eb177803bf
1. Remove the trailing comma for the last element of memref and add closing parenthesis. 2. Change integration tests to use the new format.
168 lines
6.7 KiB
MLIR
168 lines
6.7 KiB
MLIR
//--------------------------------------------------------------------------------------------------
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// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
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//
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// Set-up that's shared across all tests in this directory. In principle, this
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// config could be moved to lit.local.cfg. However, there are downstream users that
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// do not use these LIT config files. Hence why this is kept inline.
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//
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// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
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// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
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// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
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// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
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// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
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// DEFINE: %{run_opts} = -e main -entry-point-result=void
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// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
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// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs}
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//
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// DEFINE: %{env} =
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//--------------------------------------------------------------------------------------------------
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// RUN: %{compile} | %{run} | FileCheck %s
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//
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// Do the same run, but now with direct IR generation.
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// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true
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// RUN: %{compile} | %{run} | FileCheck %s
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//
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// Do the same run, but now with direct IR generation and vectorization.
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// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
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// RUN: %{compile} | %{run} | FileCheck %s
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//
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// Do the same run, but now with direct IR generation and VLA vectorization.
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// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
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#DCSR = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : compressed, d1 : compressed)
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}>
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#trait = {
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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) -> (i,j)>, // B
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affine_map<(i,j) -> (i,j)> // x (out)
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],
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iterator_types = ["parallel", "parallel"],
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doc = "X(i, j) = cmp A(i,j) B(i, j)"
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}
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//
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// Integration test that lowers a kernel annotated as sparse to
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// actual sparse code, initializes a matching sparse storage scheme
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// from file, and runs the resulting code with the JIT compiler.
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//
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module {
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func.func @cmp_all_dense(%arga: tensor<4x4xf64>,
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%argb: tensor<4x4xf64>,
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%argx: tensor<4x4xi8>) -> tensor<4x4xi8> {
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%0 = linalg.generic #trait
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ins(%arga, %argb: tensor<4x4xf64>, tensor<4x4xf64>)
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outs(%argx: tensor<4x4xi8>) {
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^bb(%a: f64, %b: f64, %x: i8):
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%0 = arith.cmpf ult, %a, %b : f64
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%1 = arith.extui %0 : i1 to i8
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linalg.yield %1 : i8
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} -> tensor<4x4xi8>
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return %0 : tensor<4x4xi8>
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}
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func.func @cmp_lhs_sparse(%arga: tensor<4x4xf64, #DCSR>,
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%argb: tensor<4x4xf64>) -> tensor<4x4xi8, #DCSR> {
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%argx = tensor.empty() : tensor<4x4xi8, #DCSR>
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%0 = linalg.generic #trait
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ins(%arga, %argb: tensor<4x4xf64, #DCSR>, tensor<4x4xf64>)
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outs(%argx: tensor<4x4xi8, #DCSR>) {
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^bb(%a: f64, %b: f64, %x: i8):
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%0 = arith.cmpf ult, %a, %b : f64
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%1 = arith.extui %0 : i1 to i8
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linalg.yield %1 : i8
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} -> tensor<4x4xi8, #DCSR>
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return %0 : tensor<4x4xi8, #DCSR>
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}
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func.func @cmp_all_sparse(%arga: tensor<4x4xf64, #DCSR>,
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%argb: tensor<4x4xf64, #DCSR>) -> tensor<4x4xi8, #DCSR> {
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%argx = tensor.empty() : tensor<4x4xi8, #DCSR>
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%0 = linalg.generic #trait
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ins(%arga, %argb: tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>)
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outs(%argx: tensor<4x4xi8, #DCSR>) {
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^bb(%a: f64, %b: f64, %x: i8):
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%0 = arith.cmpf ult, %a, %b : f64
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%1 = arith.extui %0 : i1 to i8
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linalg.yield %1 : i8
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} -> tensor<4x4xi8, #DCSR>
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return %0 : tensor<4x4xi8, #DCSR>
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}
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//
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// Main driver that constructs matrix and calls the sparse kernel to perform
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// element-wise comparison.
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//
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func.func @main() {
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%d0 = arith.constant 0 : i8
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%c0 = arith.constant 0 : index
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%lhs_dn = arith.constant dense<
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[ [ 0.0, 0.0, 1.5, 1.0],
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[ 0.0, 3.5, 0.0, 0.0],
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[ 1.0, 5.0, 2.0, 0.0],
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[ 1.0, 0.5, 0.0, 0.0] ]> : tensor<4x4xf64>
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%rhs_dn = arith.constant dense<
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[ [ 0.0, 1.5, 1.0, 1.5],
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[ 3.5, 0.0, 0.0, 0.0],
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[ 5.0, 2.0, 0.0, 2.0],
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[ 0.5, 0.0, 0.0, 0.0] ]> : tensor<4x4xf64>
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%lhs_sp = sparse_tensor.convert %lhs_dn : tensor<4x4xf64> to tensor<4x4xf64, #DCSR>
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%rhs_sp = sparse_tensor.convert %rhs_dn : tensor<4x4xf64> to tensor<4x4xf64, #DCSR>
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%output = arith.constant dense<0> : tensor<4x4xi8>
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%all_dn_out = call @cmp_all_dense(%lhs_dn, %rhs_dn, %output)
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: (tensor<4x4xf64>, tensor<4x4xf64>, tensor<4x4xi8>) -> tensor<4x4xi8>
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%lhs_sp_out = call @cmp_lhs_sparse(%lhs_sp, %rhs_dn)
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: (tensor<4x4xf64, #DCSR>, tensor<4x4xf64>) -> tensor<4x4xi8, #DCSR>
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%all_sp_out = call @cmp_all_sparse(%lhs_sp, %rhs_sp)
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: (tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>) -> tensor<4x4xi8, #DCSR>
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//
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// All should have the same boolean values.
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//
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// CHECK: ( ( 0, 1, 0, 1 ), ( 1, 0, 0, 0 ), ( 1, 0, 0, 1 ), ( 0, 0, 0, 0 ) )
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//
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// CHECK: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 16
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// CHECK-NEXT: dim = ( 4, 4 )
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// CHECK-NEXT: lvl = ( 4, 4 )
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// CHECK-NEXT: pos[0] : ( 0, 4 )
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// CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )
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// CHECK-NEXT: pos[1] : ( 0, 4, 8, 12, 16 )
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// CHECK-NEXT: crd[1] : ( 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3 )
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// CHECK-NEXT: values : ( 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0 )
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// CHECK-NEXT: ----
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//
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// CHECK: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 11
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// CHECK-NEXT: dim = ( 4, 4 )
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// CHECK-NEXT: lvl = ( 4, 4 )
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// CHECK-NEXT: pos[0] : ( 0, 4 )
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// CHECK-NEXT: crd[0] : ( 0, 1, 2, 3 )
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// CHECK-NEXT: pos[1] : ( 0, 3, 5, 9, 11 )
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// CHECK-NEXT: crd[1] : ( 1, 2, 3, 0, 1, 0, 1, 2, 3, 0, 1 )
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// CHECK-NEXT: values : ( 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0 )
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// CHECK-NEXT: ----
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//
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%v = vector.transfer_read %all_dn_out[%c0, %c0], %d0
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: tensor<4x4xi8>, vector<4x4xi8>
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vector.print %v : vector<4x4xi8>
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sparse_tensor.print %lhs_sp_out : tensor<4x4xi8, #DCSR>
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sparse_tensor.print %all_sp_out : tensor<4x4xi8, #DCSR>
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bufferization.dealloc_tensor %lhs_sp : tensor<4x4xf64, #DCSR>
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bufferization.dealloc_tensor %rhs_sp : tensor<4x4xf64, #DCSR>
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bufferization.dealloc_tensor %all_dn_out : tensor<4x4xi8>
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bufferization.dealloc_tensor %lhs_sp_out : tensor<4x4xi8, #DCSR>
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bufferization.dealloc_tensor %all_sp_out : tensor<4x4xi8, #DCSR>
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return
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}
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}
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