3dc621124f
**COO**
`lvlTypes = [ "compressed_nu", "singleton" ]` to `map = (d0, d1) -> (d0
: compressed(nonunique), d1 : singleton)`
`lvlTypes = [ "compressed_nu_no", "singleton_no" ]` to `map = (d0, d1)
-> (d0 : compressed(nonunique, nonordered), d1 : singleton(nonordered))`
**SortedCOO**
`lvlTypes = [ "compressed_nu", "singleton" ]` to `map = (d0, d1) -> (d0
: compressed(nonunique), d1 : singleton)`
**BCOO**
`lvlTypes = [ "dense", "compressed_hi_nu", "singleton" ]` to `map = (d0,
d1, d2) -> (d0 : dense, d1 : compressed(nonunique, high), d2 :
singleton)`
**BCSR**
`lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl =
affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod
3)>` to
`map = ( i, j ) ->
( i floordiv 2 : compressed,
j floordiv 3 : compressed,
i mod 2 : dense,
j mod 3 : dense
)`
**Tensor and other supported formats(e.g. CCC, CDC, CCCC)**
Currently, ELL and slice are not supported yet in the new syntax and the
CHECK tests will be updated once printing is set to output the new
syntax.
Previous PRs: #66146, #66309, #66443
103 lines
4.8 KiB
MLIR
103 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: %{sparse_compiler_opts} = enable-runtime-library=true
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// DEFINE: %{sparse_compiler_opts_sve} = enable-arm-sve=true %{sparse_compiler_opts}
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// DEFINE: %{compile} = mlir-opt %s --sparse-compiler="%{sparse_compiler_opts}"
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// DEFINE: %{compile_sve} = mlir-opt %s --sparse-compiler="%{sparse_compiler_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 entry -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: %{sparse_compiler_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: %{sparse_compiler_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<{
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map = (d0) -> (d0 : compressed)
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}>
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#SparseMatrix = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : compressed, d1 : compressed)
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}>
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#Sparse3dTensor = #sparse_tensor.encoding<{
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map = (d0, d1, d2) -> (d0 : compressed, d1 : compressed, d2 : compressed)
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}>
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module {
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func.func @reshape0(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<2x6xf64, #SparseMatrix> {
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%shape = arith.constant dense <[ 2, 6 ]> : tensor<2xi32>
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%0 = tensor.reshape %arg0(%shape) : (tensor<3x4xf64, #SparseMatrix>, tensor<2xi32>) -> tensor<2x6xf64, #SparseMatrix>
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return %0 : tensor<2x6xf64, #SparseMatrix>
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}
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func.func @reshape1(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector> {
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%shape = arith.constant dense <[ 12 ]> : tensor<1xi32>
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%0 = tensor.reshape %arg0(%shape) : (tensor<3x4xf64, #SparseMatrix>, tensor<1xi32>) -> tensor<12xf64, #SparseVector>
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return %0 : tensor<12xf64, #SparseVector>
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}
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func.func @reshape2(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<2x3x2xf64, #Sparse3dTensor> {
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%shape = arith.constant dense <[ 2, 3, 2 ]> : tensor<3xi32>
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%0 = tensor.reshape %arg0(%shape) : (tensor<3x4xf64, #SparseMatrix>, tensor<3xi32>) -> tensor<2x3x2xf64, #Sparse3dTensor>
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return %0 : tensor<2x3x2xf64, #Sparse3dTensor>
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}
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func.func @entry() {
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%m = arith.constant dense <[ [ 1.1, 0.0, 1.3, 0.0 ],
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[ 2.1, 0.0, 2.3, 0.0 ],
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[ 3.1, 0.0, 3.3, 0.0 ]]> : tensor<3x4xf64>
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%sm = sparse_tensor.convert %m : tensor<3x4xf64> to tensor<3x4xf64, #SparseMatrix>
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%reshaped0 = call @reshape0(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<2x6xf64, #SparseMatrix>
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%reshaped1 = call @reshape1(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector>
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%reshaped2 = call @reshape2(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<2x3x2xf64, #Sparse3dTensor>
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%c0 = arith.constant 0 : index
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%df = arith.constant -1.0 : f64
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// CHECK: ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3
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%b0 = sparse_tensor.values %reshaped0: tensor<2x6xf64, #SparseMatrix> to memref<?xf64>
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%v0 = vector.transfer_read %b0[%c0], %df: memref<?xf64>, vector<12xf64>
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vector.print %v0 : vector<12xf64>
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// CHECK: ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3
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%b1 = sparse_tensor.values %reshaped1: tensor<12xf64, #SparseVector> to memref<?xf64>
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%v1 = vector.transfer_read %b1[%c0], %df: memref<?xf64>, vector<12xf64>
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vector.print %v1 : vector<12xf64>
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// CHECK: ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3
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%b2 = sparse_tensor.values %reshaped2: tensor<2x3x2xf64, #Sparse3dTensor> to memref<?xf64>
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%v2 = vector.transfer_read %b2[%c0], %df: memref<?xf64>, vector<12xf64>
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vector.print %v2: vector<12xf64>
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bufferization.dealloc_tensor %sm : tensor<3x4xf64, #SparseMatrix>
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bufferization.dealloc_tensor %reshaped0 : tensor<2x6xf64, #SparseMatrix>
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bufferization.dealloc_tensor %reshaped1 : tensor<12xf64, #SparseVector>
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bufferization.dealloc_tensor %reshaped2 : tensor<2x3x2xf64, #Sparse3dTensor>
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return
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}
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}
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