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.
108 lines
4.1 KiB
MLIR
108 lines
4.1 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
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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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#SparseVector = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }>
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module {
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//
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// Sparse kernel.
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//
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func.func @sparse_dot(%a: tensor<1024xf32, #SparseVector>,
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%b: tensor<1024xf32, #SparseVector>,
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%x: tensor<f32>) -> tensor<f32> {
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%dot = linalg.dot ins(%a, %b: tensor<1024xf32, #SparseVector>,
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tensor<1024xf32, #SparseVector>)
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outs(%x: tensor<f32>) -> tensor<f32>
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return %dot : tensor<f32>
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}
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//
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// Main driver.
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//
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func.func @main() {
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// Setup two sparse vectors.
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%d1 = arith.constant sparse<
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[ [0], [1], [22], [23], [1022] ], [1.0, 2.0, 3.0, 4.0, 5.0]
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> : tensor<1024xf32>
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%d2 = arith.constant sparse<
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[ [22], [1022], [1023] ], [6.0, 7.0, 8.0]
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> : tensor<1024xf32>
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%s1 = sparse_tensor.convert %d1 : tensor<1024xf32> to tensor<1024xf32, #SparseVector>
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%s2 = sparse_tensor.convert %d2 : tensor<1024xf32> to tensor<1024xf32, #SparseVector>
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//
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// Verify the inputs.
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//
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// CHECK: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 5
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// CHECK-NEXT: dim = ( 1024 )
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// CHECK-NEXT: lvl = ( 1024 )
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// CHECK-NEXT: pos[0] : ( 0, 5 )
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// CHECK-NEXT: crd[0] : ( 0, 1, 22, 23, 1022 )
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// CHECK-NEXT: values : ( 1, 2, 3, 4, 5 )
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// CHECK-NEXT: ----
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//
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// CHECK: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 3
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// CHECK-NEXT: dim = ( 1024 )
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// CHECK-NEXT: lvl = ( 1024 )
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// CHECK-NEXT: pos[0] : ( 0, 3 )
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// CHECK-NEXT: crd[0] : ( 22, 1022, 1023 )
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// CHECK-NEXT: values : ( 6, 7, 8 )
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// CHECK-NEXT: ----
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//
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sparse_tensor.print %s1 : tensor<1024xf32, #SparseVector>
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sparse_tensor.print %s2 : tensor<1024xf32, #SparseVector>
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// Call the kernel and verify the output.
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//
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// CHECK: 53
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//
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%t = tensor.empty() : tensor<f32>
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%z = arith.constant 0.0 : f32
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%x = tensor.insert %z into %t[] : tensor<f32>
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%0 = call @sparse_dot(%s1, %s2, %x) : (tensor<1024xf32, #SparseVector>,
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tensor<1024xf32, #SparseVector>,
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tensor<f32>) -> tensor<f32>
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%1 = tensor.extract %0[] : tensor<f32>
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vector.print %1 : f32
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// Release the resources.
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bufferization.dealloc_tensor %0 : tensor<f32>
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bufferization.dealloc_tensor %s1 : tensor<1024xf32, #SparseVector>
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bufferization.dealloc_tensor %s2 : tensor<1024xf32, #SparseVector>
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return
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}
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}
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