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Differential D134971

[mlir][sparse] sorted coo co-iteration check test
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Authored by aartbik on Sep 30 2022, 9:51 AM.

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nicolasvasilache
Peiming
bixia
wrengr
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rGe202886cbbf9: [mlir][sparse] sorted coo co-iteration check test

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aartbik created this revision.Sep 30 2022, 9:51 AM
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aartbik updated this revision to Diff 464311.Sep 30 2022, 9:57 AM

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Harbormaster completed remote builds in B189716: Diff 464311.Sep 30 2022, 10:17 AM
aartbik added reviewers: Peiming, bixia, wrengr.Sep 30 2022, 1:21 PM
Peiming accepted this revision.Oct 3 2022, 10:00 AM
This revision is now accepted and ready to land.Oct 3 2022, 10:00 AM
Closed by commit rGe202886cbbf9: [mlir][sparse] sorted coo co-iteration check test (authored by aartbik). · Explain WhyOct 3 2022, 10:19 AM
This revision was automatically updated to reflect the committed changes.
aartbik added a commit: rGe202886cbbf9: [mlir][sparse] sorted coo co-iteration check test.

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mlir/
test/
Dialect/
SparseTensor/
105 lines

Diff 464728

mlir/test/Dialect/SparseTensor/sorted_coo.mlir

Show All 16 Lines indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> (j)>, // b affine_map<(i,j) -> (j)>, // b
affine_map<(i,j) -> (i)> // x (out) affine_map<(i,j) -> (i)> // x (out)
], ],
iterator_types = ["parallel","reduction"], iterator_types = ["parallel","reduction"],
doc = "x(i) += A(i,j) * b(j)" doc = "x(i) += A(i,j) * b(j)"
} }
#trait_mul = {
indexing_maps = [
affine_map<(i,j) -> (i,j)>, // X
affine_map<(i,j) -> (i,j)>, // Y
affine_map<(i,j) -> (i,j)> // Z (out)
],
iterator_types = ["parallel", "parallel"],
doc = "Z(i,j) = X(i,j) * Y(i,j)"
}
// //
// Two kernels that operate on SortedCOO format. // Kernels that operate on SortedCOO format.
// //
// CHECK-LABEL: func.func @sparse_scale( // 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-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_1:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2.000000e+00 : f32 // 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.pointers %[[VAL_0]] {dimension = 0 : index} : tensor<?x?xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
▲ Show 20 Lines • Show All 54 Lines • ▼ Show 20 Lines %0 = linalg.generic #trait_matvec
outs(%argx: tensor<32xf64>) { outs(%argx: tensor<32xf64>) {
^bb(%A: f64, %b: f64, %x: f64): ^bb(%A: f64, %b: f64, %x: f64):
%0 = arith.mulf %A, %b : f64 %0 = arith.mulf %A, %b : f64
%1 = arith.addf %x, %0 : f64 %1 = arith.addf %x, %0 : f64
linalg.yield %1 : f64 linalg.yield %1 : f64
} -> tensor<32xf64> } -> tensor<32xf64>
return %0 : tensor<32xf64> return %0 : tensor<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>
// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// 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>
// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]] {dimension = 1 : index} : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xindex>
// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ] }>> to memref<?xf64>
// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x64xf64>
// CHECK: linalg.fill ins(%[[VAL_3]] : f64) outs(%[[VAL_14]] : memref<32x64xf64>)
// CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref<?xindex>
// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_4]]] : memref<?xindex>
// CHECK: %[[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) {
// CHECK: %[[VAL_22:.*]] = arith.cmpi ult, %[[VAL_20]], %[[VAL_16]] : index
// CHECK: %[[VAL_23:.*]] = arith.cmpi ult, %[[VAL_21]], %[[VAL_18]] : index
// CHECK: %[[VAL_24:.*]] = arith.andi %[[VAL_22]], %[[VAL_23]] : i1
// CHECK: scf.condition(%[[VAL_24]]) %[[VAL_20]], %[[VAL_21]] : index, index
// CHECK: } do {
// CHECK: ^bb0(%[[VAL_25:.*]]: index, %[[VAL_26:.*]]: index):
// CHECK: %[[VAL_27:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_25]]] : memref<?xindex>
// CHECK: %[[VAL_28:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_26]]] : memref<?xindex>
// CHECK: %[[VAL_29:.*]] = arith.cmpi ult, %[[VAL_28]], %[[VAL_27]] : index
// CHECK: %[[VAL_30:.*]] = arith.select %[[VAL_29]], %[[VAL_28]], %[[VAL_27]] : index
// CHECK: %[[VAL_31:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
// CHECK: %[[VAL_32:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
// CHECK: %[[VAL_33:.*]] = arith.andi %[[VAL_31]], %[[VAL_32]] : i1
// CHECK: scf.if %[[VAL_33]] {
// CHECK: %[[VAL_34:.*]] = arith.addi %[[VAL_25]], %[[VAL_5]] : index
// CHECK: %[[VAL_35:.*]] = arith.addi %[[VAL_26]], %[[VAL_5]] : index
// CHECK: %[[VAL_36:.*]]:2 = scf.while (%[[VAL_37:.*]] = %[[VAL_25]], %[[VAL_38:.*]] = %[[VAL_26]]) : (index, index) -> (index, index) {
// CHECK: %[[VAL_39:.*]] = arith.cmpi ult, %[[VAL_37]], %[[VAL_34]] : index
// CHECK: %[[VAL_40:.*]] = arith.cmpi ult, %[[VAL_38]], %[[VAL_35]] : index
// CHECK: %[[VAL_41:.*]] = arith.andi %[[VAL_39]], %[[VAL_40]] : i1
// CHECK: scf.condition(%[[VAL_41]]) %[[VAL_37]], %[[VAL_38]] : index, index
// CHECK: } do {
// CHECK: ^bb0(%[[VAL_42:.*]]: index, %[[VAL_43:.*]]: index):
// CHECK: %[[VAL_44:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_42]]] : memref<?xindex>
// CHECK: %[[VAL_45:.*]] = memref.load %[[VAL_12]]{{\[}}%[[VAL_43]]] : memref<?xindex>
// CHECK: %[[VAL_46:.*]] = arith.cmpi ult, %[[VAL_45]], %[[VAL_44]] : index
// CHECK: %[[VAL_47:.*]] = arith.select %[[VAL_46]], %[[VAL_45]], %[[VAL_44]] : index
// CHECK: %[[VAL_48:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index
// CHECK: %[[VAL_49:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index
// CHECK: %[[VAL_50:.*]] = arith.andi %[[VAL_48]], %[[VAL_49]] : i1
// CHECK: scf.if %[[VAL_50]] {
// CHECK: %[[VAL_51:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_42]]] : memref<?xf64>
// CHECK: %[[VAL_52:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_43]]] : memref<?xf64>
// CHECK: %[[VAL_53:.*]] = arith.mulf %[[VAL_51]], %[[VAL_52]] : f64
// CHECK: memref.store %[[VAL_53]], %[[VAL_14]]{{\[}}%[[VAL_30]], %[[VAL_47]]] : memref<32x64xf64>
// CHECK: } else {
// CHECK: }
// CHECK: %[[VAL_54:.*]] = arith.cmpi eq, %[[VAL_44]], %[[VAL_47]] : index
// CHECK: %[[VAL_55:.*]] = arith.addi %[[VAL_42]], %[[VAL_5]] : index
// CHECK: %[[VAL_56:.*]] = arith.select %[[VAL_54]], %[[VAL_55]], %[[VAL_42]] : index
// CHECK: %[[VAL_57:.*]] = arith.cmpi eq, %[[VAL_45]], %[[VAL_47]] : index
// CHECK: %[[VAL_58:.*]] = arith.addi %[[VAL_43]], %[[VAL_5]] : index
// CHECK: %[[VAL_59:.*]] = arith.select %[[VAL_57]], %[[VAL_58]], %[[VAL_43]] : index
// CHECK: scf.yield %[[VAL_56]], %[[VAL_59]] : index, index
// CHECK: }
// CHECK: } else {
// CHECK: }
// CHECK: %[[VAL_60:.*]] = arith.cmpi eq, %[[VAL_27]], %[[VAL_30]] : index
// CHECK: %[[VAL_61:.*]] = arith.addi %[[VAL_25]], %[[VAL_5]] : index
// CHECK: %[[VAL_62:.*]] = arith.select %[[VAL_60]], %[[VAL_61]], %[[VAL_25]] : index
// CHECK: %[[VAL_63:.*]] = arith.cmpi eq, %[[VAL_28]], %[[VAL_30]] : index
// CHECK: %[[VAL_64:.*]] = arith.addi %[[VAL_26]], %[[VAL_5]] : index
// CHECK: %[[VAL_65:.*]] = arith.select %[[VAL_63]], %[[VAL_64]], %[[VAL_26]] : index
// CHECK: scf.yield %[[VAL_62]], %[[VAL_65]] : index, index
// CHECK: }
// CHECK: %[[VAL_66:.*]] = bufferization.to_tensor %[[VAL_14]] : memref<32x64xf64>
// CHECK: return %[[VAL_66]] : tensor<32x64xf64>
// CHECK: }
func.func @mateltmul(%argx: tensor<32x64xf64, #SortedCOO>,
%argy: tensor<32x64xf64, #SortedCOO>,
%argz: tensor<32x64xf64>) -> tensor<32x64xf64> {
%0 = linalg.generic #trait_mul
ins(%argx, %argy : tensor<32x64xf64, #SortedCOO>, tensor<32x64xf64, #SortedCOO>)
outs(%argz: tensor<32x64xf64>) {
^bb(%x: f64, %y: f64, %z: f64):
%1 = arith.mulf %x, %y : f64
linalg.yield %1 : f64
} -> tensor<32x64xf64>
return %0 : tensor<32x64xf64>
}