2a07f0fd40
**Dense** `lvlTypes = [ "dense", "dense" ]` to `map = (d0, d1) -> (d0 : dense, d1 : dense)` `lvlTypes = [ "dense", "dense" ], dimToLvl = affine_map<(i,j) -> (j,i)>` to `map = (d0, d1) -> (d1 : dense, d0 : dense)` **DCSR** `lvlTypes = [ "compressed", "compressed" ]` to `map = (d0, d1) -> (d0 : compressed, d1 : compressed)` **DCSC** `lvlTypes = [ "compressed", "compressed" ], dimToLvl = affine_map<(i,j) -> (j,i)>` to `map = (d0, d1) -> (d1 : compressed, d0 : compressed)` **Block Row** `lvlTypes = [ "compressed", "dense" ]` to `map = (d0, d1) -> (d0 : compressed, d1 : dense)` **Block Column** `lvlTypes = [ "compressed", "dense" ], dimToLvl = affine_map<(i,j) -> (j,i)>` to `map = (d0, d1) -> (d1 : compressed, d0 : dense)` This is an ongoing effort: #66146, #66309
32 lines
925 B
MLIR
32 lines
925 B
MLIR
// RUN: mlir-opt %s -sparse-compiler="vl=8" | FileCheck %s
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#Dense = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : dense, d1 : dense)
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}>
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#matvec = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)>, // A
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affine_map<(i,j) -> (j)>, // b
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affine_map<(i,j) -> (i)> // x (out)
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],
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iterator_types = ["parallel", "reduction"],
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doc = "X(i) += A(i,j) * B(j)"
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}
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// CHECK-LABEL: llvm.func @kernel_matvec
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// CHECK: llvm.intr.vector.reduce.fadd
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func.func @kernel_matvec(%arga: tensor<?x?xf32, #Dense>,
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%argb: tensor<?xf32>,
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%argx: tensor<?xf32>) -> tensor<?xf32> {
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%x = linalg.generic #matvec
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ins(%arga, %argb: tensor<?x?xf32, #Dense>, tensor<?xf32>)
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outs(%argx: tensor<?xf32>) {
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^bb(%a: f32, %b: f32, %x: f32):
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%0 = arith.mulf %a, %b : f32
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%1 = arith.addf %x, %0 : f32
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linalg.yield %1 : f32
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} -> tensor<?xf32>
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return %x : tensor<?xf32>
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}
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