16b8f4ddae
We have several ways to materialize sparse tensors (new and convert) but no explicit operation to release the underlying sparse storage scheme at runtime (other than making an explicit delSparseTensor() library call). To simplify memory management, a sparse_tensor.release operation has been introduced that lowers to the runtime library call while keeping tensors, opague pointers, and memrefs transparent in the initial IR. *Note* There is obviously some tension between the concept of immutable tensors and memory management methods. This tension is addressed by simply stating that after the "release" call, no further memref related operations are allowed on the tensor value. We expect the design to evolve over time, however, and arrive at a more satisfactory view of tensors and buffers eventually. Bug: http://llvm.org/pr52046 Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D111099
235 lines
8.1 KiB
MLIR
235 lines
8.1 KiB
MLIR
// RUN: mlir-opt %s \
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// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \
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// RUN: --sparsification --sparse-tensor-conversion \
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// RUN: --convert-vector-to-scf --convert-scf-to-std \
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// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
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// RUN: --std-bufferize --finalizing-bufferize --lower-affine \
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// RUN: --convert-vector-to-llvm --convert-memref-to-llvm \
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// RUN: --convert-std-to-llvm --reconcile-unrealized-casts | \
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// RUN: mlir-cpu-runner \
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// RUN: -e entry -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
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// RUN: FileCheck %s
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//
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// Do the same run, but now with SIMDization as well. This should not change the outcome.
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//
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// RUN: mlir-opt %s \
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// RUN: --linalg-generalize-named-ops --linalg-fuse-elementwise-ops \
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// RUN: --sparsification="vectorization-strategy=2 vl=8" --sparse-tensor-conversion \
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// RUN: --convert-vector-to-scf --convert-scf-to-std \
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// RUN: --func-bufferize --tensor-constant-bufferize --tensor-bufferize \
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// RUN: --std-bufferize --finalizing-bufferize --lower-affine \
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// RUN: --convert-vector-to-llvm --convert-memref-to-llvm \
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// RUN: --convert-std-to-llvm --reconcile-unrealized-casts | \
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// RUN: mlir-cpu-runner \
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// RUN: -e entry -entry-point-result=void \
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// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
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// RUN: FileCheck %s
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#SV = #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ] }>
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#DV = #sparse_tensor.encoding<{ dimLevelType = [ "dense" ] }>
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#trait_reduction = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // a
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affine_map<(i) -> ()> // x (scalar out)
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],
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iterator_types = ["reduction"],
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doc = "x += OPER_i a(i)"
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}
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// An example of vector reductions.
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module {
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func @sum_reduction_i32(%arga: tensor<32xi32, #SV>,
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%argx: tensor<i32>) -> tensor<i32> {
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%0 = linalg.generic #trait_reduction
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ins(%arga: tensor<32xi32, #SV>)
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outs(%argx: tensor<i32>) {
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^bb(%a: i32, %x: i32):
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%0 = addi %x, %a : i32
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linalg.yield %0 : i32
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} -> tensor<i32>
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return %0 : tensor<i32>
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}
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func @sum_reduction_f32(%arga: tensor<32xf32, #SV>,
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%argx: tensor<f32>) -> tensor<f32> {
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%0 = linalg.generic #trait_reduction
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ins(%arga: tensor<32xf32, #SV>)
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outs(%argx: tensor<f32>) {
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^bb(%a: f32, %x: f32):
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%0 = addf %x, %a : f32
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linalg.yield %0 : f32
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} -> tensor<f32>
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return %0 : tensor<f32>
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}
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func @prod_reduction_i32(%arga: tensor<32xi32, #DV>,
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%argx: tensor<i32>) -> tensor<i32> {
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%0 = linalg.generic #trait_reduction
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ins(%arga: tensor<32xi32, #DV>)
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outs(%argx: tensor<i32>) {
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^bb(%a: i32, %x: i32):
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%0 = muli %x, %a : i32
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linalg.yield %0 : i32
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} -> tensor<i32>
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return %0 : tensor<i32>
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}
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func @prod_reduction_f32(%arga: tensor<32xf32, #DV>,
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%argx: tensor<f32>) -> tensor<f32> {
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%0 = linalg.generic #trait_reduction
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ins(%arga: tensor<32xf32, #DV>)
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outs(%argx: tensor<f32>) {
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^bb(%a: f32, %x: f32):
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%0 = mulf %x, %a : f32
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linalg.yield %0 : f32
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} -> tensor<f32>
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return %0 : tensor<f32>
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}
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func @and_reduction_i32(%arga: tensor<32xi32, #DV>,
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%argx: tensor<i32>) -> tensor<i32> {
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%0 = linalg.generic #trait_reduction
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ins(%arga: tensor<32xi32, #DV>)
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outs(%argx: tensor<i32>) {
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^bb(%a: i32, %x: i32):
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%0 = and %x, %a : i32
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linalg.yield %0 : i32
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} -> tensor<i32>
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return %0 : tensor<i32>
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}
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func @or_reduction_i32(%arga: tensor<32xi32, #SV>,
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%argx: tensor<i32>) -> tensor<i32> {
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%0 = linalg.generic #trait_reduction
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ins(%arga: tensor<32xi32, #SV>)
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outs(%argx: tensor<i32>) {
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^bb(%a: i32, %x: i32):
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%0 = or %x, %a : i32
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linalg.yield %0 : i32
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} -> tensor<i32>
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return %0 : tensor<i32>
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}
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func @xor_reduction_i32(%arga: tensor<32xi32, #SV>,
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%argx: tensor<i32>) -> tensor<i32> {
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%0 = linalg.generic #trait_reduction
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ins(%arga: tensor<32xi32, #SV>)
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outs(%argx: tensor<i32>) {
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^bb(%a: i32, %x: i32):
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%0 = xor %x, %a : i32
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linalg.yield %0 : i32
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} -> tensor<i32>
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return %0 : tensor<i32>
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}
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func @dump_i32(%arg0 : memref<i32>) {
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%v = memref.load %arg0[] : memref<i32>
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vector.print %v : i32
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return
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}
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func @dump_f32(%arg0 : memref<f32>) {
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%v = memref.load %arg0[] : memref<f32>
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vector.print %v : f32
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return
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}
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func @entry() {
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%ri = constant dense< 7 > : tensor<i32>
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%rf = constant dense< 2.0 > : tensor<f32>
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%c_0_i32 = constant dense<[
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0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 4, 0, 0, 0,
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0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 9, 0
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]> : tensor<32xi32>
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%c_0_f32 = constant dense<[
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0.0, 1.0, 0.0, 0.0, 4.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 0.0, 0.0,
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0.0, 0.0, 0.0, 0.0, 2.5, 0.0, 0.0, 0.0,
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2.0, 0.0, 0.0, 0.0, 0.0, 4.0, 0.0, 9.0
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]> : tensor<32xf32>
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%c_1_i32 = constant dense<[
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1, 1, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 3
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]> : tensor<32xi32>
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%c_1_f32 = constant dense<[
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1.0, 1.0, 1.0, 3.5, 1.0, 1.0, 1.0, 1.0,
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1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0,
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1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 1.0,
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1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 4.0
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]> : tensor<32xf32>
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// Convert constants to annotated tensors.
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%sparse_input_i32 = sparse_tensor.convert %c_0_i32
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: tensor<32xi32> to tensor<32xi32, #SV>
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%sparse_input_f32 = sparse_tensor.convert %c_0_f32
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: tensor<32xf32> to tensor<32xf32, #SV>
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%dense_input_i32 = sparse_tensor.convert %c_1_i32
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: tensor<32xi32> to tensor<32xi32, #DV>
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%dense_input_f32 = sparse_tensor.convert %c_1_f32
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: tensor<32xf32> to tensor<32xf32, #DV>
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// Call the kernels.
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%0 = call @sum_reduction_i32(%sparse_input_i32, %ri)
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: (tensor<32xi32, #SV>, tensor<i32>) -> tensor<i32>
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%1 = call @sum_reduction_f32(%sparse_input_f32, %rf)
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: (tensor<32xf32, #SV>, tensor<f32>) -> tensor<f32>
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%2 = call @prod_reduction_i32(%dense_input_i32, %ri)
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: (tensor<32xi32, #DV>, tensor<i32>) -> tensor<i32>
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%3 = call @prod_reduction_f32(%dense_input_f32, %rf)
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: (tensor<32xf32, #DV>, tensor<f32>) -> tensor<f32>
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%4 = call @and_reduction_i32(%dense_input_i32, %ri)
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: (tensor<32xi32, #DV>, tensor<i32>) -> tensor<i32>
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%5 = call @or_reduction_i32(%sparse_input_i32, %ri)
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: (tensor<32xi32, #SV>, tensor<i32>) -> tensor<i32>
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%6 = call @xor_reduction_i32(%sparse_input_i32, %ri)
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: (tensor<32xi32, #SV>, tensor<i32>) -> tensor<i32>
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// Verify results.
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//
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// CHECK: 26
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// CHECK: 27.5
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// CHECK: 3087
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// CHECK: 168
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// CHECK: 1
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// CHECK: 15
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// CHECK: 10
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//
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%m0 = memref.buffer_cast %0 : memref<i32>
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call @dump_i32(%m0) : (memref<i32>) -> ()
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%m1 = memref.buffer_cast %1 : memref<f32>
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call @dump_f32(%m1) : (memref<f32>) -> ()
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%m2 = memref.buffer_cast %2 : memref<i32>
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call @dump_i32(%m2) : (memref<i32>) -> ()
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%m3 = memref.buffer_cast %3 : memref<f32>
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call @dump_f32(%m3) : (memref<f32>) -> ()
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%m4 = memref.buffer_cast %4 : memref<i32>
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call @dump_i32(%m4) : (memref<i32>) -> ()
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%m5 = memref.buffer_cast %5 : memref<i32>
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call @dump_i32(%m5) : (memref<i32>) -> ()
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%m6 = memref.buffer_cast %6 : memref<i32>
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call @dump_i32(%m6) : (memref<i32>) -> ()
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// Release the resources.
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sparse_tensor.release %sparse_input_i32 : tensor<32xi32, #SV>
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sparse_tensor.release %sparse_input_f32 : tensor<32xf32, #SV>
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sparse_tensor.release %dense_input_i32 : tensor<32xi32, #DV>
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sparse_tensor.release %dense_input_f32 : tensor<32xf32, #DV>
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memref.dealloc %m0 : memref<i32>
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memref.dealloc %m1 : memref<f32>
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memref.dealloc %m2 : memref<i32>
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memref.dealloc %m3 : memref<f32>
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memref.dealloc %m4 : memref<i32>
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memref.dealloc %m5 : memref<i32>
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memref.dealloc %m6 : memref<i32>
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return
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}
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}
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