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
122 lines
4.9 KiB
MLIR
122 lines
4.9 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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// REDEFINE: %{env} = TENSOR0="%mlir_src_dir/test/Integration/data/test.tns"
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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: %{sparse_compiler_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: %{sparse_compiler_opts} = enable-runtime-library=false 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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//
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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} | env %{env} %{run_sve} | FileCheck %s %}
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!Filename = !llvm.ptr<i8>
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#SparseTensor = #sparse_tensor.encoding<{
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// Note that any dimToLvl permutation should give the same results
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// since, even though it impacts the sparse storage scheme layout,
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// it should not change the semantics.
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map = (d0, d1, d2, d3,
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d4, d5, d6, d7) -> (d7 : compressed, d6 : compressed,
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d1 : compressed, d2 : compressed,
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d0 : compressed, d3 : compressed,
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d4 : compressed, d5 : compressed)
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}>
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#trait_flatten = {
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indexing_maps = [
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affine_map<(i,j,k,l,m,n,o,p) -> (i,j,k,l,m,n,o,p)>, // A
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affine_map<(i,j,k,l,m,n,o,p) -> (i,j)> // X (out)
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],
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iterator_types = [ "parallel", "parallel", "reduction", "reduction",
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"reduction", "reduction", "reduction", "reduction" ],
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doc = "X(i,j) += A(i,j,k,l,m,n,o,p)"
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}
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//
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// Integration test that lowers a kernel annotated as sparse to
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// actual sparse code, initializes a matching sparse storage scheme
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// from file, and runs the resulting code with the JIT compiler.
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//
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module {
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//
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// A kernel that flattens a rank 8 tensor into a dense matrix.
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//
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func.func @kernel_flatten(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>,
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%argx: tensor<7x3xf64>)
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-> tensor<7x3xf64> {
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%0 = linalg.generic #trait_flatten
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ins(%arga: tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>)
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outs(%argx: tensor<7x3xf64>) {
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^bb(%a: f64, %x: f64):
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%0 = arith.addf %x, %a : f64
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linalg.yield %0 : f64
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} -> tensor<7x3xf64>
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return %0 : tensor<7x3xf64>
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}
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func.func private @getTensorFilename(index) -> (!Filename)
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func.func private @printMemrefF64(%ptr : tensor<*xf64>)
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//
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// Main driver that reads tensor from file and calls the sparse kernel.
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//
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func.func @entry() {
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%d0 = arith.constant 0.0 : f64
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%c3 = arith.constant 3 : index
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%c7 = arith.constant 7 : index
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// Setup matrix memory that is initialized to zero.
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%x = arith.constant dense<0.000000e+00> : tensor<7x3xf64>
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// Read the sparse tensor from file, construct sparse storage.
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%fileName = call @getTensorFilename(%c0) : (index) -> (!Filename)
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%a = sparse_tensor.new %fileName : !Filename to tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>
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// Call the kernel.
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%0 = call @kernel_flatten(%a, %x)
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: (tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>, tensor<7x3xf64>) -> tensor<7x3xf64>
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// Print the result for verification.
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//
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// CHECK: {{\[}}[6.25, 0, 0],
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// CHECK-NEXT: [4.224, 6.21, 0],
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// CHECK-NEXT: [0, 0, 15.455],
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// CHECK-NEXT: [0, 0, 0],
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// CHECK-NEXT: [0, 0, 0],
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// CHECK-NEXT: [0, 0, 0],
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// CHECK-NEXT: [7, 0, 0]]
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//
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%1 = tensor.cast %0 : tensor<7x3xf64> to tensor<*xf64>
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call @printMemrefF64(%1) : (tensor<*xf64>) -> ()
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// Release the resources.
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bufferization.dealloc_tensor %a : tensor<7x3x3x3x3x3x5x3xf64, #SparseTensor>
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return
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}
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}
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