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//--------------------------------------------------------------------------------------------------
// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
//
// Set-up that's shared across all tests in this directory. In principle, this
// config could be moved to lit.local.cfg. However, there are downstream users that
// do not use these LIT config files. Hence why this is kept inline.
//
// DEFINE: %{sparse_compiler_opts} = enable-runtime-library=true
// DEFINE: %{sparse_compiler_opts_sve} = enable-arm-sve=true %{sparse_compiler_opts}
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler="%{sparse_compiler_opts}"
// DEFINE: %{compile_sve} = mlir-opt %s --sparse-compiler="%{sparse_compiler_opts_sve}"
// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
// DEFINE: %{run_opts} = -e entry -entry-point-result=void
// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs}
//
// DEFINE: %{env} =
//--------------------------------------------------------------------------------------------------
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{sparse_compiler_opts} = enable-runtime-library=false
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{sparse_compiler_opts} = enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true
// RUN: %{compile} | %{run} | FileCheck %s
//
// Do the same run, but now with direct IR generation and VLA vectorization.
// RUN: %if mlir_arm_sve_tests %{ %{compile_sve} | %{run_sve} | FileCheck %s %}
#CSC = #sparse_tensor.encoding<{
map = (d0, d1) -> (d1 : dense, d0 : compressed)
}>
module {
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
//
// Column-wise storage forces the ijk loop to permute into jki
// so that access pattern expansion (workspace) needs to be
// done along dimension with size 8.
//
func.func @matmul(%A: tensor<8x2xf64, #CSC>,
%B: tensor<2x4xf64, #CSC>) -> tensor<8x4xf64, #CSC> {
%C = bufferization.alloc_tensor() : tensor<8x4xf64, #CSC>
%D = linalg.matmul
ins(%A, %B: tensor<8x2xf64, #CSC>, tensor<2x4xf64, #CSC>)
outs(%C: tensor<8x4xf64, #CSC>) -> tensor<8x4xf64, #CSC>
return %D: tensor<8x4xf64, #CSC>
}
//
// Main driver.
//
func.func @entry() {
%c0 = arith.constant 0 : index
%d1 = arith.constant -1.0 : f64
// Initialize various dense matrices for stress testing.
%da = arith.constant dense<[
[ 1.1, 2.1 ],
[ 1.2, 2.2 ],
[ 1.3, 2.3 ],
[ 1.4, 2.4 ],
[ 1.5, 2.5 ],
[ 1.6, 2.6 ],
[ 1.7, 2.7 ],
[ 1.8, 2.8 ]
]> : tensor<8x2xf64>
%db = arith.constant dense<[
[ 10.1, 11.1, 12.1, 13.1 ],
[ 10.2, 11.2, 12.2, 13.2 ]
]> : tensor<2x4xf64>
// Convert all these matrices to sparse format.
%x1 = sparse_tensor.convert %da : tensor<8x2xf64> to tensor<8x2xf64, #CSC>
%x2 = sparse_tensor.convert %db : tensor<2x4xf64> to tensor<2x4xf64, #CSC>
// Call kernels with dense.
%x3 = call @matmul(%x1, %x2)
: (tensor<8x2xf64, #CSC>,
tensor<2x4xf64, #CSC>) -> tensor<8x4xf64, #CSC>
// CHECK: {{\[}}[32.53, 35.73, 38.93, 42.13],
// CHECK-NEXT: [34.56, 37.96, 41.36, 44.76],
// CHECK-NEXT: [36.59, 40.19, 43.79, 47.39],
// CHECK-NEXT: [38.62, 42.42, 46.22, 50.02],
// CHECK-NEXT: [40.65, 44.65, 48.65, 52.65],
// CHECK-NEXT: [42.68, 46.88, 51.08, 55.28],
// CHECK-NEXT: [44.71, 49.11, 53.51, 57.91],
// CHECK-NEXT: [46.74, 51.34, 55.94, 60.54]]
//
%xc = sparse_tensor.convert %x3 : tensor<8x4xf64, #CSC> to tensor<8x4xf64>
%xu = tensor.cast %xc : tensor<8x4xf64> to tensor<*xf64>
call @printMemrefF64(%xu) : (tensor<*xf64>) -> ()
// Release the resources.
bufferization.dealloc_tensor %x1 : tensor<8x2xf64, #CSC>
bufferization.dealloc_tensor %x2 : tensor<2x4xf64, #CSC>
bufferization.dealloc_tensor %x3 : tensor<8x4xf64, #CSC>
return
}
}
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