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clang-p2996/mlir/test/Dialect/SparseTensor/sparse_concat_codegen.mlir
wren romano 84cd51bb97 [mlir][sparse] Renaming "pointer/index" to "position/coordinate" 
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 "`Value`s".)  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}

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D144773
2023-03-06 12:23:33 -08:00

431 lines
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// RUN: mlir-opt %s --post-sparsification-rewrite="enable-runtime-library=false enable-convert=false" \
// RUN: | FileCheck %s
#DCSR = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#DENSE = #sparse_tensor.encoding<{dimLevelType = ["dense", "dense"]}>
#DENSE_P = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
// CHECK-LABEL: @concat_sparse_sparse(
// 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.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.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.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: %[[RET_6:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A5:.*]] = %[[A4]])
// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
// CHECK: %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
// CHECK: %[[NEW_3:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A5]][%[[TMP_29]], %[[TMP_27]]] : tensor<9x4xf64, #sparse_tensor
// CHECK: scf.yield %[[NEW_3]]
// CHECK: }
// CHECK: scf.yield %[[RET_6]]
// CHECK: }
// CHECK: %[[TMP_23:.*]] = sparse_tensor.load %[[RET_3]] hasInserts
// CHECK: return %[[TMP_23]] : tensor<9x4xf64, #sparse_tensor
func.func @concat_sparse_sparse(%arg0: tensor<2x4xf64, #DCSR>,
%arg1: tensor<3x4xf64, #DCSR>,
%arg2: tensor<4x4xf64, #DCSR>)
-> tensor<9x4xf64, #DCSR> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #DCSR>,
tensor<3x4xf64, #DCSR>,
tensor<4x4xf64, #DCSR> to tensor<9x4xf64, #DCSR>
return %0 : tensor<9x4xf64, #DCSR>
}
// CHECK-LABEL: @concat_sparse_sparse_dynamic(
// 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-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.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.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.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: %[[RET_6:.*]] = scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]] iter_args(%[[A5:.*]] = %[[A4]])
// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
// CHECK: %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
// CHECK: %[[NEW_3:.*]] = sparse_tensor.insert %[[TMP_28]] into %[[A5]][%[[TMP_29]], %[[TMP_27]]] : tensor<?x?xf64, #sparse_tensor
// CHECK: scf.yield %[[NEW_3]]
// CHECK: }
// CHECK: scf.yield %[[RET_6]]
// CHECK: }
// CHECK: %[[TMP_23:.*]] = sparse_tensor.load %[[RET_3]] hasInserts
// CHECK: return %[[TMP_23]] : tensor<?x?xf64, #sparse_tensor
func.func @concat_sparse_sparse_dynamic(%arg0: tensor<2x4xf64, #DCSR>,
%arg1: tensor<3x4xf64, #DCSR>,
%arg2: tensor<4x4xf64, #DCSR>)
-> tensor<?x?xf64, #DCSR> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #DCSR>,
tensor<3x4xf64, #DCSR>,
tensor<4x4xf64, #DCSR> to tensor<?x?xf64, #DCSR>
return %0 : tensor<?x?xf64, #DCSR>
}
// CHECK-LABEL: @concat_sparse_sparse_dense(
// 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-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-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.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.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.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: scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
// CHECK: %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
// CHECK: memref.store %[[TMP_28]], %[[A]]{{\[}}%[[TMP_29]], %[[TMP_27]]] : memref<?x?xf64>
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = bufferization.to_tensor %[[A]] : memref<?x?xf64>
// CHECK: return %[[R]] : tensor<?x?xf64>
func.func @concat_sparse_sparse_dense(%arg0: tensor<2x4xf64, #DCSR>,
%arg1: tensor<3x4xf64, #DCSR>,
%arg2: tensor<4x4xf64, #DCSR>)
-> tensor<?x?xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #DCSR>,
tensor<3x4xf64, #DCSR>,
tensor<4x4xf64, #DCSR> to tensor<?x?xf64>
return %0 : tensor<?x?xf64>
}
// CHECK-LABEL: @concat_sparse_sparse_annotated_dense(
// 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-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: %[[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.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.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.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: scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
// CHECK: %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
// CHECK: memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_29]], %[[TMP_27]]] : memref<?x?xf64>
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[TMP_0]]
// CHECK: return %[[R]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ] }>>
func.func @concat_sparse_sparse_annotated_dense(%arg0: tensor<2x4xf64, #DCSR>,
%arg1: tensor<3x4xf64, #DCSR>,
%arg2: tensor<4x4xf64, #DCSR>)
-> tensor<?x?xf64, #DENSE> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #DCSR>,
tensor<3x4xf64, #DCSR>,
tensor<4x4xf64, #DCSR> to tensor<?x?xf64, #DENSE>
return %0 : tensor<?x?xf64, #DENSE>
}
// CHECK-LABEL: @concat_sparse_sparse_annotated_dense_permute(
// 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-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: %[[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.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.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.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: scf.for %[[TMP_arg4:.*]] = %[[TMP_25]] to %[[TMP_26]] step %[[TMP_c1]]
// CHECK: %[[TMP_27:.*]] = memref.load %[[TMP_18]][%[[TMP_arg4]]] : memref<?xindex>
// CHECK: %[[TMP_28:.*]] = memref.load %[[TMP_19]][%[[TMP_arg4]]] : memref<?xf64>
// CHECK: %[[TMP_29:.*]] = arith.addi %[[TMP_23]], %[[TMP_c5]] : index
// CHECK: memref.store %[[TMP_28]], %[[VAL_1]][%[[TMP_27]], %[[TMP_29]]] : memref<?x?xf64>
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = sparse_tensor.convert %[[TMP_0]]
// CHECK: return %[[R]] : tensor<?x?xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
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>
}
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