dce7a7cf69
The changes in this p.r. mostly center around the tests that use the flag sparse_compiler (also: sparse-compiler).
107 lines
4.1 KiB
MLIR
107 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 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: %{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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#CSC = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d1 : dense, d0 : compressed)
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}>
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module {
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func.func private @printMemrefF64(%ptr : tensor<*xf64>)
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//
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// Column-wise storage forces the ijk loop to permute into jki
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// so that access pattern expansion (workspace) needs to be
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// done along dimension with size 8.
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//
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func.func @matmul(%A: tensor<8x2xf64, #CSC>,
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%B: tensor<2x4xf64, #CSC>) -> tensor<8x4xf64, #CSC> {
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%C = tensor.empty() : tensor<8x4xf64, #CSC>
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%D = linalg.matmul
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ins(%A, %B: tensor<8x2xf64, #CSC>, tensor<2x4xf64, #CSC>)
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outs(%C: tensor<8x4xf64, #CSC>) -> tensor<8x4xf64, #CSC>
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return %D: tensor<8x4xf64, #CSC>
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}
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//
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// Main driver.
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//
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func.func @entry() {
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%c0 = arith.constant 0 : index
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%d1 = arith.constant -1.0 : f64
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// Initialize various dense matrices for stress testing.
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%da = arith.constant dense<[
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[ 1.1, 2.1 ],
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[ 1.2, 2.2 ],
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[ 1.3, 2.3 ],
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[ 1.4, 2.4 ],
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[ 1.5, 2.5 ],
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[ 1.6, 2.6 ],
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[ 1.7, 2.7 ],
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[ 1.8, 2.8 ]
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]> : tensor<8x2xf64>
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%db = arith.constant dense<[
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[ 10.1, 11.1, 12.1, 13.1 ],
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[ 10.2, 11.2, 12.2, 13.2 ]
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]> : tensor<2x4xf64>
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// Convert all these matrices to sparse format.
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%x1 = sparse_tensor.convert %da : tensor<8x2xf64> to tensor<8x2xf64, #CSC>
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%x2 = sparse_tensor.convert %db : tensor<2x4xf64> to tensor<2x4xf64, #CSC>
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// Call kernels with dense.
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%x3 = call @matmul(%x1, %x2)
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: (tensor<8x2xf64, #CSC>,
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tensor<2x4xf64, #CSC>) -> tensor<8x4xf64, #CSC>
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// CHECK: {{\[}}[32.53, 35.73, 38.93, 42.13],
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// CHECK-NEXT: [34.56, 37.96, 41.36, 44.76],
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// CHECK-NEXT: [36.59, 40.19, 43.79, 47.39],
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// CHECK-NEXT: [38.62, 42.42, 46.22, 50.02],
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// CHECK-NEXT: [40.65, 44.65, 48.65, 52.65],
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// CHECK-NEXT: [42.68, 46.88, 51.08, 55.28],
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// CHECK-NEXT: [44.71, 49.11, 53.51, 57.91],
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// CHECK-NEXT: [46.74, 51.34, 55.94, 60.54]]
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//
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%xc = sparse_tensor.convert %x3 : tensor<8x4xf64, #CSC> to tensor<8x4xf64>
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%xu = tensor.cast %xc : tensor<8x4xf64> to tensor<*xf64>
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call @printMemrefF64(%xu) : (tensor<*xf64>) -> ()
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// Release the resources.
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bufferization.dealloc_tensor %x1 : tensor<8x2xf64, #CSC>
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bufferization.dealloc_tensor %x2 : tensor<2x4xf64, #CSC>
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bufferization.dealloc_tensor %x3 : tensor<8x4xf64, #CSC>
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return
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}
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}
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