76647fce13
This is a major step along the way towards the new STEA design. While a great deal of this patch is simple renaming, there are several significant changes as well. I've done my best to ensure that this patch retains the previous behavior and error-conditions, even though those are at odds with the eventual intended semantics of the `dimToLvl` mapping. Since the majority of the compiler does not yet support non-permutations, I've also added explicit assertions in places that previously had implicitly assumed it was dealing with permutations. Reviewed By: aartbik Differential Revision: https://reviews.llvm.org/D151505
103 lines
3.7 KiB
MLIR
103 lines
3.7 KiB
MLIR
// DEFINE: %{option} = enable-runtime-library=true
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// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
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// DEFINE: %{run} = mlir-cpu-runner \
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// DEFINE: -e entry -entry-point-result=void \
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// DEFINE: -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils | \
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// DEFINE: FileCheck %s
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//
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// RUN: %{compile} | %{run}
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//
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// Do the same run, but now with direct IR generation.
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// RUN: %{compile} | %{run}
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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: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
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// RUN: %{compile} | %{run}
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// Do the same run, but now with direct IR generation and, if available, VLA
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// vectorization.
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// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
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// REDEFINE: %{run} = %lli_host_or_aarch64_cmd \
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// REDEFINE: --entry-function=entry_lli \
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// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
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// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
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// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext --dlopen=%mlir_runner_utils | \
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// REDEFINE: FileCheck %s
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// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
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#CSC = #sparse_tensor.encoding<{
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lvlTypes = [ "dense", "compressed" ],
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dimToLvl = affine_map<(i,j) -> (j,i)>
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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 = bufferization.alloc_tensor() : 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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