138 lines
4.8 KiB
MLIR
138 lines
4.8 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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// REDEFINE: %{env} = TENSOR0="%mlir_src_dir/test/Integration/data/ds.mtx"
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// RUN: %{compile} | env %{env} %{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} | env %{env} %{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 enable-buffer-initialization=true vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
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// RUN: %{compile} | env %{env} %{run} | FileCheck %s
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!Filename = !llvm.ptr
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#CSR = #sparse_tensor.encoding<{
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map = (i, j) -> ( i : dense, j : compressed)
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}>
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#CSR_hi = #sparse_tensor.encoding<{
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map = (i, j) -> ( i : dense, j : loose_compressed)
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}>
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#NV_24 = #sparse_tensor.encoding<{
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map = ( i, j ) -> ( i : dense,
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j floordiv 4 : dense,
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j mod 4 : structured[2, 4]),
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crdWidth = 8
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}>
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#NV_58 = #sparse_tensor.encoding<{
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map = ( i, j ) -> ( i : dense,
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j floordiv 8 : dense,
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j mod 8 : structured[5, 8]),
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crdWidth = 8
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}>
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module {
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func.func private @getTensorFilename(index) -> (!Filename)
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//
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// Input matrix:
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//
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// [[0.0, 0.0, 1.0, 2.0, 0.0, 3.0, 0.0, 4.0],
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// [0.0, 5.0, 6.0, 0.0, 7.0, 0.0, 0.0, 8.0],
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// [9.0, 0.0, 10.0, 0.0, 11.0, 12.0, 0.0, 0.0]]
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//
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func.func @main() {
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%c0 = arith.constant 0 : index
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%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
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%A1 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #CSR>
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%A2 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #CSR_hi>
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%A3 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #NV_24>
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%A4 = sparse_tensor.new %fileName : !Filename to tensor<?x?xf64, #NV_58>
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//
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// CSR:
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//
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// CHECK: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 12
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// CHECK-NEXT: dim = ( 3, 8 )
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// CHECK-NEXT: lvl = ( 3, 8 )
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// CHECK-NEXT: pos[1] : ( 0, 4, 8, 12,
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// CHECK-NEXT: crd[1] : ( 2, 3, 5, 7, 1, 2, 4, 7, 0, 2, 4, 5,
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// CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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// CHECK-NEXT: ----
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//
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sparse_tensor.print %A1 : tensor<?x?xf64, #CSR>
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//
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// CSR_hi:
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//
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// CHECK-NEXT: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 12
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// CHECK-NEXT: dim = ( 3, 8 )
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// CHECK-NEXT: lvl = ( 3, 8 )
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// CHECK-NEXT: pos[1] : ( 0, 4, 4, 8, 8, 12,
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// CHECK-NEXT: crd[1] : ( 2, 3, 5, 7, 1, 2, 4, 7, 0, 2, 4, 5,
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// CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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// CHECK-NEXT: ----
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//
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sparse_tensor.print %A2 : tensor<?x?xf64, #CSR_hi>
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//
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// NV_24:
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//
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// CHECK-NEXT: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 12
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// CHECK-NEXT: dim = ( 3, 8 )
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// CHECK-NEXT: lvl = ( 3, 2, 4 )
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// CHECK-NEXT: crd[2] : ( 2, 3, 1, 3, 1, 2, 0, 3, 0, 2, 0, 1,
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// CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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// CHECK-NEXT: ----
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// CHECK-NEXT: ---- Sparse Tensor ----
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//
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sparse_tensor.print %A3 : tensor<?x?xf64, #NV_24>
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//
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// NV_58:
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//
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// CHECK-NEXT: nse = 12
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// CHECK-NEXT: dim = ( 3, 8 )
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// CHECK-NEXT: lvl = ( 3, 1, 8 )
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// CHECK-NEXT: crd[2] : ( 2, 3, 5, 7, 1, 2, 4, 7, 0, 2, 4, 5,
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// CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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// CHECK-NEXT: ----
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//
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sparse_tensor.print %A4 : tensor<?x?xf64, #NV_58>
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// Release the resources.
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bufferization.dealloc_tensor %A1: tensor<?x?xf64, #CSR>
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bufferization.dealloc_tensor %A2: tensor<?x?xf64, #CSR_hi>
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bufferization.dealloc_tensor %A3: tensor<?x?xf64, #NV_24>
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bufferization.dealloc_tensor %A4: tensor<?x?xf64, #NV_58>
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return
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}
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}
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