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Differential D121660

[mlir][sparse] more test cases for linalg.index
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Authored by aartbik on Mar 14 2022, 7:35 PM.

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rG1f3c482b76ef: [mlir][sparse] more test cases for linalg.index

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Closed by commit rG1f3c482b76ef: [mlir][sparse] more test cases for linalg.index (authored by aartbik). · Explain WhyMar 15 2022, 10:37 AM
This revision was automatically updated to reflect the committed changes.
aartbik added a commit: rG1f3c482b76ef: [mlir][sparse] more test cases for linalg.index.

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mlir/
test/
Integration/
Dialect/
SparseTensor/
CPU/
185 lines

Diff 415496

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

// RUN: mlir-opt %s --sparse-compiler | \ // RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner -e entry -entry-point-result=void \ // RUN: mlir-cpu-runner -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s // RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
}>
#SparseMatrix = #sparse_tensor.encoding<{ #SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"] dimLevelType = ["compressed", "compressed"]
}> }>
#trait = { #trait_1d = {
indexing_maps = [
affine_map<(i) -> (i)>, // a
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "X(i) = a(i) op i"
}
#trait_2d = {
indexing_maps = [ indexing_maps = [
affine_map<(i,j) -> (i,j)>, // A affine_map<(i,j) -> (i,j)>, // A
affine_map<(i,j) -> (i,j)> // X (out) affine_map<(i,j) -> (i,j)> // X (out)
], ],
iterator_types = ["parallel", "parallel"], iterator_types = ["parallel", "parallel"],
doc = "X(i,j) = A(i,j) * i * j" doc = "X(i,j) = A(i,j) op i op j"
} }
//
// Test with indices. Note that a lot of results are actually
// dense, but this is done to stress test all the operations.
//
module { module {
// //
// Kernel that uses indices in the index notation. // Kernel that uses index in the index notation (conjunction).
//
func @sparse_index_1d_conj(%arga: tensor<8xi64, #SparseVector>)
-> tensor<8xi64, #SparseVector> {
%d0 = arith.constant 8 : index
%init = sparse_tensor.init [%d0] : tensor<8xi64, #SparseVector>
%r = linalg.generic #trait_1d
ins(%arga: tensor<8xi64, #SparseVector>)
outs(%init: tensor<8xi64, #SparseVector>) {
^bb(%a: i64, %x: i64):
%i = linalg.index 0 : index
%ii = arith.index_cast %i : index to i64
%m1 = arith.muli %a, %ii : i64
linalg.yield %m1 : i64
} -> tensor<8xi64, #SparseVector>
return %r : tensor<8xi64, #SparseVector>
}
//
// Kernel that uses index in the index notation (disjunction).
//
func @sparse_index_1d_disj(%arga: tensor<8xi64, #SparseVector>)
-> tensor<8xi64, #SparseVector> {
%d0 = arith.constant 8 : index
%init = sparse_tensor.init [%d0] : tensor<8xi64, #SparseVector>
%r = linalg.generic #trait_1d
ins(%arga: tensor<8xi64, #SparseVector>)
outs(%init: tensor<8xi64, #SparseVector>) {
^bb(%a: i64, %x: i64):
%i = linalg.index 0 : index
%ii = arith.index_cast %i : index to i64
%m1 = arith.addi %a, %ii : i64
linalg.yield %m1 : i64
} -> tensor<8xi64, #SparseVector>
return %r : tensor<8xi64, #SparseVector>
}
//
// Kernel that uses indices in the index notation (conjunction).
// //
func @sparse_index(%arga: tensor<3x4xi64, #SparseMatrix>) func @sparse_index_2d_conj(%arga: tensor<3x4xi64, #SparseMatrix>)
-> tensor<3x4xi64, #SparseMatrix> { -> tensor<3x4xi64, #SparseMatrix> {
%d0 = arith.constant 3 : index %d0 = arith.constant 3 : index
%d1 = arith.constant 4 : index %d1 = arith.constant 4 : index
%init = sparse_tensor.init [%d0, %d1] : tensor<3x4xi64, #SparseMatrix> %init = sparse_tensor.init [%d0, %d1] : tensor<3x4xi64, #SparseMatrix>
%r = linalg.generic #trait %r = linalg.generic #trait_2d
ins(%arga: tensor<3x4xi64, #SparseMatrix>) ins(%arga: tensor<3x4xi64, #SparseMatrix>)
outs(%init: tensor<3x4xi64, #SparseMatrix>) { outs(%init: tensor<3x4xi64, #SparseMatrix>) {
^bb(%a: i64, %x: i64): ^bb(%a: i64, %x: i64):
%i = linalg.index 0 : index %i = linalg.index 0 : index
%j = linalg.index 1 : index %j = linalg.index 1 : index
%ii = arith.index_cast %i : index to i64 %ii = arith.index_cast %i : index to i64
%jj = arith.index_cast %j : index to i64 %jj = arith.index_cast %j : index to i64
%m1 = arith.muli %ii, %a : i64 %m1 = arith.muli %ii, %a : i64
%m2 = arith.muli %jj, %m1 : i64 %m2 = arith.muli %jj, %m1 : i64
linalg.yield %m2 : i64 linalg.yield %m2 : i64
} -> tensor<3x4xi64, #SparseMatrix> } -> tensor<3x4xi64, #SparseMatrix>
return %r : tensor<3x4xi64, #SparseMatrix> return %r : tensor<3x4xi64, #SparseMatrix>
} }
// //
// Kernel that uses indices in the index notation (disjunction).
//
func @sparse_index_2d_disj(%arga: tensor<3x4xi64, #SparseMatrix>)
-> tensor<3x4xi64, #SparseMatrix> {
%d0 = arith.constant 3 : index
%d1 = arith.constant 4 : index
%init = sparse_tensor.init [%d0, %d1] : tensor<3x4xi64, #SparseMatrix>
%r = linalg.generic #trait_2d
ins(%arga: tensor<3x4xi64, #SparseMatrix>)
outs(%init: tensor<3x4xi64, #SparseMatrix>) {
^bb(%a: i64, %x: i64):
%i = linalg.index 0 : index
%j = linalg.index 1 : index
%ii = arith.index_cast %i : index to i64
%jj = arith.index_cast %j : index to i64
%m1 = arith.addi %ii, %a : i64
%m2 = arith.addi %jj, %m1 : i64
linalg.yield %m2 : i64
} -> tensor<3x4xi64, #SparseMatrix>
return %r : tensor<3x4xi64, #SparseMatrix>
}
//
// Main driver. // Main driver.
// //
func @entry() { func @entry() {
%c0 = arith.constant 0 : index %c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c4 = arith.constant 4 : index
%du = arith.constant -1 : i64 %du = arith.constant -1 : i64
// Setup input "sparse" matrix. // Setup input sparse vector.
%d = arith.constant dense <[ %v1 = arith.constant sparse<[[2], [4]], [ 10, 20]> : tensor<8xi64>
[ 1, 1, 1, 1 ], %sv = sparse_tensor.convert %v1 : tensor<8xi64> to tensor<8xi64, #SparseVector>
[ 1, 1, 1, 1 ],
[ 1, 1, 1, 1 ] // Setup input "sparse" vector.
]> : tensor<3x4xi64> %v2 = arith.constant dense<[ 1, 2, 4, 8, 16, 32, 64, 128 ]> : tensor<8xi64>
%a = sparse_tensor.convert %d : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix> %dv = sparse_tensor.convert %v2 : tensor<8xi64> to tensor<8xi64, #SparseVector>
// Setup input sparse matrix.
%m1 = arith.constant sparse<[[1,1], [2,3]], [10, 20]> : tensor<3x4xi64>
%sm = sparse_tensor.convert %m1 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix>
// Call the kernel. // Setup input "sparse" matrix.
%0 = call @sparse_index(%a) : (tensor<3x4xi64, #SparseMatrix>) -> tensor<3x4xi64, #SparseMatrix> %m2 = arith.constant dense <[ [ 1, 1, 1, 1 ],
[ 1, 2, 1, 1 ],
[ 1, 1, 3, 4 ] ]> : tensor<3x4xi64>
%dm = sparse_tensor.convert %m2 : tensor<3x4xi64> to tensor<3x4xi64, #SparseMatrix>
// Call the kernels.
%0 = call @sparse_index_1d_conj(%sv) : (tensor<8xi64, #SparseVector>)
-> tensor<8xi64, #SparseVector>
%1 = call @sparse_index_1d_disj(%sv) : (tensor<8xi64, #SparseVector>)
-> tensor<8xi64, #SparseVector>
%2 = call @sparse_index_1d_conj(%dv) : (tensor<8xi64, #SparseVector>)
-> tensor<8xi64, #SparseVector>
%3 = call @sparse_index_1d_disj(%dv) : (tensor<8xi64, #SparseVector>)
-> tensor<8xi64, #SparseVector>
%4 = call @sparse_index_2d_conj(%sm) : (tensor<3x4xi64, #SparseMatrix>)
-> tensor<3x4xi64, #SparseMatrix>
%5 = call @sparse_index_2d_disj(%sm) : (tensor<3x4xi64, #SparseMatrix>)
-> tensor<3x4xi64, #SparseMatrix>
%6 = call @sparse_index_2d_conj(%dm) : (tensor<3x4xi64, #SparseMatrix>)
-> tensor<3x4xi64, #SparseMatrix>
%7 = call @sparse_index_2d_disj(%dm) : (tensor<3x4xi64, #SparseMatrix>)
-> tensor<3x4xi64, #SparseMatrix>
// //
// Verify result. // Verify result.
// //
// CHECK: ( ( 0, 0, 0, 0 ), ( 0, 1, 2, 3 ), ( 0, 2, 4, 6 ) ) // CHECK: ( 20, 80, -1, -1, -1, -1, -1, -1 )
// // CHECK-NEXT: ( 0, 1, 12, 3, 24, 5, 6, 7 )
%x = sparse_tensor.convert %0 : tensor<3x4xi64, #SparseMatrix> to tensor<3x4xi64> // CHECK-NEXT: ( 0, 2, 8, 24, 64, 160, 384, 896 )
%m = bufferization.to_memref %x : memref<3x4xi64> // CHECK-NEXT: ( 1, 3, 6, 11, 20, 37, 70, 135 )
%v = vector.transfer_read %m[%c0, %c0], %du: memref<3x4xi64>, vector<3x4xi64> // CHECK-NEXT: ( 10, 120, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 )
vector.print %v : vector<3x4xi64> // CHECK-NEXT: ( 0, 1, 2, 3, 1, 12, 3, 4, 2, 3, 4, 25 )
// CHECK-NEXT: ( 0, 0, 0, 0, 0, 2, 2, 3, 0, 2, 12, 24 )
// CHECK-NEXT: ( 1, 2, 3, 4, 2, 4, 4, 5, 3, 4, 7, 9 )
//
%8 = sparse_tensor.values %0 : tensor<8xi64, #SparseVector> to memref<?xi64>
%9 = sparse_tensor.values %1 : tensor<8xi64, #SparseVector> to memref<?xi64>
%10 = sparse_tensor.values %2 : tensor<8xi64, #SparseVector> to memref<?xi64>
%11 = sparse_tensor.values %3 : tensor<8xi64, #SparseVector> to memref<?xi64>
%12 = sparse_tensor.values %4 : tensor<3x4xi64, #SparseMatrix> to memref<?xi64>
%13 = sparse_tensor.values %5 : tensor<3x4xi64, #SparseMatrix> to memref<?xi64>
%14 = sparse_tensor.values %6 : tensor<3x4xi64, #SparseMatrix> to memref<?xi64>
%15 = sparse_tensor.values %7 : tensor<3x4xi64, #SparseMatrix> to memref<?xi64>
%16 = vector.transfer_read %8[%c0], %du: memref<?xi64>, vector<8xi64>
%17 = vector.transfer_read %9[%c0], %du: memref<?xi64>, vector<8xi64>
%18 = vector.transfer_read %10[%c0], %du: memref<?xi64>, vector<8xi64>
%19 = vector.transfer_read %11[%c0], %du: memref<?xi64>, vector<8xi64>
%20 = vector.transfer_read %12[%c0], %du: memref<?xi64>, vector<12xi64>
%21 = vector.transfer_read %13[%c0], %du: memref<?xi64>, vector<12xi64>
%22 = vector.transfer_read %14[%c0], %du: memref<?xi64>, vector<12xi64>
%23 = vector.transfer_read %15[%c0], %du: memref<?xi64>, vector<12xi64>
vector.print %16 : vector<8xi64>
vector.print %17 : vector<8xi64>
vector.print %18 : vector<8xi64>
vector.print %19 : vector<8xi64>
vector.print %20 : vector<12xi64>
vector.print %21 : vector<12xi64>
vector.print %22 : vector<12xi64>
vector.print %23 : vector<12xi64>
// Release resources. // Release resources.
sparse_tensor.release %a : tensor<3x4xi64, #SparseMatrix> sparse_tensor.release %sv : tensor<8xi64, #SparseVector>
sparse_tensor.release %0 : tensor<3x4xi64, #SparseMatrix> sparse_tensor.release %dv : tensor<8xi64, #SparseVector>
memref.dealloc %m : memref<3x4xi64> sparse_tensor.release %0 : tensor<8xi64, #SparseVector>
sparse_tensor.release %1 : tensor<8xi64, #SparseVector>
sparse_tensor.release %2 : tensor<8xi64, #SparseVector>
sparse_tensor.release %3 : tensor<8xi64, #SparseVector>
sparse_tensor.release %sm : tensor<3x4xi64, #SparseMatrix>
sparse_tensor.release %dm : tensor<3x4xi64, #SparseMatrix>
sparse_tensor.release %4 : tensor<3x4xi64, #SparseMatrix>
sparse_tensor.release %5 : tensor<3x4xi64, #SparseMatrix>
sparse_tensor.release %6 : tensor<3x4xi64, #SparseMatrix>
sparse_tensor.release %7 : tensor<3x4xi64, #SparseMatrix>
return return
} }
} }