From 1f3c482b76ef851c7a24f7787907b76382a2f432 Mon Sep 17 00:00:00 2001 From: Aart Bik Date: Mon, 14 Mar 2022 19:31:23 -0700 Subject: [PATCH] [mlir][sparse] more test cases for linalg.index Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D121660 --- .../Dialect/SparseTensor/CPU/sparse_index.mlir | 185 ++++++++++++++++++--- 1 file changed, 161 insertions(+), 24 deletions(-) diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir index 36a0521..cd5a5aee 100644 --- a/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir +++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_index.mlir @@ -3,30 +3,85 @@ // RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \ // RUN: FileCheck %s +#SparseVector = #sparse_tensor.encoding<{ + dimLevelType = ["compressed"] +}> + #SparseMatrix = #sparse_tensor.encoding<{ 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 = [ affine_map<(i,j) -> (i,j)>, // A affine_map<(i,j) -> (i,j)> // X (out) ], 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 { // - // 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>) - -> tensor<3x4xi64, #SparseMatrix> { + func @sparse_index_2d_conj(%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 + %r = linalg.generic #trait_2d ins(%arga: tensor<3x4xi64, #SparseMatrix>) outs(%init: tensor<3x4xi64, #SparseMatrix>) { ^bb(%a: i64, %x: i64): @@ -42,39 +97,121 @@ module { } // + // 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. // func @entry() { %c0 = arith.constant 0 : index - %c1 = arith.constant 1 : index - %c4 = arith.constant 4 : index %du = arith.constant -1 : i64 + // Setup input sparse vector. + %v1 = arith.constant sparse<[[2], [4]], [ 10, 20]> : tensor<8xi64> + %sv = sparse_tensor.convert %v1 : tensor<8xi64> to tensor<8xi64, #SparseVector> + + // Setup input "sparse" vector. + %v2 = arith.constant dense<[ 1, 2, 4, 8, 16, 32, 64, 128 ]> : tensor<8xi64> + %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> + // Setup input "sparse" matrix. - %d = arith.constant dense <[ - [ 1, 1, 1, 1 ], - [ 1, 1, 1, 1 ], - [ 1, 1, 1, 1 ] - ]> : tensor<3x4xi64> - %a = sparse_tensor.convert %d : tensor<3x4xi64> to 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 kernel. - %0 = call @sparse_index(%a) : (tensor<3x4xi64, #SparseMatrix>) -> 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. // - // 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 ) + // CHECK-NEXT: ( 0, 2, 8, 24, 64, 160, 384, 896 ) + // CHECK-NEXT: ( 1, 3, 6, 11, 20, 37, 70, 135 ) + // CHECK-NEXT: ( 10, 120, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1 ) + // 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 ) // - %x = sparse_tensor.convert %0 : tensor<3x4xi64, #SparseMatrix> to tensor<3x4xi64> - %m = bufferization.to_memref %x : memref<3x4xi64> - %v = vector.transfer_read %m[%c0, %c0], %du: memref<3x4xi64>, vector<3x4xi64> - vector.print %v : vector<3x4xi64> + %8 = sparse_tensor.values %0 : tensor<8xi64, #SparseVector> to memref + %9 = sparse_tensor.values %1 : tensor<8xi64, #SparseVector> to memref + %10 = sparse_tensor.values %2 : tensor<8xi64, #SparseVector> to memref + %11 = sparse_tensor.values %3 : tensor<8xi64, #SparseVector> to memref + %12 = sparse_tensor.values %4 : tensor<3x4xi64, #SparseMatrix> to memref + %13 = sparse_tensor.values %5 : tensor<3x4xi64, #SparseMatrix> to memref + %14 = sparse_tensor.values %6 : tensor<3x4xi64, #SparseMatrix> to memref + %15 = sparse_tensor.values %7 : tensor<3x4xi64, #SparseMatrix> to memref + %16 = vector.transfer_read %8[%c0], %du: memref, vector<8xi64> + %17 = vector.transfer_read %9[%c0], %du: memref, vector<8xi64> + %18 = vector.transfer_read %10[%c0], %du: memref, vector<8xi64> + %19 = vector.transfer_read %11[%c0], %du: memref, vector<8xi64> + %20 = vector.transfer_read %12[%c0], %du: memref, vector<12xi64> + %21 = vector.transfer_read %13[%c0], %du: memref, vector<12xi64> + %22 = vector.transfer_read %14[%c0], %du: memref, vector<12xi64> + %23 = vector.transfer_read %15[%c0], %du: memref, 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. - sparse_tensor.release %a : tensor<3x4xi64, #SparseMatrix> - sparse_tensor.release %0 : tensor<3x4xi64, #SparseMatrix> - memref.dealloc %m : memref<3x4xi64> + sparse_tensor.release %sv : tensor<8xi64, #SparseVector> + sparse_tensor.release %dv : tensor<8xi64, #SparseVector> + 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 } -- 2.7.4