bfad07268b
Run sparse_tanh with vectorization. Reviewed By: aartbik Differential Revision: https://reviews.llvm.org/D139958
88 lines
3.2 KiB
MLIR
88 lines
3.2 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)> // X (out)
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],
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iterator_types = ["parallel"],
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doc = "X(i) = OP X(i)"
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}
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module {
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// Performs zero-preserving math to sparse vector.
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func.func @sparse_tanh(%vec: tensor<?xf64, #SparseVector>)
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-> tensor<?xf64, #SparseVector> {
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%0 = linalg.generic #trait_op
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outs(%vec: tensor<?xf64, #SparseVector>) {
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^bb(%x: f64):
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%1 = math.tanh %x : f64
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linalg.yield %1 : 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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// Dumps a sparse vector of type f64.
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func.func @dump_vec_f64(%arg0: tensor<?xf64, #SparseVector>) {
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// Dump the values array to verify only sparse contents are stored.
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%c0 = arith.constant 0 : index
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%d0 = arith.constant -1.0 : f64
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%n = sparse_tensor.number_of_entries %arg0: tensor<?xf64, #SparseVector>
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vector.print %n : index
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%0 = sparse_tensor.values %arg0
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: tensor<?xf64, #SparseVector> to memref<?xf64>
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%1 = vector.transfer_read %0[%c0], %d0: memref<?xf64>, vector<9xf64>
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vector.print %1 : vector<9xf64>
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// Dump the dense vector to verify structure is correct.
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%dv = sparse_tensor.convert %arg0
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: tensor<?xf64, #SparseVector> to tensor<?xf64>
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%3 = vector.transfer_read %dv[%c0], %d0: tensor<?xf64>, vector<32xf64>
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vector.print %3 : vector<32xf64>
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return
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}
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// Driver method to call and verify vector kernels.
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func.func @entry() {
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// Setup sparse vector.
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%v1 = arith.constant sparse<
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[ [0], [3], [11], [17], [20], [21], [28], [29], [31] ],
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[ -1.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 100.0 ]
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> : tensor<32xf64>
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%sv1 = sparse_tensor.convert %v1
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: tensor<32xf64> to tensor<?xf64, #SparseVector>
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// Call sparse vector kernel.
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%0 = call @sparse_tanh(%sv1) : (tensor<?xf64, #SparseVector>)
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-> tensor<?xf64, #SparseVector>
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//
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// Verify the results (within some precision).
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//
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// CHECK: 9
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// CHECK-NEXT: {{( -0.761[0-9]*, 0.761[0-9]*, 0.96[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 0.99[0-9]*, 1 )}}
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// CHECK-NEXT: {{( -0.761[0-9]*, 0, 0, 0.761[0-9]*, 0, 0, 0, 0, 0, 0, 0, 0.96[0-9]*, 0, 0, 0, 0, 0, 0.99[0-9]*, 0, 0, 0.99[0-9]*, 0.99[0-9]*, 0, 0, 0, 0, 0, 0, 0.99[0-9]*, 0.99[0-9]*, 0, 1 )}}
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//
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call @dump_vec_f64(%0) : (tensor<?xf64, #SparseVector>) -> ()
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// Release the resources.
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bufferization.dealloc_tensor %sv1 : tensor<?xf64, #SparseVector>
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return
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}
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}
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