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llvm-project/mlir/test/Dialect/SparseTensor/sparse_affine.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

500 lines
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// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
// RUN: mlir-opt %s -sparsification | FileCheck %s
#SpVec = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
#CSR = #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ] }>
#Row = #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ] }>
#EncDenseVec = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
#trait1 = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> (3)>, // b
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) += a(i) * b(3)"
}
// 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.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: %[[VAL_17:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_16]]] : memref<32xf32>
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xf32>
// CHECK: %[[VAL_19:.*]] = arith.mulf %[[VAL_18]], %[[VAL_12]] : f32
// CHECK: %[[VAL_20:.*]] = arith.addf %[[VAL_17]], %[[VAL_19]] : f32
// CHECK: memref.store %[[VAL_20]], %[[VAL_11]]{{\[}}%[[VAL_16]]] : memref<32xf32>
// CHECK: }
// CHECK: %[[VAL_21:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<32xf32>
// CHECK: return %[[VAL_21]] : tensor<32xf32>
// CHECK: }
func.func @mul_inv_dense1d(%arga: tensor<32xf32, #SpVec>,
%argb: tensor<4xf32>,
%argx: tensor<32xf32>) -> tensor<32xf32> {
%0 = linalg.generic #trait1
ins(%arga, %argb: tensor<32xf32, #SpVec>, tensor<4xf32>)
outs(%argx: tensor<32xf32>) {
^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.mulf %a, %b : f32
%1 = arith.addf %x, %0 : f32
linalg.yield %1 : f32
} -> tensor<32xf32>
return %0 : tensor<32xf32>
}
// 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.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.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: %[[VAL_21:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_2]]] : memref<?xindex>
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref<?xindex>
// CHECK: %[[VAL_23:.*]] = scf.for %[[VAL_24:.*]] = %[[VAL_21]] to %[[VAL_22]] step %[[VAL_3]] iter_args(%[[VAL_25:.*]] = %[[VAL_16]]) -> (tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_24]]] : memref<?xf32>
// CHECK: %[[VAL_27:.*]] = arith.mulf %[[VAL_26]], %[[VAL_20]] : f32
// CHECK: %[[VAL_28:.*]] = arith.addf %[[VAL_27]], %[[VAL_5]] : f32
// CHECK: %[[VAL_29:.*]] = sparse_tensor.insert %[[VAL_28]] into %[[VAL_25]]{{\[}}%[[VAL_17]]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: scf.yield %[[VAL_29]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_30:.*]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_16]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_31:.*]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: %[[VAL_32:.*]] = sparse_tensor.load %[[VAL_33:.*]] hasInserts : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_32]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
func.func @mul_inv_sparse1d(%arga: tensor<32xf32, #SpVec>,
%argb: tensor<4xf32, #SpVec>) -> tensor<32xf32, #SpVec> {
%argx = bufferization.alloc_tensor() : tensor<32xf32, #SpVec>
%0 = linalg.generic #trait1
ins(%arga, %argb: tensor<32xf32, #SpVec>, tensor<4xf32, #SpVec>)
outs(%argx: tensor<32xf32, #SpVec>) {
^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.mulf %a, %b : f32
%1 = arith.addf %x, %0 : f32
linalg.yield %1 : f32
} -> tensor<32xf32, #SpVec>
return %0 : tensor<32xf32, #SpVec>
}
// CHECK-LABEL: func.func @mul_inv_enc_dense1d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> {
// CHECK: %[[VAL_2:.*]] = arith.constant 32 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 3 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_6]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32>
// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_3]]] : memref<?xf32>
// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_5]] {
// CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK: %[[VAL_14:.*]] = arith.mulf %[[VAL_13]], %[[VAL_10]] : f32
// CHECK: %[[VAL_15:.*]] = arith.addf %[[VAL_12]], %[[VAL_14]] : f32
// CHECK: memref.store %[[VAL_15]], %[[VAL_9]]{{\[}}%[[VAL_11]]] : memref<?xf32>
// CHECK: }
// CHECK: %[[VAL_16:.*]] = sparse_tensor.load %[[VAL_6]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_16]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
func.func @mul_inv_enc_dense1d(%arga: tensor<32xf32, #EncDenseVec>,
%argb: tensor<4xf32, #EncDenseVec>) -> tensor<32xf32, #EncDenseVec> {
%argx = bufferization.alloc_tensor() : tensor<32xf32, #EncDenseVec>
%0 = linalg.generic #trait1
ins(%arga, %argb: tensor<32xf32, #EncDenseVec>, tensor<4xf32, #EncDenseVec>)
outs(%argx: tensor<32xf32, #EncDenseVec>) {
^bb(%a: f32, %b: f32, %x: f32):
%0 = arith.mulf %a, %b : f32
%1 = arith.addf %x, %0 : f32
linalg.yield %1 : f32
} -> tensor<32xf32, #EncDenseVec>
return %0 : tensor<32xf32, #EncDenseVec>
}
#trait2 = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i+2)>, // b
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = a(i) & b(i+2)"
}
// 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.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: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref<?xi32>
// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_15]], %[[VAL_5]] : index
// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<34xi32>
// CHECK: %[[VAL_19:.*]] = arith.andi %[[VAL_16]], %[[VAL_18]] : i32
// CHECK: memref.store %[[VAL_19]], %[[VAL_11]]{{\[}}%[[VAL_15]]] : memref<32xi32>
// CHECK: }
// CHECK: %[[VAL_20:.*]] = bufferization.to_tensor %[[VAL_11]] : memref<32xi32>
// CHECK: return %[[VAL_20]] : tensor<32xi32>
// CHECK: }
func.func @and_affine_dense1d(%arga: tensor<32xi32, #SpVec>,
%argb: tensor<34xi32>,
%argx: tensor<32xi32>) -> tensor<32xi32> {
%0 = linalg.generic #trait2
ins(%arga, %argb: tensor<32xi32, #SpVec>, tensor<34xi32>)
outs(%argx: tensor<32xi32>) {
^bb(%a: i32, %b: i32, %x: i32):
%0 = arith.andi %a, %b : i32
linalg.yield %0 : i32
} -> tensor<32xi32>
return %0 : tensor<32xi32>
}
// 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.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.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: %[[VAL_21:.*]] = scf.for %[[VAL_22:.*]] = %[[VAL_19]] to %[[VAL_20]] step %[[VAL_3]] iter_args(%[[VAL_23:.*]] = %[[VAL_16]]) -> (tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_22]]] : memref<?xindex>
// CHECK: %[[VAL_25:.*]] = arith.addi %[[VAL_17]], %[[VAL_4]] : index
// CHECK: %[[VAL_26:.*]] = arith.cmpi eq, %[[VAL_24]], %[[VAL_25]] : index
// CHECK: %[[VAL_27:.*]] = scf.if %[[VAL_26]] -> (tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]]] : memref<?xi32>
// CHECK: %[[VAL_29:.*]] = arith.andi %[[VAL_18]], %[[VAL_28]] : i32
// CHECK: %[[VAL_30:.*]] = sparse_tensor.insert %[[VAL_29]] into %[[VAL_23]]{{\[}}%[[VAL_17]]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: scf.yield %[[VAL_30]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_23]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_31:.*]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_32:.*]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: %[[VAL_33:.*]] = sparse_tensor.load %[[VAL_34:.*]] hasInserts : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_33]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>
func.func @and_affine_sparse1d(%arga: tensor<32xi32, #SpVec>,
%argb: tensor<34xi32, #SpVec>) -> tensor<32xi32, #SpVec> {
%argx = bufferization.alloc_tensor() : tensor<32xi32, #SpVec>
%0 = linalg.generic #trait2
ins(%arga, %argb: tensor<32xi32, #SpVec>, tensor<34xi32, #SpVec>)
outs(%argx: tensor<32xi32, #SpVec>) {
^bb(%a: i32, %b: i32, %x: i32):
%0 = arith.andi %a, %b : i32
linalg.yield %0 : i32
} -> tensor<32xi32, #SpVec>
return %0 : tensor<32xi32, #SpVec>
}
#trait3 = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // a
affine_map<(i,j) -> (i+2,j+3)>, // b
affine_map<(i,j) -> (i,j)> // x (out)
],
iterator_types = ["parallel","parallel"],
doc = "x(i,j) += a(i,j) * b(i+2,j+3)"
}
// CHECK-LABEL: func @mul_affine_dense2d(
// 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.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: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_18]]] : memref<?xindex>
// CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_19]]] : memref<32x16xf64>
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_18]]] : memref<?xf64>
// CHECK: %[[VAL_22:.*]] = arith.addi %[[VAL_14]], %[[VAL_6]] : index
// CHECK: %[[VAL_23:.*]] = arith.addi %[[VAL_19]], %[[VAL_7]] : index
// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_22]], %[[VAL_23]]] : memref<34x19xf64>
// CHECK: %[[VAL_25:.*]] = arith.mulf %[[VAL_21]], %[[VAL_24]] : f64
// CHECK: %[[VAL_26:.*]] = arith.addf %[[VAL_20]], %[[VAL_25]] : f64
// CHECK: memref.store %[[VAL_26]], %[[VAL_13]]{{\[}}%[[VAL_14]], %[[VAL_19]]] : memref<32x16xf64>
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_27:.*]] = bufferization.to_tensor %[[VAL_13]] : memref<32x16xf64>
// CHECK: return %[[VAL_27]] : tensor<32x16xf64>
// CHECK: }
func.func @mul_affine_dense2d(%arga: tensor<32x16xf64, #CSR>,
%argb: tensor<34x19xf64>,
%argx: tensor<32x16xf64>) -> tensor<32x16xf64> {
%0 = linalg.generic #trait3
ins(%arga, %argb: tensor<32x16xf64, #CSR>, tensor<34x19xf64>)
outs(%argx: tensor<32x16xf64>) {
^bb(%a: f64, %b: f64, %x: f64):
%0 = arith.mulf %a, %b : f64
%1 = arith.addf %x, %0 : f64
linalg.yield %1 : f64
} -> tensor<32x16xf64>
return %0 : tensor<32x16xf64>
}
// CHECK-LABEL: func.func @mul_affine_sparse2d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> {
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 32 : index
// 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.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.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: %[[VAL_26:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_23]]] : memref<?xf64>
// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_18]]] : memref<?xindex>
// CHECK: %[[VAL_28:.*]] = arith.addi %[[VAL_18]], %[[VAL_4]] : index
// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_28]]] : memref<?xindex>
// CHECK: %[[VAL_30:.*]]:3 = scf.for %[[VAL_31:.*]] = %[[VAL_27]] to %[[VAL_29]] step %[[VAL_4]] iter_args(%[[VAL_32:.*]] = %[[VAL_6]], %[[VAL_200:.*]] = %[[VAL_FALSE]], %[[VAL_33:.*]] = %[[VAL_24]]) -> (f64, i1, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_34:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_31]]] : memref<?xindex>
// CHECK: %[[VAL_35:.*]] = arith.addi %[[VAL_25]], %[[VAL_7]] : index
// CHECK: %[[VAL_36:.*]] = arith.cmpi eq, %[[VAL_34]], %[[VAL_35]] : index
// CHECK: %[[VAL_37:.*]]:3 = scf.if %[[VAL_36]] -> (f64, i1, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_38:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_31]]] : memref<?xf64>
// CHECK: %[[VAL_39:.*]] = arith.mulf %[[VAL_26]], %[[VAL_38]] : f64
// CHECK: %[[VAL_40:.*]] = arith.addf %[[VAL_32]], %[[VAL_39]] : f64
// CHECK: scf.yield %[[VAL_40]], %[[VAL_TRUE]], %[[VAL_33]] : f64, i1, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_32]], %[[VAL_200]], %[[VAL_33]] : f64, i1, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_41:.*]]#0, %[[VAL_41]]#1, %[[VAL_41]]#2 : f64, i1, tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: %[[VAL_201:.*]] = scf.if %[[VAL_30]]#1 -> (tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>) {
// CHECK: %[[VAL_42:.*]] = sparse_tensor.insert %[[VAL_30]]#0 into %[[VAL_30]]#2{{\[}}%[[VAL_16]], %[[VAL_25]]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: scf.yield %[[VAL_42]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: } else {
// CHECK: scf.yield %[[VAL_30]]#2 : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_201]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: scf.yield %[[VAL_44:.*]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: }
// CHECK: %[[VAL_45:.*]] = sparse_tensor.load %[[VAL_46:.*]] hasInserts : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
// CHECK: return %[[VAL_45]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>
func.func @mul_affine_sparse2d(%arga: tensor<32x16xf64, #CSR>,
%argb: tensor<34x19xf64, #CSR>) -> tensor<32x16xf64, #CSR> {
%argx = bufferization.alloc_tensor() : tensor<32x16xf64, #CSR>
%0 = linalg.generic #trait3
ins(%arga, %argb: tensor<32x16xf64, #CSR>, tensor<34x19xf64, #CSR>)
outs(%argx: tensor<32x16xf64, #CSR>) {
^bb(%a: f64, %b: f64, %x: f64):
%0 = arith.mulf %a, %b : f64
%1 = arith.addf %x, %0 : f64
linalg.yield %1 : f64
} -> tensor<32x16xf64, #CSR>
return %0 : tensor<32x16xf64, #CSR>
}
#trait4 = {
indexing_maps = [
affine_map<(i,j) -> (i+2,j)>, // a
affine_map<(i,j) -> (i,j+3)>, // b
affine_map<(i,j) -> (i,j)> // x (out)
],
iterator_types = ["parallel","parallel"],
doc = "x(i,j) += a(i+2,j) * b(i,j+3)"
}
// CHECK-LABEL: func.func @mul_affine_dense_dim_2d(
// 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.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.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: %[[VAL_21:.*]] = arith.addi %[[VAL_19]], %[[VAL_5]] : index
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_21]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_23:.*]] = %[[VAL_20]] to %[[VAL_22]] step %[[VAL_5]] {
// CHECK: %[[VAL_24:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_23]]] : memref<?xindex>
// CHECK: %[[VAL_25:.*]] = arith.addi %[[VAL_24]], %[[VAL_7]] : index
// CHECK: %[[VAL_26:.*]] = arith.muli %[[VAL_17]], %[[VAL_3]] : index
// CHECK: %[[VAL_27:.*]] = arith.addi %[[VAL_26]], %[[VAL_25]] : index
// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_18]], %[[VAL_24]]] : memref<32x16xf64>
// CHECK: %[[VAL_29:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_23]]] : memref<?xf64>
// CHECK: %[[VAL_30:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_27]]] : memref<?xf64>
// CHECK: %[[VAL_31:.*]] = arith.mulf %[[VAL_29]], %[[VAL_30]] : f64
// CHECK: %[[VAL_32:.*]] = arith.addf %[[VAL_28]], %[[VAL_31]] : f64
// CHECK: memref.store %[[VAL_32]], %[[VAL_14]]{{\[}}%[[VAL_18]], %[[VAL_24]]] : memref<32x16xf64>
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_33:.*]] = bufferization.to_tensor %[[VAL_14]] : memref<32x16xf64>
// CHECK: return %[[VAL_33]] : tensor<32x16xf64>
// CHECK: }
func.func @mul_affine_dense_dim_2d(%arga: tensor<34x16xf64, #CSR>,
%argb: tensor<32x19xf64, #Row>,
%argx: tensor<32x16xf64>) -> tensor<32x16xf64> {
%0 = linalg.generic #trait4
ins(%arga, %argb: tensor<34x16xf64, #CSR>, tensor<32x19xf64, #Row>)
outs(%argx: tensor<32x16xf64>) {
^bb(%a: f64, %b: f64, %x: f64):
%0 = arith.mulf %a, %b : f64
%1 = arith.addf %x, %0 : f64
linalg.yield %1 : f64
} -> tensor<32x16xf64>
return %0 : tensor<32x16xf64>
}
#trait5 = {
indexing_maps = [
affine_map<(i,j) -> (2,j)>, // a
affine_map<(i,j) -> (i,3)>, // b
affine_map<(i,j) -> (i,j)> // x (out)
],
iterator_types = ["parallel","parallel"],
doc = "x(i,j) += a(2,j) * b(i,3)"
}
// CHECK-LABEL: func.func @mul_const_affine_dense_dim_2d(
// 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.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.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
// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_20]]] : memref<?xf64>
// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_23:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref<?xindex>
// CHECK: scf.for %[[VAL_24:.*]] = %[[VAL_22]] to %[[VAL_23]] step %[[VAL_6]] {
// CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_24]]] : memref<?xindex>
// CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_14]]{{\[}}%[[VAL_18]], %[[VAL_25]]] : memref<32x16xf64>
// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_24]]] : memref<?xf64>
// CHECK: %[[VAL_28:.*]] = arith.mulf %[[VAL_27]], %[[VAL_21]] : f64
// CHECK: %[[VAL_29:.*]] = arith.addf %[[VAL_26]], %[[VAL_28]] : f64
// CHECK: memref.store %[[VAL_29]], %[[VAL_14]]{{\[}}%[[VAL_18]], %[[VAL_25]]] : memref<32x16xf64>
// CHECK: }
// CHECK: }
// CHECK: %[[VAL_30:.*]] = bufferization.to_tensor %[[VAL_14]] : memref<32x16xf64>
// CHECK: return %[[VAL_30]] : tensor<32x16xf64>
// CHECK: }
func.func @mul_const_affine_dense_dim_2d(%arga: tensor<34x16xf64, #CSR>,
%argb: tensor<32x19xf64, #Row>,
%argx: tensor<32x16xf64>) -> tensor<32x16xf64> {
%0 = linalg.generic #trait5
ins(%arga, %argb: tensor<34x16xf64, #CSR>, tensor<32x19xf64, #Row>)
outs(%argx: tensor<32x16xf64>) {
^bb(%a: f64, %b: f64, %x: f64):
%0 = arith.mulf %a, %b : f64
%1 = arith.addf %x, %0 : f64
linalg.yield %1 : f64
} -> tensor<32x16xf64>
return %0 : tensor<32x16xf64>
}
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