const {
-    return getSpecifierField(builder, loc, StorageSpecifierKind::IdxMemSize,
+  Value getCrdMemSize(OpBuilder &builder, Location loc, Level lvl) const {
+    return getSpecifierField(builder, loc, StorageSpecifierKind::CrdMemSize,
                              lvl);
   }
 
   Value getValMemSize(OpBuilder &builder, Location loc) const {
     return getSpecifierField(builder, loc, StorageSpecifierKind::ValMemSize,
                              std::nullopt);
   }
 
   Type getMemRefElementType(SparseTensorFieldKind kind,
                             std::optional<Level> lvl) const {
     return getMemRefType(getMemRefField(kind, lvl)).getElementType();
   }
 
   Value getField(FieldIndex fidx) const {
     assert(fidx < fields.size());
     return fields[fidx];
   }
 
   ValueRange getMemRefFields() const {
     // Drop the last metadata fields.
     return fields.drop_back();
   }
 
-  std::pair<FieldIndex, unsigned> getIdxMemRefIndexAndStride(Level lvl) const {
+  std::pair<FieldIndex, unsigned> getCrdMemRefIndexAndStride(Level lvl) const {
     StorageLayout layout(rType.getEncoding());
-    return layout.getFieldIndexAndStride(SparseTensorFieldKind::IdxMemRef, lvl);
+    return layout.getFieldIndexAndStride(SparseTensorFieldKind::CrdMemRef, lvl);
   }
 
   Value getAOSMemRef() const {
     const Level cooStart = getCOOStart(rType.getEncoding());
     assert(cooStart < rType.getLvlRank());
-    return getMemRefField(SparseTensorFieldKind::IdxMemRef, cooStart);
+    return getMemRefField(SparseTensorFieldKind::CrdMemRef, cooStart);
   }
 
   RankedTensorType getRankedTensorType() const { return rType; }
   ValueArrayRef getFields() const { return fields; }
 
 protected:
   SparseTensorType rType;
   ValueArrayRef fields;
 };
 
 /// Uses ValueRange for immutable descriptors.
 class SparseTensorDescriptor : public SparseTensorDescriptorImpl<ValueRange> {
 public:
   SparseTensorDescriptor(SparseTensorType stt, ValueRange buffers)
       : SparseTensorDescriptorImpl<ValueRange>(stt, buffers) {}
 
-  Value getIdxMemRefOrView(OpBuilder &builder, Location loc, Level lvl) const;
+  Value getCrdMemRefOrView(OpBuilder &builder, Location loc, Level lvl) const;
 };
 
 /// Uses SmallVectorImpl<Value> & for mutable descriptors.
 /// Using SmallVector for mutable descriptor allows users to reuse it as a
 /// tmp buffers to append value for some special cases, though users should
 /// be responsible to restore the buffer to legal states after their use. It
 /// is probably not a clean way, but it is the most efficient way to avoid
 /// copying the fields into another SmallVector. If a more clear way is
void setMemRefField(FieldIndex fidx, Value v) {
     fields[fidx] = v;
   }
 
   void setField(FieldIndex fidx, Value v) {
     assert(fidx < fields.size());
     fields[fidx] = v;
   }
 
-  // FIXME: see note [CLARIFY_DIM_LVL].
   void setSpecifierField(OpBuilder &builder, Location loc,
-                         StorageSpecifierKind kind, std::optional<unsigned> dim,
+                         StorageSpecifierKind kind, std::optional<Level> lvl,
                          Value v) {
     SparseTensorSpecifier md(fields.back());
-    md.setSpecifierField(builder, loc, v, kind, dim);
+    md.setSpecifierField(builder, loc, v, kind, lvl);
     fields.back() = md;
   }
 
   void setValMemSize(OpBuilder &builder, Location loc, Value v) {
     setSpecifierField(builder, loc, StorageSpecifierKind::ValMemSize,
                       std::nullopt, v);
   }
 
-  void setIdxMemSize(OpBuilder &builder, Location loc, Level lvl, Value v) {
-    setSpecifierField(builder, loc, StorageSpecifierKind::IdxMemSize, lvl, v);
+  void setCrdMemSize(OpBuilder &builder, Location loc, Level lvl, Value v) {
+    setSpecifierField(builder, loc, StorageSpecifierKind::CrdMemSize, lvl, v);
   }
 
-  void setPtrMemSize(OpBuilder &builder, Location loc, Level lvl, Value v) {
-    setSpecifierField(builder, loc, StorageSpecifierKind::PtrMemSize, lvl, v);
+  void setPosMemSize(OpBuilder &builder, Location loc, Level lvl, Value v) {
+    setSpecifierField(builder, loc, StorageSpecifierKind::PosMemSize, lvl, v);
   }
 
-  // FIXME: see note [CLARIFY_DIM_LVL].
-  void setDimSize(OpBuilder &builder, Location loc, unsigned dim, Value v) {
-    setSpecifierField(builder, loc, StorageSpecifierKind::DimSize, dim, v);
+  void setLvlSize(OpBuilder &builder, Location loc, Level lvl, Value v) {
+    setSpecifierField(builder, loc, StorageSpecifierKind::LvlSize, lvl, v);
   }
 };
 
 /// Returns the "tuple" value of the adapted tensor.
 inline UnrealizedConversionCastOp getTuple(Value tensor) {
   return llvm::cast<UnrealizedConversionCastOp>(tensor.getDefiningOp());
 }
 

mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorStorageLayout.cpp

 
 using namespace mlir;
 using namespace sparse_tensor;
 
 //===----------------------------------------------------------------------===//
 // Private helper methods.
 //===----------------------------------------------------------------------===//
 
-static IntegerAttr fromOptionalInt(MLIRContext *ctx,
-                                   std::optional<unsigned> dim) {
-  if (!dim)
-    return nullptr;
-  return IntegerAttr::get(IndexType::get(ctx), dim.value());
+/// Constructs a nullable `LevelAttr` from the `std::optional<Level>`.
+static IntegerAttr optionalLevelAttr(MLIRContext *ctx,
+                                     std::optional<Level> lvl) {
+  return lvl ? IntegerAttr::get(IndexType::get(ctx), lvl.value())
+             : IntegerAttr();
 }
 
 // This is only ever called from `SparseTensorTypeToBufferConverter`,
 // which is why the first argument is `RankedTensorType` rather than
 // `SparseTensorType`.
 static std::optional<LogicalResult>
 convertSparseTensorType(RankedTensorType rtp, SmallVectorImpl<Type> &fields) {
   const SparseTensorType stt(rtp);

 Value SparseTensorSpecifier::getInitValue(OpBuilder &builder, Location loc,
                                           SparseTensorType stt) {
   return builder.create<StorageSpecifierInitOp>(
       loc, StorageSpecifierType::get(stt.getEncoding()));
 }
 
 Value SparseTensorSpecifier::getSpecifierField(OpBuilder &builder, Location loc,
                                                StorageSpecifierKind kind,
-                                               std::optional<unsigned> dim) {
+                                               std::optional<Level> lvl) {
   return builder.create<GetStorageSpecifierOp>(
-      loc, specifier, kind, fromOptionalInt(specifier.getContext(), dim));
+      loc, specifier, kind, optionalLevelAttr(specifier.getContext(), lvl));
 }
 
 void SparseTensorSpecifier::setSpecifierField(OpBuilder &builder, Location loc,
                                               Value v,
                                               StorageSpecifierKind kind,
-                                              std::optional<unsigned> dim) {
+                                              std::optional<Level> lvl) {
+  // TODO: make `v` have type `TypedValue<IndexType>` instead.
   assert(v.getType().isIndex());
   specifier = builder.create<SetStorageSpecifierOp>(
-      loc, specifier, kind, fromOptionalInt(specifier.getContext(), dim), v);
+      loc, specifier, kind, optionalLevelAttr(specifier.getContext(), lvl), v);
 }
 
 //===----------------------------------------------------------------------===//
 // SparseTensorDescriptor methods.
 //===----------------------------------------------------------------------===//
 
-Value sparse_tensor::SparseTensorDescriptor::getIdxMemRefOrView(
-    OpBuilder &builder, Location loc, Level idxLvl) const {
+Value sparse_tensor::SparseTensorDescriptor::getCrdMemRefOrView(
+    OpBuilder &builder, Location loc, Level lvl) const {
   const Level cooStart = getCOOStart(rType.getEncoding());
-  if (idxLvl < cooStart)
-    return getMemRefField(SparseTensorFieldKind::IdxMemRef, idxLvl);
+  if (lvl < cooStart)
+    return getMemRefField(SparseTensorFieldKind::CrdMemRef, lvl);
 
   Value stride = constantIndex(builder, loc, rType.getLvlRank() - cooStart);
-  Value size = getIdxMemSize(builder, loc, cooStart);
+  Value size = getCrdMemSize(builder, loc, cooStart);
   size = builder.create<arith::DivUIOp>(loc, size, stride);
   return builder.create<memref::SubViewOp>(
-      loc, getMemRefField(SparseTensorFieldKind::IdxMemRef, cooStart),
-      /*offset=*/ValueRange{constantIndex(builder, loc, idxLvl - cooStart)},
+      loc, getMemRefField(SparseTensorFieldKind::CrdMemRef, cooStart),
+      /*offset=*/ValueRange{constantIndex(builder, loc, lvl - cooStart)},
       /*size=*/ValueRange{size},
       /*step=*/ValueRange{stride});
 }
 
 //===----------------------------------------------------------------------===//
 // Public methods.
 //===----------------------------------------------------------------------===//
 
 constexpr FieldIndex kDataFieldStartingIdx = 0;
 
 void sparse_tensor::foreachFieldInSparseTensor(
     const SparseTensorEncodingAttr enc,
     llvm::function_ref<bool(FieldIndex, SparseTensorFieldKind, Level,
                             DimLevelType)>
         callback) {
   assert(enc);
 
-#define RETURN_ON_FALSE(idx, kind, dim, dlt)                                   \
-  if (!(callback(idx, kind, dim, dlt)))                                        \
+#define RETURN_ON_FALSE(fidx, kind, dim, dlt)                                  \
+  if (!(callback(fidx, kind, dim, dlt)))                                       \
     return;
 
   const auto lvlTypes = enc.getDimLevelType();
   const Level lvlRank = enc.getLvlRank();
   const Level cooStart = getCOOStart(enc);
   const Level end = cooStart == lvlRank ? cooStart : cooStart + 1;
   FieldIndex fieldIdx = kDataFieldStartingIdx;
   // Per-dimension storage.
   for (Level l = 0; l < end; l++) {
     // Dimension level types apply in order to the reordered dimension.
     // As a result, the compound type can be constructed directly in the given
     // order.
     const auto dlt = lvlTypes[l];
     if (isCompressedDLT(dlt)) {
-      RETURN_ON_FALSE(fieldIdx++, SparseTensorFieldKind::PtrMemRef, l, dlt);
-      RETURN_ON_FALSE(fieldIdx++, SparseTensorFieldKind::IdxMemRef, l, dlt);
+      RETURN_ON_FALSE(fieldIdx++, SparseTensorFieldKind::PosMemRef, l, dlt);
+      RETURN_ON_FALSE(fieldIdx++, SparseTensorFieldKind::CrdMemRef, l, dlt);
     } else if (isSingletonDLT(dlt)) {
-      RETURN_ON_FALSE(fieldIdx++, SparseTensorFieldKind::IdxMemRef, l, dlt);
+      RETURN_ON_FALSE(fieldIdx++, SparseTensorFieldKind::CrdMemRef, l, dlt);
     } else {
       assert(isDenseDLT(dlt)); // no fields
     }
   }
   // The values array.
   RETURN_ON_FALSE(fieldIdx++, SparseTensorFieldKind::ValMemRef, -1u,
                   DimLevelType::Undef);
 
   // Put metadata at the end.
   RETURN_ON_FALSE(fieldIdx++, SparseTensorFieldKind::StorageSpec, -1u,
                   DimLevelType::Undef);
 
 #undef RETURN_ON_FALSE
 }
 
 void sparse_tensor::foreachFieldAndTypeInSparseTensor(
     SparseTensorType stt,
     llvm::function_ref<bool(Type, FieldIndex, SparseTensorFieldKind, Level,
                             DimLevelType)>
         callback) {
   assert(stt.hasEncoding());
   // Construct the basic types.
-  Type idxType = stt.getIndexType();
-  Type ptrType = stt.getPointerType();
-  Type eltType = stt.getElementType();
-
-  Type metaDataType = StorageSpecifierType::get(stt.getEncoding());
-  // memref<? x ptr>  pointers
-  Type ptrMemType = MemRefType::get({ShapedType::kDynamic}, ptrType);
-  // memref<? x idx>  indices
-  Type idxMemType = MemRefType::get({ShapedType::kDynamic}, idxType);
+  const Type crdType = stt.getCrdType();
+  const Type posType = stt.getPosType();
+  const Type eltType = stt.getElementType();
+
+  const Type metaDataType = StorageSpecifierType::get(stt.getEncoding());
+  // memref<? x pos>  positions
+  const Type posMemType = MemRefType::get({ShapedType::kDynamic}, posType);
+  // memref<? x crd>  coordinates
+  const Type crdMemType = MemRefType::get({ShapedType::kDynamic}, crdType);
   // memref<? x eltType> values
-  Type valMemType = MemRefType::get({ShapedType::kDynamic}, eltType);
+  const Type valMemType = MemRefType::get({ShapedType::kDynamic}, eltType);
 
   foreachFieldInSparseTensor(
       stt.getEncoding(),
-      [metaDataType, ptrMemType, idxMemType, valMemType,
+      [metaDataType, posMemType, crdMemType, valMemType,
        callback](FieldIndex fieldIdx, SparseTensorFieldKind fieldKind,
                  Level lvl, DimLevelType dlt) -> bool {
         switch (fieldKind) {
         case SparseTensorFieldKind::StorageSpec:
           return callback(metaDataType, fieldIdx, fieldKind, lvl, dlt);
-        case SparseTensorFieldKind::PtrMemRef:
-          return callback(ptrMemType, fieldIdx, fieldKind, lvl, dlt);
-        case SparseTensorFieldKind::IdxMemRef:
-          return callback(idxMemType, fieldIdx, fieldKind, lvl, dlt);
+        case SparseTensorFieldKind::PosMemRef:
+          return callback(posMemType, fieldIdx, fieldKind, lvl, dlt);
+        case SparseTensorFieldKind::CrdMemRef:
+          return callback(crdMemType, fieldIdx, fieldKind, lvl, dlt);
         case SparseTensorFieldKind::ValMemRef:
           return callback(valMemType, fieldIdx, fieldKind, lvl, dlt);
         };
         llvm_unreachable("unrecognized field kind");
       });
 }
 
 unsigned sparse_tensor::getNumFieldsFromEncoding(SparseTensorEncodingAttr enc) {

mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp

 ///   enableVLAVectorization: enables scalable vectors (viz. ARMSve)
 ///   enableSIMDIndex32: uses 32-bit indices in gather/scatter for efficiency
 struct VL {
   unsigned vectorLength;
   bool enableVLAVectorization;
   bool enableSIMDIndex32;
 };
 
-/// Helper to test for given index value.
-static bool isIntValue(Value val, int64_t idx) {
-  if (auto ival = getConstantIntValue(val))
-    return *ival == idx;
-  return false;
-}
-
 /// Helper test for invariant value (defined outside given block).
 static bool isInvariantValue(Value val, Block *block) {
   return val.getDefiningOp() && val.getDefiningOp()->getBlock() != block;
 }
 
 /// Helper test for invariant argument (defined outside given block).
 static bool isInvariantArg(BlockArgument arg, Block *block) {
   return arg.getOwner() != block;
 }
 
 /// Constructs vector type for element type.
 static VectorType vectorType(VL vl, Type etp) {
   unsigned numScalableDims = vl.enableVLAVectorization;
   return VectorType::get(vl.vectorLength, etp, numScalableDims);
 }
 
-/// Constructs vector type from pointer.
-static VectorType vectorType(VL vl, Value ptr) {
-  return vectorType(vl, getMemRefType(ptr).getElementType());
+/// Constructs vector type from a memref value.
+static VectorType vectorType(VL vl, Value mem) {
+  return vectorType(vl, getMemRefType(mem).getElementType());
 }
 
 /// Constructs vector iteration mask.
 static Value genVectorMask(PatternRewriter &rewriter, Location loc, VL vl,
                            Value iv, Value lo, Value hi, Value step) {
   VectorType mtp = vectorType(vl, rewriter.getI1Type());
   // Special case if the vector length evenly divides the trip count (for
   // example, "for i = 0, 128, 16"). A constant all-true mask is generated
static Value genVectorInvariantValue(PatternRewriter &rewriter, VL vl,
   return rewriter.create<vector::BroadcastOp>(val.getLoc(), vtp, val);
 }
 
 /// Generates a vectorized load lhs = a[ind[lo:hi]] or lhs = a[lo:hi],
 /// where 'lo' denotes the current index and 'hi = lo + vl - 1'. Note
 /// that the sparse compiler can only generate indirect loads in
 /// the last index, i.e. back().
 static Value genVectorLoad(PatternRewriter &rewriter, Location loc, VL vl,
-                           Value ptr, ArrayRef<Value> idxs, Value vmask) {
-  VectorType vtp = vectorType(vl, ptr);
+                           Value mem, ArrayRef<Value> idxs, Value vmask) {
+  VectorType vtp = vectorType(vl, mem);
   Value pass = constantZero(rewriter, loc, vtp);
   if (idxs.back().getType().isa<VectorType>()) {
     SmallVector<Value> scalarArgs(idxs.begin(), idxs.end());
     Value indexVec = idxs.back();
     scalarArgs.back() = constantIndex(rewriter, loc, 0);
-    return rewriter.create<vector::GatherOp>(loc, vtp, ptr, scalarArgs,
+    return rewriter.create<vector::GatherOp>(loc, vtp, mem, scalarArgs,
                                              indexVec, vmask, pass);
   }
-  return rewriter.create<vector::MaskedLoadOp>(loc, vtp, ptr, idxs, vmask,
+  return rewriter.create<vector::MaskedLoadOp>(loc, vtp, mem, idxs, vmask,
                                                pass);
 }
 
 /// Generates a vectorized store a[ind[lo:hi]] = rhs or a[lo:hi] = rhs
 /// where 'lo' denotes the current index and 'hi = lo + vl - 1'. Note
 /// that the sparse compiler can only generate indirect stores in
 /// the last index, i.e. back().
-static void genVectorStore(PatternRewriter &rewriter, Location loc, Value ptr,
+static void genVectorStore(PatternRewriter &rewriter, Location loc, Value mem,
                            ArrayRef<Value> idxs, Value vmask, Value rhs) {
   if (idxs.back().getType().isa<VectorType>()) {
     SmallVector<Value> scalarArgs(idxs.begin(), idxs.end());
     Value indexVec = idxs.back();
     scalarArgs.back() = constantIndex(rewriter, loc, 0);
-    rewriter.create<vector::ScatterOp>(loc, ptr, scalarArgs, indexVec, vmask,
+    rewriter.create<vector::ScatterOp>(loc, mem, scalarArgs, indexVec, vmask,
                                        rhs);
     return;
   }
-  rewriter.create<vector::MaskedStoreOp>(loc, ptr, idxs, vmask, rhs);
+  rewriter.create<vector::MaskedStoreOp>(loc, mem, idxs, vmask, rhs);
 }
 
 /// Detects a vectorizable reduction operations and returns the
 /// combining kind of reduction on success in `kind`.
 static bool isVectorizableReduction(Value red, Value iter,
                                     vector::CombiningKind &kind) {
   if (auto addf = red.getDefiningOp<arith::AddFOp>()) {
     kind = vector::CombiningKind::ADD;

 /// call (codegen) yields the proper vector form in the output parameter
 /// vector 'idxs'. This mechanism ensures that analysis and rewriting code
 /// stay in sync. Note that the analyis part is simple because the sparse
 /// compiler only generates relatively simple subscript expressions.
 ///
 /// See https://llvm.org/docs/GetElementPtr.html for some background on
 /// the complications described below.
 ///
-/// We need to generate a pointer/index load from the sparse storage scheme.
-/// Narrower data types need to be zero extended before casting the value
-/// into the index type used for looping and indexing.
+/// We need to generate a position/coordinate load from the sparse storage
+/// scheme.  Narrower data types need to be zero extended before casting
+/// the value into the `index` type used for looping and indexing.
 ///
 /// For the scalar case, subscripts simply zero extend narrower indices
 /// into 64-bit values before casting to an index type without a performance
 /// penalty. Indices that already are 64-bit, in theory, cannot express the
 /// full range since the LLVM backend defines addressing in terms of an
 /// unsigned pointer/signed index pair.
 static bool vectorizeSubscripts(PatternRewriter &rewriter, scf::ForOp forOp,
                                 VL vl, ValueRange subs, bool codegen,
static bool vectorizeExpr(PatternRewriter &rewriter, scf::ForOp forOp, VL vl,
   Block *block = &forOp.getRegion().front();
   if (def->getBlock() != block) {
     if (codegen)
       vexp = genVectorInvariantValue(rewriter, vl, exp);
     return true;
   }
   // Proper load operations. These are either values involved in the
   // actual computation, such as a[i] = b[i] becomes a[lo:hi] = b[lo:hi],
-  // or index values inside the computation that are now fetched from
-  // the sparse storage index arrays, such as a[i] = i becomes
+  // or coordinate values inside the computation that are now fetched from
+  // the sparse storage coordinates arrays, such as a[i] = i becomes
   // a[lo:hi] = ind[lo:hi], where 'lo' denotes the current index
   // and 'hi = lo + vl - 1'.
   if (auto load = dyn_cast<memref::LoadOp>(def)) {
     auto subs = load.getIndices();
     SmallVector<Value> idxs;
     if (vectorizeSubscripts(rewriter, forOp, vl, subs, codegen, vmask, idxs)) {
       if (codegen)
         vexp = genVectorLoad(rewriter, loc, vl, load.getMemRef(), idxs, vmask);
ForOpRewriter(MLIRContext *context, unsigned vectorLength,
       : OpRewritePattern(context), vl{vectorLength, enableVLAVectorization,
                                       enableSIMDIndex32} {}
 
   LogicalResult matchAndRewrite(scf::ForOp op,
                                 PatternRewriter &rewriter) const override {
     // Check for single block, unit-stride for-loop that is generated by
     // sparse compiler, which means no data dependence analysis is required,
     // and its loop-body is very restricted in form.
-    if (!op.getRegion().hasOneBlock() || !isIntValue(op.getStep(), 1) ||
+    if (!op.getRegion().hasOneBlock() || !isConstantIntValue(op.getStep(), 1) ||
         !op->hasAttr(LoopEmitter::getLoopEmitterLoopAttrName()))
       return failure();
     // Analyze (!codegen) and rewrite (codegen) loop-body.
     if (vectorizeStmt(rewriter, op, vl, /*codegen=*/false) &&
         vectorizeStmt(rewriter, op, vl, /*codegen=*/true))
       return success();
     return failure();
   }

mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp

if (!ta.isa<AffineConstantExpr>()) {
       finder.setPickedIterType(utils::IteratorType::reduction);
       finder.walkPostOrder(ta);
       ta = finder.getDimExpr();
       tldx = finder.getDimExpr().getPosition();
     }
   }
 }
 
-/// Computes a topologically sorted iteration graph for the linalg
-/// operation. Ensures all tensors are visited in natural index order. This
-/// is essential for sparse storage formats since these only support access
-/// along fixed dimensions. Even for dense storage formats, however, the
-/// natural index order yields innermost unit-stride access with better
-/// spatial locality.
+/// Computes a topologically sorted iteration graph for the linalg operation.
+/// Ensures all tensors are visited in natural coordinate order.  This is
+/// essential for sparse storage formats since these only support access
+/// along fixed levels.  Even for dense storage formats, however, the natural
+/// coordinate order yields innermost unit-stride access with better spatial
+/// locality.
 static bool computeIterationGraph(CodegenEnv &env, unsigned mask,
                                   OpOperand *skip = nullptr) {
   // Set up an n x n from/to adjacency matrix of the iteration graph
   // for the implicit loop indices i_0 .. i_n-1.
   const unsigned n = env.merger().getNumLoops();
   std::vector<std::vector<bool>> adjM(n, std::vector<bool>(n, false));
   std::vector<unsigned> inDegree(n, 0); // in-degree of each node.
   const auto iteratorTypes = env.op().getIteratorTypesArray();
env.emitter().initializeLoopEmit(
           builder.create<linalg::FillOp>(loc, ValueRange{zero},
                                          ValueRange{init});
         }
         return init;
       });
 }
 
 /// Generates index for load/store on sparse tensor.
+// FIXME: It's not entirely clear what "index" means here (i.e., is it
+// a "coordinate", or "Ldx", or what).  So the function should be renamed
+// and/or the documentation expanded in order to clarify.
 static Value genIndex(CodegenEnv &env, OpOperand *t) {
   auto map = env.op().getMatchingIndexingMap(t);
   const auto stt = getSparseTensorType(t->get());
   const Level lvlRank = stt.getLvlRank();
   assert(static_cast<Level>(map.getNumResults()) == lvlRank);
   // FIXME: `toOrigDim` is deprecated.
   AffineExpr a = map.getResult(toOrigDim(stt.getEncoding(), lvlRank - 1));
   assert(a.getKind() == AffineExprKind::DimId);
static Value genSubscript(CodegenEnv &env, OpBuilder &builder, OpOperand *t,
   return env.emitter().getValBuffer()[tensor];
 }
 
 /// Generates insertion code to implement dynamic tensor load.
 static Value genInsertionLoad(CodegenEnv &env, OpBuilder &builder,
                               OpOperand *t) {
   linalg::GenericOp op = env.op();
   Location loc = op.getLoc();
-  // Direct lexicographic index order, tensor loads as zero.
+  // Direct lexicographic coordinate order, tensor loads as zero.
   if (!env.isExpand()) {
     Type tp = getElementTypeOrSelf(t->get().getType());
     return constantZero(builder, loc, tp);
   }
   // Load from expanded access pattern.
   Value index = genIndex(env, t);
   return builder.create<memref::LoadOp>(loc, env.getExpandValues(), index);
 }
 
 /// Generates insertion code to implement dynamic tensor load for reduction.
 static Value genInsertionLoadReduce(CodegenEnv &env, OpBuilder &builder,
                                     OpOperand *t) {
   linalg::GenericOp op = env.op();
   Location loc = op.getLoc();
   Value identity = env.getCustomRedId();
-  // Direct lexicographic index order, tensor loads as identity.
+  // Direct lexicographic coordinate order, tensor loads as identity.
   if (!env.isExpand())
     return identity;
   // Load from expanded access pattern if filled, identity otherwise.
   Value values = env.getExpandValues();
   Value filled = env.getExpandFilled();
   Value index = genIndex(env, t);
   Value isFilled = builder.create<memref::LoadOp>(loc, filled, index);
   Value valAtIndex = builder.create<memref::LoadOp>(loc, values, index);
   return builder.create<arith::SelectOp>(loc, isFilled, valAtIndex, identity);
 }
 
 /// Generates insertion code to implement dynamic tensor store.
 static void genInsertionStore(CodegenEnv &env, OpBuilder &builder, OpOperand *t,
                               Value rhs) {
   linalg::GenericOp op = env.op();
   Location loc = op.getLoc();
-  // Direct insertion in lexicographic index order.
+  // Direct insertion in lexicographic coordinate order.
   if (!env.isExpand()) {
     unsigned rank = op.getRank(t);
+    // FIXME: It's not entirely clear what "indices" means here (i.e.,
+    // are they "coordinates"? and if so, then are they level-coords or
+    // dim-coords?)
     SmallVector<Value> indices;
     for (unsigned i = 0; i < rank; i++) {
       assert(env.emitter().getLoopIV(i));
       indices.push_back(env.emitter().getLoopIV(i));
     }
     Value chain = env.getInsertionChain();
     if (!env.getValidLexInsert()) {
       env.updateInsertionChain(
static void genTensorStore(CodegenEnv &env, OpBuilder &builder, unsigned exp,
   builder.create<memref::StoreOp>(loc, rhs, ptr, args);
 }
 
 /// Generates an invariant value.
 inline static Value genInvariantValue(CodegenEnv &env, unsigned exp) {
   return env.exp(exp).val;
 }
 
-/// Generates an index value.
-inline static Value genIndexValue(CodegenEnv &env, unsigned idx) {
-  return env.getLoopIdxValue(idx);
-}
-
 /// Semi-ring branches are simply inlined by the sparse compiler. Prior
 /// analysis has verified that all computations are "local" to the inlined
 /// branch or otherwise invariantly defined outside the loop nest, with the
 /// exception of index computations, which need to be relinked to actual
 /// inlined cloned code.
 static Value relinkBranch(CodegenEnv &env, RewriterBase &rewriter, Block *block,
                           Value e, unsigned ldx) {
   if (Operation *def = e.getDefiningOp()) {
     if (auto indexOp = dyn_cast<linalg::IndexOp>(def))
-      return genIndexValue(env, indexOp.getDim());
+      return env.getLoopIdxValue(indexOp.getDim());
     if (def->getBlock() == block) {
       for (unsigned i = 0, n = def->getNumOperands(); i < n; i++) {
         rewriter.updateRootInPlace(def, [&]() {
           def->setOperand(
               i, relinkBranch(env, rewriter, block, def->getOperand(i), ldx));
         });
       }
     }
static Value genExp(CodegenEnv &env, RewriterBase &rewriter, unsigned exp,
 
   if (exp == -1u)
     return Value();
   if (env.exp(exp).kind == Kind::kTensor)
     return genTensorLoad(env, rewriter, exp);
   if (env.exp(exp).kind == Kind::kInvariant)
     return genInvariantValue(env, exp);
   if (env.exp(exp).kind == Kind::kIndex)
-    return genIndexValue(env, env.exp(exp).index);
+    return env.getLoopIdxValue(env.exp(exp).index);
 
   if (env.exp(exp).kind == Kind::kReduce)
     env.startCustomReduc(exp); // enter custom
 
   Value v0 = genExp(env, rewriter, env.exp(exp).children.e0, ldx);
   Value v1 = genExp(env, rewriter, env.exp(exp).children.e1, ldx);
   Value ee = env.merger().buildExp(rewriter, loc, exp, v0, v1);
   if (ee && (env.exp(exp).kind == Kind::kUnary ||
for (OpOperand *t : env.op().getDpsInputOperands()) {
       //
       // TODO: investigate fusing the conversion with computation,
       //       especially if it is a direct yield!
       //
       auto srcTp = getRankedTensorType(tval);
       auto dstEnc = SparseTensorEncodingAttr::get(
           getContext(), srcEnc.getDimLevelType(),
           permute(env, env.op().getMatchingIndexingMap(t)), // new order
-          srcEnc.getHigherOrdering(), srcEnc.getPointerBitWidth(),
-          srcEnc.getIndexBitWidth());
+          srcEnc.getHigherOrdering(), srcEnc.getPosWidth(),
+          srcEnc.getCrdWidth());
       auto dstTp = RankedTensorType::get(srcTp.getShape(),
                                          srcTp.getElementType(), dstEnc);
       auto convert = rewriter.create<ConvertOp>(tval.getLoc(), dstTp, tval);
       rewriter.updateRootInPlace(
           env.op(), [&]() { env.op()->setOperand(tensor, convert); });
       rewriter.setInsertionPointAfter(env.op());
       rewriter.create<bufferization::DeallocTensorOp>(tval.getLoc(), convert);
       return success();

mlir/lib/ExecutionEngine/SparseTensor/NNZ.cpp

for (uint64_t l = 0, lvlrank = getLvlRank(); l < lvlrank; ++l) {
     } else {
       MLIR_SPARSETENSOR_FATAL("unsupported level type: %d\n",
                               static_cast<uint8_t>(dlt));
     }
     sz = detail::checkedMul(sz, lvlSizes[l]);
   }
 }
 
-void SparseTensorNNZ::forallIndices(uint64_t stopLvl,
-                                    SparseTensorNNZ::NNZConsumer yield) const {
+void SparseTensorNNZ::forallCoords(uint64_t stopLvl,
   assert(stopLvl < getLvlRank() && "Level out of bounds");
   assert(isCompressedDLT(lvlTypes[stopLvl]) &&
          "Cannot look up non-compressed levels");
-  forallIndices(yield, stopLvl, 0, 0);
+  forallCoords(yield, stopLvl, 0, 0);
 }
 
-void SparseTensorNNZ::add(const std::vector<uint64_t> &lvlInd) {
+void SparseTensorNNZ::add(const std::vector<uint64_t> &lvlCoords) {
   uint64_t parentPos = 0;
   for (uint64_t l = 0, lvlrank = getLvlRank(); l < lvlrank; ++l) {
     if (isCompressedDLT(lvlTypes[l]))
       nnz[l][parentPos]++;
-    parentPos = parentPos * lvlSizes[l] + lvlInd[l];
+    parentPos = parentPos * lvlSizes[l] + lvlCoords[l];
   }
 }
 
-void SparseTensorNNZ::forallIndices(SparseTensorNNZ::NNZConsumer yield,
-                                    uint64_t stopLvl, uint64_t parentPos,
-                                    uint64_t l) const {
+void SparseTensorNNZ::forallCoords(SparseTensorNNZ::NNZConsumer yield,
   assert(l <= stopLvl);
   if (l == stopLvl) {
     assert(parentPos < nnz[l].size() && "Cursor is out of range");
     yield(nnz[l][parentPos]);
   } else {
     const uint64_t sz = lvlSizes[l];
     const uint64_t pstart = parentPos * sz;
     for (uint64_t i = 0; i < sz; ++i)
-      forallIndices(yield, stopLvl, pstart + i, l + 1);
+      forallCoords(yield, stopLvl, pstart + i, l + 1);
   }
 }

mlir/lib/ExecutionEngine/SparseTensor/Storage.cpp

#define IMPL_NEWENUMERATOR(VNAME, V)                                           \
   void SparseTensorStorageBase::newEnumerator(                                 \
       SparseTensorEnumeratorBase<V> **, uint64_t, const uint64_t *, uint64_t,  \
       const uint64_t *) const {                                                \
     FATAL_PIV("newEnumerator" #VNAME);                                         \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_NEWENUMERATOR)
 #undef IMPL_NEWENUMERATOR
 
-#define IMPL_GETPOINTERS(PNAME, P)                                             \
-  void SparseTensorStorageBase::getPointers(std::vector<P> **, uint64_t) {     \
-    FATAL_PIV("getPointers" #PNAME);                                           \
-  }
-MLIR_SPARSETENSOR_FOREVERY_FIXED_O(IMPL_GETPOINTERS)
-#undef IMPL_GETPOINTERS
-
-#define IMPL_GETINDICES(INAME, I)                                              \
-  void SparseTensorStorageBase::getIndices(std::vector<I> **, uint64_t) {      \
-    FATAL_PIV("getIndices" #INAME);                                            \
+#define IMPL_GETPOSITIONS(PNAME, P)                                            \
+  void SparseTensorStorageBase::getPositions(std::vector<P> **, uint64_t) {    \
+    FATAL_PIV("getPositions" #PNAME);                                          \
+  }
+MLIR_SPARSETENSOR_FOREVERY_FIXED_O(IMPL_GETPOSITIONS)
+#undef IMPL_GETPOSITIONS
+
+#define IMPL_GETCOORDINATES(CNAME, C)                                          \
+  void SparseTensorStorageBase::getCoordinates(std::vector<C> **, uint64_t) {  \
+    FATAL_PIV("getCoordinates" #CNAME);                                        \
   }
-MLIR_SPARSETENSOR_FOREVERY_FIXED_O(IMPL_GETINDICES)
-#undef IMPL_GETINDICES
+MLIR_SPARSETENSOR_FOREVERY_FIXED_O(IMPL_GETCOORDINATES)
+#undef IMPL_GETCOORDINATES
 
 #define IMPL_GETVALUES(VNAME, V)                                               \
   void SparseTensorStorageBase::getValues(std::vector<V> **) {                 \
     FATAL_PIV("getValues" #VNAME);                                             \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_GETVALUES)
 #undef IMPL_GETVALUES
 

mlir/lib/ExecutionEngine/SparseTensorRuntime.cpp

 // support library.  The functionality provided in this library is meant
 // to simplify benchmarking, testing, and debugging of MLIR code operating
 // on sparse tensors.  However, the provided functionality is **not**
 // part of core MLIR itself.
 //
 // The following memory-resident sparse storage schemes are supported:
 //
 // (a) A coordinate scheme for temporarily storing and lexicographically
-//     sorting a sparse tensor by index (SparseTensorCOO).
+//     sorting a sparse tensor by coordinate (SparseTensorCOO).
 //
 // (b) A "one-size-fits-all" sparse tensor storage scheme defined by
 //     per-dimension sparse/dense annnotations together with a dimension
 //     ordering used by MLIR compiler-generated code (SparseTensorStorage).
 //
 // The following external formats are supported:
 //
 // (1) Matrix Market Exchange (MME): *.mtx

 // functions to support non-permutations, we'll need to figure out how
 // to do so without putting undue burden on clients.
 
 /// Initializes sparse tensor from an external COO-flavored format.
 /// The `rank` argument is both dimension-rank and level-rank, and the
 /// `dim2lvl` argument must be a permutation.
 /// Used by `IMPL_CONVERTTOMLIRSPARSETENSOR`.
 //
-// TODO: generalize beyond 64-bit indices.
+// TODO: generalize beyond 64-bit overhead types.
 template <typename V>
 static SparseTensorStorage<uint64_t, uint64_t, V> *
 toMLIRSparseTensor(uint64_t rank, uint64_t nse, const uint64_t *dimSizes,
-                   const V *values, const uint64_t *dimIndices,
+                   const V *values, const uint64_t *dimCoordinates,
                    const uint64_t *dim2lvl, const DimLevelType *lvlTypes) {
 #ifndef NDEBUG
   // Verify that the sparsity values are supported.
   // TODO: update this check to match what we actually support.
   for (uint64_t i = 0; i < rank; ++i)
     if (lvlTypes[i] != DimLevelType::Dense &&
         lvlTypes[i] != DimLevelType::Compressed)
       MLIR_SPARSETENSOR_FATAL("unsupported level type: %d\n",
                               static_cast<uint8_t>(lvlTypes[i]));
 #endif
   // Verify that `dim2lvl` is a permutation of `[0..(rank-1)]`.
   // NOTE: The construction of `lvlSizes` and `lvl2dim` don't generalize
-  // to arbitrary `dim2lvl` mappings.  Whereas constructing `lvlInd` from
-  // `dimInd` does (though the details would have to be updated, just
+  // to arbitrary `dim2lvl` mappings.  Whereas constructing `lvlCoords` from
+  // `dimCoords` does (though the details would have to be updated, just
   // like for `IMPL_ADDELT`).
-  detail::PermutationRef d2l(rank, dim2lvl);
+  const detail::PermutationRef d2l(rank, dim2lvl);
   // Convert external format to internal COO.
-  auto lvlSizes = d2l.pushforward(rank, dimSizes);
+  const auto lvlSizes = d2l.pushforward(rank, dimSizes);
   auto *lvlCOO = new SparseTensorCOO<V>(lvlSizes, nse);
-  std::vector<uint64_t> lvlInd(rank);
-  const uint64_t *dimInd = dimIndices;
+  std::vector<uint64_t> lvlCoords(rank);
+  const uint64_t *dimCoords = dimCoordinates;
   for (uint64_t i = 0; i < nse; ++i) {
-    d2l.pushforward(rank, dimInd, lvlInd.data());
-    lvlCOO->add(lvlInd, values[i]);
-    dimInd += rank;
+    d2l.pushforward(rank, dimCoords, lvlCoords.data());
+    lvlCOO->add(lvlCoords, values[i]);
+    dimCoords += rank;
   }
   // Return sparse tensor storage format as opaque pointer.
-  auto lvl2dim = d2l.inverse();
+  const auto lvl2dim = d2l.inverse();
   auto *tensor = SparseTensorStorage<uint64_t, uint64_t, V>::newFromCOO(
       rank, dimSizes, rank, lvlTypes, lvl2dim.data(), *lvlCOO);
   delete lvlCOO;
   return tensor;
 }
 
 /// Converts a sparse tensor to an external COO-flavored format.
 /// Used by `IMPL_CONVERTFROMMLIRSPARSETENSOR`.
 //
 // TODO: Currently, values are copied from SparseTensorStorage to
 // SparseTensorCOO, then to the output.  We may want to reduce the number
 // of copies.
 //
-// TODO: generalize beyond 64-bit indices, no dim ordering, all dimensions
-// compressed
+// TODO: generalize beyond 64-bit overhead types, no dim ordering,
+// all dimensions compressed
 template <typename V>
 static void
 fromMLIRSparseTensor(const SparseTensorStorage<uint64_t, uint64_t, V> *tensor,
                      uint64_t *pRank, uint64_t *pNse, uint64_t **pShape,
-                     V **pValues, uint64_t **pIndices) {
+                     V **pValues, uint64_t **pCoordinates) {
   assert(tensor && "Received nullptr for tensor");
-  uint64_t dimRank = tensor->getDimRank();
+  const uint64_t dimRank = tensor->getDimRank();
   const auto &dimSizes = tensor->getDimSizes();
   std::vector<uint64_t> identityPerm(dimRank);
   std::iota(identityPerm.begin(), identityPerm.end(), 0);
   SparseTensorCOO<V> *coo =
       tensor->toCOO(dimRank, dimSizes.data(), dimRank, identityPerm.data());
 
   const std::vector<Element<V>> &elements = coo->getElements();
-  uint64_t nse = elements.size();
+  const uint64_t nse = elements.size();
 
   const auto &cooSizes = coo->getDimSizes();
   assert(cooSizes.size() == dimRank && "Rank mismatch");
-  uint64_t *shape = new uint64_t[dimRank];
-  std::memcpy((void *)shape, (const void *)cooSizes.data(),
+  uint64_t *dimShape = new uint64_t[dimRank];
+  std::memcpy(static_cast<void *>(dimShape),
+              static_cast<const void *>(cooSizes.data()),
               sizeof(uint64_t) * dimRank);
 
   V *values = new V[nse];
-  uint64_t *indices = new uint64_t[dimRank * nse];
+  uint64_t *coordinates = new uint64_t[dimRank * nse];
 
   for (uint64_t i = 0, base = 0; i < nse; ++i) {
     values[i] = elements[i].value;
     for (uint64_t d = 0; d < dimRank; ++d)
-      indices[base + d] = elements[i].indices[d];
+      coordinates[base + d] = elements[i].coords[d];
     base += dimRank;
   }
 
   delete coo;
   *pRank = dimRank;
   *pNse = nse;
-  *pShape = shape;
+  *pShape = dimShape;
   *pValues = values;
-  *pIndices = indices;
+  *pCoordinates = coordinates;
 }
 
 //===----------------------------------------------------------------------===//
 //
 // Utilities for manipulating `StridedMemRefType`.
 //
 //===----------------------------------------------------------------------===//
 

 
 //===----------------------------------------------------------------------===//
 //
 // Public functions which operate on MLIR buffers (memrefs) to interact
 // with sparse tensors (which are only visible as opaque pointers externally).
 //
 //===----------------------------------------------------------------------===//
 
-#define CASE(p, i, v, P, I, V)                                                 \
-  if (ptrTp == (p) && indTp == (i) && valTp == (v)) {                          \
+#define CASE(p, c, v, P, C, V)                                                 \
+  if (posTp == (p) && crdTp == (c) && valTp == (v)) {                          \
     switch (action) {                                                          \
     case Action::kEmpty:                                                       \
-      return SparseTensorStorage<P, I, V>::newEmpty(                           \
+      return SparseTensorStorage<P, C, V>::newEmpty(                           \
           dimRank, dimSizes, lvlRank, lvlSizes, lvlTypes, lvl2dim);            \
     case Action::kFromCOO: {                                                   \
       assert(ptr && "Received nullptr for SparseTensorCOO object");            \
       auto &coo = *static_cast<SparseTensorCOO<V> *>(ptr);                     \
-      return SparseTensorStorage<P, I, V>::newFromCOO(                         \
+      return SparseTensorStorage<P, C, V>::newFromCOO(                         \
           dimRank, dimSizes, lvlRank, lvlTypes, lvl2dim, coo);                 \
     }                                                                          \
     case Action::kSparseToSparse: {                                            \
       assert(ptr && "Received nullptr for SparseTensorStorage object");        \
       auto &tensor = *static_cast<SparseTensorStorageBase *>(ptr);             \
-      return SparseTensorStorage<P, I, V>::newFromSparseTensor(                \
+      return SparseTensorStorage<P, C, V>::newFromSparseTensor(                \
           dimRank, dimSizes, lvlRank, lvlSizes, lvlTypes, lvl2dim, dimRank,    \
           dim2lvl, tensor);                                                    \
     }                                                                          \
     case Action::kEmptyCOO:                                                    \
       return new SparseTensorCOO<V>(lvlRank, lvlSizes);                        \
     case Action::kToCOO: {                                                     \
       assert(ptr && "Received nullptr for SparseTensorStorage object");        \
-      auto &tensor = *static_cast<SparseTensorStorage<P, I, V> *>(ptr);        \
+      auto &tensor = *static_cast<SparseTensorStorage<P, C, V> *>(ptr);        \
       return tensor.toCOO(lvlRank, lvlSizes, dimRank, dim2lvl);                \
     }                                                                          \
     case Action::kToIterator: {                                                \
       assert(ptr && "Received nullptr for SparseTensorStorage object");        \
-      auto &tensor = *static_cast<SparseTensorStorage<P, I, V> *>(ptr);        \
+      auto &tensor = *static_cast<SparseTensorStorage<P, C, V> *>(ptr);        \
       auto *coo = tensor.toCOO(lvlRank, lvlSizes, dimRank, dim2lvl);           \
       return new SparseTensorIterator<V>(coo);                                 \
     }                                                                          \
     }                                                                          \
     MLIR_SPARSETENSOR_FATAL("unknown action: %d\n",                            \
                             static_cast<uint32_t>(action));                    \
   }
 

 // the first two arguments are "sizes" vs "shapes", (2) whether the "lvl"
 // arguments are actually storage-levels vs target tensor-dimensions,
 // (3) whether all the arguments are actually used/required.
 void *_mlir_ciface_newSparseTensor( // NOLINT
     StridedMemRefType<index_type, 1> *dimSizesRef,
     StridedMemRefType<index_type, 1> *lvlSizesRef,
     StridedMemRefType<DimLevelType, 1> *lvlTypesRef,
     StridedMemRefType<index_type, 1> *lvl2dimRef,
-    StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType ptrTp,
-    OverheadType indTp, PrimaryType valTp, Action action, void *ptr) {
+    StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType posTp,
+    OverheadType crdTp, PrimaryType valTp, Action action, void *ptr) {
   ASSERT_NO_STRIDE(dimSizesRef);
   ASSERT_NO_STRIDE(lvlSizesRef);
   ASSERT_NO_STRIDE(lvlTypesRef);
   ASSERT_NO_STRIDE(lvl2dimRef);
   ASSERT_NO_STRIDE(dim2lvlRef);
   const uint64_t dimRank = MEMREF_GET_USIZE(dimSizesRef);
   const uint64_t lvlRank = MEMREF_GET_USIZE(lvlSizesRef);
   ASSERT_USIZE_EQ(dim2lvlRef, dimRank);
   ASSERT_USIZE_EQ(lvlTypesRef, lvlRank);
   ASSERT_USIZE_EQ(lvl2dimRef, lvlRank);
   const index_type *dimSizes = MEMREF_GET_PAYLOAD(dimSizesRef);
   const index_type *lvlSizes = MEMREF_GET_PAYLOAD(lvlSizesRef);
   const DimLevelType *lvlTypes = MEMREF_GET_PAYLOAD(lvlTypesRef);
   const index_type *lvl2dim = MEMREF_GET_PAYLOAD(lvl2dimRef);
   const index_type *dim2lvl = MEMREF_GET_PAYLOAD(dim2lvlRef);
 
   // Rewrite kIndex to kU64, to avoid introducing a bunch of new cases.
   // This is safe because of the static_assert above.
-  if (ptrTp == OverheadType::kIndex)
-    ptrTp = OverheadType::kU64;
-  if (indTp == OverheadType::kIndex)
-    indTp = OverheadType::kU64;
+  if (posTp == OverheadType::kIndex)
+    posTp = OverheadType::kU64;
+  if (crdTp == OverheadType::kIndex)
+    crdTp = OverheadType::kU64;
 
   // Double matrices with all combinations of overhead storage.
   CASE(OverheadType::kU64, OverheadType::kU64, PrimaryType::kF64, uint64_t,
        uint64_t, double);
   CASE(OverheadType::kU64, OverheadType::kU32, PrimaryType::kF64, uint64_t,
        uint32_t, double);
   CASE(OverheadType::kU64, OverheadType::kU16, PrimaryType::kF64, uint64_t,
        uint16_t, double);
void *_mlir_ciface_newSparseTensor( // NOLINT
 
   // Complex matrices with wide overhead.
   CASE_SECSAME(OverheadType::kU64, PrimaryType::kC64, uint64_t, complex64);
   CASE_SECSAME(OverheadType::kU64, PrimaryType::kC32, uint64_t, complex32);
 
   // Unsupported case (add above if needed).
   // TODO: better pretty-printing of enum values!
   MLIR_SPARSETENSOR_FATAL(
-      "unsupported combination of types: <P=%d, I=%d, V=%d>\n",
-      static_cast<int>(ptrTp), static_cast<int>(indTp),
+      "unsupported combination of types: <P=%d, C=%d, V=%d>\n",
+      static_cast<int>(posTp), static_cast<int>(crdTp),
       static_cast<int>(valTp));
 }
 #undef CASE
 #undef CASE_SECSAME
 
 #define IMPL_SPARSEVALUES(VNAME, V)                                            \
   void _mlir_ciface_sparseValues##VNAME(StridedMemRefType<V, 1> *ref,          \
                                         void *tensor) {                        \
     assert(ref &&tensor);                                                      \
     std::vector<V> *v;                                                         \
     static_cast<SparseTensorStorageBase *>(tensor)->getValues(&v);             \
     assert(v);                                                                 \
     aliasIntoMemref(v->size(), v->data(), *ref);                               \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_SPARSEVALUES)
 #undef IMPL_SPARSEVALUES
 
 #define IMPL_GETOVERHEAD(NAME, TYPE, LIB)                                      \
   void _mlir_ciface_##NAME(StridedMemRefType<TYPE, 1> *ref, void *tensor,      \
-                           index_type d) {                                     \
+                           index_type lvl) {                                   \
     assert(ref &&tensor);                                                      \
     std::vector<TYPE> *v;                                                      \
-    static_cast<SparseTensorStorageBase *>(tensor)->LIB(&v, d);                \
+    static_cast<SparseTensorStorageBase *>(tensor)->LIB(&v, lvl);              \
     assert(v);                                                                 \
     aliasIntoMemref(v->size(), v->data(), *ref);                               \
   }
-#define IMPL_SPARSEPOINTERS(PNAME, P)                                          \
-  IMPL_GETOVERHEAD(sparsePointers##PNAME, P, getPointers)
-MLIR_SPARSETENSOR_FOREVERY_O(IMPL_SPARSEPOINTERS)
-#undef IMPL_SPARSEPOINTERS
-
-#define IMPL_SPARSEINDICES(INAME, I)                                           \
-  IMPL_GETOVERHEAD(sparseIndices##INAME, I, getIndices)
-MLIR_SPARSETENSOR_FOREVERY_O(IMPL_SPARSEINDICES)
-#undef IMPL_SPARSEINDICES
+#define IMPL_SPARSEPOSITIONS(PNAME, P)                                         \
+  IMPL_GETOVERHEAD(sparsePositions##PNAME, P, getPositions)
+MLIR_SPARSETENSOR_FOREVERY_O(IMPL_SPARSEPOSITIONS)
+#undef IMPL_SPARSEPOSITIONS
+
+#define IMPL_SPARSECOORDINATES(CNAME, C)                                       \
+  IMPL_GETOVERHEAD(sparseCoordinates##CNAME, C, getCoordinates)
+MLIR_SPARSETENSOR_FOREVERY_O(IMPL_SPARSECOORDINATES)
+#undef IMPL_SPARSECOORDINATES
 #undef IMPL_GETOVERHEAD
 
 // TODO: while this API design will work for arbitrary dim2lvl mappings,
-// we should probably move the `dimInd`-to-`lvlInd` computation into codegen
-// (since that could enable optimizations to remove the intermediate memref).
+// we should probably move the `dimCoords`-to-`lvlCoords` computation into
+// codegen (since that could enable optimizations to remove the intermediate
+// memref).
 #define IMPL_ADDELT(VNAME, V)                                                  \
   void *_mlir_ciface_addElt##VNAME(                                            \
       void *lvlCOO, StridedMemRefType<V, 0> *vref,                             \
-      StridedMemRefType<index_type, 1> *dimIndRef,                             \
+      StridedMemRefType<index_type, 1> *dimCoordsRef,                          \
       StridedMemRefType<index_type, 1> *dim2lvlRef) {                          \
     assert(lvlCOO &&vref);                                                     \
-    ASSERT_NO_STRIDE(dimIndRef);                                               \
+    ASSERT_NO_STRIDE(dimCoordsRef);                                            \
     ASSERT_NO_STRIDE(dim2lvlRef);                                              \
-    const uint64_t rank = MEMREF_GET_USIZE(dimIndRef);                         \
+    const uint64_t rank = MEMREF_GET_USIZE(dimCoordsRef);                      \
     ASSERT_USIZE_EQ(dim2lvlRef, rank);                                         \
-    const index_type *dimInd = MEMREF_GET_PAYLOAD(dimIndRef);                  \
+    const index_type *dimCoords = MEMREF_GET_PAYLOAD(dimCoordsRef);            \
     const index_type *dim2lvl = MEMREF_GET_PAYLOAD(dim2lvlRef);                \
-    std::vector<index_type> lvlInd(rank);                                      \
+    std::vector<index_type> lvlCoords(rank);                                   \
     for (uint64_t d = 0; d < rank; ++d)                                        \
-      lvlInd[dim2lvl[d]] = dimInd[d];                                          \
+      lvlCoords[dim2lvl[d]] = dimCoords[d];                                    \
     V *value = MEMREF_GET_PAYLOAD(vref);                                       \
-    static_cast<SparseTensorCOO<V> *>(lvlCOO)->add(lvlInd, *value);            \
+    static_cast<SparseTensorCOO<V> *>(lvlCOO)->add(lvlCoords, *value);         \
     return lvlCOO;                                                             \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_ADDELT)
 #undef IMPL_ADDELT
 
+// NOTE: the `cref` argument uses the same coordinate-space as the `iter`
+// (which can be either dim- or lvl-coords, depending on context).
 #define IMPL_GETNEXT(VNAME, V)                                                 \
   bool _mlir_ciface_getNext##VNAME(void *iter,                                 \
-                                   StridedMemRefType<index_type, 1> *iref,     \
+                                   StridedMemRefType<index_type, 1> *cref,     \
                                    StridedMemRefType<V, 0> *vref) {            \
     assert(iter &&vref);                                                       \
-    ASSERT_NO_STRIDE(iref);                                                    \
-    index_type *indx = MEMREF_GET_PAYLOAD(iref);                               \
+    ASSERT_NO_STRIDE(cref);                                                    \
+    index_type *coords = MEMREF_GET_PAYLOAD(cref);                             \
     V *value = MEMREF_GET_PAYLOAD(vref);                                       \
-    const uint64_t isize = MEMREF_GET_USIZE(iref);                             \
+    const uint64_t rank = MEMREF_GET_USIZE(cref);                              \
     const Element<V> *elem =                                                   \
         static_cast<SparseTensorIterator<V> *>(iter)->getNext();               \
     if (elem == nullptr)                                                       \
       return false;                                                            \
-    for (uint64_t r = 0; r < isize; r++)                                       \
-      indx[r] = elem->indices[r];                                              \
+    for (uint64_t d = 0; d < rank; d++)                                        \
+      coords[d] = elem->coords[d];                                             \
     *value = elem->value;                                                      \
     return true;                                                               \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_GETNEXT)
 #undef IMPL_GETNEXT
 
 #define IMPL_LEXINSERT(VNAME, V)                                               \
-  void _mlir_ciface_lexInsert##VNAME(void *tensor,                             \
-                                     StridedMemRefType<index_type, 1> *cref,   \
-                                     StridedMemRefType<V, 0> *vref) {          \
-    assert(tensor &&vref);                                                     \
-    ASSERT_NO_STRIDE(cref);                                                    \
-    index_type *cursor = MEMREF_GET_PAYLOAD(cref);                             \
-    assert(cursor);                                                            \
+  void _mlir_ciface_lexInsert##VNAME(                                          \
+      void *t, StridedMemRefType<index_type, 1> *lvlCoordsRef,                 \
+      StridedMemRefType<V, 0> *vref) {                                         \
+    assert(t &&vref);                                                          \
+    auto &tensor = *static_cast<SparseTensorStorageBase *>(t);                 \
+    ASSERT_NO_STRIDE(lvlCoordsRef);                                            \
+    index_type *lvlCoords = MEMREF_GET_PAYLOAD(lvlCoordsRef);                  \
+    assert(lvlCoords);                                                         \
     V *value = MEMREF_GET_PAYLOAD(vref);                                       \
-    static_cast<SparseTensorStorageBase *>(tensor)->lexInsert(cursor, *value); \
+    tensor.lexInsert(lvlCoords, *value);                                       \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_LEXINSERT)
 #undef IMPL_LEXINSERT
 
 #define IMPL_EXPINSERT(VNAME, V)                                               \
   void _mlir_ciface_expInsert##VNAME(                                          \
-      void *tensor, StridedMemRefType<index_type, 1> *cref,                    \
+      void *t, StridedMemRefType<index_type, 1> *lvlCoordsRef,                 \
       StridedMemRefType<V, 1> *vref, StridedMemRefType<bool, 1> *fref,         \
       StridedMemRefType<index_type, 1> *aref, index_type count) {              \
-    assert(tensor);                                                            \
-    ASSERT_NO_STRIDE(cref);                                                    \
+    assert(t);                                                                 \
+    auto &tensor = *static_cast<SparseTensorStorageBase *>(t);                 \
+    ASSERT_NO_STRIDE(lvlCoordsRef);                                            \
     ASSERT_NO_STRIDE(vref);                                                    \
     ASSERT_NO_STRIDE(fref);                                                    \
     ASSERT_NO_STRIDE(aref);                                                    \
     ASSERT_USIZE_EQ(vref, MEMREF_GET_USIZE(fref));                             \
-    index_type *cursor = MEMREF_GET_PAYLOAD(cref);                             \
+    index_type *lvlCoords = MEMREF_GET_PAYLOAD(lvlCoordsRef);                  \
     V *values = MEMREF_GET_PAYLOAD(vref);                                      \
     bool *filled = MEMREF_GET_PAYLOAD(fref);                                   \
     index_type *added = MEMREF_GET_PAYLOAD(aref);                              \
-    static_cast<SparseTensorStorageBase *>(tensor)->expInsert(                 \
-        cursor, values, filled, added, count);                                 \
+    tensor.expInsert(lvlCoords, values, filled, added, count);                 \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_EXPINSERT)
 #undef IMPL_EXPINSERT
 
 void *_mlir_ciface_createCheckedSparseTensorReader(
     char *filename, StridedMemRefType<index_type, 1> *dimShapeRef,
     PrimaryType valTp) {
   ASSERT_NO_STRIDE(dimShapeRef);
   const uint64_t dimRank = MEMREF_GET_USIZE(dimShapeRef);
   const index_type *dimShape = MEMREF_GET_PAYLOAD(dimShapeRef);
   auto *reader = SparseTensorReader::create(filename, dimRank, dimShape, valTp);
   return static_cast<void *>(reader);
 }
 
 // FIXME: update `SparseTensorCodegenPass` to use
 // `_mlir_ciface_getSparseTensorReaderDimSizes` instead.
 void _mlir_ciface_copySparseTensorReaderDimSizes(
-    void *p, StridedMemRefType<index_type, 1> *dref) {
+    void *p, StridedMemRefType<index_type, 1> *dimSizesRef) {
   assert(p);
   SparseTensorReader &reader = *static_cast<SparseTensorReader *>(p);
-  ASSERT_NO_STRIDE(dref);
-  const uint64_t dimRank = MEMREF_GET_USIZE(dref);
-  ASSERT_USIZE_EQ(dref, reader.getRank());
-  index_type *dimSizes = MEMREF_GET_PAYLOAD(dref);
+  ASSERT_NO_STRIDE(dimSizesRef);
+  const uint64_t dimRank = MEMREF_GET_USIZE(dimSizesRef);
+  ASSERT_USIZE_EQ(dimSizesRef, reader.getRank());
+  index_type *dimSizes = MEMREF_GET_PAYLOAD(dimSizesRef);
   const index_type *fileSizes = reader.getDimSizes();
   for (uint64_t d = 0; d < dimRank; ++d)
     dimSizes[d] = fileSizes[d];
 }
 
 void _mlir_ciface_getSparseTensorReaderDimSizes(
     StridedMemRefType<index_type, 1> *out, void *p) {
   assert(out && p);
   SparseTensorReader &reader = *static_cast<SparseTensorReader *>(p);
   auto *dimSizes = const_cast<uint64_t *>(reader.getDimSizes());
   aliasIntoMemref(reader.getRank(), dimSizes, *out);
 }
 
 #define IMPL_GETNEXT(VNAME, V)                                                 \
   void _mlir_ciface_getSparseTensorReaderNext##VNAME(                          \
-      void *p, StridedMemRefType<index_type, 1> *iref,                         \
+      void *p, StridedMemRefType<index_type, 1> *dimCoordsRef,                 \
       StridedMemRefType<V, 0> *vref) {                                         \
     assert(p &&vref);                                                          \
     auto &reader = *static_cast<SparseTensorReader *>(p);                      \
-    ASSERT_NO_STRIDE(iref);                                                    \
-    const uint64_t rank = MEMREF_GET_USIZE(iref);                              \
-    index_type *indices = MEMREF_GET_PAYLOAD(iref);                            \
+    ASSERT_NO_STRIDE(dimCoordsRef);                                            \
+    const uint64_t dimRank = MEMREF_GET_USIZE(dimCoordsRef);                   \
+    index_type *dimCoords = MEMREF_GET_PAYLOAD(dimCoordsRef);                  \
     V *value = MEMREF_GET_PAYLOAD(vref);                                       \
-    *value = reader.readCOOElement<V>(rank, indices);                          \
+    *value = reader.readElement<V>(dimRank, dimCoords);                        \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_GETNEXT)
 #undef IMPL_GETNEXT
 
+// FIXME: This function name is weird; should rename to
+// "sparseTensorReaderReadToBuffers".
 #define IMPL_GETNEXT(VNAME, V, CNAME, C)                                       \
   bool _mlir_ciface_getSparseTensorReaderRead##CNAME##VNAME(                   \
       void *p, StridedMemRefType<index_type, 1> *dim2lvlRef,                   \
       StridedMemRefType<C, 1> *cref, StridedMemRefType<V, 1> *vref) {          \
     assert(p);                                                                 \
     auto &reader = *static_cast<SparseTensorReader *>(p);                      \
     ASSERT_NO_STRIDE(cref);                                                    \
     ASSERT_NO_STRIDE(vref);                                                    \
     ASSERT_NO_STRIDE(dim2lvlRef);                                              \
     const uint64_t cSize = MEMREF_GET_USIZE(cref);                             \
     const uint64_t vSize = MEMREF_GET_USIZE(vref);                             \
     const uint64_t lvlRank = reader.getRank();                                 \
     assert(vSize *lvlRank <= cSize);                                           \
-    assert(vSize >= reader.getNNZ() && "Not enough space in buffers");         \
+    assert(vSize >= reader.getNSE() && "Not enough space in buffers");         \
     ASSERT_USIZE_EQ(dim2lvlRef, lvlRank);                                      \
     (void)cSize;                                                               \
     (void)vSize;                                                               \
     (void)lvlRank;                                                             \
     C *lvlCoordinates = MEMREF_GET_PAYLOAD(cref);                              \
     V *values = MEMREF_GET_PAYLOAD(vref);                                      \
     index_type *dim2lvl = MEMREF_GET_PAYLOAD(dim2lvlRef);                      \
     return reader.readToBuffers<C, V>(lvlRank, dim2lvl, lvlCoordinates,        \
                                       values);                                 \
   }
 MLIR_SPARSETENSOR_FOREVERY_V_O(IMPL_GETNEXT)
 #undef IMPL_GETNEXT
 
 void *_mlir_ciface_newSparseTensorFromReader(
     void *p, StridedMemRefType<index_type, 1> *lvlSizesRef,
     StridedMemRefType<DimLevelType, 1> *lvlTypesRef,
     StridedMemRefType<index_type, 1> *lvl2dimRef,
-    StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType ptrTp,
-    OverheadType indTp, PrimaryType valTp) {
+    StridedMemRefType<index_type, 1> *dim2lvlRef, OverheadType posTp,
+    OverheadType crdTp, PrimaryType valTp) {
   assert(p);
   SparseTensorReader &reader = *static_cast<SparseTensorReader *>(p);
   ASSERT_NO_STRIDE(lvlSizesRef);
   ASSERT_NO_STRIDE(lvlTypesRef);
   ASSERT_NO_STRIDE(lvl2dimRef);
   ASSERT_NO_STRIDE(dim2lvlRef);
   const uint64_t dimRank = reader.getRank();
   const uint64_t lvlRank = MEMREF_GET_USIZE(lvlSizesRef);
   ASSERT_USIZE_EQ(lvlTypesRef, lvlRank);
   ASSERT_USIZE_EQ(lvl2dimRef, lvlRank);
   ASSERT_USIZE_EQ(dim2lvlRef, dimRank);
   (void)dimRank;
   const index_type *lvlSizes = MEMREF_GET_PAYLOAD(lvlSizesRef);
   const DimLevelType *lvlTypes = MEMREF_GET_PAYLOAD(lvlTypesRef);
   const index_type *lvl2dim = MEMREF_GET_PAYLOAD(lvl2dimRef);
   const index_type *dim2lvl = MEMREF_GET_PAYLOAD(dim2lvlRef);
   //
   // FIXME(wrengr): Really need to define a separate x-macro for handling
   // all this. (Or ideally some better, entirely-different approach)
-#define CASE(p, i, v, P, I, V)                                                 \
-  if (ptrTp == OverheadType::p && indTp == OverheadType::i &&                  \
+#define CASE(p, c, v, P, C, V)                                                 \
+  if (posTp == OverheadType::p && crdTp == OverheadType::c &&                  \
       valTp == PrimaryType::v)                                                 \
-    return static_cast<void *>(reader.readSparseTensor<P, I, V>(               \
+    return static_cast<void *>(reader.readSparseTensor<P, C, V>(               \
         lvlRank, lvlSizes, lvlTypes, lvl2dim, dim2lvl));
 #define CASE_SECSAME(p, v, P, V) CASE(p, p, v, P, P, V)
   // Rewrite kIndex to kU64, to avoid introducing a bunch of new cases.
   // This is safe because of the static_assert above.
-  if (ptrTp == OverheadType::kIndex)
-    ptrTp = OverheadType::kU64;
-  if (indTp == OverheadType::kIndex)
-    indTp = OverheadType::kU64;
+  if (posTp == OverheadType::kIndex)
+    posTp = OverheadType::kU64;
+  if (crdTp == OverheadType::kIndex)
+    crdTp = OverheadType::kU64;
   // Double matrices with all combinations of overhead storage.
   CASE(kU64, kU64, kF64, uint64_t, uint64_t, double);
   CASE(kU64, kU32, kF64, uint64_t, uint32_t, double);
   CASE(kU64, kU16, kF64, uint64_t, uint16_t, double);
   CASE(kU64, kU8, kF64, uint64_t, uint8_t, double);
   CASE(kU32, kU64, kF64, uint32_t, uint64_t, double);
   CASE(kU32, kU32, kF64, uint32_t, uint32_t, double);
   CASE(kU32, kU16, kF64, uint32_t, uint16_t, double);
#define CASE_SECSAME(p, v, P, V) CASE(p, p, v, P, P, V)
   CASE_SECSAME(kU8, kI8, uint8_t, int8_t);
   // Complex matrices with wide overhead.
   CASE_SECSAME(kU64, kC64, uint64_t, complex64);
   CASE_SECSAME(kU64, kC32, uint64_t, complex32);
 
   // Unsupported case (add above if needed).
   // TODO: better pretty-printing of enum values!
   MLIR_SPARSETENSOR_FATAL(
-      "unsupported combination of types: <P=%d, I=%d, V=%d>\n",
-      static_cast<int>(ptrTp), static_cast<int>(indTp),
+      "unsupported combination of types: <P=%d, C=%d, V=%d>\n",
+      static_cast<int>(posTp), static_cast<int>(crdTp),
       static_cast<int>(valTp));
 #undef CASE_SECSAME
 #undef CASE
 }
 
 void _mlir_ciface_outSparseTensorWriterMetaData(
-    void *p, index_type rank, index_type nnz,
-    StridedMemRefType<index_type, 1> *dref) {
+    void *p, index_type dimRank, index_type nse,
+    StridedMemRefType<index_type, 1> *dimSizesRef) {
   assert(p);
-  ASSERT_NO_STRIDE(dref);
-  assert(rank != 0);
-  index_type *dimSizes = MEMREF_GET_PAYLOAD(dref);
+  ASSERT_NO_STRIDE(dimSizesRef);
+  assert(dimRank != 0);
+  index_type *dimSizes = MEMREF_GET_PAYLOAD(dimSizesRef);
   SparseTensorWriter &file = *static_cast<SparseTensorWriter *>(p);
-  file << rank << " " << nnz << std::endl;
-  for (index_type r = 0; r < rank - 1; ++r)
-    file << dimSizes[r] << " ";
-  file << dimSizes[rank - 1] << std::endl;
+  file << dimRank << " " << nse << std::endl;
+  for (index_type d = 0; d < dimRank - 1; ++d)
+    file << dimSizes[d] << " ";
+  file << dimSizes[dimRank - 1] << std::endl;
 }
 
 #define IMPL_OUTNEXT(VNAME, V)                                                 \
   void _mlir_ciface_outSparseTensorWriterNext##VNAME(                          \
-      void *p, index_type rank, StridedMemRefType<index_type, 1> *iref,        \
+      void *p, index_type dimRank,                                             \
+      StridedMemRefType<index_type, 1> *dimCoordsRef,                          \
       StridedMemRefType<V, 0> *vref) {                                         \
     assert(p &&vref);                                                          \
-    ASSERT_NO_STRIDE(iref);                                                    \
-    index_type *indices = MEMREF_GET_PAYLOAD(iref);                            \
+    ASSERT_NO_STRIDE(dimCoordsRef);                                            \
+    const index_type *dimCoords = MEMREF_GET_PAYLOAD(dimCoordsRef);            \
     SparseTensorWriter &file = *static_cast<SparseTensorWriter *>(p);          \
-    for (index_type r = 0; r < rank; ++r)                                      \
-      file << (indices[r] + 1) << " ";                                         \
+    for (index_type d = 0; d < dimRank; ++d)                                   \
+      file << (dimCoords[d] + 1) << " ";                                       \
     V *value = MEMREF_GET_PAYLOAD(vref);                                       \
     file << *value << std::endl;                                               \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_OUTNEXT)
 #undef IMPL_OUTNEXT
 
 //===----------------------------------------------------------------------===//
 //
void readSparseTensorShape(char *filename, std::vector<uint64_t> *out) {
   const uint64_t *dimSizes = reader.getDimSizes();
   out->reserve(dimRank);
   out->assign(dimSizes, dimSizes + dimRank);
 }
 
 // We can't use `static_cast` here because `DimLevelType` is an enum-class.
 #define IMPL_CONVERTTOMLIRSPARSETENSOR(VNAME, V)                               \
   void *convertToMLIRSparseTensor##VNAME(                                      \
-      uint64_t rank, uint64_t nse, uint64_t *shape, V *values,                 \
-      uint64_t *indices, uint64_t *perm, uint8_t *sparse) {                    \
-    return toMLIRSparseTensor<V>(rank, nse, shape, values, indices, perm,      \
-                                 reinterpret_cast<DimLevelType *>(sparse));    \
+      uint64_t rank, uint64_t nse, uint64_t *dimSizes, V *values,              \
+      uint64_t *dimCoordinates, uint64_t *dim2lvl, uint8_t *lvlTypes) {        \
+    return toMLIRSparseTensor<V>(rank, nse, dimSizes, values, dimCoordinates,  \
+                                 dim2lvl,                                      \
+                                 reinterpret_cast<DimLevelType *>(lvlTypes));  \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_CONVERTTOMLIRSPARSETENSOR)
 #undef IMPL_CONVERTTOMLIRSPARSETENSOR
 
 #define IMPL_CONVERTFROMMLIRSPARSETENSOR(VNAME, V)                             \
-  void convertFromMLIRSparseTensor##VNAME(void *tensor, uint64_t *pRank,       \
-                                          uint64_t *pNse, uint64_t **pShape,   \
-                                          V **pValues, uint64_t **pIndices) {  \
+  void convertFromMLIRSparseTensor##VNAME(                                     \
+      void *tensor, uint64_t *pRank, uint64_t *pNse, uint64_t **pShape,        \
+      V **pValues, uint64_t **pCoordinates) {                                  \
     fromMLIRSparseTensor<V>(                                                   \
         static_cast<SparseTensorStorage<uint64_t, uint64_t, V> *>(tensor),     \
-        pRank, pNse, pShape, pValues, pIndices);                               \
+        pRank, pNse, pShape, pValues, pCoordinates);                           \
   }
 MLIR_SPARSETENSOR_FOREVERY_V(IMPL_CONVERTFROMMLIRSPARSETENSOR)
 #undef IMPL_CONVERTFROMMLIRSPARSETENSOR
 
 // FIXME: update `SparseTensorCodegenPass` to use
 // `_mlir_ciface_createCheckedSparseTensorReader` instead.
 void *createSparseTensorReader(char *filename) {
   SparseTensorReader *reader = new SparseTensorReader(filename);
   reader->openFile();
   reader->readHeader();
   return static_cast<void *>(reader);
 }
 
 index_type getSparseTensorReaderRank(void *p) {
   return static_cast<SparseTensorReader *>(p)->getRank();
 }
 
 bool getSparseTensorReaderIsSymmetric(void *p) {
   return static_cast<SparseTensorReader *>(p)->isSymmetric();
 }
 
-index_type getSparseTensorReaderNNZ(void *p) {
-  return static_cast<SparseTensorReader *>(p)->getNNZ();
+index_type getSparseTensorReaderNSE(void *p) {
+  return static_cast<SparseTensorReader *>(p)->getNSE();
 }
 
 index_type getSparseTensorReaderDimSize(void *p, index_type d) {
   return static_cast<SparseTensorReader *>(p)->getDimSize(d);
 }
 
 void delSparseTensorReader(void *p) {
   delete static_cast<SparseTensorReader *>(p);

mlir/test/CAPI/sparse_tensor.c

 static int testRoundtripEncoding(MlirContext ctx) {
   fprintf(stderr, "testRoundtripEncoding()\n");
   // clang-format off
   const char *originalAsm =
     "#sparse_tensor.encoding<{ "
     "dimLevelType = [ \"dense\", \"compressed\", \"compressed\"], "
     "dimOrdering = affine_map<(d0, d1, d2) -> (d0, d1, d2)>, "
     "higherOrdering = affine_map<(d0, d1)[s0] -> (s0, d0, d1)>, "
-    "pointerBitWidth = 32, indexBitWidth = 64 }>";
+    "posWidth = 32, crdWidth = 64 }>";
   // clang-format on
   MlirAttribute originalAttr =
       mlirAttributeParseGet(ctx, mlirStringRefCreateFromCString(originalAsm));
   // CHECK: isa: 1
   fprintf(stderr, "isa: %d\n",
           mlirAttributeIsASparseTensorEncodingAttr(originalAttr));
   MlirAffineMap dimOrdering =
       mlirSparseTensorEncodingAttrGetDimOrdering(originalAttr);
   // CHECK: (d0, d1, d2) -> (d0, d1, d2)
   mlirAffineMapDump(dimOrdering);
   MlirAffineMap higherOrdering =
       mlirSparseTensorEncodingAttrGetHigherOrdering(originalAttr);
   // CHECK: (d0, d1)[s0] -> (s0, d0, d1)
   mlirAffineMapDump(higherOrdering);
   // CHECK: level_type: 4
   // CHECK: level_type: 8
   // CHECK: level_type: 8
-  int numLevelTypes = mlirSparseTensorEncodingGetNumDimLevelTypes(originalAttr);
+  int lvlRank = mlirSparseTensorEncodingGetLvlRank(originalAttr);
   enum MlirSparseTensorDimLevelType *levelTypes =
-      malloc(sizeof(enum MlirSparseTensorDimLevelType) * numLevelTypes);
-  for (int i = 0; i < numLevelTypes; ++i) {
-    levelTypes[i] =
-        mlirSparseTensorEncodingAttrGetDimLevelType(originalAttr, i);
-    fprintf(stderr, "level_type: %d\n", levelTypes[i]);
+      malloc(sizeof(enum MlirSparseTensorDimLevelType) * lvlRank);
+  for (int l = 0; l < lvlRank; ++l) {
+    levelTypes[l] =
+        mlirSparseTensorEncodingAttrGetDimLevelType(originalAttr, l);
+    fprintf(stderr, "level_type: %d\n", levelTypes[l]);
   }
-  // CHECK: pointer: 32
-  int pointerBitWidth =
-      mlirSparseTensorEncodingAttrGetPointerBitWidth(originalAttr);
-  fprintf(stderr, "pointer: %d\n", pointerBitWidth);
-  // CHECK: index: 64
-  int indexBitWidth =
-      mlirSparseTensorEncodingAttrGetIndexBitWidth(originalAttr);
-  fprintf(stderr, "index: %d\n", indexBitWidth);
-
-  MlirAttribute newAttr = mlirSparseTensorEncodingAttrGet(
-      ctx, numLevelTypes, levelTypes, dimOrdering, higherOrdering,
-      pointerBitWidth, indexBitWidth);
+  // CHECK: posWidth: 32
+  int posWidth = mlirSparseTensorEncodingAttrGetPosWidth(originalAttr);
+  fprintf(stderr, "posWidth: %d\n", posWidth);
+  // CHECK: crdWidth: 64
+  int crdWidth = mlirSparseTensorEncodingAttrGetCrdWidth(originalAttr);
+  fprintf(stderr, "crdWidth: %d\n", crdWidth);
+
+  MlirAttribute newAttr =
+      mlirSparseTensorEncodingAttrGet(ctx, lvlRank, levelTypes, dimOrdering,
+                                      higherOrdering, posWidth, crdWidth);
   mlirAttributeDump(newAttr); // For debugging filecheck output.
   // CHECK: equal: 1
   fprintf(stderr, "equal: %d\n", mlirAttributeEqual(originalAttr, newAttr));
 
   free(levelTypes);
   return 0;
 }
 

mlir/test/Dialect/SparseTensor/codegen.mlir

 // RUN: mlir-opt %s --sparse-tensor-codegen  --canonicalize -cse | FileCheck %s
 
 #SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
 
 #SparseVector = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed" ],
-  indexBitWidth = 64,
-  pointerBitWidth = 32
+  crdWidth = 64,
+  posWidth = 32
 }>
 
 #Dense2D = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "dense" ],
-  indexBitWidth = 64,
-  pointerBitWidth = 32
+  crdWidth = 64,
+  posWidth = 32
 }>
 
 #Row = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed", "dense" ],
-  indexBitWidth = 64,
-  pointerBitWidth = 32
+  crdWidth = 64,
+  posWidth = 32
 }>
 
 #CSR = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "compressed" ],
-  indexBitWidth = 64,
-  pointerBitWidth = 32
+  crdWidth = 64,
+  posWidth = 32
 }>
 
 #UCSR = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "compressed-no" ]
 }>
 
 #CSC = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "compressed" ],
   dimOrdering = affine_map<(i, j) -> (j, i)>
 }>
 
 #DCSR = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed", "compressed" ],
-  indexBitWidth = 64,
-  pointerBitWidth = 32
+  crdWidth = 64,
+  posWidth = 32
 }>
 
 #Dense3D = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "dense", "dense" ],
   dimOrdering = affine_map<(i, j, k) -> (k, i, j)>
 }>
 
 #Coo = #sparse_tensor.encoding<{

 //
 // Querying for dimension 1 in the tensor type needs to be permuted
 // into querying for dimension 2 in the stored sparse tensor scheme,
 // since the latter honors the dimOrdering.
 //
 // CHECK-LABEL: func @sparse_dense_3d_dyn(
 //  CHECK-SAME: %[[A0:.*]]: memref<?xf64>,
 //  CHECK-SAME: %[[A1:.*]]: !sparse_tensor.storage_specifier
-//       CHECK: %[[A2:.*]] = sparse_tensor.storage_specifier.get %[[A1]] dim_sz at 2
+//       CHECK: %[[A2:.*]] = sparse_tensor.storage_specifier.get %[[A1]] lvl_sz at 2
 //       CHECK: return %[[A2]] : index
 func.func @sparse_dense_3d_dyn(%arg0: tensor<?x?x?xf64, #Dense3D>) -> index {
   %c = arith.constant 1 : index
   %0 = tensor.dim %arg0, %c : tensor<?x?x?xf64, #Dense3D>
   return %0 : index
 }
 
-// CHECK-LABEL: func @sparse_pointers_dcsr(
+// CHECK-LABEL: func @sparse_positions_dcsr(
 //  CHECK-SAME: %[[A0:.*0]]: memref<?xi32>,
 //  CHECK-SAME: %[[A1:.*1]]: memref<?xi64>,
 //  CHECK-SAME: %[[A2:.*2]]: memref<?xi32>,
 //  CHECK-SAME: %[[A3:.*3]]: memref<?xi64>,
 //  CHECK-SAME: %[[A4:.*4]]: memref<?xf64>,
 //  CHECK-SAME: %[[A5:.*5]]: !sparse_tensor.storage_specifier
 //       CHECK: return %[[A2]] : memref<?xi32>
-func.func @sparse_pointers_dcsr(%arg0: tensor<?x?xf64, #DCSR>) -> memref<?xi32> {
-  %0 = sparse_tensor.pointers %arg0 { dimension = 1 : index } : tensor<?x?xf64, #DCSR> to memref<?xi32>
+func.func @sparse_positions_dcsr(%arg0: tensor<?x?xf64, #DCSR>) -> memref<?xi32> {
+  %0 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<?x?xf64, #DCSR> to memref<?xi32>
   return %0 : memref<?xi32>
 }
 
 // CHECK-LABEL: func @sparse_indices_dcsr(
 //  CHECK-SAME: %[[A0:.*0]]: memref<?xi32>,
 //  CHECK-SAME: %[[A1:.*1]]: memref<?xi64>,
 //  CHECK-SAME: %[[A2:.*2]]: memref<?xi32>,
 //  CHECK-SAME: %[[A3:.*3]]: memref<?xi64>,
 //  CHECK-SAME: %[[A4:.*4]]: memref<?xf64>,
 //  CHECK-SAME: %[[A5:.*5]]: !sparse_tensor.storage_specifier
 //       CHECK: return %[[A3]] : memref<?xi64>
 func.func @sparse_indices_dcsr(%arg0: tensor<?x?xf64, #DCSR>) -> memref<?xi64> {
-  %0 = sparse_tensor.indices %arg0 { dimension = 1 : index } : tensor<?x?xf64, #DCSR> to memref<?xi64>
+  %0 = sparse_tensor.coordinates %arg0 { level = 1 : index } : tensor<?x?xf64, #DCSR> to memref<?xi64>
   return %0 : memref<?xi64>
 }
 
 // CHECK-LABEL: func @sparse_values_dcsr(
 //  CHECK-SAME: %[[A0:.*0]]: memref<?xi32>,
 //  CHECK-SAME: %[[A1:.*1]]: memref<?xi64>,
 //  CHECK-SAME: %[[A2:.*2]]: memref<?xi32>,
 //  CHECK-SAME: %[[A3:.*3]]: memref<?xi64>,

 // CHECK-LABEL:   func.func @sparse_indices_coo(
 //  CHECK-SAME: %[[A0:.*0]]: memref<?xindex>,
 //  CHECK-SAME: %[[A1:.*1]]: memref<?xindex>,
 //  CHECK-SAME: %[[A2:.*2]]: memref<?xindex>,
 //  CHECK-SAME: %[[A3:.*3]]: memref<?xindex>,
 //  CHECK-SAME: %[[A4:.*4]]: memref<?xf64>,
 //  CHECK-SAME: %[[A5:.*5]]: !sparse_tensor.storage_specifier
 //       CHECK: %[[C2:.*]] = arith.constant 2 : index
-//       CHECK: %[[S0:.*]] = sparse_tensor.storage_specifier.get %[[A5]]  idx_mem_sz at 1
+//       CHECK: %[[S0:.*]] = sparse_tensor.storage_specifier.get %[[A5]]  crd_mem_sz at 1
 //       CHECK: %[[S2:.*]] = arith.divui %[[S0]], %[[C2]] : index
 //       CHECK: %[[R1:.*]] = memref.subview %[[A3]][0] {{\[}}%[[S2]]] [2] : memref<?xindex> to memref<?xindex, strided<[2]>>
 //       CHECK: %[[R2:.*]] = memref.cast %[[R1]] : memref<?xindex, strided<[2]>> to memref<?xindex, strided<[?], offset: ?>>
 //       CHECK: return %[[R2]] : memref<?xindex, strided<[?], offset: ?>>
 func.func @sparse_indices_coo(%arg0: tensor<?x?x?xf64, #ccoo>) -> memref<?xindex, strided<[?], offset: ?>> {
-  %0 = sparse_tensor.indices  %arg0 { dimension = 1 : index } : tensor<?x?x?xf64, #ccoo> to memref<?xindex, strided<[?], offset: ?>>
+  %0 = sparse_tensor.coordinates  %arg0 { level = 1 : index } : tensor<?x?x?xf64, #ccoo> to memref<?xindex, strided<[?], offset: ?>>
   return %0 : memref<?xindex, strided<[?], offset: ?>>
 }
 
 // CHECK-LABEL:   func.func @sparse_indices_buffer_coo(
 //  CHECK-SAME: %[[A0:.*0]]: memref<?xindex>,
 //  CHECK-SAME: %[[A1:.*1]]: memref<?xindex>,
 //  CHECK-SAME: %[[A2:.*2]]: memref<?xindex>,
 //  CHECK-SAME: %[[A3:.*3]]: memref<?xindex>,
 //  CHECK-SAME: %[[A4:.*4]]: memref<?xf64>,
 //  CHECK-SAME: %[[A5:.*5]]: !sparse_tensor.storage_specifier
 //       CHECK: return %[[A3]] : memref<?xindex>
 func.func @sparse_indices_buffer_coo(%arg0: tensor<?x?x?xf64, #ccoo>) -> memref<?xindex> {
-  %0 = sparse_tensor.indices_buffer  %arg0 : tensor<?x?x?xf64, #ccoo> to memref<?xindex>
+  %0 = sparse_tensor.coordinates_buffer  %arg0 : tensor<?x?x?xf64, #ccoo> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // CHECK-LABEL: func @sparse_noe(
 //  CHECK-SAME: %[[A0:.*]]: memref<?xi32>,
 //  CHECK-SAME: %[[A1:.*]]: memref<?xi64>,
 //  CHECK-SAME: %[[A2:.*]]: memref<?xf64>,
 //  CHECK-SAME: %[[A3:.*]]: !sparse_tensor.storage_specifier

 //   CHECK-DAG:     %[[A2:.*]] = arith.constant 0 : index
 //       CHECK:     %[[A3:.*]] = memref.alloc() : memref<16xindex>
 //       CHECK:     %[[A4:.*]] = memref.cast %[[A3]] : memref<16xindex> to memref<?xindex>
 //       CHECK:     %[[A5:.*]] = memref.alloc() : memref<16xindex>
 //       CHECK:     %[[A6:.*]] = memref.cast %[[A5]] : memref<16xindex> to memref<?xindex>
 //       CHECK:     %[[A7:.*]] = memref.alloc() : memref<16xf64>
 //       CHECK:     %[[A8:.*]] = memref.cast %[[A7]] : memref<16xf64> to memref<?xf64>
 //       CHECK:     %[[A9:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier
-//       CHECK:     %[[A11:.*]] = sparse_tensor.storage_specifier.set %[[A9]]  dim_sz at 0 with %[[A0]] : !sparse_tensor.storage_specifier
-//       CHECK:     %[[A12:.*]] = sparse_tensor.storage_specifier.set %[[A11]]  dim_sz at 1 with %[[A1]] : !sparse_tensor.storage_specifier
-//       CHECK:     %[[A14:.*]] = sparse_tensor.storage_specifier.get %[[A12]]  ptr_mem_sz at 1 : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A11:.*]] = sparse_tensor.storage_specifier.set %[[A9]]  lvl_sz at 0 with %[[A0]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A12:.*]] = sparse_tensor.storage_specifier.set %[[A11]]  lvl_sz at 1 with %[[A1]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A14:.*]] = sparse_tensor.storage_specifier.get %[[A12]]  pos_mem_sz at 1 : !sparse_tensor.storage_specifier
 //       CHECK:     %[[A15:.*]], %[[A17:.*]] = sparse_tensor.push_back %[[A14]], %[[A4]], %[[A2]] : index, memref<?xindex>, index
-//       CHECK:     %[[A18:.*]] = sparse_tensor.storage_specifier.set %[[A12]]  ptr_mem_sz at 1 with %[[A17]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A18:.*]] = sparse_tensor.storage_specifier.set %[[A12]]  pos_mem_sz at 1 with %[[A17]] : !sparse_tensor.storage_specifier
 //       CHECK:     %[[A23:.*]], %[[A25:.*]] = sparse_tensor.push_back %[[A17]], %[[A15]], %[[A2]], %[[A0]] : index, memref<?xindex>, index, index
-//       CHECK:     %[[A26:.*]] = sparse_tensor.storage_specifier.set %[[A18]]  ptr_mem_sz at 1 with %[[A25]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A26:.*]] = sparse_tensor.storage_specifier.set %[[A18]]  pos_mem_sz at 1 with %[[A25]] : !sparse_tensor.storage_specifier
 //       CHECK:     return %[[A23]], %[[A6]], %[[A8]], %[[A26]] : memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
 func.func @sparse_alloc_csc(%arg0: index) -> tensor<10x?xf64, #CSC> {
   %0 = bufferization.alloc_tensor(%arg0) : tensor<10x?xf64, #CSC>
   %1 = sparse_tensor.load %0 : tensor<10x?xf64, #CSC>
   return %1 : tensor<10x?xf64, #CSC>
 }
 
 // CHECK-LABEL:   func.func @sparse_alloc_3d() -> (memref<?xf64>, !sparse_tensor.storage_specifier
 //   CHECK-DAG:     %[[A0:.*]] = arith.constant 6000 : index
 //   CHECK-DAG:     %[[A1:.*]] = arith.constant 20 : index
 //   CHECK-DAG:     %[[A2:.*]] = arith.constant 10 : index
 //   CHECK-DAG:     %[[A3:.*]] = arith.constant 30 : index
 //   CHECK-DAG:     %[[A4:.*]] = arith.constant 0.000000e+00 : f64
 //       CHECK:     %[[A5:.*]] = memref.alloc() : memref<6000xf64>
 //       CHECK:     %[[A6:.*]] = memref.cast %[[A5]] : memref<6000xf64> to memref<?xf64>
 //       CHECK:     %[[A7:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier
-//       CHECK:     %[[A8:.*]] = sparse_tensor.storage_specifier.set %[[A7]]  dim_sz at 0 with %[[A3]] : !sparse_tensor.storage_specifier
-//       CHECK:     %[[A9:.*]] = sparse_tensor.storage_specifier.set %[[A8]]  dim_sz at 1 with %[[A2]] : !sparse_tensor.storage_specifier
-//       CHECK:     %[[A10:.*]] = sparse_tensor.storage_specifier.set %[[A9]]  dim_sz at 2 with %[[A1]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A8:.*]] = sparse_tensor.storage_specifier.set %[[A7]]  lvl_sz at 0 with %[[A3]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A9:.*]] = sparse_tensor.storage_specifier.set %[[A8]]  lvl_sz at 1 with %[[A2]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A10:.*]] = sparse_tensor.storage_specifier.set %[[A9]]  lvl_sz at 2 with %[[A1]] : !sparse_tensor.storage_specifier
 //       CHECK:     %[[A12:.*]] = sparse_tensor.storage_specifier.get %[[A10]]  val_mem_sz : !sparse_tensor.storage_specifier
 //       CHECK:     %[[A15:.*]], %[[A14:.*]] = sparse_tensor.push_back %[[A12]], %[[A6]], %[[A4]], %[[A0]] : index, memref<?xf64>, f64, index
 //       CHECK:     %[[A16:.*]] = sparse_tensor.storage_specifier.set %[[A10]]  val_mem_sz with %[[A14]] : !sparse_tensor.storage_specifier
 //       CHECK:     return %[[A15]], %[[A16]] : memref<?xf64>, !sparse_tensor.storage_specifier
 func.func @sparse_alloc_3d() -> tensor<10x20x30xf64, #Dense3D> {
   %0 = bufferization.alloc_tensor() : tensor<10x20x30xf64, #Dense3D>
   %1 = sparse_tensor.load %0 : tensor<10x20x30xf64, #Dense3D>
   return %1 : tensor<10x20x30xf64, #Dense3D>

 //       CHECK:       %[[A22:.*]]:4 = func.call @_insert_dense_compressed_8_8_f64_64_32(%[[A16]], %[[A17]], %[[A18]], %[[A19]], %[[A8]], %[[A20]], %[[A21]]) : (memref<?xi32>, memref<?xi64>, memref<?xf64>, !sparse_tensor.storage_specifier
 //       CHECK:       memref.store %[[A11]], %[[A4]]{{\[}}%[[A20]]] : memref<?xf64>
 //       CHECK:       memref.store %[[A10]], %[[A5]]{{\[}}%[[A20]]] : memref<?xi1>
 //       CHECK:       scf.yield %[[A22]]#0, %[[A22]]#1, %[[A22]]#2, %[[A22]]#3 : memref<?xi32>, memref<?xi64>, memref<?xf64>, !sparse_tensor.storage_specifier
 //       CHECK:     }
 //       CHECK:     memref.dealloc %[[A4]] : memref<?xf64>
 //       CHECK:     memref.dealloc %[[A5]] : memref<?xi1>
 //       CHECK:     memref.dealloc %[[A6]] : memref<?xindex>
-//       CHECK:     %[[A25:.*]] = sparse_tensor.storage_specifier.get %[[A24:.*]]#3  ptr_mem_sz at 1 : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A25:.*]] = sparse_tensor.storage_specifier.get %[[A24:.*]]#3  pos_mem_sz at 1 : !sparse_tensor.storage_specifier
 //       CHECK:     %[[A26:.*]] = memref.load %[[A24]]#0{{\[}}%[[A13]]] : memref<?xi32>
 //       CHECK:     %[[A27:.*]] = scf.for %[[A28:.*]] = %[[A12]] to %[[A25]] step %[[A12]] iter_args(%[[A29:.*]] = %[[A26]]) -> (i32) {
 //       CHECK:       %[[A30:.*]] = memref.load %[[A24]]#0{{\[}}%[[A28]]] : memref<?xi32>
 //       CHECK:       %[[A31:.*]] = arith.cmpi eq, %[[A30]], %[[A9]] : i32
 //       CHECK:       %[[A32:.*]] = arith.select %[[A31]], %[[A29]], %[[A30]] : i32
 //       CHECK:       scf.if %[[A31]] {
 //       CHECK:         memref.store %[[A29]], %[[A24]]#0{{\[}}%[[A28]]] : memref<?xi32>
 //       CHECK:       }

 //       CHECK:       %[[A21:.*]]:4 = func.call @"_insert_dense_compressed-no_8_8_f64_0_0"(%[[A15]], %[[A16]], %[[A17]], %[[A18]], %[[A8]], %[[A19]], %[[A20]]) : (memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
 //       CHECK:       memref.store %[[A10]], %[[A4]]{{\[}}%[[A19]]] : memref<?xf64>
 //       CHECK:       memref.store %[[A9]], %[[A5]]{{\[}}%[[A19]]] : memref<?xi1>
 //       CHECK:       scf.yield %[[A21]]#0, %[[A21]]#1, %[[A21]]#2, %[[A21]]#3 : memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
 //       CHECK:     }
 //       CHECK:     memref.dealloc %[[A4]] : memref<?xf64>
 //       CHECK:     memref.dealloc %[[A5]] : memref<?xi1>
 //       CHECK:     memref.dealloc %[[A6]] : memref<?xindex>
-//       CHECK:     %[[A24:.*]] = sparse_tensor.storage_specifier.get %[[A23:.*]]#3  ptr_mem_sz at 1 : !sparse_tensor.storage_specifier
+//       CHECK:     %[[A24:.*]] = sparse_tensor.storage_specifier.get %[[A23:.*]]#3  pos_mem_sz at 1 : !sparse_tensor.storage_specifier
 //       CHECK:     %[[A25:.*]] = memref.load %[[A23]]#0{{\[}}%[[A11]]] : memref<?xindex>
 //       CHECK:     %[[A26:.*]] = scf.for %[[A27:.*]] = %[[A12]] to %[[A24]] step %[[A12]] iter_args(%[[A28:.*]] = %[[A25]]) -> (index) {
 //       CHECK:       %[[A29:.*]] = memref.load %[[A23]]#0{{\[}}%[[A27]]] : memref<?xindex>
 //       CHECK:       %[[A30:.*]] = arith.cmpi eq, %[[A29]], %[[A11]] : index
 //       CHECK:       %[[A31:.*]] = arith.select %[[A30]], %[[A28]], %[[A29]] : index
 //       CHECK:       scf.if %[[A30]] {
 //       CHECK:         memref.store %[[A28]], %[[A23]]#0{{\[}}%[[A27]]] : memref<?xindex>
 //       CHECK:       }

 //   CHECK-DAG: %[[A3:.*]] = arith.constant 0 : index
 //   CHECK-DAG: %[[A4:.*]] = arith.constant 2 : index
 //       CHECK: %[[A5:.*]] = call @createSparseTensorReader(%[[A0]])
 //       CHECK: %[[A6:.*]] = memref.alloca() : memref<2xindex>
 //       CHECK: %[[A7:.*]] = memref.cast %[[A6]] : memref<2xindex> to memref<?xindex>
 //       CHECK: call @copySparseTensorReaderDimSizes(%[[A5]], %[[A7]]) : (!llvm.ptr<i8>, memref<?xindex>) -> ()
 //       CHECK: %[[A8:.*]] = memref.load %[[A6]]{{\[}}%[[A3]]] : memref<2xindex>
 //       CHECK: %[[A9:.*]] = memref.load %[[A6]]{{\[}}%[[A2]]] : memref<2xindex>
-//       CHECK: %[[A10:.*]] = call @getSparseTensorReaderNNZ(%[[A5]])
+//       CHECK: %[[A10:.*]] = call @getSparseTensorReaderNSE(%[[A5]])
 //       CHECK: %[[A11:.*]] = arith.muli %[[A10]], %[[A4]] : index
 //       CHECK: %[[A12:.*]] = memref.alloc() : memref<2xindex>
 //       CHECK: %[[A13:.*]] = memref.cast %[[A12]] : memref<2xindex> to memref<?xindex>
 //       CHECK: %[[A14:.*]] = memref.alloc(%[[A11]]) : memref<?xindex>
 //       CHECK: %[[A15:.*]] = memref.alloc(%[[A10]]) : memref<?xf32>
 //       CHECK: %[[A16:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ dimLevelType = [ "compressed", "singleton" ] }>>
-//       CHECK: %[[A18:.*]] = sparse_tensor.storage_specifier.set %[[A16]]  dim_sz at 0 with %[[A8]]
-//       CHECK: %[[A19:.*]] = sparse_tensor.storage_specifier.get %[[A18]]  ptr_mem_sz at 0
+//       CHECK: %[[A18:.*]] = sparse_tensor.storage_specifier.set %[[A16]]  lvl_sz at 0 with %[[A8]]
+//       CHECK: %[[A19:.*]] = sparse_tensor.storage_specifier.get %[[A18]]  pos_mem_sz at 0
 //       CHECK: %[[A21:.*]], %[[A22:.*]] = sparse_tensor.push_back %[[A19]], %[[A13]], %[[A3]]
-//       CHECK: %[[A24:.*]] = sparse_tensor.storage_specifier.set %[[A18]]  ptr_mem_sz at 0 with %[[A22]]
-//       CHECK: %[[A26:.*]] = sparse_tensor.storage_specifier.set %[[A24]]  dim_sz at 1 with %[[A9]]
+//       CHECK: %[[A24:.*]] = sparse_tensor.storage_specifier.set %[[A18]]  pos_mem_sz at 0 with %[[A22]]
+//       CHECK: %[[A26:.*]] = sparse_tensor.storage_specifier.set %[[A24]]  lvl_sz at 1 with %[[A9]]
 //       CHECK: %[[A27:.*]], %[[A28:.*]] = sparse_tensor.push_back %[[A22]], %[[A21]], %[[A3]], %[[A2]]
-//       CHECK: %[[A30:.*]] = sparse_tensor.storage_specifier.set %[[A26]]  ptr_mem_sz at 0 with %[[A28]]
+//       CHECK: %[[A30:.*]] = sparse_tensor.storage_specifier.set %[[A26]]  pos_mem_sz at 0 with %[[A28]]
 //       CHECK: %[[A31:.*]] = memref.alloca() : memref<2xindex>
 //       CHECK: %[[A32:.*]] = memref.cast %[[A31]] : memref<2xindex> to memref<?xindex>
 //       CHECK: memref.store %[[A3]], %[[A31]]{{\[}}%[[A3]]] : memref<2xindex>
 //       CHECK: memref.store %[[A2]], %[[A31]]{{\[}}%[[A2]]] : memref<2xindex>
 //       CHECK: %[[A33:.*]] = call @getSparseTensorReaderRead0F32(%[[A5]], %[[A32]], %[[A14]], %[[A15]])
 //       CHECK: %[[A34:.*]] = arith.cmpi eq, %[[A33]], %[[A1]] : i1
 //       CHECK: scf.if %[[A34]] {
 //       CHECK:   sparse_tensor.sort_coo  hybrid_quick_sort %[[A10]], %[[A14]] jointly %[[A15]] {nx = 2 : index, ny = 0 : index} : memref<?xindex> jointly memref<?xf32>
 //       CHECK: }
 //       CHECK: memref.store %[[A10]], %[[A27]]{{\[}}%[[A2]]] : memref<?xindex>
-//       CHECK: %[[A36:.*]] = sparse_tensor.storage_specifier.set %[[A30]]  idx_mem_sz at 0 with %[[A11]]
+//       CHECK: %[[A36:.*]] = sparse_tensor.storage_specifier.set %[[A30]]  crd_mem_sz at 0 with %[[A11]]
 //       CHECK: %[[A38:.*]] = sparse_tensor.storage_specifier.set %[[A36]]  val_mem_sz with %[[A10]]
 //       CHECK: call @delSparseTensorReader(%[[A5]]) : (!llvm.ptr<i8>) -> ()
 //       CHECK: return %[[A27]], %[[A14]], %[[A15]], %[[A38]]
 func.func @sparse_new_coo(%arg0: !llvm.ptr<i8>) -> tensor<?x?xf32, #Coo> {
   %0 = sparse_tensor.new %arg0 : !llvm.ptr<i8> to tensor<?x?xf32, #Coo>
   return %0 : tensor<?x?xf32, #Coo>
 }
 
 // CHECK-LABEL: func.func @sparse_new_coo_permute_no(
 //  CHECK-SAME: %[[A0:.*]]: !llvm.ptr<i8>) -> (memref<?xindex>, memref<?xindex>, memref<?xf32>, !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ dimLevelType = [ "compressed", "singleton" ] }>>) {
 //   CHECK-DAG: %[[A1:.*]] = arith.constant 1 : index
 //   CHECK-DAG: %[[A2:.*]] = arith.constant 0 : index
 //   CHECK-DAG: %[[A3:.*]] = arith.constant 2 : index
 //       CHECK: %[[A4:.*]] = call @createSparseTensorReader(%[[A0]])
 //       CHECK: %[[A5:.*]] = memref.alloca() : memref<2xindex>
 //       CHECK: %[[A6:.*]] = memref.cast %[[A5]] : memref<2xindex> to memref<?xindex>
 //       CHECK: call @copySparseTensorReaderDimSizes(%[[A4]], %[[A6]])
 //       CHECK: %[[A7:.*]] = memref.load %[[A5]]{{\[}}%[[A2]]] : memref<2xindex>
 //       CHECK: %[[A8:.*]] = memref.load %[[A5]]{{\[}}%[[A1]]] : memref<2xindex>
-//       CHECK: %[[A9:.*]] = call @getSparseTensorReaderNNZ(%[[A4]])
+//       CHECK: %[[A9:.*]] = call @getSparseTensorReaderNSE(%[[A4]])
 //       CHECK: %[[A10:.*]] = arith.muli %[[A9]], %[[A3]] : index
 //       CHECK: %[[A11:.*]] = memref.alloc() : memref<2xindex>
 //       CHECK: %[[A12:.*]] = memref.cast %[[A11]] : memref<2xindex> to memref<?xindex>
 //       CHECK: %[[A13:.*]] = memref.alloc(%[[A10]]) : memref<?xindex>
 //       CHECK: %[[A14:.*]] = memref.alloc(%[[A9]]) : memref<?xf32>
 //       CHECK: %[[A15:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier<#sparse_tensor.encoding<{ dimLevelType = [ "compressed", "singleton" ] }>>
-//       CHECK: %[[A17:.*]] = sparse_tensor.storage_specifier.set %[[A15]]  dim_sz at 0 with %[[A8]]
-//       CHECK: %[[A18:.*]] = sparse_tensor.storage_specifier.get %[[A17]]  ptr_mem_sz at 0
+//       CHECK: %[[A17:.*]] = sparse_tensor.storage_specifier.set %[[A15]]  lvl_sz at 0 with %[[A8]]
+//       CHECK: %[[A18:.*]] = sparse_tensor.storage_specifier.get %[[A17]]  pos_mem_sz at 0
 //       CHECK: %[[A20:.*]], %[[A21:.*]] = sparse_tensor.push_back %[[A18]], %[[A12]], %[[A2]]
-//       CHECK: %[[A23:.*]] = sparse_tensor.storage_specifier.set %[[A17]]  ptr_mem_sz at 0 with %[[A21]]
-//       CHECK: %[[A25:.*]] = sparse_tensor.storage_specifier.set %[[A23]]  dim_sz at 1 with %[[A7]]
+//       CHECK: %[[A23:.*]] = sparse_tensor.storage_specifier.set %[[A17]]  pos_mem_sz at 0 with %[[A21]]
+//       CHECK: %[[A25:.*]] = sparse_tensor.storage_specifier.set %[[A23]]  lvl_sz at 1 with %[[A7]]
 //       CHECK: %[[A26:.*]], %[[A27:.*]] = sparse_tensor.push_back %[[A21]], %[[A20]], %[[A2]], %[[A1]]
-//       CHECK: %[[A29:.*]] = sparse_tensor.storage_specifier.set %[[A25]]  ptr_mem_sz at 0 with %[[A27]]
+//       CHECK: %[[A29:.*]] = sparse_tensor.storage_specifier.set %[[A25]]  pos_mem_sz at 0 with %[[A27]]
 //       CHECK: %[[A30:.*]] = memref.alloca() : memref<2xindex>
 //       CHECK: %[[A31:.*]] = memref.cast %[[A30]] : memref<2xindex> to memref<?xindex>
 //       CHECK: memref.store %[[A1]], %[[A30]]{{\[}}%[[A2]]] : memref<2xindex>
 //       CHECK: memref.store %[[A2]], %[[A30]]{{\[}}%[[A1]]] : memref<2xindex>
 //       CHECK: %[[A32:.*]] = call @getSparseTensorReaderRead0F32(%[[A4]], %[[A31]], %[[A13]], %[[A14]])
 //       CHECK: memref.store %[[A9]], %[[A26]]{{\[}}%[[A1]]] : memref<?xindex>
-//       CHECK: %[[A34:.*]] = sparse_tensor.storage_specifier.set %[[A29]]  idx_mem_sz at 0 with %[[A10]]
+//       CHECK: %[[A34:.*]] = sparse_tensor.storage_specifier.set %[[A29]]  crd_mem_sz at 0 with %[[A10]]
 //       CHECK: %[[A36:.*]] = sparse_tensor.storage_specifier.set %[[A34]]  val_mem_sz with %[[A9]]
 //       CHECK: call @delSparseTensorReader(%[[A4]]) : (!llvm.ptr<i8>) -> ()
 //       CHECK: return %[[A26]], %[[A13]], %[[A14]], %[[A36]]
 func.func @sparse_new_coo_permute_no(%arg0: !llvm.ptr<i8>) -> tensor<?x?xf32, #CooPNo> {
   %0 = sparse_tensor.new %arg0 : !llvm.ptr<i8> to tensor<?x?xf32, #CooPNo>
   return %0 : tensor<?x?xf32, #CooPNo>
-}
+}
 

mlir/test/Dialect/SparseTensor/codegen_buffer_initialization.mlir

 //       CHECK:     linalg.fill ins(%[[VAL_3]] : index) outs(%[[VAL_4]] : memref<16xindex>)
 //       CHECK:     %[[VAL_6:.*]] = memref.alloc() : memref<16xindex>
 //       CHECK:     %[[VAL_7:.*]] = memref.cast %[[VAL_6]] : memref<16xindex> to memref<?xindex>
 //       CHECK:     linalg.fill ins(%[[VAL_3]] : index) outs(%[[VAL_6]] : memref<16xindex>)
 //       CHECK:     %[[VAL_8:.*]] = memref.alloc() : memref<16xf64>
 //       CHECK:     %[[VAL_9:.*]] = memref.cast %[[VAL_8]] : memref<16xf64> to memref<?xf64>
 //       CHECK:     linalg.fill ins(%[[VAL_2]] : f64) outs(%[[VAL_8]] : memref<16xf64>)
 //       CHECK:     %[[VAL_10:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier
-//       CHECK:     %[[VAL_12:.*]] = sparse_tensor.storage_specifier.set %[[VAL_10]]  dim_sz at 0 with %[[VAL_0]] : !sparse_tensor.storage_specifier
-//       CHECK:     %[[VAL_14:.*]] = sparse_tensor.storage_specifier.get %[[VAL_12]]  ptr_mem_sz at 0 : !sparse_tensor.storage_specifier
+//       CHECK:     %[[VAL_12:.*]] = sparse_tensor.storage_specifier.set %[[VAL_10]]  lvl_sz at 0 with %[[VAL_0]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[VAL_14:.*]] = sparse_tensor.storage_specifier.get %[[VAL_12]]  pos_mem_sz at 0 : !sparse_tensor.storage_specifier
 //       CHECK:     %[[VAL_15:.*]], %[[VAL_17:.*]] = sparse_tensor.push_back %[[VAL_14]], %[[VAL_5]], %[[VAL_3]] : index, memref<?xindex>, index
-//       CHECK:     %[[VAL_18:.*]] = sparse_tensor.storage_specifier.set %[[VAL_12]]  ptr_mem_sz at 0 with %[[VAL_17]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[VAL_18:.*]] = sparse_tensor.storage_specifier.set %[[VAL_12]]  pos_mem_sz at 0 with %[[VAL_17]] : !sparse_tensor.storage_specifier
 //       CHECK:     %[[VAL_19:.*]], %[[VAL_21:.*]] = sparse_tensor.push_back %[[VAL_17]], %[[VAL_15]], %[[VAL_3]], %[[VAL_1]] : index, memref<?xindex>, index, index
-//       CHECK:     %[[VAL_22:.*]] = sparse_tensor.storage_specifier.set %[[VAL_18]]  ptr_mem_sz at 0 with %[[VAL_21]] : !sparse_tensor.storage_specifier
+//       CHECK:     %[[VAL_22:.*]] = sparse_tensor.storage_specifier.set %[[VAL_18]]  pos_mem_sz at 0 with %[[VAL_21]] : !sparse_tensor.storage_specifier
 //       CHECK:     return %[[VAL_19]], %[[VAL_7]], %[[VAL_9]], %[[VAL_22]] : memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
 func.func @sparse_alloc_sparse_vector(%arg0: index) -> tensor<?xf64, #SV> {
   %0 = bufferization.alloc_tensor(%arg0) : tensor<?xf64, #SV>
   %1 = sparse_tensor.load %0 : tensor<?xf64, #SV>
   return %1 : tensor<?xf64, #SV>
 }

mlir/test/Dialect/SparseTensor/conversion.mlir

 // RUN: mlir-opt %s --sparse-tensor-conversion --canonicalize --cse | FileCheck %s
 
 #SparseVector = #sparse_tensor.encoding<{
   dimLevelType = ["compressed"]
 }>
 
 #SparseVector64 = #sparse_tensor.encoding<{
   dimLevelType = ["compressed"],
-  pointerBitWidth = 64,
-  indexBitWidth = 64
+  posWidth = 64,
+  crdWidth = 64
 }>
 
 #SparseVector32 = #sparse_tensor.encoding<{
   dimLevelType = ["compressed"],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 #CSR = #sparse_tensor.encoding<{
   dimLevelType = ["dense", "compressed"]
 }>
 
 #CSC = #sparse_tensor.encoding<{
   dimLevelType = ["dense", "compressed"],

 // CHECK-LABEL: func @sparse_nop_cast(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>) -> !llvm.ptr<i8>
 //       CHECK: return %[[A]] : !llvm.ptr<i8>
 func.func @sparse_nop_cast(%arg0: tensor<64xf32, #SparseVector>) -> tensor<?xf32, #SparseVector> {
   %0 = tensor.cast %arg0 : tensor<64xf32, #SparseVector> to tensor<?xf32, #SparseVector>
   return %0 : tensor<?xf32, #SparseVector>
 }
 
-// CHECK-LABEL: func @sparse_pointers(
+// CHECK-LABEL: func @sparse_positions(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: %[[C:.*]] = arith.constant 0 : index
-//       CHECK: %[[T:.*]] = call @sparsePointers0(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+//       CHECK: %[[T:.*]] = call @sparsePositions0(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 //       CHECK: return %[[T]] : memref<?xindex>
-func.func @sparse_pointers(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
-  %0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
+func.func @sparse_positions(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
+  %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
-// CHECK-LABEL: func @sparse_pointers64(
+// CHECK-LABEL: func @sparse_positions64(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: %[[C:.*]] = arith.constant 0 : index
-//       CHECK: %[[T:.*]] = call @sparsePointers64(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xi64>
+//       CHECK: %[[T:.*]] = call @sparsePositions64(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xi64>
 //       CHECK: return %[[T]] : memref<?xi64>
-func.func @sparse_pointers64(%arg0: tensor<128xf64, #SparseVector64>) -> memref<?xi64> {
-  %0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<128xf64, #SparseVector64> to memref<?xi64>
+func.func @sparse_positions64(%arg0: tensor<128xf64, #SparseVector64>) -> memref<?xi64> {
+  %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector64> to memref<?xi64>
   return %0 : memref<?xi64>
 }
 
-// CHECK-LABEL: func @sparse_pointers32(
+// CHECK-LABEL: func @sparse_positions32(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: %[[C:.*]] = arith.constant 0 : index
-//       CHECK: %[[T:.*]] = call @sparsePointers32(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xi32>
+//       CHECK: %[[T:.*]] = call @sparsePositions32(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xi32>
 //       CHECK: return %[[T]] : memref<?xi32>
-func.func @sparse_pointers32(%arg0: tensor<128xf64, #SparseVector32>) -> memref<?xi32> {
-  %0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<128xf64, #SparseVector32> to memref<?xi32>
+func.func @sparse_positions32(%arg0: tensor<128xf64, #SparseVector32>) -> memref<?xi32> {
+  %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector32> to memref<?xi32>
   return %0 : memref<?xi32>
 }
 
 // CHECK-LABEL: func @sparse_indices(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: %[[C:.*]] = arith.constant 0 : index
-//       CHECK: %[[T:.*]] = call @sparseIndices0(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+//       CHECK: %[[T:.*]] = call @sparseCoordinates0(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 //       CHECK: return %[[T]] : memref<?xindex>
 func.func @sparse_indices(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
-  %0 = sparse_tensor.indices %arg0 { dimension = 0 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
+  %0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // CHECK-LABEL: func @sparse_indices64(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: %[[C:.*]] = arith.constant 0 : index
-//       CHECK: %[[T:.*]] = call @sparseIndices64(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xi64>
+//       CHECK: %[[T:.*]] = call @sparseCoordinates64(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xi64>
 //       CHECK: return %[[T]] : memref<?xi64>
 func.func @sparse_indices64(%arg0: tensor<128xf64, #SparseVector64>) -> memref<?xi64> {
-  %0 = sparse_tensor.indices %arg0 { dimension = 0 : index } : tensor<128xf64, #SparseVector64> to memref<?xi64>
+  %0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector64> to memref<?xi64>
   return %0 : memref<?xi64>
 }
 
 // CHECK-LABEL: func @sparse_indices32(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: %[[C:.*]] = arith.constant 0 : index
-//       CHECK: %[[T:.*]] = call @sparseIndices32(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xi32>
+//       CHECK: %[[T:.*]] = call @sparseCoordinates32(%[[A]], %[[C]]) : (!llvm.ptr<i8>, index) -> memref<?xi32>
 //       CHECK: return %[[T]] : memref<?xi32>
 func.func @sparse_indices32(%arg0: tensor<128xf64, #SparseVector32>) -> memref<?xi32> {
-  %0 = sparse_tensor.indices %arg0 { dimension = 0 : index } : tensor<128xf64, #SparseVector32> to memref<?xi32>
+  %0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector32> to memref<?xi32>
   return %0 : memref<?xi32>
 }
 
 // CHECK-LABEL: func @sparse_valuesf64(
 //  CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: %[[T:.*]] = call @sparseValuesF64(%[[A]]) : (!llvm.ptr<i8>) -> memref<?xf64>
 //       CHECK: return %[[T]] : memref<?xf64>
 func.func @sparse_valuesf64(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xf64> {

mlir/test/Dialect/SparseTensor/convert_dense2sparse.mlir

 //       CHECK-RWT:     %[[F0:.*]] = arith.constant sparse<{{\[\[}}0, 0], [1, 6]], [1.000000e+00, 5.000000e+00]> : tensor<8x7xf32>
 //       CHECK-RWT:     %[[T0:.*]] = bufferization.alloc_tensor()
 //       CHECK-RWT:     %[[T1:.*]] = sparse_tensor.foreach in %[[F0]] init(%[[T0]])
 //       CHECK-RWT:     ^bb0(%[[L0I0:.*]]: index, %[[L0I1:.*]]: index, %[[L0V:.*]]: f32, %[[L0T:.*]]: tensor
 //       CHECK-RWT:       %[[L0T2:.*]] = sparse_tensor.insert %[[L0V]] into %[[L0T]]{{\[}}%[[L0I1]], %[[L0I0]]]
 //       CHECK-RWT:       sparse_tensor.yield %[[L0T2]]
 //       CHECK-RWT:     }
 //       CHECK-RWT:     %[[COO:.*]] = sparse_tensor.load %[[T1]] hasInserts
-//       CHECK-RWT:     %[[NNZ:.*]] = sparse_tensor.number_of_entries %[[COO]]
+//       CHECK-RWT:     %[[NSE:.*]] = sparse_tensor.number_of_entries %[[COO]]
 //       CHECK-RWT:     %[[V:.*]] = sparse_tensor.values %[[COO]]
-//       CHECK-RWT:     %[[I:.*]] = sparse_tensor.indices_buffer %[[COO]]
-//       CHECK-RWT:     sparse_tensor.sort_coo hybrid_quick_sort %[[NNZ]], %[[I]] jointly %[[V]] {nx = 2 : index, ny = 0 : index}
+//       CHECK-RWT:     %[[I:.*]] = sparse_tensor.coordinates_buffer %[[COO]]
+//       CHECK-RWT:     sparse_tensor.sort_coo hybrid_quick_sort %[[NSE]], %[[I]] jointly %[[V]] {nx = 2 : index, ny = 0 : index}
 //       CHECK-RWT:     %[[T3:.*]] = bufferization.alloc_tensor()
 //       CHECK-RWT:     %[[T4:.*]] = sparse_tensor.foreach in %[[COO]] init(%[[T3]])
 //       CHECK-RWT:     ^bb0(%[[L1I0:.*]]: index, %[[L1I1:.*]]: index, %[[L1V:.*]]: f32, %[[L1T:.*]]: tensor
 //       CHECK-RWT:       %[[L1T2:.*]] = sparse_tensor.insert %[[L1V]] into %[[L1T]]{{\[}}%[[L1I1]], %[[L1I0]]]
 //       CHECK-RWT:       sparse_tensor.yield %[[L1T2]]
 //       CHECK-RWT:     }
 //       CHECK-RWT:     %[[T5:.*]] = sparse_tensor.load %[[T4]] hasInserts
 //       CHECK-RWT:     %[[T6:.*]] = sparse_tensor.convert %[[T5]]

mlir/test/Dialect/SparseTensor/convert_sparse2sparse.mlir

 // First use with `kViaCOO` for sparse2sparse conversion (the old way).
 // RUN: mlir-opt %s --sparse-tensor-conversion="s2s-strategy=1" \
 // RUN:    --canonicalize --cse | FileCheck %s -check-prefix=CHECK-COO
 //
 // Now again with `kAuto` (the new default).
 // RUN: mlir-opt %s --sparse-tensor-conversion="s2s-strategy=0" \
 // RUN:    --canonicalize --cse | FileCheck %s -check-prefixes=CHECK-AUTO,CHECK
 
 // RUN: mlir-opt %s --post-sparsification-rewrite="enable-runtime-library=false enable-foreach=false" \
 // RUN: --canonicalize --cse | FileCheck %s --check-prefix=CHECK-RWT
 
 #SparseVector64 = #sparse_tensor.encoding<{
   dimLevelType = ["compressed"],
-  pointerBitWidth = 64,
-  indexBitWidth = 64
+  posWidth = 64,
+  crdWidth = 64
 }>
 
 #SparseVector32 = #sparse_tensor.encoding<{
   dimLevelType = ["compressed"],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 #SparseVector = #sparse_tensor.encoding<{
   dimLevelType = ["compressed"]
 }>
 
 #SortedCOO3D = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ]

 
 func.func @sparse_convert(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32, #SparseVector32> {
   %0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseVector64> to tensor<?xf32, #SparseVector32>
   return %0 : tensor<?xf32, #SparseVector32>
 }
 
 #SparseSingleton64 = #sparse_tensor.encoding<{
   dimLevelType = ["singleton"],
-  pointerBitWidth = 64,
-  indexBitWidth = 64
+  posWidth = 64,
+  crdWidth = 64
 }>
 
 #SparseSingleton32 = #sparse_tensor.encoding<{
   dimLevelType = ["singleton"],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 // CHECK-COO-LABEL: func @sparse_convert_singleton(
 //  CHECK-COO-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //   CHECK-COO-DAG: %[[ToCOO:.*]] = arith.constant 5 : i32
 //   CHECK-COO-DAG: %[[FromCOO:.*]] = arith.constant 2 : i32
 //   CHECK-COO-DAG: %[[LvlTypes:.*]] = memref.alloca() : memref<1xi8>
 //   CHECK-COO-DAG: %[[DimSizes:.*]] = memref.alloca() : memref<1xindex>

mlir/test/Dialect/SparseTensor/convert_sparse2sparse_element.mlir

 // RUN: mlir-opt %s --sparse-tensor-codegen --canonicalize --cse | FileCheck %s
 
 #SparseVector64 = #sparse_tensor.encoding<{
   dimLevelType = ["compressed"],
-  pointerBitWidth = 64,
-  indexBitWidth = 64
+  posWidth = 64,
+  crdWidth = 64
 }>
 
 #SparseVector32 = #sparse_tensor.encoding<{
   dimLevelType = ["compressed"],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 
 // CHECK-LABEL:   func.func @sparse_convert(
 // CHECK-SAME:      %[[VAL_0:.*0]]: memref<?xi64>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: memref<?xi64>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: memref<?xf32>,
 // CHECK-SAME:      %[[VAL_3:.*3]]: !sparse_tensor.storage_specifier

mlir/test/Dialect/SparseTensor/fold.mlir

 //       CHECK: return
 func.func @sparse_dce_convert(%arg0: tensor<64xf32>) {
   %0 = sparse_tensor.convert %arg0 : tensor<64xf32> to tensor<64xf32, #SparseVector>
   return
 }
 
 // CHECK-LABEL: func @sparse_dce_getters(
 //  CHECK-SAME: %[[A:.*]]: tensor<64xf32, #sparse_tensor.encoding<{{{.*}}}>>)
-//   CHECK-NOT: sparse_tensor.pointers
-//   CHECK-NOT: sparse_tensor.indices
+//   CHECK-NOT: sparse_tensor.positions
+//   CHECK-NOT: sparse_tensor.coordinates
 //   CHECK-NOT: sparse_tensor.values
 //       CHECK: return
 func.func @sparse_dce_getters(%arg0: tensor<64xf32, #SparseVector>) {
-  %0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<64xf32, #SparseVector> to memref<?xindex>
-  %1 = sparse_tensor.indices %arg0 { dimension = 0 : index } : tensor<64xf32, #SparseVector> to memref<?xindex>
+  %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<64xf32, #SparseVector> to memref<?xindex>
+  %1 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<64xf32, #SparseVector> to memref<?xindex>
   %2 = sparse_tensor.values %arg0 : tensor<64xf32, #SparseVector> to memref<?xf32>
   return
 }
 
 // CHECK-LABEL: func @sparse_concat_dce(
 //   CHECK-NOT: sparse_tensor.concatenate
 //       CHECK: return
 func.func @sparse_concat_dce(%arg0: tensor<2xf64, #SparseVector>,

 // CHECK-LABEL: func @sparse_get_specifier_dce_fold(
 //  CHECK-SAME:  %[[A0:.*]]: !sparse_tensor.storage_specifier
 //  CHECK-SAME:  %[[A1:.*]]: index,
 //  CHECK-SAME:  %[[A2:.*]]: index)
 //   CHECK-NOT:  sparse_tensor.storage_specifier.set
 //   CHECK-NOT:  sparse_tensor.storage_specifier.get
 //       CHECK:  return %[[A1]]
 func.func @sparse_get_specifier_dce_fold(%arg0: !sparse_tensor.storage_specifier<#SparseVector>, %arg1: index, %arg2: index) -> index {
-  %0 = sparse_tensor.storage_specifier.set %arg0 dim_sz at 0 with %arg1
+  %0 = sparse_tensor.storage_specifier.set %arg0 lvl_sz at 0 with %arg1
        : !sparse_tensor.storage_specifier<#SparseVector>
-  %1 = sparse_tensor.storage_specifier.set %0 ptr_mem_sz at 0 with %arg2
+  %1 = sparse_tensor.storage_specifier.set %0 pos_mem_sz at 0 with %arg2
        : !sparse_tensor.storage_specifier<#SparseVector>
-  %2 = sparse_tensor.storage_specifier.get %1 dim_sz at 0
+  %2 = sparse_tensor.storage_specifier.get %1 lvl_sz at 0
        : !sparse_tensor.storage_specifier<#SparseVector>
   return %2 : index
 }

mlir/test/Dialect/SparseTensor/invalid.mlir

 // RUN: mlir-opt %s -split-input-file -verify-diagnostics
 
 func.func @invalid_new_dense(%arg0: !llvm.ptr<i8>) -> tensor<32xf32> {
   // expected-error@+1 {{'sparse_tensor.new' op result #0 must be sparse tensor of any type values, but got 'tensor<32xf32>'}}
   %0 = sparse_tensor.new %arg0 : !llvm.ptr<i8> to tensor<32xf32>
   return %0 : tensor<32xf32>
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
-func.func @non_static_pack_ret(%data: tensor<6xf64>, %index: tensor<6x1xi32>)
+func.func @non_static_pack_ret(%values: tensor<6xf64>, %coordinates: tensor<6x1xi32>)
                             -> tensor<?xf64, #SparseVector> {
-  // expected-error@+1 {{all input types must be statically shaped}}
-  %0 = sparse_tensor.pack %data, %index : tensor<6xf64>, tensor<6x1xi32>
-                                       to tensor<?xf64, #SparseVector>
+  // expected-error@+1 {{the sparse-tensor must have static shape}}
+  %0 = sparse_tensor.pack %values, %coordinates
+     : tensor<6xf64>, tensor<6x1xi32> to tensor<?xf64, #SparseVector>
   return %0 : tensor<?xf64, #SparseVector>
 }
 
 // -----
 
-#DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"], indexBitWidth=32}>
+#DenseVector = #sparse_tensor.encoding<{dimLevelType = ["dense"], crdWidth=32}>
 
-func.func @invalid_pack_dense(%data: tensor<6xf64>, %index: tensor<6x1xi32>)
+func.func @invalid_pack_dense(%values: tensor<6xf64>, %coordinates: tensor<6x1xi32>)
                             -> tensor<100xf64, #DenseVector> {
-  // expected-error@+1 {{must operate on a COO tensor}}
-  %0 = sparse_tensor.pack %data, %index : tensor<6xf64>, tensor<6x1xi32>
-                                       to tensor<100xf64, #DenseVector>
+  // expected-error@+1 {{the sparse-tensor must have a COO type}}
+  %0 = sparse_tensor.pack %values, %coordinates
+     : tensor<6xf64>, tensor<6x1xi32> to tensor<100xf64, #DenseVector>
   return %0 : tensor<100xf64, #DenseVector>
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
-func.func @invalid_pack_data(%data: tensor<6x1xf64>, %index: tensor<6x1xi32>)
+func.func @invalid_pack_data(%values: tensor<6x1xf64>, %coordinates: tensor<6x1xi32>)
                             -> tensor<100xf64, #SparseVector> {
   // expected-error@+1 {{'sparse_tensor.pack' op operand #0 must be 1D tensor of any type values}}
-  %0 = sparse_tensor.pack %data, %index : tensor<6x1xf64>, tensor<6x1xi32>
-                                       to tensor<100xf64, #SparseVector>
+  %0 = sparse_tensor.pack %values, %coordinates
+     : tensor<6x1xf64>, tensor<6x1xi32> to tensor<100xf64, #SparseVector>
   return %0 : tensor<100xf64, #SparseVector>
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
-func.func @invalid_pack_type(%data: tensor<6xf64>, %index: tensor<6x1xi32>)
+func.func @invalid_pack_type(%values: tensor<6xf64>, %coordinates: tensor<6x1xi32>)
                             -> tensor<100xf32, #SparseVector> {
-  // expected-error@+1 {{unmatched type between input and output}}
-  %0 = sparse_tensor.pack %data, %index : tensor<6xf64>, tensor<6x1xi32>
-                                       to tensor<100xf32, #SparseVector>
+  // expected-error@+1 {{input/output element-types don't match}}
+  %0 = sparse_tensor.pack %values, %coordinates
+     : tensor<6xf64>, tensor<6x1xi32> to tensor<100xf32, #SparseVector>
   return %0 : tensor<100xf32, #SparseVector>
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
-func.func @invalid_pack_type(%data: tensor<5xf64>, %index: tensor<6x1xi32>)
+func.func @invalid_pack_type(%values: tensor<5xf64>, %coordinates: tensor<6x1xi32>)
                             -> tensor<100xf64, #SparseVector> {
-  // expected-error@+1 {{unmatched number of elements in data and indices}}
-  %0 = sparse_tensor.pack %data, %index : tensor<5xf64>, tensor<6x1xi32>
-                                       to tensor<100xf64, #SparseVector>
+  // expected-error@+1 {{values/coordinates number-of-elements don't match}}
+  %0 = sparse_tensor.pack %values, %coordinates
+     : tensor<5xf64>, tensor<6x1xi32> to tensor<100xf64, #SparseVector>
   return %0 : tensor<100xf64, #SparseVector>
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
-func.func @invalid_pack_type(%data: tensor<6xf64>, %index: tensor<6x2xi32>)
+func.func @invalid_pack_type(%values: tensor<6xf64>, %coordinates: tensor<6x2xi32>)
                             -> tensor<100xf64, #SparseVector> {
-  // expected-error@+1 {{unmatched rank between input and output}}
-  %0 = sparse_tensor.pack %data, %index : tensor<6xf64>, tensor<6x2xi32>
-                                       to tensor<100xf64, #SparseVector>
+  // expected-error@+1 {{input/output level-ranks don't match}}
+  %0 = sparse_tensor.pack %values, %coordinates
+     : tensor<6xf64>, tensor<6x2xi32> to tensor<100xf64, #SparseVector>
   return %0 : tensor<100xf64, #SparseVector>
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
 func.func @invalid_unpack_type(%sp: tensor<100xf32, #SparseVector>)
                             -> (tensor<6xf64>, tensor<6x1xi32>, i32) {
-  // expected-error@+1 {{unmatched type between input and output}}
-  %d, %i, %n = sparse_tensor.unpack %sp :  tensor<100xf32, #SparseVector>
-                                        to tensor<6xf64>, tensor<6x1xi32>, i32
-  return %d, %i, %n : tensor<6xf64>, tensor<6x1xi32>, i32
+  // expected-error@+1 {{input/output element-types don't match}}
+  %values, %coordinates, %nse = sparse_tensor.unpack %sp
+     : tensor<100xf32, #SparseVector> to tensor<6xf64>, tensor<6x1xi32>, i32
+  return %values, %coordinates, %nse : tensor<6xf64>, tensor<6x1xi32>, i32
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
 func.func @invalid_unpack_type(%sp: tensor<100xf32, #SparseVector>)
                             -> (tensor<5xf32>, tensor<6x1xi32>, i32) {
-  // expected-error@+1 {{unmatched number of elements in data and indices}}
-  %d, %i, %n = sparse_tensor.unpack %sp :  tensor<100xf32, #SparseVector>
-                                        to tensor<5xf32>, tensor<6x1xi32>, i32
-  return %d, %i, %n : tensor<5xf32>, tensor<6x1xi32>, i32
+  // expected-error@+1 {{values/coordinates number-of-elements don't match}}
+  %values, %coordinates, %nse = sparse_tensor.unpack %sp
+     : tensor<100xf32, #SparseVector> to tensor<5xf32>, tensor<6x1xi32>, i32
+  return %values, %coordinates, %nse : tensor<5xf32>, tensor<6x1xi32>, i32
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
 func.func @invalid_unpack_type(%sp: tensor<100xf32, #SparseVector>)
                             -> (tensor<6xf32>, tensor<6x2xi32>, i32) {
-  // expected-error@+1 {{unmatched rank between input and output}}
-  %d, %i, %n = sparse_tensor.unpack %sp :  tensor<100xf32, #SparseVector>
-                                        to tensor<6xf32>, tensor<6x2xi32>, i32
-  return %d, %i, %n : tensor<6xf32>, tensor<6x2xi32>, i32
+  // expected-error@+1 {{input/output level-ranks don't match}}
+  %values, %coordinates, %nse = sparse_tensor.unpack %sp
+     : tensor<100xf32, #SparseVector> to tensor<6xf32>, tensor<6x2xi32>, i32
+  return %values, %coordinates, %nse : tensor<6xf32>, tensor<6x2xi32>, i32
 }
 
 // -----
 
-func.func @invalid_pointers_dense(%arg0: tensor<128xf64>) -> memref<?xindex> {
-  // expected-error@+1 {{'sparse_tensor.pointers' op operand #0 must be sparse tensor of any type values, but got 'tensor<128xf64>'}}
-  %0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<128xf64> to memref<?xindex>
+func.func @invalid_positions_dense(%arg0: tensor<128xf64>) -> memref<?xindex> {
+  // expected-error@+1 {{'sparse_tensor.positions' op operand #0 must be sparse tensor of any type values, but got 'tensor<128xf64>'}}
+  %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<128xf64> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
-func.func @invalid_pointers_unranked(%arg0: tensor<*xf64>) -> memref<?xindex> {
-  // expected-error@+1 {{'sparse_tensor.pointers' op operand #0 must be sparse tensor of any type values, but got 'tensor<*xf64>'}}
-  %0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<*xf64> to memref<?xindex>
+func.func @invalid_positions_unranked(%arg0: tensor<*xf64>) -> memref<?xindex> {
+  // expected-error@+1 {{'sparse_tensor.positions' op operand #0 must be sparse tensor of any type values, but got 'tensor<*xf64>'}}
+  %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<*xf64> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], pointerBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], posWidth=32}>
 
-func.func @mismatch_pointers_types(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
-  // expected-error@+1 {{unexpected type for pointers}}
-  %0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
+func.func @mismatch_positions_types(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
+  // expected-error@+1 {{unexpected type for positions}}
+  %0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
-func.func @pointers_oob(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
-  // expected-error@+1 {{requested pointers dimension out of bounds}}
-  %0 = sparse_tensor.pointers %arg0 { dimension = 1 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
+func.func @positions_oob(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
+  // expected-error@+1 {{requested level is out of bounds}}
+  %0 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 func.func @invalid_indices_dense(%arg0: tensor<10x10xi32>) -> memref<?xindex> {
-  // expected-error@+1 {{'sparse_tensor.indices' op operand #0 must be sparse tensor of any type values, but got 'tensor<10x10xi32>'}}
-  %0 = sparse_tensor.indices %arg0 { dimension = 1 : index } : tensor<10x10xi32> to memref<?xindex>
+  // expected-error@+1 {{'sparse_tensor.coordinates' op operand #0 must be sparse tensor of any type values, but got 'tensor<10x10xi32>'}}
+  %0 = sparse_tensor.coordinates %arg0 { level = 1 : index } : tensor<10x10xi32> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 func.func @invalid_indices_unranked(%arg0: tensor<*xf64>) -> memref<?xindex> {
-  // expected-error@+1 {{'sparse_tensor.indices' op operand #0 must be sparse tensor of any type values, but got 'tensor<*xf64>'}}
-  %0 = sparse_tensor.indices %arg0 { dimension = 0 : index } : tensor<*xf64> to memref<?xindex>
+  // expected-error@+1 {{'sparse_tensor.coordinates' op operand #0 must be sparse tensor of any type values, but got 'tensor<*xf64>'}}
+  %0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<*xf64> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 func.func @mismatch_indices_types(%arg0: tensor<?xf64, #SparseVector>) -> memref<?xi32> {
-  // expected-error@+1 {{unexpected type for indices}}
-  %0 = sparse_tensor.indices %arg0 { dimension = 0 : index } : tensor<?xf64, #SparseVector> to memref<?xi32>
+  // expected-error@+1 {{unexpected type for coordinates}}
+  %0 = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<?xf64, #SparseVector> to memref<?xi32>
   return %0 : memref<?xi32>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 func.func @indices_oob(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
-  // expected-error@+1 {{requested indices dimension out of bounds}}
-  %0 = sparse_tensor.indices %arg0 { dimension = 1 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
+  // expected-error@+1 {{requested level is out of bounds}}
+  %0 = sparse_tensor.coordinates %arg0 { level = 1 : index } : tensor<128xf64, #SparseVector> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 func.func @invalid_values_dense(%arg0: tensor<1024xf32>) -> memref<?xf32> {
   // expected-error@+1 {{'sparse_tensor.values' op operand #0 must be sparse tensor of any type values, but got 'tensor<1024xf32>'}}
   %0 = sparse_tensor.values %arg0 : tensor<1024xf32> to memref<?xf32>
   return %0 : memref<?xf32>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 func.func @indices_buffer_noncoo(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
   // expected-error@+1 {{expected sparse tensor with a COO region}}
-  %0 = sparse_tensor.indices_buffer %arg0 : tensor<128xf64, #SparseVector> to memref<?xindex>
+  %0 = sparse_tensor.coordinates_buffer %arg0 : tensor<128xf64, #SparseVector> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 func.func @indices_buffer_dense(%arg0: tensor<1024xf32>) -> memref<?xindex> {
   // expected-error@+1 {{must be sparse tensor of any type values}}
-  %0 = sparse_tensor.indices_buffer %arg0 : tensor<1024xf32> to memref<?xindex>
+  %0 = sparse_tensor.coordinates_buffer %arg0 : tensor<1024xf32> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 func.func @mismatch_values_types(%arg0: tensor<?xf64, #SparseVector>) -> memref<?xf32> {

 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>) -> index {
   // expected-error@+1 {{missing level argument}}
-  %0 = sparse_tensor.storage_specifier.get %arg0 idx_mem_sz
+  %0 = sparse_tensor.storage_specifier.get %arg0 crd_mem_sz
        : !sparse_tensor.storage_specifier<#SparseVector>
   return %0 : index
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>) -> index {
-  // expected-error@+1 {{requested level out of bound}}
-  %0 = sparse_tensor.storage_specifier.get %arg0 dim_sz at 1
+  // expected-error@+1 {{requested level is out of bounds}}
+  %0 = sparse_tensor.storage_specifier.get %arg0 lvl_sz at 1
        : !sparse_tensor.storage_specifier<#SparseVector>
   return %0 : index
 }
 
 // -----
 
 #COO = #sparse_tensor.encoding<{dimLevelType = ["compressed-nu", "singleton"]}>
 
 func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#COO>) -> index {
-  // expected-error@+1 {{requested pointer memory size on a singleton level}}
-  %0 = sparse_tensor.storage_specifier.get %arg0 ptr_mem_sz at 1
+  // expected-error@+1 {{requested position memory size on a singleton level}}
+  %0 = sparse_tensor.storage_specifier.get %arg0 pos_mem_sz at 1
        : !sparse_tensor.storage_specifier<#COO>
   return %0 : index
 }
 
 // -----
 
 func.func @sparse_unannotated_load(%arg0: tensor<16x32xf64>) -> tensor<16x32xf64> {
   // expected-error@+1 {{'sparse_tensor.load' op operand #0 must be sparse tensor of any type values, but got 'tensor<16x32xf64>'}}
func.func @sparse_unannotated_insert(%arg0: tensor<128xf64>, %arg1: index, %arg2: f64) {
   return
 }
 
 // -----
 
 #CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
 
 func.func @sparse_wrong_arity_insert(%arg0: tensor<128x64xf64, #CSR>, %arg1: index, %arg2: f64) {
-  // expected-error@+1 {{'sparse_tensor.insert' op incorrect number of indices}}
+  // expected-error@+1 {{'sparse_tensor.insert' op incorrect number of coordinates}}
   sparse_tensor.insert %arg2 into %arg0[%arg1] : tensor<128x64xf64, #CSR>
   return
 }
 
 // -----
 
 func.func @sparse_push_back(%arg0: index, %arg1: memref<?xf64>, %arg2: f32) -> (memref<?xf64>, index) {
   // expected-error@+1 {{'sparse_tensor.push_back' op failed to verify that value type matches element type of inBuffer}}

 #CSR = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
 
 func.func @sparse_wrong_arity_compression(%arg0: memref<?xf64>,
                                           %arg1: memref<?xi1>,
                                           %arg2: memref<?xindex>,
                                           %arg3: index,
                                           %arg4: tensor<8x8xf64, #CSR>,
                                           %arg5: index) {
-  // expected-error@+1 {{'sparse_tensor.compress' op incorrect number of indices}}
+  // expected-error@+1 {{'sparse_tensor.compress' op incorrect number of coordinates}}
   sparse_tensor.compress %arg0, %arg1, %arg2, %arg3 into %arg4[%arg5,%arg5]
     : memref<?xf64>, memref<?xi1>, memref<?xindex>, tensor<8x8xf64, #CSR>
   return
 }
 
 // -----
 
 func.func @sparse_convert_unranked(%arg0: tensor<*xf32>) -> tensor<10xf32> {

mlir/test/Dialect/SparseTensor/invalid_encoding.mlir

 // -----
 
 // expected-error@+1 {{expected a permutation affine map for dimension ordering}}
 #a = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"], dimOrdering = affine_map<(i,j) -> (i,i)>}>
 func.func private @tensor_no_permutation(%arg0: tensor<16x32xf32, #a>) -> ()
 
 // -----
 
-#a = #sparse_tensor.encoding<{pointerBitWidth = "x"}> // expected-error {{expected an integral pointer bitwidth}}
+#a = #sparse_tensor.encoding<{posWidth = "x"}> // expected-error {{expected an integral position bitwidth}}
 func.func private @tensor_no_int_ptr(%arg0: tensor<16x32xf32, #a>) -> ()
 
 // -----
 
-#a = #sparse_tensor.encoding<{pointerBitWidth = 42}> // expected-error {{unexpected pointer bitwidth: 42}}
+#a = #sparse_tensor.encoding<{posWidth = 42}> // expected-error {{unexpected position bitwidth: 42}}
 func.func private @tensor_invalid_int_ptr(%arg0: tensor<16x32xf32, #a>) -> ()
 
 // -----
 
-#a = #sparse_tensor.encoding<{indexBitWidth = "not really"}> // expected-error {{expected an integral index bitwidth}}
+#a = #sparse_tensor.encoding<{crdWidth = "not really"}> // expected-error {{expected an integral index bitwidth}}
 func.func private @tensor_no_int_index(%arg0: tensor<16x32xf32, #a>) -> ()
 
 // -----
 
-#a = #sparse_tensor.encoding<{indexBitWidth = 128}> // expected-error {{unexpected index bitwidth: 128}}
+#a = #sparse_tensor.encoding<{crdWidth = 128}> // expected-error {{unexpected coordinate bitwidth: 128}}
 func.func private @tensor_invalid_int_index(%arg0: tensor<16x32xf32, #a>) -> ()
 
 // -----
 
 #a = #sparse_tensor.encoding<{key = 1}> // expected-error {{unexpected key: key}}
 func.func private @tensor_invalid_key(%arg0: tensor<16x32xf32, #a>) -> ()
 
 // -----

mlir/test/Dialect/SparseTensor/roundtrip.mlir

 // RUN: mlir-opt %s -split-input-file | mlir-opt -split-input-file | FileCheck %s
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 // CHECK-LABEL: func @sparse_new(
 // CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
 //       CHECK: %[[T:.*]] = sparse_tensor.new %[[A]] : !llvm.ptr<i8> to tensor<128xf64, #{{.*}}>
 //       CHECK: return %[[T]] : tensor<128xf64, #{{.*}}>
 func.func @sparse_new(%arg0: !llvm.ptr<i8>) -> tensor<128xf64, #SparseVector> {
   %0 = sparse_tensor.new %arg0 : !llvm.ptr<i8> to tensor<128xf64, #SparseVector>
   return %0 : tensor<128xf64, #SparseVector>
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
 // CHECK-LABEL: func @sparse_pack(
 // CHECK-SAME: %[[D:.*]]: tensor<6xf64>,
 // CHECK-SAME: %[[I:.*]]: tensor<6x1xi32>)
 //       CHECK: %[[R:.*]] = sparse_tensor.pack %[[D]], %[[I]]
 //       CHECK: return %[[R]] : tensor<100xf64, #{{.*}}>
 func.func @sparse_pack(%data: tensor<6xf64>, %index: tensor<6x1xi32>)
                             -> tensor<100xf64, #SparseVector> {
   %0 = sparse_tensor.pack %data, %index : tensor<6xf64>, tensor<6x1xi32>
                                        to tensor<100xf64, #SparseVector>
   return %0 : tensor<100xf64, #SparseVector>
 }
 
 // -----
 
-#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], indexBitWidth=32}>
+#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"], crdWidth=32}>
 
 // CHECK-LABEL: func @sparse_unpack(
 //  CHECK-SAME: %[[T:.*]]: tensor<100xf64, #
 //       CHECK: %[[D:.*]], %[[I:.*]], %[[N:.*]] = sparse_tensor.unpack %[[T]]
 //       CHECK: return %[[D]], %[[I]], %[[N]]
 func.func @sparse_unpack(%sp : tensor<100xf64, #SparseVector>)
                        -> (tensor<6xf64>, tensor<6x1xi32>, i32) {
   %data, %indices, %nnz = sparse_tensor.unpack %sp : tensor<100xf64, #SparseVector>
func.func @sparse_convert_3d_from_sparse(%arg0: tensor<8x8x8xf64, #SparseTensor>) -> tensor<8x8x8xf64> {
   %0 = sparse_tensor.convert %arg0 : tensor<8x8x8xf64, #SparseTensor> to tensor<8x8x8xf64>
   return %0 : tensor<8x8x8xf64>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
-// CHECK-LABEL: func @sparse_pointers(
+// CHECK-LABEL: func @sparse_positions(
 //  CHECK-SAME: %[[A:.*]]: tensor<128xf64, #{{.*}}>)
-//       CHECK: %[[T:.*]] = sparse_tensor.pointers %[[A]] {dimension = 0 : index} : tensor<128xf64, #{{.*}}> to memref<?xindex>
+//       CHECK: %[[T:.*]] = sparse_tensor.positions %[[A]] {level = 0 : index} : tensor<128xf64, #{{.*}}> to memref<?xindex>
 //       CHECK: return %[[T]] : memref<?xindex>
-func.func @sparse_pointers(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
-  %0 = sparse_tensor.pointers %arg0 {dimension = 0 : index} : tensor<128xf64, #SparseVector> to memref<?xindex>
+func.func @sparse_positions(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
+  %0 = sparse_tensor.positions %arg0 {level = 0 : index} : tensor<128xf64, #SparseVector> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 #COO = #sparse_tensor.encoding<{dimLevelType = ["compressed-nu", "singleton"]}>
 
 // CHECK-LABEL: func @sparse_indices_buffer(
 //  CHECK-SAME: %[[A:.*]]: tensor<?x?xf64, #{{.*}}>)
-//       CHECK: %[[T:.*]] = sparse_tensor.indices_buffer %[[A]] : tensor<?x?xf64, #{{.*}}> to memref<?xindex>
+//       CHECK: %[[T:.*]] = sparse_tensor.coordinates_buffer %[[A]] : tensor<?x?xf64, #{{.*}}> to memref<?xindex>
 //       CHECK: return %[[T]] : memref<?xindex>
 func.func @sparse_indices_buffer(%arg0: tensor<?x?xf64, #COO>) -> memref<?xindex> {
-  %0 = sparse_tensor.indices_buffer %arg0 : tensor<?x?xf64, #COO> to memref<?xindex>
+  %0 = sparse_tensor.coordinates_buffer %arg0 : tensor<?x?xf64, #COO> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 // CHECK-LABEL: func @sparse_indices(
 //  CHECK-SAME: %[[A:.*]]: tensor<128xf64, #{{.*}}>)
-//       CHECK: %[[T:.*]] = sparse_tensor.indices %[[A]] {dimension = 0 : index} : tensor<128xf64, #{{.*}}> to memref<?xindex>
+//       CHECK: %[[T:.*]] = sparse_tensor.coordinates %[[A]] {level = 0 : index} : tensor<128xf64, #{{.*}}> to memref<?xindex>
 //       CHECK: return %[[T]] : memref<?xindex>
 func.func @sparse_indices(%arg0: tensor<128xf64, #SparseVector>) -> memref<?xindex> {
-  %0 = sparse_tensor.indices %arg0 {dimension = 0 : index} : tensor<128xf64, #SparseVector> to memref<?xindex>
+  %0 = sparse_tensor.coordinates %arg0 {level = 0 : index} : tensor<128xf64, #SparseVector> to memref<?xindex>
   return %0 : memref<?xindex>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 // CHECK-LABEL: func @sparse_values(

 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 // CHECK-LABEL: func @sparse_get_md(
 //  CHECK-SAME: %[[A:.*]]: !sparse_tensor.storage_specifier<#{{.*}}>
-//       CHECK: %[[T:.*]] = sparse_tensor.storage_specifier.get %[[A]] dim_sz at 0
+//       CHECK: %[[T:.*]] = sparse_tensor.storage_specifier.get %[[A]] lvl_sz at 0
 //       CHECK: return %[[T]] : index
 func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>) -> index {
-  %0 = sparse_tensor.storage_specifier.get %arg0 dim_sz at 0
+  %0 = sparse_tensor.storage_specifier.get %arg0 lvl_sz at 0
        : !sparse_tensor.storage_specifier<#SparseVector>
   return %0 : index
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 
 // CHECK-LABEL: func @sparse_set_md(
 //  CHECK-SAME: %[[A:.*]]: !sparse_tensor.storage_specifier<#{{.*}}>,
 //  CHECK-SAME: %[[I:.*]]: index)
-//       CHECK: %[[T:.*]] = sparse_tensor.storage_specifier.set %[[A]] dim_sz at 0 with %[[I]]
+//       CHECK: %[[T:.*]] = sparse_tensor.storage_specifier.set %[[A]] lvl_sz at 0 with %[[I]]
 //       CHECK: return %[[T]] : !sparse_tensor.storage_specifier<#{{.*}}>
 func.func @sparse_set_md(%arg0: !sparse_tensor.storage_specifier<#SparseVector>, %arg1: index)
           -> !sparse_tensor.storage_specifier<#SparseVector> {
-  %0 = sparse_tensor.storage_specifier.set %arg0 dim_sz at 0 with %arg1
+  %0 = sparse_tensor.storage_specifier.set %arg0 lvl_sz at 0 with %arg1
        : !sparse_tensor.storage_specifier<#SparseVector>
   return %0 : !sparse_tensor.storage_specifier<#SparseVector>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
 

mlir/test/Dialect/SparseTensor/roundtrip_encoding.mlir

 // RUN: mlir-opt %s -split-input-file | mlir-opt | FileCheck %s
 
 // CHECK-LABEL: func private @sparse_1d_tensor(
 // CHECK-SAME: tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>)
 func.func private @sparse_1d_tensor(tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = ["compressed"] }>>)
 
 // -----
 
 #CSR = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "compressed" ],
   dimOrdering = affine_map<(i,j) -> (i,j)>,
-  pointerBitWidth = 64,
-  indexBitWidth = 64
+  posWidth = 64,
+  crdWidth = 64
 }>
 
 // CHECK-LABEL: func private @sparse_csr(
-// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 64, indexBitWidth = 64 }>>)
+// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], posWidth = 64, crdWidth = 64 }>>)
 func.func private @sparse_csr(tensor<?x?xf32, #CSR>)
 
 // -----
 
 #CSC = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "compressed" ],
   dimOrdering = affine_map<(i,j) -> (j,i)>,
-  pointerBitWidth = 0,
-  indexBitWidth = 0
+  posWidth = 0,
+  crdWidth = 0
 }>
 
 // CHECK-LABEL: func private @sparse_csc(
 // CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>)
 func.func private @sparse_csc(tensor<?x?xf32, #CSC>)
 
 // -----
 
 #DCSC = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed", "compressed" ],
   dimOrdering = affine_map<(i,j) -> (j,i)>,
-  pointerBitWidth = 0,
-  indexBitWidth = 64
+  posWidth = 0,
+  crdWidth = 64
 }>
 
 // CHECK-LABEL: func private @sparse_dcsc(
-// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, indexBitWidth = 64 }>>)
+// CHECK-SAME: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, crdWidth = 64 }>>)
 func.func private @sparse_dcsc(tensor<?x?xf32, #DCSC>)
 
 // -----
 
 #COO = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed-nu-no", "singleton-no" ]
 }>
 

mlir/test/Dialect/SparseTensor/sorted_coo.mlir

 // Kernels that operate on SortedCOO format.
 //
 
 // CHECK-LABEL: func.func @sparse_scale(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> {
 // CHECK-DAG:     %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_2:.*]] = arith.constant 1 : index
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 2.000000e+00 : f32
-// CHECK-DAG:     %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
 // CHECK-DAG:     %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf32>
 // CHECK:         %[[VAL_6:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:         %[[VAL_7:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_8:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_2]] {
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_8]]] : memref<?xf32>
 // CHECK:           %[[VAL_10:.*]] = arith.mulf %[[VAL_9]], %[[VAL_3]] : f32
 // CHECK:           memref.store %[[VAL_10]], %[[VAL_5]]{{\[}}%[[VAL_8]]] : memref<?xf32>
 // CHECK:         }

 }
 
 // CHECK-LABEL: func.func @matvec(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<64xf64>,
 // CHECK-SAME:    %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:     %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
-// CHECK-DAG:     %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG:     %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:     %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:     %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
 // CHECK-DAG:     %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
 // CHECK-DAG:     %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
 // CHECK-DAG:     %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK-DAG:     %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:     %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_4]] {
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex, strided<[?], offset: ?>>
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_14]]] : memref<32xf64>

 
 // CHECK-LABEL: func.func @mateltmul(
 // CHECK-SAME:    %[[VAL_0:.*0]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
 // CHECK-SAME:    %[[VAL_1:.*1]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>>,
 // CHECK-SAME:    %[[VAL_2:.*2]]: tensor<32x64xf64>) -> tensor<32x64xf64> {
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f64
 // CHECK-DAG:     %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:     %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
-// CHECK-DAG:     %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG:     %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:     %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:     %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
 // CHECK-DAG:     %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
-// CHECK-DAG:     %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
-// CHECK-DAG:     %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
-// CHECK-DAG:     %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:     %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
+// CHECK-DAG:     %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
+// CHECK-DAG:     %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex, strided<[?], offset: ?>>
 // CHECK-DAG:     %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
 // CHECK-DAG:     %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x64xf64>
 // CHECK-DAG:     linalg.fill ins(%[[VAL_3]] : f64) outs(%[[VAL_14]] : memref<32x64xf64>)
 // CHECK-DAG:     %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:     %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-DAG:     %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:     %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:         %[[VAL_19:.*]]:2 = scf.while (%[[VAL_20:.*]] = %[[VAL_15]], %[[VAL_21:.*]] = %[[VAL_17]]) : (index, index) -> (index, index) {

mlir/test/Dialect/SparseTensor/sparse_1d.mlir

 // CHECK-LABEL:   func @add_s(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: f32,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_11]] : memref<32xf32>)
 // CHECK:           %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
 // CHECK:             scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index
func.func @add_s(%arga: tensor<32xf32, #SV>, %argb: f32, %argx: tensor<32xf32>) -> tensor<32xf32> {
   return %0 : tensor<32xf32>
 }
 
 // CHECK-LABEL:   func @repeated_add_s(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]]
 // CHECK-DAG:       %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_8]] : memref<32xf32>)
 // CHECK:           scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_3]] {
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xf32>

 }
 
 // CHECK-LABEL:   func @mul_s(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: f32,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_9]] : memref<32xf32>)
 // CHECK-DAG:       %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xf32>

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32xf32>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32xf32>)
 // CHECK-DAG:       %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           %[[VAL_15:.*]]:2 = scf.while (%[[VAL_16:.*]] = %[[VAL_13]], %[[VAL_17:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_18:.*]] = arith.cmpi ult, %[[VAL_16]], %[[VAL_14]] : index
 // CHECK:             scf.condition(%[[VAL_18]]) %[[VAL_16]], %[[VAL_17]] : index, index

 
 // CHECK-LABEL:   func @mul_ds(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32xf32>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_10]] : memref<32xf32>)
 // CHECK-DAG:       %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_14]]] : memref<32xf32>

 // CHECK-LABEL:   func @add_sd(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32xf32>)
 // CHECK-DAG:       %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           %[[VAL_15:.*]]:2 = scf.while (%[[VAL_16:.*]] = %[[VAL_13]], %[[VAL_17:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_18:.*]] = arith.cmpi ult, %[[VAL_16]], %[[VAL_14]] : index

 }
 
 // CHECK-LABEL:   func @mul_sd(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_10]] : memref<32xf32>)
 // CHECK-DAG:       %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>

 }
 
 // CHECK-LABEL:   func @add_ss(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32xf32>)
 // CHECK-DAG:       %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_17:.*]]:2 = scf.while (%[[VAL_18:.*]] = %[[VAL_13]], %[[VAL_19:.*]] = %[[VAL_15]]) : (index, index) -> (index, index) {

 }
 
 // CHECK-LABEL:   func @mul_ss(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32xf32>)
 // CHECK-DAG:       %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_17:.*]]:2 = scf.while (%[[VAL_18:.*]] = %[[VAL_13]], %[[VAL_19:.*]] = %[[VAL_15]]) : (index, index) -> (index, index) {

 
 // CHECK-LABEL:   func @two_way_inv(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: f32,
 // CHECK-SAME:      %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> {
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<16xf32>)
 // CHECK-DAG:       %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_14]], %[[VAL_20:.*]] = %[[VAL_16]]) : (index, index) -> (index, index) {

 
 // CHECK-LABEL:   func @two_way_inv_alt(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: f32,
 // CHECK-SAME:      %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> {
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<16xf32>)
 // CHECK-DAG:       %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_14]], %[[VAL_20:.*]] = %[[VAL_16]]) : (index, index) -> (index, index) {
#trait_sum_reduction = {
   doc = "x += SUM_i a(i)"
 }
 
 // CHECK-LABEL:   func @sum_reduction(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
 // CHECK-DAG:       %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = memref.load %[[VAL_6]][] : memref<f32>
 // CHECK:           %[[VAL_11:.*]] = scf.for %[[VAL_12:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_13:.*]] = %[[VAL_10]]) -> (f32) {
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xf32>
 // CHECK:             %[[VAL_15:.*]] = arith.addf %[[VAL_13]], %[[VAL_14]] : f32

 }
 
 // CHECK-LABEL:   func @sum_reduction_ss(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<f32>) -> tensor<f32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<f32>
 // CHECK-DAG:           %[[VAL_13:.*]] = memref.load %[[VAL_11]][] : memref<f32>
 // CHECK-DAG:           %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:           %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:           %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-DAG:           %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]]:3 = scf.while (%[[VAL_19:.*]] = %[[VAL_14]], %[[VAL_20:.*]] = %[[VAL_16]], %[[VAL_21:.*]] = %[[VAL_13]]) : (index, index, f32) -> (index, index, f32) {

 
 // CHECK-LABEL:   func @sum_reduction_inv(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<f32>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_3:.*3]]: tensor<f32>) -> tensor<f32> {
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_2]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_2]] {dimension = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 0 : index} : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]] : memref<f32>
 // CHECK-DAG:       %[[VAL_15:.*]] = memref.load %[[VAL_13]][] : memref<f32>
 // CHECK-DAG:       %[[VAL_16:.*]] = memref.load %[[VAL_9]][] : memref<f32>
 // CHECK-DAG:       %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_5]]] : memref<?xindex>

 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<?xf64>,
 // CHECK-SAME:      %[[VAL_3:.*3]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_4:.*]]: tensor<?xf64>) -> tensor<?xf64> {
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<?xf64>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?xf64>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_3]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_3]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_3]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_3]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_3]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
 // CHECK-DAG:       %[[VAL_16:.*]] = tensor.dim %[[VAL_0]], %[[VAL_5]] : tensor<?xf64>
 // CHECK-DAG:       %[[VAL_18:.*]] = bufferization.to_memref %[[VAL_4]]
 // CHECK-DAG:       linalg.fill ins(%{{.*}} : f64) outs(%[[VAL_18]] : memref<?xf64>)
 // CHECK-DAG:       %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_21:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_22:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_7]]] : memref<?xindex>

 
 // CHECK-LABEL:   func @red3s(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_3:.*3]]: tensor<f64>) -> tensor<f64> {
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_2]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_2]] {dimension = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 0 : index} : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
 // CHECK-DAG:       %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_3]] : memref<f64>
 // CHECK-DAG:       %[[VAL_17:.*]] = memref.load %[[VAL_15]][] : memref<f64>
 // CHECK-DAG:       %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_22:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_4]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_2d.mlir

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<32x16xf32>)
 // CHECK:           scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_7]] {
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex>
 // CHECK:             %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_7]] : index
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>

 
 // CHECK-LABEL:   func @mul_ds(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_11]] : memref<32x16xf32>)
 // CHECK:           scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK:             %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<32x16xf32>)
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex>
 // CHECK:           %[[VAL_16:.*]]:2 = scf.while (%[[VAL_17:.*]] = %[[VAL_14]], %[[VAL_18:.*]] = %[[VAL_6]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_19:.*]] = arith.cmpi ult, %[[VAL_17]], %[[VAL_15]] : index

 
 // CHECK-LABEL:   func @mul_sd(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_11]] : memref<32x16xf32>)
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xindex>

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
 // CHECK-DAG:       %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_15]] : memref<32x16xf32>)
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_16]], %[[VAL_20:.*]] = %[[VAL_6]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_17]] : index

 }
 
 // CHECK-LABEL:   func @mul_ss(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_12]] : memref<32x16xf32>)
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_15]]] : memref<?xindex>

 }
 
 // CHECK-LABEL:   func @add_ss_ss(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_16]] : memref<32x16xf32>)
 // CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_21:.*]]:2 = scf.while (%[[VAL_22:.*]] = %[[VAL_17]], %[[VAL_23:.*]] = %[[VAL_19]]) : (index, index) -> (index, index) {

 }
 
 // CHECK-LABEL:   func @mul_ss_ss(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_16]] : memref<32x16xf32>)
 // CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_21:.*]]:2 = scf.while (%[[VAL_22:.*]] = %[[VAL_17]], %[[VAL_23:.*]] = %[[VAL_19]]) : (index, index) -> (index, index) {

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_15]] : memref<32x16xf32>)
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]]:2 = scf.while (%[[VAL_19:.*]] = %[[VAL_16]], %[[VAL_20:.*]] = %[[VAL_5]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_21:.*]] = arith.cmpi ult, %[[VAL_19]], %[[VAL_17]] : index
 // CHECK:             scf.condition(%[[VAL_21]]) %[[VAL_19]], %[[VAL_20]] : index, index

 
 // CHECK-LABEL:   func @mul_sd_ds(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32>
 // CHECK:           linalg.fill ins(%{{.*}} : f32) outs(%[[VAL_13]] : memref<32x16xf32>)
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex>

 
 // CHECK-LABEL:   func @matvec(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<16xf32>) -> tensor<16xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<16xf32>
 // CHECK:           scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK-DAG:         %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK-DAG:         %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
 // CHECK-DAG:         %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
 // CHECK-DAG:         %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_12]]] : memref<16xf32>

 }
 
 // CHECK-LABEL:   func @sum_reduction(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 10 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32>
 // CHECK:           %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_9]]) -> (f32) {
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             %[[VAL_14:.*]] = arith.addi %[[VAL_11]], %[[VAL_3]] : index
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_14]]] : memref<?xindex>
 // CHECK:             %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_3]] iter_args(%[[VAL_18:.*]] = %[[VAL_12]]) -> (f32) {

 }
 
 // CHECK-LABEL:   func @scale(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<?x?xf64>) -> tensor<?x?xf64> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f64
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
 // CHECK-DAG:       %[[VAL_8:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?xf64>
 // CHECK:           linalg.fill ins(%{{.*}} : f64) outs(%[[VAL_11]] : memref<?x?xf64>)
 // CHECK:           scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK:             %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_4]] : index
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_14]]] : memref<?xindex>

 
 // CHECK-LABEL:   func.func @sampled_dense_dense(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<?x?xf32>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<?x?xf32>,
 // CHECK-SAME:      %[[VAL_3:.*3]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor<?x?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?x?xf32>
 // CHECK-DAG:       %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?x?xf32>
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_4]] {

 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_3:.*3]]: tensor<?xf32>,
 // CHECK-SAME:      %[[VAL_4:.*4]]: tensor<f32>,
 // CHECK-SAME:      %[[VAL_5:.*5]]: tensor<?xf32>) -> tensor<?xf32> {
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant true
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_17:.*]] = sparse_tensor.pointers %[[VAL_2]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_18:.*]] = sparse_tensor.indices %[[VAL_2]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_17:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_18:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_19:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_20:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?xf32>
 // CHECK-DAG:       %[[VAL_21:.*]] = bufferization.to_memref %[[VAL_4]] : memref<f32>
 // CHECK-DAG:       %[[VAL_22:.*]] = tensor.dim %[[VAL_2]], %[[VAL_6]] : tensor<?x?xf32,
 // CHECK-DAG:       %[[VAL_24:.*]] = bufferization.to_memref %[[VAL_5]] : memref<?xf32>
 // CHECK:           %[[VAL_25:.*]] = memref.load %[[VAL_21]][] : memref<f32>
 // CHECK:           %[[VAL_26:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           %[[VAL_27:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_3d.mlir

 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_9:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_15]] : memref<32x16x8xf32>)
 // CHECK:           scf.for %[[VAL_16:.*]] = %[[VAL_7]] to %[[VAL_4]] step %[[VAL_9]] {
 // CHECK:             scf.for %[[VAL_17:.*]] = %[[VAL_7]] to %[[VAL_5]] step %[[VAL_9]] {
 // CHECK:               %[[VAL_18:.*]] = arith.muli %[[VAL_16]], %[[VAL_5]] : index
 // CHECK:               %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_17]] : index

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16x8xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_13]] : memref<32x16x8xf32>)
 // CHECK:           scf.for %[[VAL_14:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] {
 // CHECK:             scf.for %[[VAL_15:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] {
 // CHECK:               %[[VAL_16:.*]] = arith.muli %[[VAL_14]], %[[VAL_5]] : index
 // CHECK:               %[[VAL_17:.*]] = arith.addi %[[VAL_16]], %[[VAL_15]] : index

 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_14]] : memref<32x16x8xf32>)
 // CHECK:           scf.for %[[VAL_15:.*]] = %[[VAL_7]] to %[[VAL_3]] step %[[VAL_8]] {
 // CHECK:             %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_15]]] : memref<?xindex>
 // CHECK:             %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_8]] : index
 // CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex>

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16x8xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_12]] : memref<32x16x8xf32>)
 // CHECK:           scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK:             %[[VAL_15:.*]] = arith.addi %[[VAL_13]], %[[VAL_6]] : index
 // CHECK:             %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>

 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_9:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_17]] : memref<32x16x8xf32>)
 // CHECK:           scf.for %[[VAL_18:.*]] = %[[VAL_8]] to %[[VAL_4]] step %[[VAL_9]] {
 // CHECK:             %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_18]]] : memref<?xindex>
 // CHECK:             %[[VAL_20:.*]] = arith.addi %[[VAL_18]], %[[VAL_9]] : index
 // CHECK:             %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xindex>

 // CHECK-LABEL:   func @mul_dss(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16x8xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_14]] : memref<32x16x8xf32>)
 // CHECK:           scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
 // CHECK:             %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
 // CHECK:             %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_6]] : index
 // CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex>

 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_14]] : memref<32x16x8xf32>)
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_8]]] : memref<?xindex>
 // CHECK:           %[[VAL_17:.*]]:2 = scf.while (%[[VAL_18:.*]] = %[[VAL_15]], %[[VAL_19:.*]] = %[[VAL_7]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_20:.*]] = arith.cmpi ult, %[[VAL_18]], %[[VAL_16]] : index

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16x8xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_12]] : memref<32x16x8xf32>)
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_6]] {
 // CHECK:             %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex>

 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_9:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_17]] : memref<32x16x8xf32>)
 // CHECK:           %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref<?xindex>
 // CHECK:           %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_9]]] : memref<?xindex>
 // CHECK:           %[[VAL_20:.*]]:2 = scf.while (%[[VAL_21:.*]] = %[[VAL_18]], %[[VAL_22:.*]] = %[[VAL_8]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_21]], %[[VAL_19]] : index

 // CHECK-LABEL:   func @mul_sds(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16x8xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_14]] : memref<32x16x8xf32>)
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_6]] {
 // CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xindex>

 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_16]] : memref<32x16x8xf32>)
 // CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_8]]] : memref<?xindex>
 // CHECK:           %[[VAL_19:.*]]:2 = scf.while (%[[VAL_20:.*]] = %[[VAL_17]], %[[VAL_21:.*]] = %[[VAL_7]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_18]] : index

 // CHECK-LABEL:   func @mul_ssd(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16x8xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_13]] : memref<32x16x8xf32>)
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>

 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 16 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_9:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_19:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_19]] : memref<32x16x8xf32>)
 // CHECK:           %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref<?xindex>
 // CHECK:           %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_9]]] : memref<?xindex>
 // CHECK:           %[[VAL_22:.*]]:2 = scf.while (%[[VAL_23:.*]] = %[[VAL_20]], %[[VAL_24:.*]] = %[[VAL_8]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_25:.*]] = arith.cmpi ult, %[[VAL_23]], %[[VAL_21]] : index

 
 // CHECK-LABEL:   func @mul_sss(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<32x16x8xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32>
 // CHECK-DAG:       %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_15]] : memref<32x16x8xf32>)
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_18:.*]] = %[[VAL_16]] to %[[VAL_17]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>

 
 // CHECK-LABEL:   func @kernel_3d(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<?x?xf32>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<?x?xf32>,
 // CHECK-SAME:      %[[VAL_3:.*3]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 2 : index} : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 2 : index} : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_10:.*]] = tensor.dim %[[VAL_1]], %[[VAL_6]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<?x?xf32>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<?x?xf32>
 // CHECK-DAG:       %[[VAL_13:.*]] = tensor.dim %[[VAL_1]], %[[VAL_5]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_14:.*]] = tensor.dim %[[VAL_2]], %[[VAL_6]] : tensor<?x?xf32>
 // CHECK-DAG:       %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_0]] : memref<?x?xf32>
 // CHECK:           scf.for %[[VAL_17:.*]] = %[[VAL_5]] to %[[VAL_13]] step %[[VAL_6]] {
#trait_sum_reduction = {
   doc = "x += SUM_ijk A(i,j,k)"
 }
 
 // CHECK-LABEL:   func @sum_reduction(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<f32>) -> tensor<f32> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f32>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_11]]) -> (f32) {
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_15]]] : memref<?xindex>
 // CHECK:             %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_3]] : index

mlir/test/Dialect/SparseTensor/sparse_affine.mlir

 
 // CHECK-LABEL:   func @mul_inv_dense1d(
 // CHECK-SAME:                          %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                          %[[VAL_1:.*]]: tensor<4xf32>,
 // CHECK-SAME:                          %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 3 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<4xf32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<4xf32>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>

 // CHECK-LABEL:   func.func @mul_inv_sparse1d(
 // CHECK-SAME:                                %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                                %[[VAL_1:.*]]: tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>>)
 // CHECK:           %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK:           %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK:           %[[VAL_4:.*]] = arith.constant 3 : index
 // CHECK:           %[[VAL_5:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK:           %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:           %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_6]]) -> (tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>) {
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_15]]] : memref<?xindex>
 // CHECK:             %[[VAL_18:.*]] = arith.cmpi eq, %[[VAL_17]], %[[VAL_4]] : index
 // CHECK:             %[[VAL_19:.*]] = scf.if %[[VAL_18]] -> (tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>) {
 // CHECK:               %[[VAL_20:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_15]]] : memref<?xf32>

 // CHECK-LABEL:   func @and_affine_dense1d(
 // CHECK-SAME:                             %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                             %[[VAL_1:.*]]: tensor<34xi32>,
 // CHECK-SAME:                             %[[VAL_2:.*]]: tensor<32xi32>) -> tensor<32xi32> {
 // CHECK-DAG:       %[[ZERO:.*]] = arith.constant 0 : i32
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 2 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34xi32>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : i32) outs(%[[VAL_11]] : memref<32xi32>)
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]]] : memref<?xindex>

 
 // CHECK-LABEL:   func.func @and_affine_sparse1d(
 // CHECK-SAME:                                   %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                                   %[[VAL_1:.*]]: tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>>)
 // CHECK:           %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK:           %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK:           %[[VAL_4:.*]] = arith.constant 2 : index
 // CHECK:           %[[VAL_5:.*]] = bufferization.alloc_tensor() : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi32>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi32>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_5]]) -> (tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>) {
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
 // CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xi32>
 // CHECK:             %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:             %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_3]]] : memref<?xindex>

 // CHECK-SAME:                             %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                             %[[VAL_1:.*]]: tensor<34x19xf64>,
 // CHECK-SAME:                             %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 32 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 2 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 3 : index
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34x19xf64>
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf64>
 // CHECK:           scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_3]] {
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xindex>
 // CHECK:             %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_3]] : index
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK:             scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_3]] {

 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 2 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 0.000000e+00 : f64
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 3 : index
 // CHECK-DAG:       %[[VAL_TRUE:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_FALSE:.*]] = arith.constant false
 // CHECK:           %[[VAL_8:.*]] = bufferization.alloc_tensor() : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
-// CHECK:           %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:           %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_3]] to %[[VAL_2]] step %[[VAL_4]] iter_args(%[[VAL_17:.*]] = %[[VAL_8]]) -> (tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>) {
 // CHECK:             %[[VAL_18:.*]] = arith.addi %[[VAL_16]], %[[VAL_5]] : index
 // CHECK:             %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK:             %[[VAL_20:.*]] = arith.addi %[[VAL_16]], %[[VAL_4]] : index
 // CHECK:             %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex>
 // CHECK:             %[[VAL_22:.*]] = scf.for %[[VAL_23:.*]] = %[[VAL_19]] to %[[VAL_21]] step %[[VAL_4]] iter_args(%[[VAL_24:.*]] = %[[VAL_17]]) -> (tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>) {
 // CHECK:               %[[VAL_25:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_23]]] : memref<?xindex>

 // CHECK-SAME:                                       %[[VAL_0:.*]]: tensor<34x16xf64, #sparse_tensor.encoding
 // CHECK-SAME:                                       %[[VAL_1:.*]]: tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                                       %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 19 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 2 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 3 : index
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
-// CHECK:           %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:           %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf64>
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_17]]] : memref<?xindex>
 // CHECK:             %[[VAL_19:.*]] = arith.addi %[[VAL_18]], %[[VAL_6]] : index
 // CHECK:             %[[VAL_20:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_19]]] : memref<?xindex>

 // CHECK-SAME:                                             %[[VAL_0:.*]]: tensor<34x16xf64,
 // CHECK-SAME:                                             %[[VAL_1:.*]]: tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                                             %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 19 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 2 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 3 : index
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<34x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
-// CHECK:           %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:           %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf64>
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_6]] {
 // CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_17]]] : memref<?xindex>
 // CHECK:             %[[VAL_19:.*]] = arith.muli %[[VAL_17]], %[[VAL_3]] : index
 // CHECK:             %[[VAL_20:.*]] = arith.addi %[[VAL_19]], %[[VAL_7]] : index

mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir

 }
 
 // CHECK-LABEL:  @main(
 //  CHECK-SAME:  %[[TMP_arg0:.*]]: tensor<4x5xi32,
 //   CHECK-DAG:  %[[TMP_c3:.*]] = arith.constant 3 : index
 //   CHECK-DAG:  %[[TMP_c0:.*]] = arith.constant 0 : index
 //   CHECK-DAG:  %[[TMP_c1:.*]] = arith.constant 1 : index
 //       CHECK:  %[[TMP_0:.*]] = bufferization.alloc_tensor()
-//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index}
-//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index}
-//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index}
-//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index}
+//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index}
+//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index}
+//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index}
+//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index}
 //       CHECK:  %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]]
 //       CHECK:  %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  %[[T:.*]] = scf.for %[[TMP_arg1:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] {{.*}} {
 //       CHECK:    %[[TMP_9:.*]] = memref.load %[[TMP_2]][%[[TMP_arg1]]] : memref<?xindex>
 //       CHECK:    %[[L1:.*]] = scf.for %[[TMP_arg2:.*]] = %[[TMP_c0]] to %[[TMP_c3]] step %[[TMP_c1]] {{.*}} {
 //       CHECK:      %[[TMP_10:.*]] = memref.load %[[TMP_3]][%[[TMP_arg1]]] : memref<?xindex>
 //       CHECK:      %[[TMP_11:.*]] = arith.addi %[[TMP_arg1]], %[[TMP_c1]] : index

mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir

 //  CHECK-SAME:  %[[TMP_arg0:.*]]: tensor<2x4xf64, #sparse_tensor
 //  CHECK-SAME:  %[[TMP_arg1:.*]]: tensor<3x4xf64, #sparse_tensor
 //  CHECK-SAME:  %[[TMP_arg2:.*]]: tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_c0:.*]] = arith.constant 0 : index
 //       CHECK:  %[[TMP_c1:.*]] = arith.constant 1 : index
 //       CHECK:  %[[TMP_c5:.*]] = arith.constant 5 : index
 //       CHECK:  %[[TMP_c2:.*]] = arith.constant 2 : index
 //       CHECK:  %[[TMP_0:.*]] = bufferization.alloc_tensor() : tensor<9x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  %[[RET_1:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] iter_args(%[[A0:.*]] = %[[TMP_0]])
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    %[[RET_4:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A1:.*]] = %[[A0]])
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[NEW_1:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A1]][%[[TMP_23]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
 //       CHECK:      scf.yield %[[NEW_1]]
 //       CHECK:    }
 //       CHECK:    scf.yield %[[RET_4]]
 //       CHECK:  }
-//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  %[[RET_2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]] iter_args(%[[A2:.*]] = %[[RET_1]])
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    %[[RET_5:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A3:.*]] = %[[A2]])
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
 //       CHECK:      %[[NEW_2:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A3]][%[[TMP_29]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
 //       CHECK:      scf.yield %[[NEW_2]]
 //       CHECK:    }
 //       CHECK:    scf.yield %[[RET_5]]
 //       CHECK:  }
-//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  %[[RET_3:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]] iter_args(%[[A4:.*]] = %[[RET_2]])
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>

 //  CHECK-SAME:  %[[TMP_arg2:.*]]: tensor<4x4xf64, #sparse_tensor
 //   CHECK-DAG:  %[[TMP_c0:.*]] = arith.constant 0 : index
 //   CHECK-DAG:  %[[TMP_c1:.*]] = arith.constant 1 : index
 //   CHECK-DAG:  %[[TMP_c5:.*]] = arith.constant 5 : index
 //   CHECK-DAG:  %[[TMP_c2:.*]] = arith.constant 2 : index
 //   CHECK-DAG:  %[[TMP_c9:.*]] = arith.constant 9 : index
 //   CHECK-DAG:  %[[TMP_c4:.*]] = arith.constant 4 : index
 //       CHECK:  %[[TMP_0:.*]] = bufferization.alloc_tensor(%[[TMP_c9]], %[[TMP_c4]]) : tensor<?x?xf64, #sparse_tensor
-//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  %[[RET_1:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]] iter_args(%[[A0:.*]] = %[[TMP_0]])
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    %[[RET_4:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A1:.*]] = %[[A0]])
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[NEW_1:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A1]][%[[TMP_23]], %[[TMP_27]]] : tensor<?x?xf64, #sparse_tensor
 //       CHECK:      scf.yield %[[NEW_1]]
 //       CHECK:    }
 //       CHECK:    scf.yield %[[RET_4]]
 //       CHECK:  }
-//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  %[[RET_2:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]] iter_args(%[[A2:.*]] = %[[RET_1]])
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    %[[RET_5:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A3:.*]] = %[[A2]])
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
 //       CHECK:      %[[NEW_2:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A3]][%[[TMP_29]], %[[TMP_27]]] : tensor<?x?xf64, #sparse_tensor
 //       CHECK:      scf.yield %[[NEW_2]]
 //       CHECK:    }
 //       CHECK:    scf.yield %[[RET_5]]
 //       CHECK:  }
-//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  %[[RET_3:.*]] = scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]] iter_args(%[[A4:.*]] = %[[RET_2]])
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>

 //   CHECK-DAG:  %[[TMP_c1:.*]] = arith.constant 1 : index
 //   CHECK-DAG:  %[[TMP_c5:.*]] = arith.constant 5 : index
 //   CHECK-DAG:  %[[TMP_c2:.*]] = arith.constant 2 : index
 //   CHECK-DAG:  %[[TMP_c9:.*]] = arith.constant 9 : index
 //   CHECK-DAG:  %[[TMP_c4:.*]] = arith.constant 4 : index
 //   CHECK-DAG:  %[[TMP_d0:.*]] = arith.constant 0.000000e+00 : f64
 //       CHECK:  %[[A:.*]] = memref.alloc(%[[TMP_c9]], %[[TMP_c4]]) : memref<?x?xf64>
 //       CHECK:  linalg.fill ins(%[[TMP_d0]] : f64) outs(%[[A]] : memref<?x?xf64>)
-//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      memref.store %[[TMP_28]], %[[A]]{{\[}}%[[TMP_23]], %[[TMP_27]]] : memref<?x?xf64>
 //       CHECK:    }
 //       CHECK:  }
-//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
 //       CHECK:      memref.store %[[TMP_28]], %[[A]]{{\[}}%[[TMP_29]], %[[TMP_27]]] : memref<?x?xf64>
 //       CHECK:    }
 //       CHECK:  }
-//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>

 //   CHECK-DAG:  %[[TMP_c9:.*]] = arith.constant 9 : index
 //   CHECK-DAG:  %[[TMP_c4:.*]] = arith.constant 4 : index
 //       CHECK:  %[[TMP_0:.*]] = bufferization.alloc_tensor(%[[TMP_c9]], %[[TMP_c4]]) : tensor<?x?xf64, #sparse_tensor
 //       CHECK:  %[[VAL_0:.*]] = sparse_tensor.values %[[TMP_0]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>> to memref<?xf64>
 //       CHECK:  %[[DIM_0:.*]] = memref.alloca() : memref<2xindex>
 //       CHECK:  memref.store %[[TMP_c9]], %[[DIM_0]][%[[TMP_c0]]] : memref<2xindex>
 //       CHECK:  memref.store %[[TMP_c4]], %[[DIM_0]][%[[TMP_c1]]] : memref<2xindex>
 //       CHECK:  %[[VAL_1:.*]] = memref.reshape %[[VAL_0]](%[[DIM_0]]) : (memref<?xf64>, memref<2xindex>) -> memref<?x?xf64>
-//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_23]], %[[TMP_27]]] : memref<?x?xf64>
 //       CHECK:    }
 //       CHECK:  }
-//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
 //       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_29]], %[[TMP_27]]] : memref<?x?xf64>
 //       CHECK:    }
 //       CHECK:  }
-//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>

 //   CHECK-DAG:  %[[TMP_c9:.*]] = arith.constant 9 : index
 //   CHECK-DAG:  %[[TMP_c4:.*]] = arith.constant 4 : index
 //       CHECK:  %[[TMP_0:.*]] = bufferization.alloc_tensor(%[[TMP_c9]], %[[TMP_c4]]) : tensor<?x?xf64, #sparse_tensor
 //       CHECK:  %[[VAL_0:.*]] = sparse_tensor.values %[[TMP_0]] : tensor<?x?xf64, #sparse_tensor
 //       CHECK:  %[[DIM_0:.*]] = memref.alloca() : memref<2xindex>
 //       CHECK:  memref.store %[[TMP_c4]], %[[DIM_0]][%[[TMP_c0]]] : memref<2xindex>
 //       CHECK:  memref.store %[[TMP_c9]], %[[DIM_0]][%[[TMP_c1]]] : memref<2xindex>
 //       CHECK:  %[[VAL_1:.*]] = memref.reshape %[[VAL_0]](%[[DIM_0]]) : (memref<?xf64>, memref<2xindex>) -> memref<?x?xf64>
-//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 0 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_4:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index} : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_5:.*]] = sparse_tensor.values %[[TMP_arg0]] : tensor<2x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_6:.*]] = memref.load %[[TMP_1]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_7:.*]] = memref.load %[[TMP_1]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_6]] to %[[TMP_7]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_2]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_3]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_3]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_4]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_5]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_27]], %[[TMP_23]]] : memref<?x?xf64>
 //       CHECK:    }
 //       CHECK:  }
-//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.pointers %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.indices %[[TMP_arg1]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_8:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_9:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_10:.*]] = sparse_tensor.positions %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_11:.*]] = sparse_tensor.coordinates %[[TMP_arg1]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_12:.*]] = sparse_tensor.values %[[TMP_arg1]] : tensor<3x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_13:.*]] = memref.load %[[TMP_8]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_14:.*]] = memref.load %[[TMP_8]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_13]] to %[[TMP_14]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_9]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_25:.*]] = memref.load %[[TMP_10]][%[[TMP_arg3]]] : memref<?xindex>
 //   CHECK-DAG:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_10]][%[[TMP_24]]] : memref<?xindex>
 //       CHECK:    scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
 //       CHECK:      %[[TMP_27:.*]] = memref.load %[[TMP_11]][%[[TMP_arg4]]] : memref<?xindex>
 //       CHECK:      %[[TMP_28:.*]] = memref.load %[[TMP_12]][%[[TMP_arg4]]] : memref<?xf64>
 //       CHECK:      %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c2]] : index
 //       CHECK:      memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_27]], %[[TMP_29]]] : memref<?x?xf64>
 //       CHECK:    }
 //       CHECK:  }
-//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 0 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.pointers %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
-//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.indices %[[TMP_arg2]] {dimension = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_15:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_16:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 0 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_17:.*]] = sparse_tensor.positions %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
+//       CHECK:  %[[TMP_18:.*]] = sparse_tensor.coordinates %[[TMP_arg2]] {level = 1 : index} : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_19:.*]] = sparse_tensor.values %[[TMP_arg2]] : tensor<4x4xf64, #sparse_tensor
 //       CHECK:  %[[TMP_20:.*]] = memref.load %[[TMP_15]][%[[TMP_c0]]] : memref<?xindex>
 //       CHECK:  %[[TMP_21:.*]] = memref.load %[[TMP_15]][%[[TMP_c1]]] : memref<?xindex>
 //       CHECK:  scf.for %[[TMP_arg3:.*]] = %[[TMP_20]] to %[[TMP_21]] step %[[TMP_c1]]
 //       CHECK:    %[[TMP_23:.*]] = memref.load %[[TMP_16]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_25:.*]] = memref.load %[[TMP_17]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_24:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_26:.*]] = memref.load %[[TMP_17]][%[[TMP_24]]] : memref<?xindex>
func.func @concat_sparse_sparse_annotated_dense_permute(%arg0: tensor<2x4xf64, #DCSR>,
                                 %arg1: tensor<3x4xf64, #DCSR>,
                                 %arg2: tensor<4x4xf64, #DCSR>)
                                 -> tensor<?x?xf64, #DENSE_P> {
     %0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
          : tensor<2x4xf64, #DCSR>,
            tensor<3x4xf64, #DCSR>,
            tensor<4x4xf64, #DCSR> to tensor<?x?xf64, #DENSE_P>
     return %0 : tensor<?x?xf64, #DENSE_P>
-}
+}
 

mlir/test/Dialect/SparseTensor/sparse_fill_zero.mlir

 // CHECK:           %[[VAL_20:.*]] = memref.alloc() : memref<300xf64>
 // CHECK:           %[[VAL_21:.*]] = memref.cast %[[VAL_20]] : memref<300xf64> to memref<?xf64>
 // CHECK:           %[[VAL_22:.*]] = memref.alloc() : memref<300xi1>
 // CHECK:           %[[VAL_23:.*]] = memref.cast %[[VAL_22]] : memref<300xi1> to memref<?xi1>
 // CHECK:           %[[VAL_24:.*]] = memref.alloc() : memref<300xindex>
 // CHECK:           %[[VAL_25:.*]] = memref.cast %[[VAL_24]] : memref<300xindex> to memref<?xindex>
 // CHECK:           linalg.fill ins(%[[F0]] : f64) outs(%[[VAL_20]] : memref<300xf64>)
 // CHECK:           linalg.fill ins(%[[False]] : i1) outs(%[[VAL_22]] : memref<300xi1>)
-// CHECK:           %[[VAL_26:.*]] = call @sparsePointers0(%[[Arg0]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK:           %[[VAL_27:.*]] = call @sparseIndices0(%[[Arg0]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK:           %[[VAL_28:.*]] = call @sparsePointers0(%[[Arg0]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK:           %[[VAL_29:.*]] = call @sparseIndices0(%[[Arg0]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK:           %[[VAL_26:.*]] = call @sparsePositions0(%[[Arg0]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK:           %[[VAL_27:.*]] = call @sparseCoordinates0(%[[Arg0]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK:           %[[VAL_28:.*]] = call @sparsePositions0(%[[Arg0]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK:           %[[VAL_29:.*]] = call @sparseCoordinates0(%[[Arg0]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 // CHECK:           %[[VAL_30:.*]] = call @sparseValuesF64(%[[Arg0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
-// CHECK:           %[[VAL_31:.*]] = call @sparsePointers0(%[[Arg1]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK:           %[[VAL_32:.*]] = call @sparseIndices0(%[[Arg1]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK:           %[[VAL_33:.*]] = call @sparsePointers0(%[[Arg1]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK:           %[[VAL_34:.*]] = call @sparseIndices0(%[[Arg1]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK:           %[[VAL_31:.*]] = call @sparsePositions0(%[[Arg1]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK:           %[[VAL_32:.*]] = call @sparseCoordinates0(%[[Arg1]], %[[I0]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK:           %[[VAL_33:.*]] = call @sparsePositions0(%[[Arg1]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK:           %[[VAL_34:.*]] = call @sparseCoordinates0(%[[Arg1]], %[[I1]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 // CHECK:           %[[VAL_35:.*]] = call @sparseValuesF64(%[[Arg1]]) : (!llvm.ptr<i8>) -> memref<?xf64>
 // CHECK:           %[[VAL_36:.*]] = memref.load %[[VAL_26]]{{\[}}%[[I0]]] : memref<?xindex>
 // CHECK:           %[[VAL_37:.*]] = memref.load %[[VAL_26]]{{\[}}%[[I1]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_38:.*]] = %[[VAL_36]] to %[[VAL_37]] step %[[I1]] {
 // CHECK:             %[[VAL_39:.*]] = memref.load %[[VAL_27]]{{\[}}%[[VAL_38]]] : memref<?xindex>
 // CHECK:             %[[VAL_40:.*]] = memref.load %[[VAL_28]]{{\[}}%[[VAL_38]]] : memref<?xindex>
 // CHECK:             %[[VAL_41:.*]] = arith.addi %[[VAL_38]], %[[I1]] : index
 // CHECK:             %[[VAL_42:.*]] = memref.load %[[VAL_28]]{{\[}}%[[VAL_41]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_fp_ops.mlir

#traitc = {
   doc = "x(i) = a(i) OP c"
 }
 
 // CHECK-LABEL: func @abs(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-DAG:     %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK:         %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:         %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:         %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:         %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
 // CHECK:         %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:         %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf64>
 // CHECK:           %[[VAL_13:.*]] = math.absf %[[VAL_12]] : f64
func.func @abs(%arga: tensor<32xf64, #SV>,
   return %0 : tensor<32xf64>
 }
 
 // CHECK-LABEL: func @ceil(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-DAG:     %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK:         %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:         %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:         %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:         %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
 // CHECK:         %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:         %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf64>
 // CHECK:           %[[VAL_13:.*]] = math.ceil %[[VAL_12]] : f64
func.func @ceil(%arga: tensor<32xf64, #SV>,
   return %0 : tensor<32xf64>
 }
 
 // CHECK-LABEL: func @floor(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-DAG:     %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK:         %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:         %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:         %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:         %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
 // CHECK:         %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:         %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf64>
 // CHECK:           %[[VAL_13:.*]] = math.floor %[[VAL_12]] : f64
func.func @floor(%arga: tensor<32xf64, #SV>,
   return %0 : tensor<32xf64>
 }
 
 // CHECK-LABEL: func @neg(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-DAG:     %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK:         %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:         %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:         %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:         %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
 // CHECK:         %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:         %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf64>
 // CHECK:           %[[VAL_13:.*]] = arith.negf %[[VAL_12]] : f64

 // CHECK-LABEL: func @add(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<32xf64>,
 // CHECK-SAME:    %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:     %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:     %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK:         %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:         %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:         %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:         %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
 // CHECK:         %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK:         %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:         %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:         %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:           %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
 // CHECK:           scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index

 // CHECK-LABEL: func @sub(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<32xf64>,
 // CHECK-SAME:    %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:     %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:     %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK:         %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:         %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:         %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:         %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64>
 // CHECK:         %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
 // CHECK:         %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK:         %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:         %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:         %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:         %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
 // CHECK:         scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index

 }
 
 // CHECK-LABEL: func @mul(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<32xf64>,
 // CHECK-SAME:    %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:         %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:         %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:         %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:         %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
 // CHECK:         %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK:         %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:         %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_4]] {
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xf64>

 }
 
 // CHECK-LABEL: func @divbyc(
 // CHECK-SAME:    %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:    %[[VAL_1:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-DAG:     %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f64
 // CHECK-DAG:     %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:     %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:         %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:         %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:         %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:         %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:         %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf64>
 // CHECK:         %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:         %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:         scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xf64>
 // CHECK:           %[[VAL_14:.*]] = arith.divf %[[VAL_13]], %[[VAL_2]] : f64
func.func @divbyc(%arga: tensor<32xf64, #SV>,
   return %0 : tensor<32xf64>
 }
 
 // CHECK-LABEL:   func.func @zero_preserving_math(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> {
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
 // CHECK:           %[[VAL_3:.*]] = bufferization.alloc_tensor() : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
-// CHECK:           %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf64>
 // CHECK:           %[[VAL_7:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[T:.*]] = scf.for %[[VAL_9:.*]] = %[[VAL_7]] to %[[VAL_8]] step %[[VAL_2]] {{.*}} {
 // CHECK:             %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_9]]] : memref<?xindex>
 // CHECK:             %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref<?xf64>
 // CHECK:             %[[VAL_12:.*]] = math.absf %[[VAL_11]] : f64
 // CHECK:             %[[VAL_13:.*]] = math.ceil %[[VAL_12]] : f64

 }
 
 // CHECK-LABEL:   func.func @complex_divbyc(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> {
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
 // CHECK:           %[[VAL_3:.*]] = complex.constant [0.000000e+00, 1.000000e+00] : complex<f64>
 // CHECK:           %[[VAL_4:.*]] = bufferization.alloc_tensor() : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xcomplex<f64>>
 // CHECK:           %[[VAL_8:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[T:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_2]] {{.*}} {
 // CHECK:             %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_10]]] : memref<?xcomplex<f64>>
 // CHECK:             %[[VAL_13:.*]] = complex.div %[[VAL_12]], %[[VAL_3]] : complex<f64>
 // CHECK:             %[[Y:.*]] = sparse_tensor.insert %[[VAL_13]] into %{{.*}}[%[[VAL_11]]] : tensor<32xcomplex<f64>, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>

mlir/test/Dialect/SparseTensor/sparse_index.mlir

 
 // CHECK-LABEL:   func.func @sparse_index(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_1]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_5:.*]] = bufferization.alloc_tensor(%[[VAL_3]], %[[VAL_4]]) : tensor<?x?xi64, #sparse_tensor.encoding
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xi64, #sparse_tensor.encoding
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xi64, #sparse_tensor.encoding
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xi64, #sparse_tensor.encoding
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xi64, #sparse_tensor.encoding
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xi64, #sparse_tensor.encoding
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xi64, #sparse_tensor.encoding
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xi64, #sparse_tensor.encoding
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xi64, #sparse_tensor.encoding
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[T:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_2]] {{.*}} {
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK:             %[[VAL_16:.*]] = arith.addi %[[VAL_13]], %[[VAL_2]] : index
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_int_ops.mlir

 // CHECK-LABEL:   func @add(
 // CHECK-SAME:              %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:              %[[VAL_1:.*]]: tensor<32xi64>,
 // CHECK-SAME:              %[[VAL_2:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:           %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi64>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
 // CHECK:             scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index

 // CHECK-SAME:              %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:              %[[VAL_1:.*]]: tensor<32xi64>,
 // CHECK-SAME:              %[[VAL_2:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:           %[[VAL_6:.*]] = arith.constant 1 : index
 // CHECK-DAG:           %[[VAL_7:.*]] = arith.constant 0 : i64
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi64>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           %[[VAL_15:.*]]:2 = scf.while (%[[VAL_16:.*]] = %[[VAL_13]], %[[VAL_17:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_18:.*]] = arith.cmpi ult, %[[VAL_16]], %[[VAL_14]] : index
 // CHECK:             scf.condition(%[[VAL_18]]) %[[VAL_16]], %[[VAL_17]] : index, index

 }
 
 // CHECK-LABEL:   func @mul(
 // CHECK-SAME:              %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:              %[[VAL_1:.*]]: tensor<32xi64>,
 // CHECK-SAME:              %[[VAL_2:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi64>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xi64>

 }
 
 // CHECK-LABEL:   func @divsbyc(
 // CHECK-SAME:                  %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                  %[[VAL_1:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_2:.*]] = arith.constant 2 : i64
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi64>
 // CHECK:             %[[VAL_14:.*]] = arith.divsi %[[VAL_13]], %[[VAL_2]] : i64

 }
 
 // CHECK-LABEL:   func @divubyc(
 // CHECK-SAME:                  %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                  %[[VAL_1:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_2:.*]] = arith.constant 2 : i64
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{.*}}}>>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{.*}}}>>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi64>
 // CHECK:             %[[VAL_14:.*]] = arith.divui %[[VAL_13]], %[[VAL_2]] : i64

 }
 
 // CHECK-LABEL:   func @and(
 // CHECK-SAME:              %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:              %[[VAL_1:.*]]: tensor<32xi64>,
 // CHECK-SAME:              %[[VAL_2:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi64>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi64>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xi64>

 // CHECK-LABEL:   func @or(
 // CHECK-SAME:             %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:             %[[VAL_1:.*]]: tensor<32xi64>,
 // CHECK-SAME:             %[[VAL_2:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:           %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi64>
 // CHECK:           %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi64>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
 // CHECK:             scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index

 // CHECK-LABEL:   func @xor(
 // CHECK-SAME:             %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:             %[[VAL_1:.*]]: tensor<32xi64>,
 // CHECK-SAME:             %[[VAL_2:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:           %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi64>
 // CHECK:           %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi64>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
 // CHECK:             scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index

 }
 
 // CHECK-LABEL:   func @ashrbyc(
 // CHECK-SAME:                  %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                  %[[VAL_1:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_2:.*]] = arith.constant 2 : i64
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi64>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi64>
 // CHECK:             %[[VAL_14:.*]] = arith.shrsi %[[VAL_13]], %[[VAL_2]] : i64

 }
 
 // CHECK-LABEL:   func @lsrbyc(
 // CHECK-SAME:                 %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                 %[[VAL_1:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_2:.*]] = arith.constant 2 : i64
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi64>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi64>
 // CHECK:             %[[VAL_14:.*]] = arith.shrui %[[VAL_13]], %[[VAL_2]] : i64

 }
 
 // CHECK-LABEL:   func @lslbyc(
 // CHECK-SAME:                 %[[VAL_0:.*]]: tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-SAME:                 %[[VAL_1:.*]]: tensor<32xi64>) -> tensor<32xi64> {
 // CHECK-DAG:           %[[VAL_2:.*]] = arith.constant 2 : i64
 // CHECK-DAG:           %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:           %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi64>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xi64>
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi64>
 // CHECK:             %[[VAL_14:.*]] = arith.shli %[[VAL_13]], %[[VAL_2]] : i64

mlir/test/Dialect/SparseTensor/sparse_kernels.mlir

 // RUN: mlir-opt %s \
 // RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \
 // RUN: --sparsification | FileCheck %s
 
 #SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
 
 #DCSR = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>
 
 // CHECK-LABEL:   func.func @matmul1(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<20x30xf32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 30 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK:           %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30xf32>
 // CHECK:           %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x30xf32>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex>

 // CHECK-LABEL:   func.func @matmul_sparse_rhs(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<10x20xf32>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<20x30xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<10x30xf32>) -> tensor<10x30xf32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 10 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<10x20xf32>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index}
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index}
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index}
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index}
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index}
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index}
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index}
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index}
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]]
 // CHECK:           %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x30xf32>
 // CHECK:           scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:             scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] {
 // CHECK:               %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK:               %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]], %[[VAL_17]]] : memref<10x20xf32>

 // CHECK-LABEL:   func.func @matmul2(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant false
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<4x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<8x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
 // CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_19:.*]] = scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_3]] iter_args(%[[VAL_21:.*]] = %[[VAL_6]]) -> (tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
 // CHECK:             %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex>
 // CHECK:             %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]] = sparse_tensor.expand %[[VAL_6]] : tensor<4x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
 // CHECK:             %[[VAL_27:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex>
 // CHECK:             %[[VAL_28:.*]] = arith.addi %[[VAL_20]], %[[VAL_3]] : index

 // CHECK-LABEL:   func.func @conv2d(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<8x8xi32>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<6x6xi32>) -> tensor<6x6xi32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 6 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xi32>
 // CHECK:           %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<6x6xi32>
 // CHECK:           scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:             scf.for %[[VAL_16:.*]] = %[[VAL_14]] to %[[VAL_15]] step %[[VAL_5]] {
 // CHECK:               %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK:               scf.for %[[VAL_18:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<5x3xi8>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<5x6xi64>) -> tensor<5x6xi64> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 5 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 2 : i64
 // CHECK-DAG:       %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xi8>
 // CHECK:           %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<5x6xi64>
 // CHECK:           scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:             %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:             scf.for %[[VAL_17:.*]] = %[[VAL_15]] to %[[VAL_16]] step %[[VAL_5]] {
 // CHECK:               %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xindex>
 // CHECK:               %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_14]], %[[VAL_18]]] : memref<5x3xi8>

 }
 
 // CHECK-LABEL:   func.func @sparse_dot(
 // CHECK-SAME:      %[[VAL_0:.*0]]: tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_1:.*1]]: tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*2]]: tensor<f32>) -> tensor<f32> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK:           %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<f32>
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_11]][] : memref<f32>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_17:.*]]:3 = scf.while (%[[VAL_18:.*]] = %[[VAL_13]], %[[VAL_19:.*]] = %[[VAL_15]], %[[VAL_20:.*]] = %[[VAL_12]]) : (index, index, f32) -> (index, index, f32) {

mlir/test/Dialect/SparseTensor/sparse_lower.mlir

 
 // CHECK-HIR-LABEL:   func @matvec(
 // CHECK-HIR-SAME:      %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-HIR-SAME:      %[[VAL_1:.*]]: tensor<64xf64>,
 // CHECK-HIR-SAME:      %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-HIR-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-HIR-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-HIR-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-HIR-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK-HIR-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-HIR-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-HIR-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-HIR-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-HIR-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
 // CHECK-HIR-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK-HIR:           scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK-HIR-DAG:         %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK-HIR-DAG:         %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
 // CHECK-HIR-DAG:         %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
 // CHECK-HIR-DAG:         %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_12]]] : memref<32xf64>

 
 // CHECK-MIR-LABEL:   func @matvec(
 // CHECK-MIR-SAME:      %[[VAL_0:.*]]: !llvm.ptr<i8>,
 // CHECK-MIR-SAME:      %[[VAL_1:.*]]: tensor<64xf64>,
 // CHECK-MIR-SAME:      %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-MIR-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-MIR-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-MIR-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-MIR-DAG:       %[[VAL_6:.*]] = call @sparsePointers0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK-MIR-DAG:       %[[VAL_7:.*]] = call @sparseIndices0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-MIR-DAG:       %[[VAL_6:.*]] = call @sparsePositions0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-MIR-DAG:       %[[VAL_7:.*]] = call @sparseCoordinates0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 // CHECK-MIR-DAG:       %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
 // CHECK-MIR-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
 // CHECK-MIR-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK-MIR:           scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK-MIR-DAG:         %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
 // CHECK-MIR-DAG:         %[[VAL_16:.*]] = arith.addi %[[VAL_14]], %[[VAL_5]] : index
 // CHECK-MIR-DAG:         %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK-MIR-DAG:         %[[VAL_18:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_14]]] : memref<32xf64>

 
 // CHECK-LIR-LABEL:   func @matvec(
 // CHECK-LIR-SAME:      %[[VAL_0:.*]]: !llvm.ptr<i8>,
 // CHECK-LIR-SAME:      %[[VAL_1:.*]]: memref<64xf64>,
 // CHECK-LIR-SAME:      %[[VAL_2:.*]]: memref<32xf64>) -> memref<32xf64> {
 // CHECK-LIR-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-LIR-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-LIR-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-LIR-DAG:       %[[VAL_6:.*]] = call @sparsePointers0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK-LIR-DAG:       %[[VAL_7:.*]] = call @sparseIndices0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-LIR-DAG:       %[[VAL_6:.*]] = call @sparsePositions0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-LIR-DAG:       %[[VAL_7:.*]] = call @sparseCoordinates0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 // CHECK-LIR-DAG:       %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
 // CHECK-LIR:           scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK-LIR-DAG:         %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK-LIR-DAG:         %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
 // CHECK-LIR-DAG:         %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref<?xindex>
 // CHECK-LIR-DAG:         %[[VAL_16:.*]] = memref.load %[[VAL_2]]{{\[}}%[[VAL_12]]] : memref<32xf64>
 // CHECK-LIR:             %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_13]] to %[[VAL_15]] step %[[VAL_5]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (f64) {
 // CHECK-LIR:               %[[VAL_20:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_lower_col.mlir

 
 // CHECK-HIR-LABEL:   func @matvec(
 // CHECK-HIR-SAME:                 %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>,
 // CHECK-HIR-SAME:                 %[[VAL_1:.*]]: tensor<64xf64>,
 // CHECK-HIR-SAME:                 %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-HIR-DAG:       %[[VAL_3:.*]] = arith.constant 64 : index
 // CHECK-HIR-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-HIR-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-HIR-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
-// CHECK-HIR-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
+// CHECK-HIR-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
+// CHECK-HIR-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
 // CHECK-HIR-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xf64>
 // CHECK-HIR-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
 // CHECK-HIR-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK-HIR:           scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK-HIR-DAG:         %[[VAL_13:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<64xf64>
 // CHECK-HIR-DAG:         %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK-HIR-DAG:         %[[VAL_15:.*]] = arith.addi %[[VAL_12]], %[[VAL_5]] : index
 // CHECK-HIR-DAG:         %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_15]]] : memref<?xindex>

 
 // CHECK-MIR-LABEL:   func @matvec(
 // CHECK-MIR-SAME:                 %[[VAL_0:.*]]: !llvm.ptr<i8>,
 // CHECK-MIR-SAME:                 %[[VAL_1:.*]]: tensor<64xf64>,
 // CHECK-MIR-SAME:                 %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-MIR-DAG:       %[[VAL_3:.*]] = arith.constant 64 : index
 // CHECK-MIR-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-MIR-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-MIR-DAG:       %[[VAL_7:.*]] = call @sparsePointers0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK-MIR-DAG:       %[[VAL_8:.*]] = call @sparseIndices0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-MIR-DAG:       %[[VAL_7:.*]] = call @sparsePositions0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-MIR-DAG:       %[[VAL_8:.*]] = call @sparseCoordinates0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 // CHECK-MIR-DAG:       %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
 // CHECK-MIR-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
 // CHECK-MIR-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK-MIR:           scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
 // CHECK-MIR:             %[[VAL_16:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_15]]] : memref<64xf64>
 // CHECK-MIR:             %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
 // CHECK-MIR:             %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_6]] : index
 // CHECK-MIR:             %[[VAL_19:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_18]]] : memref<?xindex>

 
 // CHECK-LIR-LABEL:   func @matvec(
 // CHECK-LIR-SAME:                 %[[VAL_0:.*]]: !llvm.ptr<i8>,
 // CHECK-LIR-SAME:                 %[[VAL_1:.*]]: memref<64xf64>,
 // CHECK-LIR-SAME:                 %[[VAL_2:.*]]: memref<32xf64>) -> memref<32xf64> {
 // CHECK-LIR-DAG:       %[[VAL_3:.*]] = arith.constant 64 : index
 // CHECK-LIR-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-LIR-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-LIR-DAG:       %[[VAL_7:.*]] = call @sparsePointers0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK-LIR-DAG:       %[[VAL_8:.*]] = call @sparseIndices0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-LIR-DAG:       %[[VAL_7:.*]] = call @sparsePositions0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-LIR-DAG:       %[[VAL_8:.*]] = call @sparseCoordinates0(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 // CHECK-LIR-DAG:       %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
 // CHECK-LIR:           scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
 // CHECK-LIR:             %[[VAL_14:.*]] = memref.load %[[VAL_1]]{{\[}}%[[VAL_13]]] : memref<64xf64>
 // CHECK-LIR:             %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK-LIR:             %[[VAL_16:.*]] = arith.addi %[[VAL_13]], %[[VAL_6]] : index
 // CHECK-LIR:             %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK-LIR:             scf.for %[[VAL_18:.*]] = %[[VAL_15]] to %[[VAL_17]] step %[[VAL_6]] {
 // CHECK-LIR:               %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_lower_inplace.mlir

 
 // CHECK-HIR-LABEL:   func @matvec(
 // CHECK-HIR-SAME:      %[[VAL_0:.*]]: tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>,
 // CHECK-HIR-SAME:      %[[VAL_1:.*]]: tensor<64xf64>,
 // CHECK-HIR-SAME:      %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-HIR-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-HIR-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-HIR-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-HIR:           %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
-// CHECK-HIR:           %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-HIR:           %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
+// CHECK-HIR:           %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-HIR:           %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>>
 // CHECK-HIR:           %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
 // CHECK-HIR:           %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK-HIR:           scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK-HIR-DAG:         %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK-HIR-DAG:         %[[VAL_13:.*]] = arith.addi %[[VAL_11]], %[[VAL_5]] : index
 // CHECK-HIR-DAG:         %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK-HIR-DAG:         %[[VAL_15:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_11]]] : memref<32xf64>

 
 // CHECK-MIR-LABEL:   func @matvec(
 // CHECK-MIR-SAME:      %[[VAL_0:.*]]: !llvm.ptr<i8>,
 // CHECK-MIR-SAME:      %[[VAL_1:.*]]: tensor<64xf64>,
 // CHECK-MIR-SAME:      %[[VAL_2:.*]]: tensor<32xf64>) -> tensor<32xf64> {
 // CHECK-MIR-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-MIR-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-MIR-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-MIR:           %[[VAL_6:.*]] = call @sparsePointers0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK-MIR:           %[[VAL_7:.*]] = call @sparseIndices0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-MIR:           %[[VAL_6:.*]] = call @sparsePositions0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-MIR:           %[[VAL_7:.*]] = call @sparseCoordinates0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 // CHECK-MIR:           %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
 // CHECK-MIR:           %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64>
 // CHECK-MIR:           %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64>
 // CHECK-MIR:           scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK-MIR-DAG:         %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK-MIR-DAG:         %[[VAL_13:.*]] = arith.addi %[[VAL_11]], %[[VAL_5]] : index
 // CHECK-MIR-DAG:         %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK-MIR-DAG:         %[[VAL_15:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_11]]] : memref<32xf64>

 
 // CHECK-LIR-LABEL:   func @matvec(
 // CHECK-LIR-SAME:      %[[VAL_0:.*]]: !llvm.ptr<i8>,
 // CHECK-LIR-SAME:      %[[VAL_1:.*]]: memref<64xf64>,
 // CHECK-LIR-SAME:      %[[VAL_2:.*]]: memref<32xf64>) -> memref<32xf64> {
 // CHECK-LIR-DAG:       %[[VAL_3:.*]] = arith.constant 32 : index
 // CHECK-LIR-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-LIR-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-LIR:           %[[VAL_6:.*]] = call @sparsePointers0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
-// CHECK-LIR:           %[[VAL_7:.*]] = call @sparseIndices0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-LIR:           %[[VAL_6:.*]] = call @sparsePositions0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
+// CHECK-LIR:           %[[VAL_7:.*]] = call @sparseCoordinates0(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr<i8>, index) -> memref<?xindex>
 // CHECK-LIR:           %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr<i8>) -> memref<?xf64>
 // CHECK-LIR:           scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK-LIR-DAG:         %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref<?xindex>
 // CHECK-LIR-DAG:         %[[VAL_11:.*]] = arith.addi %[[VAL_9]], %[[VAL_5]] : index
 // CHECK-LIR-DAG:         %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK-LIR-DAG:         %[[VAL_13:.*]] = memref.load %[[VAL_2]]{{\[}}%[[VAL_9]]] : memref<32xf64>
 // CHECK-LIR:             %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_10]] to %[[VAL_12]] step %[[VAL_5]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (f64) {
 // CHECK-LIR:               %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_matmul_codegen.mlir

 // CHECK-SAME:      %[[VAL_4:.*4]]: index,
 // CHECK-SAME:      %[[VAL_5:.*5]]: index,
 // CHECK-SAME:      %[[VAL_6:.*6]]: f64) -> (memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
 // CHECK:           %[[VAL_7:.*]] = arith.constant false
 // CHECK:           %[[VAL_8:.*]] = arith.constant 1 : index
 // CHECK:           %[[VAL_9:.*]] = arith.addi %[[VAL_4]], %[[VAL_8]] : index
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_0]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_0]]{{\[}}%[[VAL_9]]] : memref<?xindex>
-// CHECK:           %[[VAL_13:.*]] = sparse_tensor.storage_specifier.get %[[VAL_3]]  idx_mem_sz at 1 : !sparse_tensor.storage_specifier
+// CHECK:           %[[VAL_13:.*]] = sparse_tensor.storage_specifier.get %[[VAL_3]]  crd_mem_sz at 1 : !sparse_tensor.storage_specifier
 // CHECK:           %[[VAL_14:.*]] = arith.subi %[[VAL_11]], %[[VAL_8]] : index
 // CHECK:           %[[VAL_15:.*]] = arith.cmpi ult, %[[VAL_10]], %[[VAL_11]] : index
 // CHECK:           %[[VAL_16:.*]] = scf.if %[[VAL_15]] -> (i1) {
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_1]]{{\[}}%[[VAL_14]]] : memref<?xindex>
 // CHECK:             %[[VAL_18:.*]] = arith.cmpi eq, %[[VAL_17]], %[[VAL_5]] : index
 // CHECK:             scf.yield %[[VAL_18]] : i1
 // CHECK:           } else {
 // CHECK:             memref.store %[[VAL_13]], %[[VAL_0]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:             scf.yield %[[VAL_7]] : i1
 // CHECK:           }
 // CHECK:           %[[VAL_19:.*]]:2 = scf.if %[[VAL_20:.*]] -> (memref<?xindex>, !sparse_tensor.storage_specifier
 // CHECK:             scf.yield %[[VAL_1]], %[[VAL_3]] : memref<?xindex>, !sparse_tensor.storage_specifier
 // CHECK:           } else {
 // CHECK:             %[[VAL_21:.*]] = arith.addi %[[VAL_13]], %[[VAL_8]] : index
 // CHECK:             memref.store %[[VAL_21]], %[[VAL_0]]{{\[}}%[[VAL_9]]] : memref<?xindex>
 // CHECK:             %[[VAL_22:.*]], %[[VAL_24:.*]] = sparse_tensor.push_back %[[VAL_13]], %[[VAL_1]], %[[VAL_5]] : index, memref<?xindex>, index
-// CHECK:             %[[VAL_25:.*]] = sparse_tensor.storage_specifier.set %[[VAL_3]]  idx_mem_sz at 1 with %[[VAL_24]] : !sparse_tensor.storage_specifier
+// CHECK:             %[[VAL_25:.*]] = sparse_tensor.storage_specifier.set %[[VAL_3]]  crd_mem_sz at 1 with %[[VAL_24]] : !sparse_tensor.storage_specifier
 // CHECK:             scf.yield %[[VAL_22]], %[[VAL_25]] : memref<?xindex>, !sparse_tensor.storage_specifier
 // CHECK:           }
 // CHECK:           %[[VAL_28:.*]] = sparse_tensor.storage_specifier.get %[[VAL_27:.*]]#1  val_mem_sz : !sparse_tensor.storage_specifier
 // CHECK:           %[[VAL_29:.*]], %[[VAL_30:.*]] = sparse_tensor.push_back %[[VAL_28]], %[[VAL_2]], %[[VAL_6]] : index, memref<?xf64>, f64
 // CHECK:           %[[VAL_32:.*]] = sparse_tensor.storage_specifier.set %[[VAL_27]]#1  val_mem_sz with %[[VAL_30]] : !sparse_tensor.storage_specifier
 // CHECK:           return %[[VAL_0]], %[[VAL_27]]#0, %[[VAL_29]], %[[VAL_32]] : memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
 // CHECK:         }
 

 // CHECK-DAG:       %[[VAL_13:.*]] = arith.constant true
 // CHECK:           %[[VAL_14:.*]] = memref.alloc() : memref<16xindex>
 // CHECK:           %[[VAL_15:.*]] = memref.cast %[[VAL_14]] : memref<16xindex> to memref<?xindex>
 // CHECK:           %[[VAL_16:.*]] = memref.alloc() : memref<16xindex>
 // CHECK:           %[[VAL_17:.*]] = memref.cast %[[VAL_16]] : memref<16xindex> to memref<?xindex>
 // CHECK:           %[[VAL_18:.*]] = memref.alloc() : memref<16xf64>
 // CHECK:           %[[VAL_19:.*]] = memref.cast %[[VAL_18]] : memref<16xf64> to memref<?xf64>
 // CHECK:           %[[VAL_20:.*]] = sparse_tensor.storage_specifier.init : !sparse_tensor.storage_specifier
-// CHECK:           %[[VAL_21:.*]] = sparse_tensor.storage_specifier.set %[[VAL_20]]  dim_sz at 0 with %[[VAL_8]] : !sparse_tensor.storage_specifier
-// CHECK:           %[[VAL_22:.*]] = sparse_tensor.storage_specifier.set %[[VAL_21]]  dim_sz at 1 with %[[VAL_8]] : !sparse_tensor.storage_specifier
-// CHECK:           %[[VAL_23:.*]] = sparse_tensor.storage_specifier.get %[[VAL_22]]  ptr_mem_sz at 1 : !sparse_tensor.storage_specifier
+// CHECK:           %[[VAL_21:.*]] = sparse_tensor.storage_specifier.set %[[VAL_20]]  lvl_sz at 0 with %[[VAL_8]] : !sparse_tensor.storage_specifier
+// CHECK:           %[[VAL_22:.*]] = sparse_tensor.storage_specifier.set %[[VAL_21]]  lvl_sz at 1 with %[[VAL_8]] : !sparse_tensor.storage_specifier
+// CHECK:           %[[VAL_23:.*]] = sparse_tensor.storage_specifier.get %[[VAL_22]]  pos_mem_sz at 1 : !sparse_tensor.storage_specifier
 // CHECK:           %[[VAL_24:.*]], %[[VAL_25:.*]] = sparse_tensor.push_back %[[VAL_23]], %[[VAL_15]], %[[VAL_10]] : index, memref<?xindex>, index
-// CHECK:           %[[VAL_26:.*]] = sparse_tensor.storage_specifier.set %[[VAL_22]]  ptr_mem_sz at 1 with %[[VAL_25]] : !sparse_tensor.storage_specifier
+// CHECK:           %[[VAL_26:.*]] = sparse_tensor.storage_specifier.set %[[VAL_22]]  pos_mem_sz at 1 with %[[VAL_25]] : !sparse_tensor.storage_specifier
 // CHECK:           %[[VAL_27:.*]], %[[VAL_28:.*]] = sparse_tensor.push_back %[[VAL_25]], %[[VAL_24]], %[[VAL_10]], %[[VAL_8]] : index, memref<?xindex>, index, index
-// CHECK:           %[[VAL_29:.*]] = sparse_tensor.storage_specifier.set %[[VAL_26]]  ptr_mem_sz at 1 with %[[VAL_28]] : !sparse_tensor.storage_specifier
+// CHECK:           %[[VAL_29:.*]] = sparse_tensor.storage_specifier.set %[[VAL_26]]  pos_mem_sz at 1 with %[[VAL_28]] : !sparse_tensor.storage_specifier
 // CHECK:           %[[VAL_30:.*]] = memref.alloc() : memref<4xf64>
 // CHECK:           %[[VAL_31:.*]] = m
 // CHECK:           %[[VAL_32:.*]] = memref.alloc() : memref<4xindex>
 // CHECK:           %[[VAL_33:.*]] = memref.cast %[[VAL_32]] : memref<4xindex> to memref<?xindex>
 // CHECK:           linalg.fill ins(%[[VAL_9]] : f64) outs(%[[VAL_30]] : memref<4xf64>)
 // CHECK:           linalg.fill ins(%[[VAL_12]] : i1) outs(%[[VAL_31]] : memref<4xi1>)
 // CHECK:           %[[VAL_34:.*]]:4 = scf.for %[[VAL_35:.*]] = %[[VAL_10]] to %[[VAL_8]] step %[[VAL_11]] iter_args(%[[VAL_36:.*]] = %[[VAL_27]], %[[VAL_37:.*]] = %[[VAL_17]], %[[VAL_38:.*]] = %[[VAL_19]], %[[VAL_39:.*]] = %[[VAL_29]]) -> (memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
 // CHECK:             %[[VAL_40:.*]] = memref.load %[[VAL_0]]{{\[}}%[[VAL_35]]] : memref<?xindex>

 // CHECK:               memref.store %[[VAL_12]], %[[VAL_31]]{{\[}}%[[VAL_72]]] : memref<4xi1>
 // CHECK:               scf.yield %[[VAL_74]]#0, %[[VAL_74]]#1, %[[VAL_74]]#2, %[[VAL_74]]#3 : memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
 // CHECK:             }
 // CHECK:             scf.yield %[[VAL_75:.*]]#0, %[[VAL_75]]#1, %[[VAL_75]]#2, %[[VAL_75]]#3 : memref<?xindex>, memref<?xindex>, memref<?xf64>, !sparse_tensor.storage_specifier
 // CHECK:           } {"Emitted from" = "linalg.generic"}
 // CHECK:           memref.dealloc %[[VAL_30]] : memref<4xf64>
 // CHECK:           memref.dealloc %[[VAL_31]] : memref<4xi1>
 // CHECK:           memref.dealloc %[[VAL_32]] : memref<4xindex>
-// CHECK:           %[[VAL_76:.*]] = sparse_tensor.storage_specifier.get %[[VAL_77:.*]]#3  ptr_mem_sz at 1 : !sparse_tensor.storage_specifier
+// CHECK:           %[[VAL_76:.*]] = sparse_tensor.storage_specifier.get %[[VAL_77:.*]]#3  pos_mem_sz at 1 : !sparse_tensor.storage_specifier
 // CHECK:           %[[VAL_78:.*]] = memref.load %[[VAL_77]]#0{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:           %[[VAL_79:.*]] = scf.for %[[VAL_80:.*]] = %[[VAL_11]] to %[[VAL_76]] step %[[VAL_11]] iter_args(%[[VAL_81:.*]] = %[[VAL_78]]) -> (index) {
 // CHECK:             %[[VAL_82:.*]] = memref.load %[[VAL_77]]#0{{\[}}%[[VAL_80]]] : memref<?xindex>
 // CHECK:             %[[VAL_83:.*]] = arith.cmpi eq, %[[VAL_82]], %[[VAL_10]] : index
 // CHECK:             %[[VAL_84:.*]] = arith.select %[[VAL_83]], %[[VAL_81]], %[[VAL_82]] : index
 // CHECK:             scf.if %[[VAL_83]] {
 // CHECK:               memref.store %[[VAL_81]], %[[VAL_77]]#0{{\[}}%[[VAL_80]]] : memref<?xindex>
 // CHECK:             }

mlir/test/Dialect/SparseTensor/sparse_nd.mlir

 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 20 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 30 : index
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant 60 : index
 // CHECK-DAG:       %[[VAL_9:.*]] = arith.constant 70 : index
 // CHECK-DAG:       %[[VAL_10:.*]] = arith.constant 80 : index
 // CHECK-DAG:       %[[VAL_11:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_12:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32>
-// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_17:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 3 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_16:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_17:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 4 : index} : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ] }>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_20:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32>
 // CHECK:           linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_20]] : memref<10x20x30x40x50x60x70x80xf32>
 // CHECK:           scf.for %[[VAL_21:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_12]] {
 // CHECK:             scf.for %[[VAL_22:.*]] = %[[VAL_11]] to %[[VAL_9]] step %[[VAL_12]] {
 // CHECK:               %[[VAL_23:.*]] = arith.muli %[[VAL_21]], %[[VAL_9]] : index
 // CHECK:               %[[VAL_24:.*]] = arith.addi %[[VAL_23]], %[[VAL_22]] : index
 // CHECK:               scf.for %[[VAL_25:.*]] = %[[VAL_11]] to %[[VAL_8]] step %[[VAL_12]] {

mlir/test/Dialect/SparseTensor/sparse_out.mlir

#trait_scale_inpl = {
   doc = "X(i,j) *= 2 or X(i,j) += X(i,j)"
 }
 
 // CHECK-LABEL:   func.func @sparse_simply_dynamic1(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 2.000000e+00 : f32
-// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK:           %[[VAL_7:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_9:.*]] = %[[VAL_7]] to %[[VAL_8]] step %[[VAL_2]] {
 // CHECK:             %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_9]]] : memref<?xindex>
 // CHECK:             %[[VAL_11:.*]] = arith.addi %[[VAL_9]], %[[VAL_2]] : index
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             scf.for %[[VAL_13:.*]] = %[[VAL_10]] to %[[VAL_12]] step %[[VAL_2]] {
func.func @sparse_simply_dynamic1(%argx: tensor<32x16xf32, #DCSR>) -> tensor<32x16xf32, #DCSR> {
   } -> tensor<32x16xf32, #DCSR>
   return %0 : tensor<32x16xf32, #DCSR>
 }
 
 // CHECK-LABEL:   func.func @sparse_simply_dynamic2(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[VAL_3:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_3:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK:           %[[VAL_6:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_7:.*]] = memref.load %[[VAL_3]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_8:.*]] = %[[VAL_6]] to %[[VAL_7]] step %[[VAL_2]] {
 // CHECK:             %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_8]]] : memref<?xindex>
 // CHECK:             %[[VAL_10:.*]] = arith.addi %[[VAL_8]], %[[VAL_2]] : index
 // CHECK:             %[[VAL_11:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:             scf.for %[[VAL_12:.*]] = %[[VAL_9]] to %[[VAL_11]] step %[[VAL_2]] {

 
 // CHECK-LABEL:   func.func @sparse_truly_dynamic(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 10 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 2.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_5:.*]] = bufferization.alloc_tensor() : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf32>
 // CHECK:           %[[VAL_9:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] iter_args(%[[VAL_11:.*]] = %[[VAL_5]]) -> (tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = arith.addi %[[VAL_10]], %[[VAL_3]] : index
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK:             %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_11]]) -> (tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
 // CHECK:               %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK:               %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xf32>

 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : i32
 // CHECK-DAG:       %[[VAL_FALSE:.*]] = arith.constant false
 // CHECK-DAG:       %[[VAL_TRUE:.*]] = arith.constant true
 // CHECK:           %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>>
 // CHECK:           %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>>
 // CHECK:           %[[VAL_7:.*]] = bufferization.alloc_tensor(%[[VAL_5]], %[[VAL_6]]) : tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_10:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_11:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_12:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_13:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xi32>
-// CHECK:           %[[VAL_15:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_16:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_17:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_18:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_19:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_20:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_15:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_16:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_17:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_18:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_19:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_20:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index} : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_21:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ] }>> to memref<?xi32>
 // CHECK:           %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_23:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_24:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_25:.*]] = memref.load %[[VAL_15]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_26:.*]]:3 = scf.while (%[[VAL_27:.*]] = %[[VAL_22]], %[[VAL_28:.*]] = %[[VAL_24]], %[[VAL_29:.*]] = %[[VAL_7]]) : (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> (index, index, tensor<?x?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
 // CHECK:             %[[VAL_30:.*]] = arith.cmpi ult, %[[VAL_27]], %[[VAL_23]] : index
 // CHECK:             %[[VAL_31:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_25]] : index

 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant false
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant true
 // CHECK:           %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
 // CHECK:           %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
 // CHECK:           %[[VAL_8:.*]] = bufferization.alloc_tensor(%[[VAL_6]], %[[VAL_7]]) : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
-// CHECK:           %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_17:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_14:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_15:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_16:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_17:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>
 // CHECK:           %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           %[[VAL_21:.*]] = scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_20]] step %[[VAL_3]] iter_args(%[[VAL_23:.*]] = %[[VAL_8]]) -> (tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
 // CHECK:             %[[VAL_24:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_22]]] : memref<?xindex>
 // CHECK:             %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]], %[[VAL_28:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf32>, memref<?xi1>, memref<?xindex>
 // CHECK:             %[[VAL_29:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xindex>
 // CHECK:             %[[VAL_30:.*]] = arith.addi %[[VAL_22]], %[[VAL_3]] : index

mlir/test/Dialect/SparseTensor/sparse_outbuf.mlir

 }
 
 // CHECK-LABEL:   func.func @allout_inplace(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<10xi32, #{{.*}}>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<10xf32>) -> tensor<10xf32> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xi32, #{{.*}}> to memref<?xi32>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<10xf32>
 // CHECK:           linalg.fill ins(%[[VAL_3]] : f32) outs(%[[VAL_8]] : memref<10xf32>)
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_4]] {
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi32>

 }
 
 // CHECK-LABEL:   func.func @allout_materialize(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<10xi32, #{{.*}}>) -> tensor<10xf32> {
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f32
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK:           %[[VAL_4:.*]] = bufferization.alloc_tensor() : tensor<10xf32>
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex>
-// CHECK:           %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex>
+// CHECK:           %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10xi32, #{{.*}}> to memref<?xindex>
 // CHECK:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xi32, #{{.*}}> to memref<?xi32>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_4]] : memref<10xf32>
 // CHECK:           linalg.fill ins(%[[VAL_2]] : f32) outs(%[[VAL_8]] : memref<10xf32>)
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_11:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_3]] {
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_11]]] : memref<?xindex>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xi32>
func.func @allout_materialize(%arga: tensor<10xi32, #SV>) -> tensor<10xf32> {
   return %0 : tensor<10xf32>
 }
 
 // CHECK-LABEL:   func.func @update_inplace(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<10xf32, #{{.*}}>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<10xf32>) -> tensor<10xf32> {
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<10xf32, #{{.*}}> to memref<?xindex>
-// CHECK:           %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<10xf32, #{{.*}}> to memref<?xindex>
+// CHECK:           %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<10xf32, #{{.*}}> to memref<?xindex>
+// CHECK:           %[[VAL_5:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<10xf32, #{{.*}}> to memref<?xindex>
 // CHECK:           %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xf32, #{{.*}}> to memref<?xf32>
 // CHECK:           %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<10xf32>
 // CHECK:           %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_10:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] {
 // CHECK:             %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK:             %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf32>
 // CHECK:             %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<10xf32>

mlir/test/Dialect/SparseTensor/sparse_pack.mlir

 // RUN: mlir-opt %s --canonicalize --post-sparsification-rewrite="enable-runtime-library=false" --sparse-tensor-codegen -cse | FileCheck %s
 
 #COO = #sparse_tensor.encoding<{
   dimLevelType = ["compressed-nu", "singleton"],
-  indexBitWidth=32
+  crdWidth=32
 }>
 
 // CHECK-LABEL:   func.func @sparse_pack(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<6xf64>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<6x2xi32>) -> (memref<?xindex>, memref<?xi32>, memref<?xf64>,
 // CHECK:           %[[VAL_2:.*]] = arith.constant dense<[0, 6]> : tensor<2xindex>
 // CHECK:           %[[VAL_3:.*]] = bufferization.to_memref %[[VAL_2]] : memref<2xindex>
 // CHECK:           %[[VAL_4:.*]] = memref.cast %[[VAL_3]] : memref<2xindex> to memref<?xindex>
 // CHECK:           %[[VAL_5:.*]] = bufferization.to_memref %[[VAL_1]] : memref<6x2xi32>
 // CHECK:           %[[VAL_6:.*]] = memref.collapse_shape %[[VAL_5]] {{\[\[}}0, 1]] : memref<6x2xi32> into memref<12xi32>
 // CHECK:           %[[VAL_7:.*]] = memref.cast %[[VAL_6]] : memref<12xi32> to memref<?xi32>
 // CHECK:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<6xf64>
 // CHECK:           %[[VAL_9:.*]] = memref.cast %[[VAL_8]] : memref<6xf64> to memref<?xf64>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.storage_specifier.init :
 // CHECK:           %[[VAL_11:.*]] = arith.constant 6 : index
 // CHECK:           %[[VAL_12:.*]] = arith.constant 100 : index
-// CHECK:           %[[VAL_14:.*]] = sparse_tensor.storage_specifier.set %[[VAL_10]]  dim_sz at 0 with %[[VAL_12]]
+// CHECK:           %[[VAL_14:.*]] = sparse_tensor.storage_specifier.set %[[VAL_10]]  lvl_sz at 0 with %[[VAL_12]]
 // CHECK:           %[[VAL_15:.*]] = arith.constant 2 : index
-// CHECK:           %[[VAL_17:.*]] = sparse_tensor.storage_specifier.set %[[VAL_14]]  ptr_mem_sz at 0 with %[[VAL_15]]
-// CHECK:           %[[VAL_19:.*]] = sparse_tensor.storage_specifier.set %[[VAL_17]]  idx_mem_sz at 0 with %[[VAL_11]]
-// CHECK:           %[[VAL_20:.*]] = sparse_tensor.storage_specifier.set %[[VAL_19]]  dim_sz at 1 with %[[VAL_12]]
-// CHECK:           %[[VAL_21:.*]] = sparse_tensor.storage_specifier.set %[[VAL_20]]  idx_mem_sz at 1 with %[[VAL_11]]
+// CHECK:           %[[VAL_17:.*]] = sparse_tensor.storage_specifier.set %[[VAL_14]]  pos_mem_sz at 0 with %[[VAL_15]]
+// CHECK:           %[[VAL_19:.*]] = sparse_tensor.storage_specifier.set %[[VAL_17]]  crd_mem_sz at 0 with %[[VAL_11]]
+// CHECK:           %[[VAL_20:.*]] = sparse_tensor.storage_specifier.set %[[VAL_19]]  lvl_sz at 1 with %[[VAL_12]]
+// CHECK:           %[[VAL_21:.*]] = sparse_tensor.storage_specifier.set %[[VAL_20]]  crd_mem_sz at 1 with %[[VAL_11]]
 // CHECK:           %[[VAL_22:.*]] = sparse_tensor.storage_specifier.set %[[VAL_21]]  val_mem_sz with %[[VAL_11]]
 // CHECK:           return %[[VAL_4]], %[[VAL_7]], %[[VAL_9]], %[[VAL_22]] : memref<?xindex>, memref<?xi32>, memref<?xf64>,
 // CHECK:         }
-func.func @sparse_pack(%data: tensor<6xf64>, %index: tensor<6x2xi32>)
+func.func @sparse_pack(%values: tensor<6xf64>, %coordinates: tensor<6x2xi32>)
                     -> tensor<100x100xf64, #COO> {
-  %0 = sparse_tensor.pack %data, %index : tensor<6xf64>, tensor<6x2xi32>
-                                       to tensor<100x100xf64, #COO>
+  %0 = sparse_tensor.pack %values, %coordinates
+     : tensor<6xf64>, tensor<6x2xi32> to tensor<100x100xf64, #COO>
   return %0 : tensor<100x100xf64, #COO>
 }
 
 // CHECK-LABEL:   func.func @sparse_unpack(
 // CHECK-SAME:      %[[VAL_0:.*]]: memref<?xindex>,
 // CHECK-SAME:      %[[VAL_1:.*]]: memref<?xi32>,
 // CHECK-SAME:      %[[VAL_2:.*]]: memref<?xf64>,
 // CHECK-SAME:      %[[VAL_3:.*]]: !sparse_tensor.storage_specifier

mlir/test/Dialect/SparseTensor/sparse_parallel_reduce.mlir

 }
 // CHECK-LABEL:  func.func @matvec(
 //  CHECK-SAME:    %[[TMP_arg0:.*]]: tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 //  CHECK-SAME:    %[[TMP_arg1:.*]]: tensor<32xf32>,
 //  CHECK-SAME:    %[[TMP_arg2:.*]]: tensor<16xf32>) -> tensor<16xf32> {
 //   CHECK-DAG:  %[[TMP_c16:.*]] = arith.constant 16 : index
 //   CHECK-DAG:  %[[TMP_c0:.*]] = arith.constant 0 : index
 //   CHECK-DAG:  %[[TMP_c1:.*]] = arith.constant 1 : index
-//       CHECK:  %[[TMP_0:.*]] = sparse_tensor.pointers %[[TMP_arg0]] {dimension = 1 : index}
-//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.indices %[[TMP_arg0]] {dimension = 1 : index}
+//       CHECK:  %[[TMP_0:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index}
+//       CHECK:  %[[TMP_1:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 1 : index}
 //       CHECK:  %[[TMP_2:.*]] = sparse_tensor.values %[[TMP_arg0]]
 //       CHECK:  %[[TMP_3:.*]] = bufferization.to_memref %[[TMP_arg1]] : memref<32xf32>
 //       CHECK:  %[[TMP_4:.*]] = bufferization.to_memref %[[TMP_arg2]] : memref<16xf32>
 //       CHECK:  scf.parallel (%[[TMP_arg3:.*]]) = (%[[TMP_c0]]) to (%[[TMP_c16]]) step (%[[TMP_c1]]) {
 //       CHECK:    %[[TMP_6:.*]] = memref.load %[[TMP_4]][%[[TMP_arg3]]] : memref<16xf32>
 //       CHECK:    %[[TMP_7:.*]] = memref.load %[[TMP_0]][%[[TMP_arg3]]] : memref<?xindex>
 //       CHECK:    %[[TMP_8:.*]] = arith.addi %[[TMP_arg3]], %[[TMP_c1]] : index
 //       CHECK:    %[[TMP_9:.*]] = memref.load %[[TMP_0]][%[[TMP_8]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_reshape.mlir

 // rewrite for codegen:
 //
 // CHECK-RWT-LABEL:   func.func @sparse_expand(
 // CHECK-RWT-SAME:    %[[S:.*]]:
 // CHECK-RWT-DAG:     %[[C10:.*]] = arith.constant 10 : index
 // CHECK-RWT-DAG:     %[[C0:.*]] = arith.constant 0 : index
 // CHECK-RWT-DAG:     %[[C1:.*]] = arith.constant 1 : index
 // CHECK-RWT:         %[[B:.*]] = bufferization.alloc_tensor()
-// CHECK-RWT:         %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
-// CHECK-RWT:         %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
+// CHECK-RWT:         %[[P0:.*]] = sparse_tensor.positions %[[S]] {level = 0 : index}
+// CHECK-RWT:         %[[I0:.*]] = sparse_tensor.coordinates %[[S]] {level = 0 : index}
 // CHECK-RWT:         %[[V:.*]] = sparse_tensor.values %[[S]]
 // CHECK-RWT:         %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
 // CHECK-RWT:         %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
 // CHECK-RWT:         %[[RET:.*]] = scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] iter_args(%[[R:.*]] = %[[B]])
 // CHECK-RWT:           %[[SI:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
 // CHECK-RWT:           %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[I]]] : memref<?xf64>
 // CHECK-RWT:           %[[DI0:.*]] = arith.divui %[[SI]], %[[C10]] : index
 // CHECK-RWT:           %[[DI1:.*]] = arith.remui %[[SI]], %[[C10]] : index

 // rewrite for codegen:
 //
 // CHECK-RWT-LABEL:   func.func @sparse_collapse(
 // CHECK-RWT-SAME:    %[[S:.*]]:
 // CHECK-RWT-DAG:     %[[C10:.*]] = arith.constant 10 : index
 // CHECK-RWT-DAG:     %[[C0:.*]] = arith.constant 0 : index
 // CHECK-RWT-DAG:     %[[C1:.*]] = arith.constant 1 : index
 // CHECK-RWT:         %[[B:.*]] = bufferization.alloc_tensor()
-// CHECK-RWT:         %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
-// CHECK-RWT:         %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
-// CHECK-RWT:         %[[P1:.*]] = sparse_tensor.pointers %[[S]] {dimension = 1 : index}
-// CHECK-RWT:         %[[I1:.*]] = sparse_tensor.indices %[[S]] {dimension = 1 : index}
+// CHECK-RWT:         %[[P0:.*]] = sparse_tensor.positions %[[S]] {level = 0 : index}
+// CHECK-RWT:         %[[I0:.*]] = sparse_tensor.coordinates %[[S]] {level = 0 : index}
+// CHECK-RWT:         %[[P1:.*]] = sparse_tensor.positions %[[S]] {level = 1 : index}
+// CHECK-RWT:         %[[I1:.*]] = sparse_tensor.coordinates %[[S]] {level = 1 : index}
 // CHECK-RWT:         %[[V:.*]] = sparse_tensor.values %[[S]]
 // CHECK-RWT:         %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
 // CHECK-RWT:         %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
 // CHECK-RWT:         %[[RET:.*]] = scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] iter_args(%[[A0:.*]] = %[[B]])
 // CHECK-RWT:           %[[SI0:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
 // CHECK-RWT-DAG:       %[[S1:.*]] = memref.load %[[P1]]{{\[}}%[[I]]] : memref<?xindex>
 // CHECK-RWT-DAG:       %[[PE1:.*]] = arith.addi %[[I]], %[[C1]] : index
 // CHECK-RWT:           %[[E1:.*]] = memref.load %[[P1]]{{\[}}%[[PE1]]] : memref<?xindex>

 // CHECK-RWT-LABEL:   func.func @dynamic_sparse_expand(
 // CHECK-RWT-SAME:    %[[S:.*]]:
 // CHECK-RWT-DAG:     %[[C10:.*]] = arith.constant 10 : index
 // CHECK-RWT-DAG:     %[[C0:.*]] = arith.constant 0 : index
 // CHECK-RWT-DAG:     %[[C1:.*]] = arith.constant 1 : index
 // CHECK-RWT:         %[[SD:.*]] = tensor.dim %[[S]], %[[C0]]
 // CHECK-RWT:         %[[DD0:.*]] = arith.divui %[[SD]], %[[C10]] : index
 // CHECK-RWT:         %[[B:.*]] = bufferization.alloc_tensor(%[[DD0]])
-// CHECK-RWT:         %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
-// CHECK-RWT:         %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
+// CHECK-RWT:         %[[P0:.*]] = sparse_tensor.positions %[[S]] {level = 0 : index}
+// CHECK-RWT:         %[[I0:.*]] = sparse_tensor.coordinates %[[S]] {level = 0 : index}
 // CHECK-RWT:         %[[V:.*]] = sparse_tensor.values %[[S]]
 // CHECK-RWT:         %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
 // CHECK-RWT:         %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
 // CHECK-RWT:         %[[RET:.*]] = scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] iter_args(%[[R:.*]] = %[[B]])
 // CHECK-RWT:           %[[SI:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
 // CHECK-RWT:           %[[SV:.*]] = memref.load %[[V]]{{\[}}%[[I]]] : memref<?xf64>
 // CHECK-RWT:           %[[T1:.*]] = arith.muli %[[DD0]], %[[C10]] : index
 // CHECK-RWT:           %[[T2:.*]] = arith.divui %[[T1]], %[[DD0]] : index

 // CHECK-RWT-LABEL:   func.func @dynamic_sparse_collapse(
 // CHECK-RWT-SAME:    %[[S:.*]]:
 // CHECK-RWT-DAG:     %[[C10:.*]] = arith.constant 10 : index
 // CHECK-RWT-DAG:     %[[C0:.*]] = arith.constant 0 : index
 // CHECK-RWT-DAG:     %[[C1:.*]] = arith.constant 1 : index
 // CHECK-RWT:         %[[SD1:.*]] = tensor.dim %[[S]], %[[C1]]
 // CHECK-RWT:         %[[DD0:.*]] = arith.muli %[[SD1]], %[[C10]] : index
 // CHECK-RWT:         %[[B:.*]] = bufferization.alloc_tensor(%[[DD0]])
-// CHECK-RWT:         %[[P0:.*]] = sparse_tensor.pointers %[[S]] {dimension = 0 : index}
-// CHECK-RWT:         %[[I0:.*]] = sparse_tensor.indices %[[S]] {dimension = 0 : index}
-// CHECK-RWT:         %[[P1:.*]] = sparse_tensor.pointers %[[S]] {dimension = 1 : index}
-// CHECK-RWT:         %[[I1:.*]] = sparse_tensor.indices %[[S]] {dimension = 1 : index}
+// CHECK-RWT:         %[[P0:.*]] = sparse_tensor.positions %[[S]] {level = 0 : index}
+// CHECK-RWT:         %[[I0:.*]] = sparse_tensor.coordinates %[[S]] {level = 0 : index}
+// CHECK-RWT:         %[[P1:.*]] = sparse_tensor.positions %[[S]] {level = 1 : index}
+// CHECK-RWT:         %[[I1:.*]] = sparse_tensor.coordinates %[[S]] {level = 1 : index}
 // CHECK-RWT:         %[[V:.*]] = sparse_tensor.values %[[S]]
 // CHECK-RWT:         %[[S0:.*]] = memref.load %[[P0]]{{\[}}%[[C0]]] : memref<?xindex>
 // CHECK-RWT:         %[[E0:.*]] = memref.load %[[P0]]{{\[}}%[[C1]]] : memref<?xindex>
 // CHECK-RWT:         %[[RET:.*]] = scf.for %[[I:.*]] = %[[S0]] to %[[E0]] step %[[C1]] iter_args(%[[R0:.*]] = %[[B]])
 // CHECK-RWT:           %[[SI0:.*]] = memref.load %[[I0]]{{\[}}%[[I]]] : memref<?xindex>
 // CHECK-RWT-DAG:       %[[S1:.*]] = memref.load %[[P1]]{{\[}}%[[I]]] : memref<?xindex>
 // CHECK-RWT-DAG:       %[[PE1:.*]] = arith.addi %[[I]], %[[C1]] : index
 // CHECK-RWT:           %[[E1:.*]] = memref.load %[[P1]]{{\[}}%[[PE1]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_reshape_dot.mlir

 // RUN: mlir-opt %s --linalg-generalize-named-ops --sparsification --cse --canonicalize | FileCheck %s
 
-#COO_2D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ], pointerBitWidth = 32, indexBitWidth = 32 }>
-#COO_3D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], pointerBitWidth = 32, indexBitWidth = 32 }>
+#COO_2D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
+#COO_3D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
 
 // CHECK-LABEL:   func.func @sparse_reshape_fused(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<5x6xf32>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<6x2x3xf32,
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 5 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 3 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = tensor.empty() : tensor<5x6xf32>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index}
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 0 : index}
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index}
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 2 : index}
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index}
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 0 : index}
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index}
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 2 : index}
 // CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]]
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_6]] : memref<5x6xf32>
 // CHECK:           scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xi32>
 // CHECK:             %[[VAL_15:.*]] = arith.extui %[[VAL_14]] : i32 to i64
 // CHECK:             %[[VAL_16:.*]] = arith.index_cast %[[VAL_15]] : i64 to index
 // CHECK:             %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xi32>
 // CHECK:             %[[VAL_18:.*]] = arith.extui %[[VAL_17]] : i32 to i64

mlir/test/Dialect/SparseTensor/sparse_scalars.mlir

 // CHECK-SAME:              %[[VAL_1:.*1]]: tensor<f32>,
 // CHECK-SAME:              %[[VAL_2:.*2]]: f32,
 // CHECK-SAME:              %[[VAL_3:.*3]]: f32,
 // CHECK-SAME:              %[[VAL_4:.*4]]: tensor<32x16xf32>) -> tensor<32x16xf32> {
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 2.200000e+00 : f32
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.addf %[[VAL_2]], %[[VAL_3]] : f32
-// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref<?xf32>
 // CHECK-DAG:       %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_1]] : memref<f32>
 // CHECK-DAG:       %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_4]] : memref<32x16xf32>
 // CHECK-DAG:       %[[VAL_16:.*]] = memref.load %[[VAL_14]][] : memref<f32>
 // CHECK-DAG:       %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK-DAG:       %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_19:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_7]] {
 // CHECK:             %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_19]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_sddmm.mlir

 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant dense<0.000000e+00> : tensor<8x8xf64>
 // CHECK-DAG:       %[[VAL_7:.*]] = bufferization.alloc_tensor() copy(%[[VAL_6]]) {bufferization.escape = [false]} : tensor<8x8xf64>
 // CHECK-DAG:       %[[VAL_8:.*]] = bufferization.alloc_tensor() copy(%[[VAL_6]]) {bufferization.escape = [false], memory_space = 0 : i64} : tensor<8x8xf64>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64>
-// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{{.*}}>> to memref<?xf64>
 // CHECK:           %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_8]] : memref<8x8xf64>
 // CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_19:.*]] = %[[VAL_17]] to %[[VAL_18]] step %[[VAL_5]] {
 // CHECK:             %[[VAL_20:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_19]]] : memref<?xindex>
 // CHECK:             scf.for %[[VAL_21:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] {
 // CHECK:               %[[VAL_22:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]], %[[VAL_21]]] : memref<8x8xf64>

 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant false
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_8:.*]] = arith.constant dense<0.000000e+00> : tensor<8x8xf64>
 // CHECK-DAG:       %[[VAL_9:.*]] = bufferization.alloc_tensor() copy(%[[VAL_8]]) {bufferization.escape = [false]} : tensor<8x8xf64>
 // CHECK-DAG:       %[[VAL_10:.*]] = bufferization.alloc_tensor() {bufferization.escape = [false]} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
 // CHECK-DAG:       %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64>
 // CHECK-DAG:       %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64>
-// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_16:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_15:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_16:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_17:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
 // CHECK:           %[[VAL_18:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_19:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_20:.*]] = scf.for %[[VAL_21:.*]] = %[[VAL_18]] to %[[VAL_19]] step %[[VAL_5]] iter_args(%[[VAL_22:.*]] = %[[VAL_10]]) -> (tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
 // CHECK:             %[[VAL_23:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_21]]] : memref<?xindex>
 // CHECK:             %[[VAL_24:.*]], %[[VAL_25:.*]], %[[VAL_26:.*]], %[[VAL_27:.*]] = sparse_tensor.expand %[[VAL_10]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
 // CHECK:             %[[VAL_28:.*]] = scf.for %[[VAL_29:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_30:.*]] = %[[VAL_27]]) -> (index) {
 // CHECK:               %[[VAL_31:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_23]], %[[VAL_29]]] : memref<8x8xf64>

mlir/test/Dialect/SparseTensor/sparse_sddmm_org.mlir

 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant false
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant true
 // CHECK:           %[[VAL_8:.*]] = bufferization.alloc_tensor() : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
 // CHECK:           %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<8x8xf64>
 // CHECK:           %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<8x8xf64>
-// CHECK:           %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_13:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_14:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
 // CHECK:           %[[VAL_16:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_18:.*]] = scf.for %[[VAL_19:.*]] = %[[VAL_16]] to %[[VAL_17]] step %[[VAL_5]] iter_args(%[[VAL_20:.*]] = %[[VAL_8]]) -> (tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
 // CHECK:             %[[VAL_21:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_19]]] : memref<?xindex>
 // CHECK:             %[[VAL_22:.*]], %[[VAL_23:.*]], %[[VAL_24:.*]], %[[VAL_25:.*]] = sparse_tensor.expand %[[VAL_8]] : tensor<8x8xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>, memref<?xi1>, memref<?xindex>
 // CHECK:             %[[VAL_26:.*]] = scf.for %[[VAL_27:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] iter_args(%[[VAL_28:.*]] = %[[VAL_25]]) -> (index) {
 // CHECK:               %[[VAL_29:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_21]], %[[VAL_27]]] : memref<8x8xf64>

mlir/test/Dialect/SparseTensor/sparse_storage.mlir

 // RUN: mlir-opt %s -sparsification= | FileCheck %s
 
 #SparseVector64 = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed" ],
-  pointerBitWidth = 64,
-  indexBitWidth = 64
+  posWidth = 64,
+  crdWidth = 64
 }>
 
 #SparseVector32 = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed" ],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 #trait_mul = {
   indexing_maps = [
     affine_map<(i) -> (i)>,  // a
     affine_map<(i) -> (i)>,  // b
     affine_map<(i) -> (i)>   // x (out)
   ],

mlir/test/Dialect/SparseTensor/sparse_transpose.mlir

 // TODO: improve auto-conversion followed by yield
 
 // CHECK-LABEL:   func.func @sparse_transpose_auto(
 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_3:.*]] = bufferization.alloc_tensor() : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
 // CHECK-DAG:       %[[VAL_4:.*]] = sparse_tensor.convert %[[VAL_0]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
-// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_4]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_4]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_4]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
-// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_4]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_4]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_6:.*]] = sparse_tensor.coordinates %[[VAL_4]] {level = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_4]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
+// CHECK-DAG:       %[[VAL_8:.*]] = sparse_tensor.coordinates %[[VAL_4]] {level = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
 // CHECK-DAG:       %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xf64>
 // CHECK:           %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
 // CHECK:           %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK:           %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_3]]) -> (tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) {
 // CHECK:             %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK:             %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK:             %[[VAL_17:.*]] = arith.addi %[[VAL_13]], %[[VAL_2]] : index
 // CHECK:             %[[VAL_18:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_17]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_vector.mlir

func.func @scale_d(%arga: tensor<1024xf32, #DenseVector>, %b: f32, %argx: tensor<1024xf32>) -> tensor<1024xf32> {
   } -> tensor<1024xf32>
   return %0 : tensor<1024xf32>
 }
 
 // -----
 
 #SparseVector = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed" ],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 #trait_mul_s = {
   indexing_maps = [
     affine_map<(i) -> (i)>,  // a
     affine_map<(i) -> (i)>,  // b
     affine_map<(i) -> (i)>   // x (out)
   ],
func.func @reduction_d(%arga: tensor<1024xf32, #DenseVector>,
   } -> tensor<f32>
   return %0 : tensor<f32>
 }
 
 // -----
 
 #SparseMatrix = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "compressed" ],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 #trait_mul_ds = {
   indexing_maps = [
     affine_map<(i,j) -> (i,j)>,  // A
     affine_map<(i,j) -> (i,j)>,  // B
     affine_map<(i,j) -> (i,j)>   // X (out)
   ],

mlir/test/Dialect/SparseTensor/sparse_vector_chain.mlir

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<f64>,
 // CHECK-SAME:      %[[VAL_1:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>,
 // CHECK-SAME:      %[[VAL_2:.*]]: tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<f64> {
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant dense<0.000000e+00> : vector<8xf64>
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 64 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_1]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
-// CHECK:           %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_2]] {dimension = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_2]] {dimension = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_11:.*]] = sparse_tensor.positions %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_12:.*]] = sparse_tensor.coordinates %[[VAL_2]] {level = 1 : index} : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<64x32xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
 // CHECK:           %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
 // CHECK:           %[[VAL_15:.*]] = memref.load %[[VAL_14]][] : memref<f64>
 // CHECK:           %[[VAL_16:.*]] = scf.for %[[VAL_17:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] iter_args(%[[VAL_18:.*]] = %[[VAL_15]]) -> (f64) {
 // CHECK:             %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xindex>
 // CHECK:             %[[VAL_20:.*]] = arith.addi %[[VAL_17]], %[[VAL_7]] : index
 // CHECK:             %[[VAL_21:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_20]]] : memref<?xindex>
 // CHECK:             %[[VAL_22:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_17]]] : memref<?xindex>

mlir/test/Dialect/SparseTensor/sparse_vector_index.mlir

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<8xi64> {
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant dense<0> : vector<8xi64>
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xindex>
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : i64
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_7:.*]] = tensor.empty() : tensor<8xi64>
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi64>
 // CHECK:           %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_7]] : memref<8xi64>
 // CHECK:           linalg.fill ins(%[[VAL_4]] : i64) outs(%[[VAL_11]] : memref<8xi64>)
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK:           scf.for %[[VAL_14:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_1]] {
 // CHECK:             %[[VAL_15:.*]] = affine.min #map(%[[VAL_13]], %[[VAL_14]]){{\[}}%[[VAL_1]]]
 // CHECK:             %[[VAL_16:.*]] = vector.create_mask %[[VAL_15]] : vector<8xi1>

 // CHECK-SAME:      %[[VAL_0:.*]]: tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<8xi64> {
 // CHECK-DAG:       %[[VAL_1:.*]] = arith.constant 8 : index
 // CHECK-DAG:       %[[VAL_2:.*]] = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7]> : vector<8xindex>
 // CHECK-DAG:       %[[VAL_3:.*]] = arith.constant 0 : i64
 // CHECK-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
 // CHECK-DAG:       %[[VAL_6:.*]] = arith.constant true
 // CHECK-DAG:       %[[VAL_7:.*]] = tensor.empty() : tensor<8xi64>
-// CHECK:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
-// CHECK:           %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK:           %[[VAL_9:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK:           %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<8xi64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi64>
 // CHECK:           %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_7]] : memref<8xi64>
 // CHECK:           linalg.fill ins(%[[VAL_3]] : i64) outs(%[[VAL_11]] : memref<8xi64>)
 // CHECK:           %[[VAL_12:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK:           %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK:           %[[VAL_14:.*]]:2 = scf.while (%[[VAL_15:.*]] = %[[VAL_12]], %[[VAL_16:.*]] = %[[VAL_4]]) : (index, index) -> (index, index) {
 // CHECK:             %[[VAL_17:.*]] = arith.cmpi ult, %[[VAL_15]], %[[VAL_13]] : index
 // CHECK:             scf.condition(%[[VAL_17]]) %[[VAL_15]], %[[VAL_16]] : index, index

mlir/test/Dialect/SparseTensor/sparse_vector_peeled.mlir

 // RUN: mlir-opt %s --sparsification -cse -sparse-vectorization="vl=16" -scf-for-loop-peeling -canonicalize -cse | \
 // RUN:   FileCheck %s
 
 #SparseVector = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed" ],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 #trait_mul_s = {
   indexing_maps = [
     affine_map<(i) -> (i)>,  // a
     affine_map<(i) -> (i)>,  // b
     affine_map<(i) -> (i)>   // x (out)
   ],

mlir/test/Dialect/SparseTensor/specifier_to_llvm.mlir

 }
 
 // CHECK-LABEL:   func.func @sparse_get_md(
 // CHECK-SAME:      %[[VAL_0:.*]]: !llvm.struct<(array<2 x i64>, array<3 x i64>)>) -> index {
 // CHECK:           %[[VAL_1:.*]] = llvm.extractvalue %[[VAL_0]][0, 0] : !llvm.struct<(array<2 x i64>, array<3 x i64>)>
 // CHECK:           %[[CAST:.*]] = arith.index_cast %[[VAL_1]] : i64 to index
 // CHECK:           return %[[CAST]] : index
 func.func @sparse_get_md(%arg0: !sparse_tensor.storage_specifier<#CSR>) -> index {
-  %0 = sparse_tensor.storage_specifier.get %arg0 dim_sz at 0
+  %0 = sparse_tensor.storage_specifier.get %arg0 lvl_sz at 0
        : !sparse_tensor.storage_specifier<#CSR>
   return %0 : index
 }
 
 // CHECK-LABEL:   func.func @sparse_set_md(
 // CHECK-SAME:      %[[VAL_0:.*]]: !llvm.struct<(array<2 x i64>, array<3 x i64>)>,
 // CHECK-SAME:      %[[VAL_1:.*]]: index) -> !llvm.struct<(array<2 x i64>, array<3 x i64>)> {
 // CHECK:           %[[CAST:.*]] = arith.index_cast %[[VAL_1]] : index to i64
 // CHECK:           %[[VAL_2:.*]] = llvm.insertvalue %[[CAST]], %[[VAL_0]][0, 0] : !llvm.struct<(array<2 x i64>, array<3 x i64>)>
 // CHECK:           return %[[VAL_2]] : !llvm.struct<(array<2 x i64>, array<3 x i64>)>
 func.func @sparse_set_md(%arg0: !sparse_tensor.storage_specifier<#CSR>, %arg1: index)
           -> !sparse_tensor.storage_specifier<#CSR> {
-  %0 = sparse_tensor.storage_specifier.set %arg0 dim_sz at 0 with %arg1
+  %0 = sparse_tensor.storage_specifier.set %arg0 lvl_sz at 0 with %arg1
        : !sparse_tensor.storage_specifier<#CSR>
   return %0 : !sparse_tensor.storage_specifier<#CSR>
 }

mlir/test/Dialect/SparseTensor/vectorize_reduction.mlir

 // CHECK-ON-SAME:                                                     %[[VAL_0:.*]]: tensor<f64>,
 // CHECK-ON-SAME:                                                     %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<f64> {
 // CHECK-ON-DAG:       %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:       %[[VAL_3:.*]] = arith.constant dense<1.000000e+00> : vector<8xf64>
 // CHECK-ON-DAG:       %[[VAL_4:.*]] = arith.constant dense<0.000000e+00> : vector<8xf64>
 // CHECK-ON-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
 // CHECK-ON-DAG:       %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_5]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>
-// CHECK-ON:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:           %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:           %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
 // CHECK-ON:           %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
 // CHECK-ON:           %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f64>
 // CHECK-ON:           %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_7]] step %[[VAL_6]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f64) {
 // CHECK-ON:             %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK-ON:             %[[VAL_16:.*]] = arith.addi %[[VAL_13]], %[[VAL_6]] : index
 // CHECK-ON:             %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_16]]] : memref<?xindex>
 // CHECK-ON:             %[[VAL_18:.*]] = vector.insertelement %[[VAL_14]], %[[VAL_3]]{{\[}}%[[VAL_5]] : index] : vector<8xf64>

 // CHECK-ON:         }
 //
 // CHECK-OFF-LABEL:   func.func @sparse_product_reduction_dense_sparse(
 // CHECK-OFF-SAME:                                                     %[[VAL_0:.*]]: tensor<f64>,
 // CHECK-OFF-SAME:                                                     %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>) -> tensor<f64> {
 // CHECK-OFF-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
 // CHECK-OFF:           %[[VAL_4:.*]] = tensor.dim %[[VAL_1]], %[[VAL_2]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>>
-// CHECK-OFF:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:           %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>> to memref<?xf64>
 // CHECK-OFF:           %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
 // CHECK-OFF:           %[[VAL_8:.*]] = memref.load %[[VAL_7]][] : memref<f64>
 // CHECK-OFF:           %[[VAL_9:.*]] = scf.for %[[VAL_10:.*]] = %[[VAL_2]] to %[[VAL_4]] step %[[VAL_3]] iter_args(%[[VAL_11:.*]] = %[[VAL_8]]) -> (f64) {
 // CHECK-OFF:             %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_10]]] : memref<?xindex>
 // CHECK-OFF:             %[[VAL_13:.*]] = arith.addi %[[VAL_10]], %[[VAL_3]] : index
 // CHECK-OFF:             %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_13]]] : memref<?xindex>
 // CHECK-OFF:             %[[VAL_15:.*]] = scf.for %[[VAL_16:.*]] = %[[VAL_12]] to %[[VAL_14]] step %[[VAL_3]] iter_args(%[[VAL_17:.*]] = %[[VAL_11]]) -> (f64) {

 // CHECK-ON-LABEL:   func.func @sparse_product_reduction_sparse_sparse(
 // CHECK-ON-SAME:                                                      %[[VAL_0:.*]]: tensor<f64>,
 // CHECK-ON-SAME:                                                      %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<f64> {
 // CHECK-ON-DAG:       %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:       %[[VAL_3:.*]] = arith.constant dense<1.000000e+00> : vector<8xf64>
 // CHECK-ON-DAG:       %[[VAL_4:.*]] = arith.constant dense<0.000000e+00> : vector<8xf64>
 // CHECK-ON-DAG:       %[[VAL_5:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:       %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-ON:           %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-ON:           %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:           %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:           %[[VAL_8:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:           %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
 // CHECK-ON:           %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
 // CHECK-ON:           %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref<f64>
 // CHECK-ON:           %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-ON:           %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK-ON:           %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_12]] to %[[VAL_13]] step %[[VAL_6]] iter_args(%[[VAL_16:.*]] = %[[VAL_11]]) -> (f64) {
 // CHECK-ON:             %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex>
 // CHECK-ON:             %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_6]] : index

 // CHECK-ON:           return %[[VAL_32]] : tensor<f64>
 // CHECK-ON:         }
 //
 // CHECK-OFF-LABEL:   func.func @sparse_product_reduction_sparse_sparse(
 // CHECK-OFF-SAME:                                                     %[[VAL_0:.*]]: tensor<f64>,
 // CHECK-OFF-SAME:                                                     %[[VAL_1:.*]]: tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<f64> {
 // CHECK-OFF-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:           %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
-// CHECK-OFF:           %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:           %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:           %[[VAL_5:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 1 : index} : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:           %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?x128xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to memref<?xf64>
 // CHECK-OFF:           %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f64>
 // CHECK-OFF:           %[[VAL_8:.*]] = memref.load %[[VAL_7]][] : memref<f64>
 // CHECK-OFF:           %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:           %[[VAL_10:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:           %[[VAL_11:.*]] = scf.for %[[VAL_12:.*]] = %[[VAL_9]] to %[[VAL_10]] step %[[VAL_3]] iter_args(%[[VAL_13:.*]] = %[[VAL_8]]) -> (f64) {
 // CHECK-OFF:             %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref<?xindex>
 // CHECK-OFF:             %[[VAL_15:.*]] = arith.addi %[[VAL_12]], %[[VAL_3]] : index

 // constant type for the pass-through value.
 // CHECK-ON-LABEL:   func.func @sparse_reduction_ori(
 // CHECK-ON-SAME:                                    %[[VAL_0:.*]]: tensor<i13>,
 // CHECK-ON-SAME:                                    %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i13> {
 // CHECK-ON-DAG:       %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:       %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi13>
 // CHECK-ON-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-ON:           %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:           %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi13>
 // CHECK-ON:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13>
 // CHECK-ON:           %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i13>
 // CHECK-ON:           %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-ON:           %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-ON:           %[[VAL_12:.*]] = vector.broadcast %[[VAL_9]] : i13 to vector<8xi13>
 // CHECK-ON:           %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi13>) {
 // CHECK-ON:             %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]

 // CHECK-ON:           return %[[VAL_23]] : tensor<i13>
 // CHECK-ON:         }
 //
 // CHECK-OFF-LABEL:   func.func @sparse_reduction_ori(
 // CHECK-OFF-SAME:                                    %[[VAL_0:.*]]: tensor<i13>,
 // CHECK-OFF-SAME:                                    %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i13> {
 // CHECK-OFF-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:           %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:           %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:           %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi13>
 // CHECK-OFF:           %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13>
 // CHECK-OFF:           %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i13>
 // CHECK-OFF:           %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:           %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:           %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i13) {
 // CHECK-OFF:             %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi13>
 // CHECK-OFF:             %[[VAL_14:.*]] = arith.ori %[[VAL_12]], %[[VAL_13]] : i13

 
 // CHECK-ON-LABEL:   func.func @sparse_reduction_ori_accumulator_on_rhs(
 // CHECK-ON-SAME:                                    %[[VAL_0:.*]]: tensor<i13>,
 // CHECK-ON-SAME:                                    %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i13> {
 // CHECK-ON-DAG:       %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:       %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi13>
 // CHECK-ON-DAG:       %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-ON:           %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:           %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi13>
 // CHECK-ON:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13>
 // CHECK-ON:           %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i13>
 // CHECK-ON:           %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-ON:           %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-ON:           %[[VAL_12:.*]] = vector.broadcast %[[VAL_9]] : i13 to vector<8xi13>
 // CHECK-ON:           %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi13>) {
 // CHECK-ON:             %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]

 // CHECK-ON:           return %[[VAL_23]] : tensor<i13>
 // CHECK-ON:         }
 //
 // CHECK-OFF-LABEL:   func.func @sparse_reduction_ori_accumulator_on_rhs(
 // CHECK-OFF-SAME:                                    %[[VAL_0:.*]]: tensor<i13>,
 // CHECK-OFF-SAME:                                    %[[VAL_1:.*]]: tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i13> {
 // CHECK-OFF-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:           %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:           %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:           %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi13, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi13>
 // CHECK-OFF:           %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i13>
 // CHECK-OFF:           %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i13>
 // CHECK-OFF:           %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:           %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:           %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i13) {
 // CHECK-OFF:             %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi13>
 // CHECK-OFF:             %[[VAL_14:.*]] = arith.ori %[[VAL_13]], %[[VAL_12]] : i13

 //
 // CHECK-ON-LABEL:   func.func @sparse_reduction_subi(
 // CHECK-ON-SAME:                                     %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-ON-SAME:                                     %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-ON-DAG:       %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:       %[[VAL_3:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:       %[[VAL_4:.*]] = arith.constant dense<0> : vector<8xi32>
 // CHECK-ON-DAG:       %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-ON:           %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:           %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:           %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-ON:           %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-ON:           %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
 // CHECK-ON:           %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-ON:           %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-ON:           %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_4]]{{\[}}%[[VAL_3]] : index] : vector<8xi32>
 // CHECK-ON:           %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) {
 // CHECK-ON:             %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]

 // CHECK-ON:           return %[[VAL_23]] : tensor<i32>
 // CHECK-ON:         }
 //
 // CHECK-OFF-LABEL:   func.func @sparse_reduction_subi(
 // CHECK-OFF-SAME:                                     %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-OFF-SAME:                                     %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-OFF-DAG:       %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:       %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:           %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:           %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:           %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-OFF:           %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-OFF:           %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
 // CHECK-OFF:           %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:           %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:           %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) {
 // CHECK-OFF:             %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32>
 // CHECK-OFF:             %[[VAL_14:.*]] = arith.subi %[[VAL_12]], %[[VAL_13]] : i32

 // Check that we vectorize xor.
 // CHECK-ON-LABEL: func.func @sparse_reduction_xor(
 // CHECK-ON-SAME: %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-ON-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-ON-DAG:  %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:  %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32>
 // CHECK-ON-DAG:  %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:  %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-ON:  %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:  %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:  %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-ON:  %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-ON:  %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
 // CHECK-ON:  %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-ON:  %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-ON:  %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xi32>
 // CHECK-ON:  %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) {
 // CHECK-ON:    %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]

 // CHECK-ON:  return %[[VAL_23]] : tensor<i32>
 // CHECK-ON: }
 //
 // CHECK-OFF-LABEL: func.func @sparse_reduction_xor(
 // CHECK-OFF-SAME:  %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-OFF-SAME:  %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-OFF-DAG:   %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:   %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:   %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-OFF:   %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-OFF:   %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
 // CHECK-OFF:   %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) {
 // CHECK-OFF:     %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32>
 // CHECK-OFF:     %[[VAL_14:.*]] = arith.xori %[[VAL_12]], %[[VAL_13]] : i32

 // Check that we vectorize and.
 // CHECK-ON-LABEL: func.func @sparse_reduction_and(
 // CHECK-ON-SAME:   %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-ON-SAME:   %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-ON-DAG:   %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:   %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32>
 // CHECK-ON-DAG:   %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:   %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-ON:   %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:   %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:   %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-ON:   %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-ON:   %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
 // CHECK-ON:   %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_12:.*]] = vector.broadcast %[[VAL_9]] : i32 to vector<8xi32>
 // CHECK-ON:   %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) {
 // CHECK-ON:     %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]

 // CHECK-ON:   return %[[VAL_23]] : tensor<i32>
 // CHECK-ON: }
 //
 // CHECK-OFF-LABEL: func.func @sparse_reduction_and(
 // CHECK-OFF-SAME:   %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-OFF-SAME:   %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-OFF-DAG:   %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:   %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:   %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-OFF:   %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-OFF:   %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
 // CHECK-OFF:   %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) {
 // CHECK-OFF:     %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32>
 // CHECK-OFF:     %[[VAL_14:.*]] = arith.andi %[[VAL_12]], %[[VAL_13]] : i32

 // CHECK-ON-LABEL: func.func @sparse_reduction_muli(
 // CHECK-ON-SAME:   %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-ON-SAME:   %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-ON-DAG:   %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:   %[[VAL_3:.*]] = arith.constant dense<1> : vector<8xi32>
 // CHECK-ON-DAG:   %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:   %[[VAL_5:.*]] = arith.constant dense<0> : vector<8xi32>
 // CHECK-ON-DAG:   %[[VAL_6:.*]] = arith.constant 1 : index
-// CHECK-ON:   %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:   %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:   %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-ON:   %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-ON:   %[[VAL_10:.*]] = memref.load %[[VAL_9]][] : memref<i32>
 // CHECK-ON:   %[[VAL_11:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_13:.*]] = vector.insertelement %[[VAL_10]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xi32>
 // CHECK-ON:   %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_11]] to %[[VAL_12]] step %[[VAL_2]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (vector<8xi32>) {
 // CHECK-ON:     %[[VAL_17:.*]] = affine.min #map(%[[VAL_12]], %[[VAL_15]]){{\[}}%[[VAL_2]]]

 // CHECK-ON:   return %[[VAL_24]] : tensor<i32>
 // CHECK-ON: }
 //
 // CHECK-OFF-LABEL: func.func @sparse_reduction_muli(
 // CHECK-OFF-SAME:   %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-OFF-SAME:   %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-OFF-DAG:   %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:   %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:   %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-OFF:   %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-OFF:   %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
 // CHECK-OFF:   %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) {
 // CHECK-OFF:     %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32>
 // CHECK-OFF:     %[[VAL_14:.*]] = arith.muli %[[VAL_12]], %[[VAL_13]] : i32

 // Check that we vectorize addi.
 // CHECK-ON-LABEL: func.func @sparse_reduction_addi(
 // CHECK-ON-SAME:   %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-ON-SAME:   %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-ON-DAG:   %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:   %[[VAL_3:.*]] = arith.constant dense<0> : vector<8xi32>
 // CHECK-ON-DAG:   %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:   %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-ON:   %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:   %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:   %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-ON:   %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-ON:   %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<i32>
 // CHECK-ON:   %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xi32>
 // CHECK-ON:   %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xi32>) {
 // CHECK-ON:     %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]

 // CHECK-ON:   return %[[VAL_23]] : tensor<i32>
 // CHECK-ON: }
 //
 // CHECK-OFF-LABEL: func.func @sparse_reduction_addi(
 // CHECK-OFF-SAME:   %[[VAL_0:.*]]: tensor<i32>,
 // CHECK-OFF-SAME:   %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<i32> {
 // CHECK-OFF-DAG:   %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:   %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:   %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xi32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xi32>
 // CHECK-OFF:   %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<i32>
 // CHECK-OFF:   %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<i32>
 // CHECK-OFF:   %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (i32) {
 // CHECK-OFF:     %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xi32>
 // CHECK-OFF:     %[[VAL_14:.*]] = arith.addi %[[VAL_12]], %[[VAL_13]] : i32

 // Check that we vectorize subf.
 // CHECK-ON-LABEL: func.func @sparse_reduction_subf(
 // CHECK-ON-SAME:   %[[VAL_0:.*]]: tensor<f32>,
 // CHECK-ON-SAME:   %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<f32> {
 // CHECK-ON-DAG:   %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:   %[[VAL_3:.*]] = arith.constant dense<0.000000e+00> : vector<8xf32>
 // CHECK-ON-DAG:   %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:   %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-ON:   %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:   %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:   %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-ON:   %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32>
 // CHECK-ON:   %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32>
 // CHECK-ON:   %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xf32>
 // CHECK-ON:   %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xf32>) {
 // CHECK-ON:     %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]

 // CHECK-ON:   return %[[VAL_23]] : tensor<f32>
 // CHECK-ON: }
 //
 // CHECK-OFF-LABEL: func.func @sparse_reduction_subf(
 // CHECK-OFF-SAME:   %[[VAL_0:.*]]: tensor<f32>,
 // CHECK-OFF-SAME:   %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<f32> {
 // CHECK-OFF-DAG:   %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:   %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:   %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-OFF:   %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32>
 // CHECK-OFF:   %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<f32>
 // CHECK-OFF:   %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (f32) {
 // CHECK-OFF:     %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xf32>
 // CHECK-OFF:     %[[VAL_14:.*]] = arith.subf %[[VAL_12]], %[[VAL_13]] : f32

 // Check that we vectorize addf.
 // CHECK-ON-LABEL: func.func @sparse_reduction_addf(
 // CHECK-ON-SAME:   %[[VAL_0:.*]]: tensor<f32>,
 // CHECK-ON-SAME:   %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<f32> {
 // CHECK-ON-DAG:   %[[VAL_2:.*]] = arith.constant 8 : index
 // CHECK-ON-DAG:   %[[VAL_3:.*]] = arith.constant dense<0.000000e+00> : vector<8xf32>
 // CHECK-ON-DAG:   %[[VAL_4:.*]] = arith.constant 0 : index
 // CHECK-ON-DAG:   %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-ON:   %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-ON:   %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-ON:   %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-ON:   %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32>
 // CHECK-ON:   %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref<f32>
 // CHECK-ON:   %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
 // CHECK-ON:   %[[VAL_12:.*]] = vector.insertelement %[[VAL_9]], %[[VAL_3]]{{\[}}%[[VAL_4]] : index] : vector<8xf32>
 // CHECK-ON:   %[[VAL_13:.*]] = scf.for %[[VAL_14:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] iter_args(%[[VAL_15:.*]] = %[[VAL_12]]) -> (vector<8xf32>) {
 // CHECK-ON:     %[[VAL_16:.*]] = affine.min #map(%[[VAL_11]], %[[VAL_14]]){{\[}}%[[VAL_2]]]

 // CHECK-ON:   return %[[VAL_23]] : tensor<f32>
 // CHECK-ON: }
 //
 // CHECK-OFF-LABEL: func.func @sparse_reduction_addf(
 // CHECK-OFF-SAME:    %[[VAL_0:.*]]: tensor<f32>,
 // CHECK-OFF-SAME:    %[[VAL_1:.*]]: tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>>) -> tensor<f32> {
 // CHECK-OFF-DAG:   %[[VAL_2:.*]] = arith.constant 0 : index
 // CHECK-OFF-DAG:   %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_1]] {dimension = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
+// CHECK-OFF:   %[[VAL_4:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xindex>
 // CHECK-OFF:   %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>> to memref<?xf32>
 // CHECK-OFF:   %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<f32>
 // CHECK-OFF:   %[[VAL_7:.*]] = memref.load %[[VAL_6]][] : memref<f32>
 // CHECK-OFF:   %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref<?xindex>
 // CHECK-OFF:   %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_8]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_7]]) -> (f32) {
 // CHECK-OFF:     %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_11]]] : memref<?xf32>
 // CHECK-OFF:     %[[VAL_14:.*]] = arith.addf %[[VAL_12]], %[[VAL_13]] : f32

mlir/test/Integration/Dialect/SparseTensor/CPU/reshape_dot.mlir

 // Do the same run, but now with direct IR generation.
 // REDEFINE: %{option} = "enable-runtime-library=true"
 // RUN: %{compile} | %{run}
 //
 // Do the same run, but now with direct IR generation and vectorization.
 // REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
 // RUN: %{compile} | %{run}
 
-#COO_2D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ], pointerBitWidth = 32, indexBitWidth = 32 }>
-#COO_3D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], pointerBitWidth = 32, indexBitWidth = 32 }>
+#COO_2D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
+#COO_3D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
 
 module {
   func.func private @printMemref3dF32(%ptr : tensor<?x?x?xf32>) attributes { llvm.emit_c_interface }
   func.func private @printMemref2dF32(%ptr : tensor<?x?xf32>) attributes { llvm.emit_c_interface }
 
   func.func @test_sparse_rhs(%arg0: tensor<5x6xf32>, %arg1: tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> {
     %collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32, #COO_3D> into tensor<6x6xf32, #COO_2D>
     %0 = tensor.empty() : tensor<5x6xf32>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_constant_to_sparse_tensor.mlir

func.func @entry() {
     // A tensor in COO format.
     %ti = arith.constant sparse<[[0, 0], [0, 7], [1, 2], [4, 2], [5, 3], [6, 4], [6, 6], [9, 7]],
                           [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0]> : tensor<10x8xf64>
 
     // Convert the tensor in COO format to a sparse tensor with annotation #Tensor1.
     %ts = sparse_tensor.convert %ti : tensor<10x8xf64> to tensor<10x8xf64, #Tensor1>
 
     // CHECK: ( 0, 1, 4, 5, 6, 9 )
-    %i0 = sparse_tensor.indices %ts { dimension = 0 : index } : tensor<10x8xf64, #Tensor1> to memref<?xindex>
+    %i0 = sparse_tensor.coordinates %ts { level = 0 : index } : tensor<10x8xf64, #Tensor1> to memref<?xindex>
     %i0r = vector.transfer_read %i0[%c0], %c0: memref<?xindex>, vector<6xindex>
     vector.print %i0r : vector<6xindex>
 
     // CHECK: ( 0, 7, 2, 2, 3, 4, 6, 7 )
-    %i1 = sparse_tensor.indices %ts { dimension = 1 : index } : tensor<10x8xf64, #Tensor1> to memref<?xindex>
+    %i1 = sparse_tensor.coordinates %ts { level = 1 : index } : tensor<10x8xf64, #Tensor1> to memref<?xindex>
     %i1r = vector.transfer_read %i1[%c0], %c0: memref<?xindex>, vector<8xindex>
     vector.print %i1r : vector<8xindex>
 
     // CHECK: ( 1, 2, 3, 4, 5, 6, 7, 8 )
     %v = sparse_tensor.values %ts : tensor<10x8xf64, #Tensor1> to memref<?xf64>
     %vr = vector.transfer_read %v[%c0], %d0: memref<?xf64>, vector<8xf64>
     vector.print %vr : vector<8xf64>
 
     // Release the resources.
     bufferization.dealloc_tensor %ts : tensor<10x8xf64, #Tensor1>
 
     return
   }
 }
 

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion.mlir

func.func @entry() {
     call @dumpf64(%dv) : (memref<?xf64>) -> ()
     call @dumpf64(%ev) : (memref<?xf64>) -> ()
     call @dumpf64(%fv) : (memref<?xf64>) -> ()
     call @dumpf64(%gv) : (memref<?xf64>) -> ()
     call @dumpf64(%hv) : (memref<?xf64>) -> ()
     call @dumpf64(%iv) : (memref<?xf64>) -> ()
 
     //
-    // Check indices.
+    // Check coordinates.
     //
     // CHECK-NEXT: ( 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
func.func @entry() {
     // CHECK-NEXT: ( 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2, 0 )
     //
-    %v10 = sparse_tensor.indices %1 { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %v11 = sparse_tensor.indices %1 { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %v12 = sparse_tensor.indices %1 { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %v20 = sparse_tensor.indices %2 { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %v21 = sparse_tensor.indices %2 { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %v22 = sparse_tensor.indices %2 { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %v30 = sparse_tensor.indices %3 { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %v31 = sparse_tensor.indices %3 { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %v32 = sparse_tensor.indices %3 { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-
-    %a10 = sparse_tensor.indices %a { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %a11 = sparse_tensor.indices %a { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %a12 = sparse_tensor.indices %a { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %b10 = sparse_tensor.indices %b { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %b11 = sparse_tensor.indices %b { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %b12 = sparse_tensor.indices %b { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %c10 = sparse_tensor.indices %c { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %c11 = sparse_tensor.indices %c { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-    %c12 = sparse_tensor.indices %c { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
-
-    %d20 = sparse_tensor.indices %d { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %d21 = sparse_tensor.indices %d { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %d22 = sparse_tensor.indices %d { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %e20 = sparse_tensor.indices %e { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %e21 = sparse_tensor.indices %e { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %e22 = sparse_tensor.indices %e { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %f20 = sparse_tensor.indices %f { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %f21 = sparse_tensor.indices %f { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-    %f22 = sparse_tensor.indices %f { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
-
-    %g30 = sparse_tensor.indices %g { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %g31 = sparse_tensor.indices %g { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %g32 = sparse_tensor.indices %g { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %h30 = sparse_tensor.indices %h { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %h31 = sparse_tensor.indices %h { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %h32 = sparse_tensor.indices %h { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %i30 = sparse_tensor.indices %i { dimension = 0 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %i31 = sparse_tensor.indices %i { dimension = 1 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
-    %i32 = sparse_tensor.indices %i { dimension = 2 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %v10 = sparse_tensor.coordinates %1 { level = 0 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %v11 = sparse_tensor.coordinates %1 { level = 1 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %v12 = sparse_tensor.coordinates %1 { level = 2 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %v20 = sparse_tensor.coordinates %2 { level = 0 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %v21 = sparse_tensor.coordinates %2 { level = 1 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %v22 = sparse_tensor.coordinates %2 { level = 2 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %v30 = sparse_tensor.coordinates %3 { level = 0 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %v31 = sparse_tensor.coordinates %3 { level = 1 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %v32 = sparse_tensor.coordinates %3 { level = 2 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+
+    %a10 = sparse_tensor.coordinates %a { level = 0 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %a11 = sparse_tensor.coordinates %a { level = 1 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %a12 = sparse_tensor.coordinates %a { level = 2 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %b10 = sparse_tensor.coordinates %b { level = 0 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %b11 = sparse_tensor.coordinates %b { level = 1 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %b12 = sparse_tensor.coordinates %b { level = 2 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %c10 = sparse_tensor.coordinates %c { level = 0 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %c11 = sparse_tensor.coordinates %c { level = 1 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+    %c12 = sparse_tensor.coordinates %c { level = 2 : index } : tensor<2x3x4xf64, #Tensor1> to memref<?xindex>
+
+    %d20 = sparse_tensor.coordinates %d { level = 0 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %d21 = sparse_tensor.coordinates %d { level = 1 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %d22 = sparse_tensor.coordinates %d { level = 2 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %e20 = sparse_tensor.coordinates %e { level = 0 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %e21 = sparse_tensor.coordinates %e { level = 1 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %e22 = sparse_tensor.coordinates %e { level = 2 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %f20 = sparse_tensor.coordinates %f { level = 0 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %f21 = sparse_tensor.coordinates %f { level = 1 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+    %f22 = sparse_tensor.coordinates %f { level = 2 : index } : tensor<2x3x4xf64, #Tensor2> to memref<?xindex>
+
+    %g30 = sparse_tensor.coordinates %g { level = 0 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %g31 = sparse_tensor.coordinates %g { level = 1 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %g32 = sparse_tensor.coordinates %g { level = 2 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %h30 = sparse_tensor.coordinates %h { level = 0 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %h31 = sparse_tensor.coordinates %h { level = 1 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %h32 = sparse_tensor.coordinates %h { level = 2 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %i30 = sparse_tensor.coordinates %i { level = 0 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %i31 = sparse_tensor.coordinates %i { level = 1 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
+    %i32 = sparse_tensor.coordinates %i { level = 2 : index } : tensor<2x3x4xf64, #Tensor3> to memref<?xindex>
 
     call @dumpidx(%v10) : (memref<?xindex>) -> ()
     call @dumpidx(%v11) : (memref<?xindex>) -> ()
     call @dumpidx(%v12) : (memref<?xindex>) -> ()
     call @dumpidx(%v20) : (memref<?xindex>) -> ()
     call @dumpidx(%v21) : (memref<?xindex>) -> ()
     call @dumpidx(%v22) : (memref<?xindex>) -> ()
     call @dumpidx(%v30) : (memref<?xindex>) -> ()

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_conversion_ptr.mlir

 // REDEFINE:   --extra-module=%S/Inputs/main_for_lli.ll \
 // REDEFINE:   %VLA_ARCH_ATTR_OPTIONS \
 // REDEFINE:   --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
 // REDEFINE: FileCheck %s
 // RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
 
 #DCSR  = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed", "compressed" ],
-  pointerBitWidth = 8,
-  indexBitWidth = 8
+  posWidth = 8,
+  crdWidth = 8
 }>
 
 #DCSC  = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed", "compressed" ],
   dimOrdering = affine_map<(i,j) -> (j,i)>,
-  pointerBitWidth = 64,
-  indexBitWidth = 64
+  posWidth = 64,
+  crdWidth = 64
 }>
 
 #CSC  = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "compressed" ],
   dimOrdering = affine_map<(i,j) -> (j,i)>,
-  pointerBitWidth = 16,
-  indexBitWidth = 32
+  posWidth = 16,
+  crdWidth = 32
 }>
 
 //
 // Integration test that tests conversions between sparse tensors,
-// where the pointer and index sizes in the overhead storage change
+// where the position and index sizes in the overhead storage change
 // in addition to layout.
 //
 module {
 
   //
   // Helper method to print values and indices arrays. The transfer actually
   // reads more than required to verify size of buffer as well.
   //
func.func @entry() {
     //
     // CHECK-NEXT: ( 0, 1, 63, 0, 1, 0, 63, 0 )
     // CHECK-NEXT: ( 0, 1, 31, 0, 1, 0, 31, 0 )
     // CHECK-NEXT: ( 0, 1, 31, 0, 1, 0, 31, 0 )
     // CHECK-NEXT: ( 0, 1, 31, 0, 1, 0, 31, 0 )
     // CHECK-NEXT: ( 0, 1, 63, 0, 1, 0, 63, 0 )
     // CHECK-NEXT: ( 0, 1, 63, 0, 1, 0, 63, 0 )
     //
-    %i1 = sparse_tensor.indices %1 { dimension = 1 : index } : tensor<32x64xf64, #DCSR> to memref<?xi8>
-    %i2 = sparse_tensor.indices %2 { dimension = 1 : index } : tensor<32x64xf64, #DCSC> to memref<?xi64>
-    %i3 = sparse_tensor.indices %3 { dimension = 1 : index } : tensor<32x64xf64, #CSC>  to memref<?xi32>
-    %i4 = sparse_tensor.indices %4 { dimension = 1 : index } : tensor<32x64xf64, #DCSC> to memref<?xi64>
-    %i5 = sparse_tensor.indices %5 { dimension = 1 : index } : tensor<32x64xf64, #DCSR> to memref<?xi8>
-    %i6 = sparse_tensor.indices %6 { dimension = 1 : index } : tensor<32x64xf64, #DCSR> to memref<?xi8>
+    %i1 = sparse_tensor.coordinates %1 { level = 1 : index } : tensor<32x64xf64, #DCSR> to memref<?xi8>
+    %i2 = sparse_tensor.coordinates %2 { level = 1 : index } : tensor<32x64xf64, #DCSC> to memref<?xi64>
+    %i3 = sparse_tensor.coordinates %3 { level = 1 : index } : tensor<32x64xf64, #CSC>  to memref<?xi32>
+    %i4 = sparse_tensor.coordinates %4 { level = 1 : index } : tensor<32x64xf64, #DCSC> to memref<?xi64>
+    %i5 = sparse_tensor.coordinates %5 { level = 1 : index } : tensor<32x64xf64, #DCSR> to memref<?xi8>
+    %i6 = sparse_tensor.coordinates %6 { level = 1 : index } : tensor<32x64xf64, #DCSR> to memref<?xi8>
     call @dumpi08(%i1) : (memref<?xi8>)  -> ()
     call @dumpi64(%i2) : (memref<?xi64>) -> ()
     call @dumpi32(%i3) : (memref<?xi32>) -> ()
     call @dumpi64(%i4) : (memref<?xi64>) -> ()
     call @dumpi08(%i5) : (memref<?xi08>) -> ()
     call @dumpi08(%i6) : (memref<?xi08>) -> ()
 
     // Release the resources.

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_file_io.mlir

 
 module {
 
   func.func private @getTensorFilename(index) -> (!Filename)
 
   func.func private @createSparseTensorReader(!Filename) -> (!TensorReader)
   func.func private @delSparseTensorReader(!TensorReader) -> ()
   func.func private @getSparseTensorReaderRank(!TensorReader) -> (index)
-  func.func private @getSparseTensorReaderNNZ(!TensorReader) -> (index)
+  func.func private @getSparseTensorReaderNSE(!TensorReader) -> (index)
   func.func private @getSparseTensorReaderIsSymmetric(!TensorReader) -> (i1)
   func.func private @copySparseTensorReaderDimSizes(!TensorReader,
     memref<?xindex>) -> () attributes { llvm.emit_c_interface }
   func.func private @getSparseTensorReaderRead0F32(!TensorReader,
     memref<?xindex>, memref<?xindex>, memref<?xf32>)
     -> (i1) attributes { llvm.emit_c_interface }
   func.func private @getSparseTensorReaderNextF32(!TensorReader,
     memref<?xindex>, memref<f32>) -> () attributes { llvm.emit_c_interface }
module {
   // Returns the indices and values of the tensor.
   func.func @readTensorFile(%tensor: !TensorReader)
     -> (memref<?xindex>, memref<?xf32>, i1) {
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %c2 = arith.constant 2 : index
 
     %rank = call @getSparseTensorReaderRank(%tensor) : (!TensorReader) -> index
-    %nnz = call @getSparseTensorReaderNNZ(%tensor) : (!TensorReader) -> index
+    %nse = call @getSparseTensorReaderNSE(%tensor) : (!TensorReader) -> index
 
     // Assume rank == 2.
-    %isize = arith.muli %c2, %nnz : index
+    %isize = arith.muli %c2, %nse : index
     %xs = memref.alloc(%isize) : memref<?xindex>
-    %vs = memref.alloc(%nnz) : memref<?xf32>
+    %vs = memref.alloc(%nse) : memref<?xf32>
     %dim2lvl = memref.alloca(%c2) : memref<?xindex>
     memref.store %c0, %dim2lvl[%c0] : memref<?xindex>
     memref.store %c1, %dim2lvl[%c1] : memref<?xindex>
     %isSorted =func.call @getSparseTensorReaderRead0F32(%tensor, %dim2lvl, %xs, %vs)
         : (!TensorReader, memref<?xindex>, memref<?xindex>, memref<?xf32>) -> (i1)
     return %xs, %vs, %isSorted : memref<?xindex>, memref<?xf32>, i1
   }
 
   // Reads a COO tensor from the given file name and prints its content.
   func.func @readTensorFileAndDump(%fileName: !Filename) {
     %c0 = arith.constant 0 : index
     %c1 = arith.constant 1 : index
     %c2 = arith.constant 2 : index
     %tensor = call @createSparseTensorReader(%fileName)
       : (!Filename) -> (!TensorReader)
     %rank = call @getSparseTensorReaderRank(%tensor) : (!TensorReader) -> index
     vector.print %rank : index
-    %nnz = call @getSparseTensorReaderNNZ(%tensor) : (!TensorReader) -> index
-    vector.print %nnz : index
+    %nse = call @getSparseTensorReaderNSE(%tensor) : (!TensorReader) -> index
+    vector.print %nse : index
     %symmetric = call @getSparseTensorReaderIsSymmetric(%tensor)
       : (!TensorReader) -> i1
     vector.print %symmetric : i1
     %dimSizes = memref.alloc(%rank) : memref<?xindex>
     func.call @copySparseTensorReaderDimSizes(%tensor, %dimSizes)
       : (!TensorReader, memref<?xindex>) -> ()
     call @dumpi(%dimSizes) : (memref<?xindex>) -> ()
 
     %xs, %vs, %isSorted = call @readTensorFile(%tensor)
       : (!TensorReader) -> (memref<?xindex>, memref<?xf32>, i1)
-    %x0s = memref.subview %xs[%c0][%nnz][%c2]
+    %x0s = memref.subview %xs[%c0][%nse][%c2]
       : memref<?xindex> to memref<?xindex, strided<[2], offset: ?>>
-    %x1s = memref.subview %xs[%c1][%nnz][%c2]
+    %x1s = memref.subview %xs[%c1][%nse][%c2]
       : memref<?xindex> to memref<?xindex, strided<[2], offset: ?>>
     vector.print %isSorted : i1
     call @dumpi2(%x0s) : (memref<?xindex, strided<[2], offset: ?>>) -> ()
     call @dumpi2(%x1s) : (memref<?xindex, strided<[2], offset: ?>>) -> ()
     call @dumpf(%vs) : (memref<?xf32>) -> ()
 
     // Release the resources.
     call @delSparseTensorReader(%tensor) : (!TensorReader) -> ()
func.func @createTensorFileFrom(%fileName0: !Filename, %fileName1: !Filename) {
     %c1 = arith.constant 1 : index
 
     %tensor0 = call @createSparseTensorReader(%fileName0)
       : (!Filename) -> (!TensorReader)
     %tensor1 = call @createSparseTensorWriter(%fileName1)
       : (!Filename) -> (!TensorWriter)
 
     %rank = call @getSparseTensorReaderRank(%tensor0) : (!TensorReader) -> index
-    %nnz = call @getSparseTensorReaderNNZ(%tensor0) : (!TensorReader) -> index
+    %nse = call @getSparseTensorReaderNSE(%tensor0) : (!TensorReader) -> index
     %dimSizes = memref.alloc(%rank) : memref<?xindex>
     func.call @copySparseTensorReaderDimSizes(%tensor0, %dimSizes)
       : (!TensorReader, memref<?xindex>) -> ()
-    call @outSparseTensorWriterMetaData(%tensor1, %rank, %nnz, %dimSizes)
+    call @outSparseTensorWriterMetaData(%tensor1, %rank, %nse, %dimSizes)
       : (!TensorWriter, index, index, memref<?xindex>) -> ()
 
     //TODO: handle isSymmetric.
     // Assume rank == 2.
     %indices = memref.alloc(%rank) : memref<?xindex>
     %value = memref.alloca() : memref<f32>
-    scf.for %i = %c0 to %nnz step %c1 {
+    scf.for %i = %c0 to %nse step %c1 {
       func.call @getSparseTensorReaderNextF32(%tensor0, %indices, %value)
         : (!TensorReader, memref<?xindex>, memref<f32>) -> ()
       func.call @outSparseTensorWriterNextF32(%tensor1, %rank, %indices, %value)
         : (!TensorWriter, index, memref<?xindex>, memref<f32>) -> ()
     }
 
     // Release the resources.
     call @delSparseTensorReader(%tensor0) : (!TensorReader) -> ()
func.func @entry() {
     // CHECK: 4 250 -15
     // CHECK: 4 254 16
     // CHECK: 4 256 -17
     call @createTensorFileFrom(%fileName0, %fileName1)
       : (!Filename, !Filename) -> ()
 
     return
   }
-}
+}
 

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_insert_1d.mlir

indexing_maps = [
     affine_map<(i) -> (i)>   // x (out)
   ],
   iterator_types = ["parallel"],
   doc = "x(i) = x(i) * 2.0"
 }
 
 module {
 
-  // Dumps pointers, indices, values for verification.
+  // Dumps positions, indices, values for verification.
   func.func @dump(%argx: tensor<1024xf32, #SparseVector>) {
     %c0 = arith.constant 0 : index
     %f0 = arith.constant 0.0 : f32
-    %p = sparse_tensor.pointers %argx { dimension = 0 : index }
+    %p = sparse_tensor.positions %argx { level = 0 : index }
        : tensor<1024xf32, #SparseVector> to memref<?xindex>
-    %i = sparse_tensor.indices %argx { dimension = 0 : index }
+    %i = sparse_tensor.coordinates %argx { level = 0 : index }
        : tensor<1024xf32, #SparseVector> to memref<?xindex>
     %v = sparse_tensor.values %argx
        : tensor<1024xf32, #SparseVector> to memref<?xf32>
     %vp = vector.transfer_read %p[%c0], %c0: memref<?xindex>, vector<2xindex>
     %vi = vector.transfer_read %i[%c0], %c0: memref<?xindex>, vector<8xindex>
     %vv = vector.transfer_read %v[%c0], %f0: memref<?xf32>,   vector<8xf32>
     vector.print %vp : vector<2xindex>
     vector.print %vi : vector<8xindex>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_insert_2d.mlir

func.func @dump_dense(%arg0: tensor<4x3xf64, #Dense>) {
     vector.print %vv : vector<12xf64>
     return
   }
 
   func.func @dump_coo(%arg0: tensor<4x3xf64, #SortedCOO>) {
     %c0 = arith.constant 0 : index
     %cu = arith.constant -1 : index
     %fu = arith.constant 99.0 : f64
-    %p0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<4x3xf64, #SortedCOO> to memref<?xindex>
-    %i0 = sparse_tensor.indices  %arg0 { dimension = 0 : index } : tensor<4x3xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
-    %i1 = sparse_tensor.indices  %arg0 { dimension = 1 : index } : tensor<4x3xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
+    %p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<4x3xf64, #SortedCOO> to memref<?xindex>
+    %i0 = sparse_tensor.coordinates  %arg0 { level = 0 : index } : tensor<4x3xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
+    %i1 = sparse_tensor.coordinates  %arg0 { level = 1 : index } : tensor<4x3xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
     %v = sparse_tensor.values %arg0 : tensor<4x3xf64, #SortedCOO> to memref<?xf64>
     %vp0 = vector.transfer_read %p0[%c0], %cu: memref<?xindex>, vector<2xindex>
     vector.print %vp0 : vector<2xindex>
     %vi0 = vector.transfer_read %i0[%c0], %cu: memref<?xindex, strided<[?], offset: ?>>, vector<4xindex>
     vector.print %vi0 : vector<4xindex>
     %vi1 = vector.transfer_read %i1[%c0], %cu: memref<?xindex, strided<[?], offset: ?>>, vector<4xindex>
     vector.print %vi1 : vector<4xindex>
     %vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<4xf64>
     vector.print %vv : vector<4xf64>
     return
   }
 
   func.func @dump_csr(%arg0: tensor<4x3xf64, #CSR>) {
     %c0 = arith.constant 0 : index
     %cu = arith.constant -1 : index
     %fu = arith.constant 99.0 : f64
-    %p1 = sparse_tensor.pointers %arg0 { dimension = 1 : index } : tensor<4x3xf64, #CSR> to memref<?xindex>
-    %i1 = sparse_tensor.indices  %arg0 { dimension = 1 : index } : tensor<4x3xf64, #CSR> to memref<?xindex>
+    %p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<4x3xf64, #CSR> to memref<?xindex>
+    %i1 = sparse_tensor.coordinates  %arg0 { level = 1 : index } : tensor<4x3xf64, #CSR> to memref<?xindex>
     %v = sparse_tensor.values %arg0 : tensor<4x3xf64, #CSR> to memref<?xf64>
     %vp1 = vector.transfer_read %p1[%c0], %cu: memref<?xindex>, vector<5xindex>
     vector.print %vp1 : vector<5xindex>
     %vi1 = vector.transfer_read %i1[%c0], %cu: memref<?xindex>, vector<4xindex>
     vector.print %vi1 : vector<4xindex>
     %vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<4xf64>
     vector.print %vv : vector<4xf64>
     return
   }
 
   func.func @dump_dcsr(%arg0: tensor<4x3xf64, #DCSR>) {
     %c0 = arith.constant 0 : index
     %cu = arith.constant -1 : index
     %fu = arith.constant 99.0 : f64
-    %p0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
-    %i0 = sparse_tensor.indices  %arg0 { dimension = 0 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
-    %p1 = sparse_tensor.pointers %arg0 { dimension = 1 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
-    %i1 = sparse_tensor.indices  %arg0 { dimension = 1 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
+    %p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
+    %i0 = sparse_tensor.coordinates  %arg0 { level = 0 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
+    %p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
+    %i1 = sparse_tensor.coordinates  %arg0 { level = 1 : index } : tensor<4x3xf64, #DCSR> to memref<?xindex>
     %v = sparse_tensor.values %arg0 : tensor<4x3xf64, #DCSR> to memref<?xf64>
     %vp0 = vector.transfer_read %p0[%c0], %cu: memref<?xindex>, vector<2xindex>
     vector.print %vp0 : vector<2xindex>
     %vi0 = vector.transfer_read %i0[%c0], %cu: memref<?xindex>, vector<3xindex>
     vector.print %vi0 : vector<3xindex>
     %vp1 = vector.transfer_read %p1[%c0], %cu: memref<?xindex>, vector<4xindex>
     vector.print %vp1 : vector<4xindex>
     %vi1 = vector.transfer_read %i1[%c0], %cu: memref<?xindex>, vector<4xindex>
     vector.print %vi1 : vector<4xindex>
     %vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<4xf64>
     vector.print %vv : vector<4xf64>
     return
   }
 
   func.func @dump_row(%arg0: tensor<4x3xf64, #Row>) {
     %c0 = arith.constant 0 : index
     %cu = arith.constant -1 : index
     %fu = arith.constant 99.0 : f64
-    %p0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<4x3xf64, #Row> to memref<?xindex>
-    %i0 = sparse_tensor.indices  %arg0 { dimension = 0 : index } : tensor<4x3xf64, #Row> to memref<?xindex>
+    %p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<4x3xf64, #Row> to memref<?xindex>
+    %i0 = sparse_tensor.coordinates  %arg0 { level = 0 : index } : tensor<4x3xf64, #Row> to memref<?xindex>
     %v = sparse_tensor.values %arg0 : tensor<4x3xf64, #Row> to memref<?xf64>
     %vp0 = vector.transfer_read %p0[%c0], %cu: memref<?xindex>, vector<2xindex>
     vector.print %vp0 : vector<2xindex>
     %vi0 = vector.transfer_read %i0[%c0], %cu: memref<?xindex>, vector<3xindex>
     vector.print %vi0 : vector<3xindex>
     %vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<9xf64>
     vector.print %vv : vector<9xf64>
     return

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_insert_3d.mlir

 }>
 
 
 module {
 
   func.func @dump(%arg0: tensor<5x4x3xf64, #TensorCSR>) {
     %c0 = arith.constant 0 : index
     %fu = arith.constant 99.0 : f64
-    %p0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
-    %i0 = sparse_tensor.indices  %arg0 { dimension = 0 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
-    %p2 = sparse_tensor.pointers %arg0 { dimension = 2 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
-    %i2 = sparse_tensor.indices  %arg0 { dimension = 2 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
+    %p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
+    %i0 = sparse_tensor.coordinates  %arg0 { level = 0 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
+    %p2 = sparse_tensor.positions %arg0 { level = 2 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
+    %i2 = sparse_tensor.coordinates  %arg0 { level = 2 : index } : tensor<5x4x3xf64, #TensorCSR> to memref<?xindex>
     %v = sparse_tensor.values %arg0 : tensor<5x4x3xf64, #TensorCSR> to memref<?xf64>
     %vp0 = vector.transfer_read %p0[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %vp0 : vector<2xindex>
     %vi0 = vector.transfer_read %i0[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %vi0 : vector<2xindex>
     %vp2 = vector.transfer_read %p2[%c0], %c0: memref<?xindex>, vector<9xindex>
     vector.print %vp2 : vector<9xindex>
     %vi2 = vector.transfer_read %i2[%c0], %c0: memref<?xindex>, vector<5xindex>
     vector.print %vi2 : vector<5xindex>
     %vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<5xf64>
     vector.print %vv : vector<5xf64>
     return
   }
 
   func.func @dump_row(%arg0: tensor<5x4x3xf64, #TensorRow>) {
     %c0 = arith.constant 0 : index
     %fu = arith.constant 99.0 : f64
-    %p0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
-    %i0 = sparse_tensor.indices  %arg0 { dimension = 0 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
-    %p1 = sparse_tensor.pointers %arg0 { dimension = 1 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
-    %i1 = sparse_tensor.indices  %arg0 { dimension = 1 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
+    %p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
+    %i0 = sparse_tensor.coordinates  %arg0 { level = 0 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
+    %p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
+    %i1 = sparse_tensor.coordinates  %arg0 { level = 1 : index } : tensor<5x4x3xf64, #TensorRow> to memref<?xindex>
     %v = sparse_tensor.values %arg0 : tensor<5x4x3xf64, #TensorRow> to memref<?xf64>
     %vp0 = vector.transfer_read %p0[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %vp0 : vector<2xindex>
     %vi0 = vector.transfer_read %i0[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %vi0 : vector<2xindex>
     %vp1 = vector.transfer_read %p1[%c0], %c0: memref<?xindex>, vector<3xindex>
     vector.print %vp1 : vector<3xindex>
     %vi1 = vector.transfer_read %i1[%c0], %c0: memref<?xindex>, vector<4xindex>
     vector.print %vi1 : vector<4xindex>
     %vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<12xf64>
     vector.print %vv : vector<12xf64>
     return
   }
 
 func.func @dump_ccoo(%arg0: tensor<5x4x3xf64, #CCoo>) {
     %c0 = arith.constant 0 : index
     %fu = arith.constant 99.0 : f64
-    %p0 = sparse_tensor.pointers %arg0 { dimension = 0 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
-    %i0 = sparse_tensor.indices  %arg0 { dimension = 0 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
-    %p1 = sparse_tensor.pointers %arg0 { dimension = 1 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
-    %i1 = sparse_tensor.indices  %arg0 { dimension = 1 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
-    %i2 = sparse_tensor.indices  %arg0 { dimension = 2 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
+    %p0 = sparse_tensor.positions %arg0 { level = 0 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
+    %i0 = sparse_tensor.coordinates  %arg0 { level = 0 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
+    %p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
+    %i1 = sparse_tensor.coordinates  %arg0 { level = 1 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
+    %i2 = sparse_tensor.coordinates  %arg0 { level = 2 : index } : tensor<5x4x3xf64, #CCoo> to memref<?xindex>
     %v = sparse_tensor.values %arg0 : tensor<5x4x3xf64, #CCoo> to memref<?xf64>
     %vp0 = vector.transfer_read %p0[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %vp0 : vector<2xindex>
     %vi0 = vector.transfer_read %i0[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %vi0 : vector<2xindex>
     %vp1 = vector.transfer_read %p1[%c0], %c0: memref<?xindex>, vector<3xindex>
     vector.print %vp1 : vector<3xindex>
     %vi1 = vector.transfer_read %i1[%c0], %c0: memref<?xindex>, vector<5xindex>
     vector.print %vi1 : vector<5xindex>
     %vi2 = vector.transfer_read %i2[%c0], %c0: memref<?xindex>, vector<5xindex>
     vector.print %vi2 : vector<5xindex>
     %vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<5xf64>
     vector.print %vv : vector<5xf64>
     return
   }
 
 func.func @dump_dcoo(%arg0: tensor<5x4x3xf64, #DCoo>) {
     %c0 = arith.constant 0 : index
     %fu = arith.constant 99.0 : f64
-    %p1 = sparse_tensor.pointers %arg0 { dimension = 1 : index } : tensor<5x4x3xf64, #DCoo> to memref<?xindex>
-    %i1 = sparse_tensor.indices  %arg0 { dimension = 1 : index } : tensor<5x4x3xf64, #DCoo> to memref<?xindex>
-    %i2 = sparse_tensor.indices  %arg0 { dimension = 2 : index } : tensor<5x4x3xf64, #DCoo> to memref<?xindex>
+    %p1 = sparse_tensor.positions %arg0 { level = 1 : index } : tensor<5x4x3xf64, #DCoo> to memref<?xindex>
+    %i1 = sparse_tensor.coordinates  %arg0 { level = 1 : index } : tensor<5x4x3xf64, #DCoo> to memref<?xindex>
+    %i2 = sparse_tensor.coordinates  %arg0 { level = 2 : index } : tensor<5x4x3xf64, #DCoo> to memref<?xindex>
     %v = sparse_tensor.values %arg0 : tensor<5x4x3xf64, #DCoo> to memref<?xf64>
     %vp1 = vector.transfer_read %p1[%c0], %c0: memref<?xindex>, vector<6xindex>
     vector.print %vp1 : vector<6xindex>
     %vi1 = vector.transfer_read %i1[%c0], %c0: memref<?xindex>, vector<5xindex>
     vector.print %vi1 : vector<5xindex>
     %vi2 = vector.transfer_read %i2[%c0], %c0: memref<?xindex>, vector<5xindex>
     vector.print %vi2 : vector<5xindex>
     %vv = vector.transfer_read %v[%c0], %fu: memref<?xf64>, vector<5xf64>

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_matvec.mlir

 // REDEFINE:   --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
 // REDEFINE: FileCheck %s
 // RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
 
 !Filename = !llvm.ptr<i8>
 
 #SparseMatrix = #sparse_tensor.encoding<{
   dimLevelType = [ "dense", "compressed" ],
-  pointerBitWidth = 8,
-  indexBitWidth = 8
+  posWidth = 8,
+  crdWidth = 8
 }>
 
 #matvec = {
   indexing_maps = [
     affine_map<(i,j) -> (i,j)>, // A
     affine_map<(i,j) -> (j)>,   // b
     affine_map<(i,j) -> (i)>    // x (out)
   ],

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_pack.mlir

 // TODO: Pack only support CodeGen Path
 
 #SortedCOO = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed-nu", "singleton" ]
 }>
 
 #SortedCOOI32 = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed-nu", "singleton" ],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 module {
   //
   // Main driver.
   //
   func.func @entry() {
     %c0 = arith.constant 0 : index

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_re_im.mlir

module {
   func.func @dump(%arg0: tensor<?xf32, #SparseVector>) {
     %c0 = arith.constant 0 : index
     %d0 = arith.constant -1.0 : f32
     %n = sparse_tensor.number_of_entries %arg0 : tensor<?xf32, #SparseVector>
     vector.print %n : index
     %values = sparse_tensor.values %arg0 : tensor<?xf32, #SparseVector> to memref<?xf32>
     %0 = vector.transfer_read %values[%c0], %d0: memref<?xf32>, vector<3xf32>
     vector.print %0 : vector<3xf32>
-    %indices = sparse_tensor.indices %arg0 { dimension = 0 : index } : tensor<?xf32, #SparseVector> to memref<?xindex>
-    %1 = vector.transfer_read %indices[%c0], %c0: memref<?xindex>, vector<3xindex>
+    %coordinates = sparse_tensor.coordinates %arg0 { level = 0 : index } : tensor<?xf32, #SparseVector> to memref<?xindex>
+    %1 = vector.transfer_read %coordinates[%c0], %c0: memref<?xindex>, vector<3xindex>
     vector.print %1 : vector<3xindex>
     return
   }
 
   // Driver method to call and verify functions cim and cre.
   func.func @entry() {
     // Setup sparse vectors.
     %v1 = arith.constant sparse<

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sampled_matmul.mlir

 // REDEFINE:   --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
 // REDEFINE: FileCheck %s
 // RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
 
 !Filename = !llvm.ptr<i8>
 
 #SparseMatrix = #sparse_tensor.encoding<{
   dimLevelType = [ "compressed", "compressed" ],
-  pointerBitWidth = 32,
-  indexBitWidth = 32
+  posWidth = 32,
+  crdWidth = 32
 }>
 
 #trait_sampled_dense_dense = {
   indexing_maps = [
     affine_map<(i,j,k) -> (i,j)>,  // S
     affine_map<(i,j,k) -> (i,k)>,  // A
     affine_map<(i,j,k) -> (k,j)>,  // B
     affine_map<(i,j,k) -> (i,j)>   // X (out)

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_sorted_coo.mlir

func.func @entry() {
     %4 = sparse_tensor.convert %m : tensor<5x4xf64> to tensor<?x?xf64, #SortedCOO>
 
     //
     // CHECK:      ( 0, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 0, 0, 0, 1, 1, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 126, 127, 254, 1, 253, 2, 0, 1, 3, 98, 126, 127, 128, 249, 253, 255, 0, 0, 0 )
     // CHECK-NEXT: ( -1, 2, -3, 4, -5, 6, -7, 8, -9, 10, -11, 12, -13, 14, -15, 16, -17, 0, 0, 0 )
     //
-    %p0 = sparse_tensor.pointers %0 { dimension = 0 : index }
+    %p0 = sparse_tensor.positions %0 { level = 0 : index }
       : tensor<?x?xf64, #SortedCOO> to memref<?xindex>
-    %i00 = sparse_tensor.indices %0 { dimension = 0 : index }
+    %i00 = sparse_tensor.coordinates %0 { level = 0 : index }
       : tensor<?x?xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
-    %i01 = sparse_tensor.indices %0 { dimension = 1 : index }
+    %i01 = sparse_tensor.coordinates %0 { level = 1 : index }
       : tensor<?x?xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
     %v0 = sparse_tensor.values %0
       : tensor<?x?xf64, #SortedCOO> to memref<?xf64>
     call @dumpi(%p0)  : (memref<?xindex>) -> ()
     call @dumpsi(%i00) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpsi(%i01) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpf(%v0)  : (memref<?xf64>) -> ()
 
     //
     // CHECK-NEXT: ( 0, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 0, 1, 1, 2, 3, 98, 126, 126, 127, 127, 128, 249, 253, 253, 254, 255, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 3, 1, 3, 2, 3, 3, 0, 3, 0, 3, 3, 3, 1, 3, 0, 3, 0, 0, 0 )
     // CHECK-NEXT: ( -1, 8, -5, -9, -7, 10, -11, 2, 12, -3, -13, 14, -15, 6, 16, 4, -17, 0, 0, 0 )
     //
-    %p1 = sparse_tensor.pointers %1 { dimension = 0 : index }
+    %p1 = sparse_tensor.positions %1 { level = 0 : index }
       : tensor<?x?xf64, #SortedCOOPermuted> to memref<?xindex>
-    %i10 = sparse_tensor.indices %1 { dimension = 0 : index }
+    %i10 = sparse_tensor.coordinates %1 { level = 0 : index }
       : tensor<?x?xf64, #SortedCOOPermuted> to memref<?xindex, strided<[?], offset: ?>>
-    %i11 = sparse_tensor.indices %1 { dimension = 1 : index }
+    %i11 = sparse_tensor.coordinates %1 { level = 1 : index }
       : tensor<?x?xf64, #SortedCOOPermuted> to memref<?xindex, strided<[?], offset: ?>>
     %v1 = sparse_tensor.values %1
       : tensor<?x?xf64, #SortedCOOPermuted> to memref<?xf64>
     call @dumpi(%p1)  : (memref<?xindex>) -> ()
     call @dumpsi(%i10) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpsi(%i11) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpf(%v1)  : (memref<?xf64>) -> ()
 
     //
     // CHECK-NEXT: ( 0, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 0, 1, 1, 2, 2, 2, 2, 0, 0, 0, 1, 1, 1, 1, 2, 2, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 0, 1, 1, 2, 2, 2, 2, 0, 0, 0, 1, 1, 1, 1, 2, 2, 0, 0, 0 )
     // CHECK-NEXT: ( 3, 63, 11, 100, 66, 61, 13, 43, 77, 10, 46, 61, 53, 3, 75, 22, 18, 0, 0, 0 )
     //
-    %p2 = sparse_tensor.pointers %2 { dimension = 0 : index }
+    %p2 = sparse_tensor.positions %2 { level = 0 : index }
       : tensor<?x?x?xf64, #SortedCOO3D> to memref<?xindex>
-    %i20 = sparse_tensor.indices %2 { dimension = 0 : index }
+    %i20 = sparse_tensor.coordinates %2 { level = 0 : index }
       : tensor<?x?x?xf64, #SortedCOO3D> to memref<?xindex, strided<[?], offset: ?>>
-    %i21 = sparse_tensor.indices %2 { dimension = 1 : index }
+    %i21 = sparse_tensor.coordinates %2 { level = 1 : index }
       : tensor<?x?x?xf64, #SortedCOO3D> to memref<?xindex, strided<[?], offset: ?>>
-    %i22 = sparse_tensor.indices %2 { dimension = 2 : index }
+    %i22 = sparse_tensor.coordinates %2 { level = 2 : index }
       : tensor<?x?x?xf64, #SortedCOO3D> to memref<?xindex, strided<[?], offset: ?>>
     %v2 = sparse_tensor.values %2
       : tensor<?x?x?xf64, #SortedCOO3D> to memref<?xf64>
     call @dumpi(%p2)  : (memref<?xindex>) -> ()
     call @dumpsi(%i20) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpsi(%i21) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpsi(%i21) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpf(%v2)  : (memref<?xf64>) -> ()
 
     //
     // CHECK-NEXT: ( 0, 17, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0 )
     // CHECK-NEXT: ( 66, 77, 61, 11, 61, 53, 22, 3, 100, 13, 10, 3, 18, 63, 43, 46, 75, 0, 0, 0 )
     //
-    %p3 = sparse_tensor.pointers %3 { dimension = 0 : index }
+    %p3 = sparse_tensor.positions %3 { level = 0 : index }
       : tensor<?x?x?xf64, #SortedCOO3DPermuted> to memref<?xindex>
-    %i30 = sparse_tensor.indices %3 { dimension = 0 : index }
+    %i30 = sparse_tensor.coordinates %3 { level = 0 : index }
       : tensor<?x?x?xf64, #SortedCOO3DPermuted> to memref<?xindex, strided<[?], offset: ?>>
-    %i31 = sparse_tensor.indices %3 { dimension = 1 : index }
+    %i31 = sparse_tensor.coordinates %3 { level = 1 : index }
       : tensor<?x?x?xf64, #SortedCOO3DPermuted> to memref<?xindex, strided<[?], offset: ?>>
-    %i32 = sparse_tensor.indices %3 { dimension = 2 : index }
+    %i32 = sparse_tensor.coordinates %3 { level = 2 : index }
       : tensor<?x?x?xf64, #SortedCOO3DPermuted> to memref<?xindex, strided<[?], offset: ?>>
     %v3 = sparse_tensor.values %3
       : tensor<?x?x?xf64, #SortedCOO3DPermuted> to memref<?xf64>
     call @dumpi(%p3)  : (memref<?xindex>) -> ()
     call @dumpsi(%i30) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpsi(%i31) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpsi(%i31) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpf(%v3)  : (memref<?xf64>) -> ()
 
     //
     // CHECK-NEXT: ( 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 3, 0, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
    // CHECK-NEXT: ( 6, 5, 4, 3, 2, 11, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     //
-    %p4 = sparse_tensor.pointers %4 { dimension = 0 : index }
+    %p4 = sparse_tensor.positions %4 { level = 0 : index }
       : tensor<?x?xf64, #SortedCOO> to memref<?xindex>
-    %i40 = sparse_tensor.indices %4 { dimension = 0 : index }
+    %i40 = sparse_tensor.coordinates %4 { level = 0 : index }
       : tensor<?x?xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
-    %i41 = sparse_tensor.indices %4 { dimension = 1 : index }
+    %i41 = sparse_tensor.coordinates %4 { level = 1 : index }
       : tensor<?x?xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
     %v4 = sparse_tensor.values %4
       : tensor<?x?xf64, #SortedCOO> to memref<?xf64>
     call @dumpi(%p4)  : (memref<?xindex>) -> ()
     call @dumpsi(%i40) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpsi(%i41) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpf(%v4)  : (memref<?xf64>) -> ()
 
     // And last but not least, an actual operation applied to COO.
     // Note that this performs the operation "in place".
     %5 = call @sparse_scale(%4) : (tensor<?x?xf64, #SortedCOO>) -> tensor<?x?xf64, #SortedCOO>
 
     //
     // CHECK-NEXT: ( 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 1, 2, 2, 3, 4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 0, 3, 0, 3, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     // CHECK-NEXT: ( 12, 10, 8, 6, 4, 22, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 )
     //
-    %p5 = sparse_tensor.pointers %5 { dimension = 0 : index }
+    %p5 = sparse_tensor.positions %5 { level = 0 : index }
       : tensor<?x?xf64, #SortedCOO> to memref<?xindex>
-    %i50 = sparse_tensor.indices %5 { dimension = 0 : index }
+    %i50 = sparse_tensor.coordinates %5 { level = 0 : index }
       : tensor<?x?xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
-    %i51 = sparse_tensor.indices %5 { dimension = 1 : index }
+    %i51 = sparse_tensor.coordinates %5 { level = 1 : index }
       : tensor<?x?xf64, #SortedCOO> to memref<?xindex, strided<[?], offset: ?>>
     %v5 = sparse_tensor.values %5
       : tensor<?x?xf64, #SortedCOO> to memref<?xf64>
     call @dumpi(%p5)  : (memref<?xindex>) -> ()
     call @dumpsi(%i50) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpsi(%i51) : (memref<?xindex, strided<[?], offset: ?>>) -> ()
     call @dumpf(%v5)  : (memref<?xf64>) -> ()
 

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_storage.mlir

func.func @entry() {
     //
     %5 = sparse_tensor.values %0 : tensor<10x8xf64, #Dense> to memref<?xf64>
     %6 = vector.transfer_read %5[%c0], %d0: memref<?xf64>, vector<80xf64>
     vector.print %6 : vector<80xf64>
 
     //
     // Inspect storage scheme of CSR.
     //
-    // pointers(1)
+    // positions(1)
     // indices(1)
     // values
     //
     // CHECK: ( 0, 3, 3, 4, 5, 6, 9, 12, 16, 16, 17 )
     // CHECK: ( 0, 2, 7, 2, 3, 4, 1, 2, 7, 2, 6, 7, 1, 2, 6, 7, 6 )
     // CHECK: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 )
     //
-    %7 = sparse_tensor.pointers %1 { dimension = 1 : index } : tensor<10x8xf64, #CSR> to memref<?xindex>
+    %7 = sparse_tensor.positions %1 { level = 1 : index } : tensor<10x8xf64, #CSR> to memref<?xindex>
     %8 = vector.transfer_read %7[%c0], %c0: memref<?xindex>, vector<11xindex>
     vector.print %8 : vector<11xindex>
-    %9 = sparse_tensor.indices %1 { dimension = 1 : index } : tensor<10x8xf64, #CSR> to memref<?xindex>
+    %9 = sparse_tensor.coordinates %1 { level = 1 : index } : tensor<10x8xf64, #CSR> to memref<?xindex>
     %10 = vector.transfer_read %9[%c0], %c0: memref<?xindex>, vector<17xindex>
     vector.print %10 : vector<17xindex>
     %11 = sparse_tensor.values %1 : tensor<10x8xf64, #CSR> to memref<?xf64>
     %12 = vector.transfer_read %11[%c0], %d0: memref<?xf64>, vector<17xf64>
     vector.print %12 : vector<17xf64>
 
     //
     // Inspect storage scheme of DCSR.
     //
-    // pointers(0)
+    // positions(0)
     // indices(0)
-    // pointers(1)
+    // positions(1)
     // indices(1)
     // values
     //
     // CHECK: ( 0, 8 )
     // CHECK: ( 0, 2, 3, 4, 5, 6, 7, 9 )
     // CHECK: ( 0, 3, 4, 5, 6, 9, 12, 16, 17 )
     // CHECK: ( 0, 2, 7, 2, 3, 4, 1, 2, 7, 2, 6, 7, 1, 2, 6, 7, 6 )
     // CHECK: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 )
     //
-    %13 = sparse_tensor.pointers %2 { dimension = 0 : index } : tensor<10x8xf64, #DCSR> to memref<?xindex>
+    %13 = sparse_tensor.positions %2 { level = 0 : index } : tensor<10x8xf64, #DCSR> to memref<?xindex>
     %14 = vector.transfer_read %13[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %14 : vector<2xindex>
-    %15 = sparse_tensor.indices %2 { dimension = 0 : index } : tensor<10x8xf64, #DCSR> to memref<?xindex>
+    %15 = sparse_tensor.coordinates %2 { level = 0 : index } : tensor<10x8xf64, #DCSR> to memref<?xindex>
     %16 = vector.transfer_read %15[%c0], %c0: memref<?xindex>, vector<8xindex>
     vector.print %16 : vector<8xindex>
-    %17 = sparse_tensor.pointers %2 { dimension = 1 : index } : tensor<10x8xf64, #DCSR> to memref<?xindex>
+    %17 = sparse_tensor.positions %2 { level = 1 : index } : tensor<10x8xf64, #DCSR> to memref<?xindex>
     %18 = vector.transfer_read %17[%c0], %c0: memref<?xindex>, vector<9xindex>
     vector.print %18 : vector<9xindex>
-    %19 = sparse_tensor.indices %2 { dimension = 1 : index } : tensor<10x8xf64, #DCSR> to memref<?xindex>
+    %19 = sparse_tensor.coordinates %2 { level = 1 : index } : tensor<10x8xf64, #DCSR> to memref<?xindex>
     %20 = vector.transfer_read %19[%c0], %c0: memref<?xindex>, vector<17xindex>
     vector.print %20 : vector<17xindex>
     %21 = sparse_tensor.values %2 : tensor<10x8xf64, #DCSR> to memref<?xf64>
     %22 = vector.transfer_read %21[%c0], %d0: memref<?xf64>, vector<17xf64>
     vector.print %22 : vector<17xf64>
 
     //
     // Inspect storage scheme of CSC.
     //
-    // pointers(1)
+    // positions(1)
     // indices(1)
     // values
     //
     // CHECK: ( 0, 1, 3, 8, 9, 10, 10, 13, 17 )
     // CHECK: ( 0, 5, 7, 0, 2, 5, 6, 7, 3, 4, 6, 7, 9, 0, 5, 6, 7 )
     // CHECK: ( 1, 7, 13, 2, 4, 8, 10, 14, 5, 6, 11, 15, 17, 3, 9, 12, 16 )
     //
-    %23 = sparse_tensor.pointers %3 { dimension = 1 : index } : tensor<10x8xf64, #CSC> to memref<?xindex>
+    %23 = sparse_tensor.positions %3 { level = 1 : index } : tensor<10x8xf64, #CSC> to memref<?xindex>
     %24 = vector.transfer_read %23[%c0], %c0: memref<?xindex>, vector<9xindex>
     vector.print %24 : vector<9xindex>
-    %25 = sparse_tensor.indices %3 { dimension = 1 : index } : tensor<10x8xf64, #CSC> to memref<?xindex>
+    %25 = sparse_tensor.coordinates %3 { level = 1 : index } : tensor<10x8xf64, #CSC> to memref<?xindex>
     %26 = vector.transfer_read %25[%c0], %c0: memref<?xindex>, vector<17xindex>
     vector.print %26 : vector<17xindex>
     %27 = sparse_tensor.values %3 : tensor<10x8xf64, #CSC> to memref<?xf64>
     %28 = vector.transfer_read %27[%c0], %d0: memref<?xf64>, vector<17xf64>
     vector.print %28 : vector<17xf64>
 
     //
     // Inspect storage scheme of DCSC.
     //
-    // pointers(0)
+    // positions(0)
     // indices(0)
-    // pointers(1)
+    // positions(1)
     // indices(1)
     // values
     //
     // CHECK: ( 0, 7 )
     // CHECK: ( 0, 1, 2, 3, 4, 6, 7 )
     // CHECK: ( 0, 1, 3, 8, 9, 10, 13, 17 )
     // CHECK: ( 0, 5, 7, 0, 2, 5, 6, 7, 3, 4, 6, 7, 9, 0, 5, 6, 7 )
     // CHECK: ( 1, 7, 13, 2, 4, 8, 10, 14, 5, 6, 11, 15, 17, 3, 9, 12, 16 )
     //
-    %29 = sparse_tensor.pointers %4 { dimension = 0 : index } : tensor<10x8xf64, #DCSC> to memref<?xindex>
+    %29 = sparse_tensor.positions %4 { level = 0 : index } : tensor<10x8xf64, #DCSC> to memref<?xindex>
     %30 = vector.transfer_read %29[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %30 : vector<2xindex>
-    %31 = sparse_tensor.indices %4 { dimension = 0 : index } : tensor<10x8xf64, #DCSC> to memref<?xindex>
+    %31 = sparse_tensor.coordinates %4 { level = 0 : index } : tensor<10x8xf64, #DCSC> to memref<?xindex>
     %32 = vector.transfer_read %31[%c0], %c0: memref<?xindex>, vector<7xindex>
     vector.print %32 : vector<7xindex>
-    %33 = sparse_tensor.pointers %4 { dimension = 1 : index } : tensor<10x8xf64, #DCSC> to memref<?xindex>
+    %33 = sparse_tensor.positions %4 { level = 1 : index } : tensor<10x8xf64, #DCSC> to memref<?xindex>
     %34 = vector.transfer_read %33[%c0], %c0: memref<?xindex>, vector<8xindex>
     vector.print %34 : vector<8xindex>
-    %35 = sparse_tensor.indices %4 { dimension = 1 : index } : tensor<10x8xf64, #DCSC> to memref<?xindex>
+    %35 = sparse_tensor.coordinates %4 { level = 1 : index } : tensor<10x8xf64, #DCSC> to memref<?xindex>
     %36 = vector.transfer_read %35[%c0], %c0: memref<?xindex>, vector<17xindex>
     vector.print %36 : vector<17xindex>
     %37 = sparse_tensor.values %4 : tensor<10x8xf64, #DCSC> to memref<?xf64>
     %38 = vector.transfer_read %37[%c0], %d0: memref<?xf64>, vector<17xf64>
     vector.print %38 : vector<17xf64>
 
     //
     // Inspect storage scheme of BlockRow.
     //
-    // pointers(0)
+    // positions(0)
     // indices(0)
     // values
     //
     // CHECK: ( 0, 8 )
     // CHECK: ( 0, 2, 3, 4, 5, 6, 7, 9 )
     // CHECK: ( 1, 0, 2, 0, 0, 0, 0, 3, 0, 0, 4, 0, 0, 0, 0, 0,
     // CHECK-SAME: 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, 0,
     // CHECK-SAME: 0, 7, 8, 0, 0, 0, 0, 9, 0, 0, 10, 0, 0, 0, 11, 12,
     // CHECK-SAME: 0, 13, 14, 0, 0, 0, 15, 16, 0, 0, 0, 0, 0, 0, 17, 0 )
     //
-    %39 = sparse_tensor.pointers %x { dimension = 0 : index } : tensor<10x8xf64, #BlockRow> to memref<?xindex>
+    %39 = sparse_tensor.positions %x { level = 0 : index } : tensor<10x8xf64, #BlockRow> to memref<?xindex>
     %40 = vector.transfer_read %39[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %40 : vector<2xindex>
-    %41 = sparse_tensor.indices %x { dimension = 0 : index } : tensor<10x8xf64, #BlockRow> to memref<?xindex>
+    %41 = sparse_tensor.coordinates %x { level = 0 : index } : tensor<10x8xf64, #BlockRow> to memref<?xindex>
     %42 = vector.transfer_read %41[%c0], %c0: memref<?xindex>, vector<8xindex>
     vector.print %42 : vector<8xindex>
     %43 = sparse_tensor.values %x : tensor<10x8xf64, #BlockRow> to memref<?xf64>
     %44 = vector.transfer_read %43[%c0], %d0: memref<?xf64>, vector<64xf64>
     vector.print %44 : vector<64xf64>
 
     //
     // Inspect storage scheme of BlockCol.
     //
-    // pointers(0)
+    // positions(0)
     // indices(0)
     // values
     //
     // CHECK: ( 0, 7 )
     // CHECK: ( 0, 1, 2, 3, 4, 6, 7 )
     // CHECK: ( 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 0, 13, 0, 0, 2, 0, 4, 0,
     // CHECK-SAME: 0, 8, 10, 14, 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0,
     // CHECK-SAME: 0, 0, 0, 0, 0, 0, 0, 0, 0, 11, 15, 0, 17, 3, 0, 0, 0, 0, 9, 12, 16, 0, 0 )
     //
-    %45 = sparse_tensor.pointers %y { dimension = 0 : index } : tensor<10x8xf64, #BlockCol> to memref<?xindex>
+    %45 = sparse_tensor.positions %y { level = 0 : index } : tensor<10x8xf64, #BlockCol> to memref<?xindex>
     %46 = vector.transfer_read %45[%c0], %c0: memref<?xindex>, vector<2xindex>
     vector.print %46 : vector<2xindex>
-    %47 = sparse_tensor.indices %y { dimension = 0 : index } : tensor<10x8xf64, #BlockCol> to memref<?xindex>
+    %47 = sparse_tensor.coordinates %y { level = 0 : index } : tensor<10x8xf64, #BlockCol> to memref<?xindex>
     %48 = vector.transfer_read %47[%c0], %c0: memref<?xindex>, vector<7xindex>
     vector.print %48 : vector<7xindex>
     %49 = sparse_tensor.values %y : tensor<10x8xf64, #BlockCol> to memref<?xf64>
     %50 = vector.transfer_read %49[%c0], %d0: memref<?xf64>, vector<70xf64>
     vector.print %50 : vector<70xf64>
 
     // Release the resources.
     bufferization.dealloc_tensor %0 : tensor<10x8xf64, #Dense>

mlir/test/Integration/Dialect/SparseTensor/taco/tools/mlir_pytaco.py

 from mlir.dialects.linalg.opdsl import lang
 
 from . import mlir_pytaco_utils as utils
 
 # TACO naming prefixes.
 _TACO_INDEX_PREFIX = "i"
 _TACO_TENSOR_PREFIX = "A"
 
-# Bitwidths for pointers and indices.
-_POINTER_BIT_WIDTH = 0
-_INDEX_BIT_WIDTH = 0
+# Bitwidths for positions and coordinates.
+_POS_WIDTH = 0
+_CRD_WIDTH = 0
 # The entry point to the JIT compiled program.
 _ENTRY_NAME = "main"
 
 # Type aliases for type annotation.
 _UnaryOp = Callable[[Any], Any]
 _BinaryOp = Callable[[Any, Any], Any]
 _ExprVisitor = Callable[..., None]
 _ExprInfoDict = Dict["IndexExpr", "_ExprInfo"]
class Format:
   def mlir_tensor_attr(self) -> Optional[sparse_tensor.EncodingAttr]:
     """Constructs the MLIR attributes for the tensor format."""
     order = (
         range(self.rank()) if
         (self.ordering is None) else self.ordering.ordering)
     mlir_storage_format = [f.value for f in self.format_pack.formats]
     return sparse_tensor.EncodingAttr.get(mlir_storage_format,
                                           ir.AffineMap.get_permutation(order),
-                                          None, _POINTER_BIT_WIDTH,
-                                          _INDEX_BIT_WIDTH)
+                                          None, _POS_WIDTH, _CRD_WIDTH)
 
 
 def _make_format(formats: List[ModeFormat],
                  ordering: Optional[List[int]] = None) -> Format:
   """Constructs a format from a list of ModeFormat and an optional ordering.
 
   Args:
     formats: A list of ModeFormat, one for each dimension of a tensor.

mlir/test/python/dialects/sparse_tensor/dialect.py

 
 
 # CHECK-LABEL: TEST: testEncodingAttr1D
 @run
 def testEncodingAttr1D():
   with Context() as ctx:
     parsed = Attribute.parse('#sparse_tensor.encoding<{'
                              '  dimLevelType = [ "compressed" ],'
-                             '  pointerBitWidth = 16,'
-                             '  indexBitWidth = 32'
+                             '  posWidth = 16,'
+                             '  crdWidth = 32'
                              '}>')
-    # CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 16, indexBitWidth = 32 }>
+    # CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], posWidth = 16, crdWidth = 32 }>
     print(parsed)
 
     casted = st.EncodingAttr(parsed)
     # CHECK: equal: True
     print(f"equal: {casted == parsed}")
 
     # CHECK: dim_level_types: [<DimLevelType.compressed: 8>]
     print(f"dim_level_types: {casted.dim_level_types}")
     # CHECK: dim_ordering: None
     print(f"dim_ordering: {casted.dim_ordering}")
-    # CHECK: pointer_bit_width: 16
-    print(f"pointer_bit_width: {casted.pointer_bit_width}")
-    # CHECK: index_bit_width: 32
-    print(f"index_bit_width: {casted.index_bit_width}")
+    # CHECK: pos_width: 16
+    print(f"pos_width: {casted.pos_width}")
+    # CHECK: crd_width: 32
+    print(f"crd_width: {casted.crd_width}")
 
     created = st.EncodingAttr.get(casted.dim_level_types, None, None, 0, 0)
     # CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
     print(created)
     # CHECK: created_equal: False
     print(f"created_equal: {created == casted}")
 
     # Verify that the factory creates an instance of the proper type.
     # CHECK: is_proper_instance: True
     print(f"is_proper_instance: {isinstance(created, st.EncodingAttr)}")
-    # CHECK: created_pointer_bit_width: 0
-    print(f"created_pointer_bit_width: {created.pointer_bit_width}")
+    # CHECK: created_pos_width: 0
+    print(f"created_pos_width: {created.pos_width}")
 
 
 # CHECK-LABEL: TEST: testEncodingAttr2D
 @run
 def testEncodingAttr2D():
   with Context() as ctx:
     parsed = Attribute.parse('#sparse_tensor.encoding<{'
                              '  dimLevelType = [ "dense", "compressed" ],'
                              '  dimOrdering = affine_map<(d0, d1) -> (d1, d0)>,'
-                             '  pointerBitWidth = 8,'
-                             '  indexBitWidth = 32'
+                             '  posWidth = 8,'
+                             '  crdWidth = 32'
                              '}>')
-    # CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 8, indexBitWidth = 32 }>
+    # CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
     print(parsed)
 
     casted = st.EncodingAttr(parsed)
     # CHECK: equal: True
     print(f"equal: {casted == parsed}")
 
     # CHECK: dim_level_types: [<DimLevelType.dense: 4>, <DimLevelType.compressed: 8>]
     print(f"dim_level_types: {casted.dim_level_types}")
     # CHECK: dim_ordering: (d0, d1) -> (d1, d0)
     print(f"dim_ordering: {casted.dim_ordering}")
-    # CHECK: pointer_bit_width: 8
-    print(f"pointer_bit_width: {casted.pointer_bit_width}")
-    # CHECK: index_bit_width: 32
-    print(f"index_bit_width: {casted.index_bit_width}")
+    # CHECK: pos_width: 8
+    print(f"pos_width: {casted.pos_width}")
+    # CHECK: crd_width: 32
+    print(f"crd_width: {casted.crd_width}")
 
     created = st.EncodingAttr.get(casted.dim_level_types, casted.dim_ordering,
                                   casted.higher_ordering, 8, 32)
-    # CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 8, indexBitWidth = 32 }>
+    # CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, posWidth = 8, crdWidth = 32 }>
     print(created)
     # CHECK: created_equal: True
     print(f"created_equal: {created == casted}")
 
 
 # CHECK-LABEL: TEST: testEncodingAttrOnTensorType
 @run
 def testEncodingAttrOnTensorType():
   with Context() as ctx, Location.unknown():
     encoding = st.EncodingAttr(
         Attribute.parse('#sparse_tensor.encoding<{'
                         '  dimLevelType = [ "compressed" ], '
-                        '  pointerBitWidth = 64,'
-                        '  indexBitWidth = 32'
+                        '  posWidth = 64,'
+                        '  crdWidth = 32'
                         '}>'))
     tt = RankedTensorType.get((1024,), F32Type.get(), encoding=encoding)
-    # CHECK: tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 64, indexBitWidth = 32 }>>
+    # CHECK: tensor<1024xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], posWidth = 64, crdWidth = 32 }>>
     print(tt)
-    # CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 64, indexBitWidth = 32 }>
+    # CHECK: #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], posWidth = 64, crdWidth = 32 }>
     print(tt.encoding)
     assert tt.encoding == encoding