128 lines
4.8 KiB
MLIR
128 lines
4.8 KiB
MLIR
// DEFINE: %{option} = enable-runtime-library=true
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// DEFINE: %{command} = mlir-opt %s --sparse-compiler=%{option} | \
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// DEFINE: mlir-cpu-runner \
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// DEFINE: -e entry -entry-point-result=void \
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// DEFINE: -shared-libs=%mlir_lib_dir/libmlir_c_runner_utils%shlibext | \
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// DEFINE: FileCheck %s
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//
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// RUN: %{command}
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//
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// Do the same run, but now with direct IR generation.
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// REDEFINE: %{option} = enable-runtime-library=false
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// RUN: %{command}
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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: %{command}
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#SparseVector = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
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#trait_op = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // a
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affine_map<(i) -> (i)> // x (out)
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],
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iterator_types = ["parallel"],
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doc = "x(i) = OP a(i)"
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}
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module {
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func.func @sparse_absf(%arg0: tensor<?xf64, #SparseVector>)
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-> tensor<?xf64, #SparseVector> {
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%c0 = arith.constant 0 : index
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%d = tensor.dim %arg0, %c0 : tensor<?xf64, #SparseVector>
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%xin = bufferization.alloc_tensor(%d) : tensor<?xf64, #SparseVector>
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%0 = linalg.generic #trait_op
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ins(%arg0: tensor<?xf64, #SparseVector>)
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outs(%xin: tensor<?xf64, #SparseVector>) {
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^bb0(%a: f64, %x: f64) :
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%result = math.absf %a : f64
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linalg.yield %result : f64
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} -> tensor<?xf64, #SparseVector>
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return %0 : tensor<?xf64, #SparseVector>
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}
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func.func @sparse_absi(%arg0: tensor<?xi32, #SparseVector>)
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-> tensor<?xi32, #SparseVector> {
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%c0 = arith.constant 0 : index
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%d = tensor.dim %arg0, %c0 : tensor<?xi32, #SparseVector>
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%xin = bufferization.alloc_tensor(%d) : tensor<?xi32, #SparseVector>
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%0 = linalg.generic #trait_op
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ins(%arg0: tensor<?xi32, #SparseVector>)
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outs(%xin: tensor<?xi32, #SparseVector>) {
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^bb0(%a: i32, %x: i32) :
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%result = math.absi %a : i32
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linalg.yield %result : i32
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} -> tensor<?xi32, #SparseVector>
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return %0 : tensor<?xi32, #SparseVector>
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}
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// Driver method to call and verify sign kernel.
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func.func @entry() {
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%c0 = arith.constant 0 : index
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%df = arith.constant 99.99 : f64
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%di = arith.constant 9999 : i32
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%pnan = arith.constant 0x7FF0000001000000 : f64
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%nnan = arith.constant 0xFFF0000001000000 : f64
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%pinf = arith.constant 0x7FF0000000000000 : f64
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%ninf = arith.constant 0xFFF0000000000000 : f64
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// Setup sparse vectors.
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%v1 = arith.constant sparse<
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[ [0], [3], [5], [11], [13], [17], [18], [20], [21], [28], [29], [31] ],
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[ -1.5, 1.5, -10.2, 11.3, 1.0, -1.0,
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0x7FF0000001000000, // +NaN
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0xFFF0000001000000, // -NaN
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0x7FF0000000000000, // +Inf
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0xFFF0000000000000, // -Inf
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-0.0, // -Zero
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0.0 // +Zero
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]
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> : tensor<32xf64>
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%v2 = arith.constant sparse<
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[ [0], [3], [5], [11], [13], [17], [18], [21], [31] ],
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[ -2147483648, -2147483647, -1000, -1, 0,
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1, 1000, 2147483646, 2147483647
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]
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> : tensor<32xi32>
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%sv1 = sparse_tensor.convert %v1
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: tensor<32xf64> to tensor<?xf64, #SparseVector>
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%sv2 = sparse_tensor.convert %v2
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: tensor<32xi32> to tensor<?xi32, #SparseVector>
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// Call abs kernels.
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%0 = call @sparse_absf(%sv1) : (tensor<?xf64, #SparseVector>)
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-> tensor<?xf64, #SparseVector>
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%1 = call @sparse_absi(%sv2) : (tensor<?xi32, #SparseVector>)
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-> tensor<?xi32, #SparseVector>
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//
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// Verify the results.
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//
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// CHECK: 12
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// CHECK-NEXT: ( 1.5, 1.5, 10.2, 11.3, 1, 1, nan, nan, inf, inf, 0, 0 )
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// CHECK-NEXT: 9
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// CHECK-NEXT: ( -2147483648, 2147483647, 1000, 1, 0, 1, 1000, 2147483646, 2147483647 )
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//
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%x = sparse_tensor.values %0 : tensor<?xf64, #SparseVector> to memref<?xf64>
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%y = sparse_tensor.values %1 : tensor<?xi32, #SparseVector> to memref<?xi32>
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%a = vector.transfer_read %x[%c0], %df: memref<?xf64>, vector<12xf64>
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%b = vector.transfer_read %y[%c0], %di: memref<?xi32>, vector<9xi32>
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%na = sparse_tensor.number_of_entries %0 : tensor<?xf64, #SparseVector>
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%nb = sparse_tensor.number_of_entries %1 : tensor<?xi32, #SparseVector>
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vector.print %na : index
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vector.print %a : vector<12xf64>
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vector.print %nb : index
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vector.print %b : vector<9xi32>
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// Release the resources.
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bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
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bufferization.dealloc_tensor %sv2 : tensor<?xi32, #SparseVector>
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bufferization.dealloc_tensor %0 : tensor<?xf64, #SparseVector>
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bufferization.dealloc_tensor %1 : tensor<?xi32, #SparseVector>
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return
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}
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}
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