147 lines
5.4 KiB
MLIR
147 lines
5.4 KiB
MLIR
// DEFINE: %{option} = "enable-runtime-library=false"
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// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
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// DEFINE: %{run} = mlir-cpu-runner \
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// DEFINE: -e entry -entry-point-result=void \
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// DEFINE: -shared-libs=%mlir_c_runner_utils | \
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// DEFINE: FileCheck %s
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//
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// RUN: %{compile} | %{run}
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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: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
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// RUN: %{compile} | %{run}
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// Do the same run, but now with direct IR generation and, if available, VLA
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// vectorization.
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// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
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// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
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// REDEFINE: --entry-function=entry_lli \
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// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
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// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
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// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
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// REDEFINE: FileCheck %s
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// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
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#DCSR = #sparse_tensor.encoding<{
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lvlTypes = [ "compressed", "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 = bufferization.alloc_tensor() : 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 = bufferization.alloc_tensor() : 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 @entry() {
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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 result.
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//
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// CHECK-COUNT-3: ( ( 0, 1, 0, 1 ), ( 1, 0, 0, 0 ), ( 1, 0, 0, 1 ), ( 0, 0, 0, 0 ) )
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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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%lhs_sp_ret = sparse_tensor.convert %lhs_sp_out
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: tensor<4x4xi8, #DCSR> to tensor<4x4xi8>
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%v1 = vector.transfer_read %lhs_sp_ret[%c0, %c0], %d0
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: tensor<4x4xi8>, vector<4x4xi8>
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vector.print %v1 : vector<4x4xi8>
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%rhs_sp_ret = sparse_tensor.convert %all_sp_out
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: tensor<4x4xi8, #DCSR> to tensor<4x4xi8>
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%v2 = vector.transfer_read %rhs_sp_ret[%c0, %c0], %d0
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: tensor<4x4xi8>, vector<4x4xi8>
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vector.print %v2 : vector<4x4xi8>
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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 %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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