eb177803bf
1. Remove the trailing comma for the last element of memref and add closing parenthesis. 2. Change integration tests to use the new format.
137 lines
4.9 KiB
MLIR
Executable File
137 lines
4.9 KiB
MLIR
Executable File
//--------------------------------------------------------------------------------------------------
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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 main -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 enable-buffer-initialization=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 vectorization.
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// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false enable-buffer-initialization=true 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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// Test that test-bufferization-analysis-only works. This option is useful
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// for understanding why buffer copies were inserted.
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// RUN: mlir-opt %s --sparsifier="test-bufferization-analysis-only" -o /dev/null
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#Sparse1 = #sparse_tensor.encoding<{
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map = (i, j, k) -> (
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j : compressed,
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k : compressed,
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i : dense
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)
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}>
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#Sparse2 = #sparse_tensor.encoding<{
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map = (i, j, k) -> (
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i floordiv 2 : compressed,
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j floordiv 2 : compressed,
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k floordiv 2 : compressed,
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i mod 2 : dense,
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j mod 2 : dense,
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k mod 2 : dense)
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}>
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module {
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//
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// Main driver that tests sparse tensor storage.
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//
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func.func @main() {
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%c0 = arith.constant 0 : index
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%i0 = arith.constant 0 : i32
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// Setup input dense tensor and convert to two sparse tensors.
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%d = arith.constant dense <[
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[ // i=0
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[ 1, 0, 0, 0 ],
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[ 0, 0, 0, 0 ],
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[ 0, 0, 0, 0 ],
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[ 0, 0, 5, 0 ] ],
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[ // i=1
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[ 2, 0, 0, 0 ],
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[ 0, 0, 0, 0 ],
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[ 0, 0, 0, 0 ],
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[ 0, 0, 6, 0 ] ],
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[ //i=2
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[ 3, 0, 0, 0 ],
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[ 0, 0, 0, 0 ],
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[ 0, 0, 0, 0 ],
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[ 0, 0, 7, 0 ] ],
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//i=3
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[ [ 4, 0, 0, 0 ],
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[ 0, 0, 0, 0 ],
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[ 0, 0, 0, 0 ],
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[ 0, 0, 8, 0 ] ]
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]> : tensor<4x4x4xi32>
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%a = sparse_tensor.convert %d : tensor<4x4x4xi32> to tensor<4x4x4xi32, #Sparse1>
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%b = sparse_tensor.convert %d : tensor<4x4x4xi32> to tensor<4x4x4xi32, #Sparse2>
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//
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// If we store the two "fibers" [1,2,3,4] starting at index (0,0,0) and
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// ending at index (3,0,0) and [5,6,7,8] starting at index (0,3,2) and
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// ending at index (3,3,2)) with a “DCSR-flavored” along (j,k) with
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// dense “fibers” in the i-dim, we end up with 8 stored entries.
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//
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// CHECK: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 8
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// CHECK-NEXT: dim = ( 4, 4, 4 )
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// CHECK-NEXT: lvl = ( 4, 4, 4 )
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// CHECK-NEXT: pos[0] : ( 0, 2 )
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// CHECK-NEXT: crd[0] : ( 0, 3 )
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// CHECK-NEXT: pos[1] : ( 0, 1, 2 )
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// CHECK-NEXT: crd[1] : ( 0, 2 )
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// CHECK-NEXT: values : ( 1, 2, 3, 4, 5, 6, 7, 8 )
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// CHECK-NEXT: ----
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//
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sparse_tensor.print %a : tensor<4x4x4xi32, #Sparse1>
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//
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// If we store full 2x2x2 3-D blocks in the original index order
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// in a compressed fashion, we end up with 4 blocks to incorporate
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// all the nonzeros, and thus 32 stored entries.
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//
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// CHECK: ---- Sparse Tensor ----
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// CHECK-NEXT: nse = 32
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// CHECK-NEXT: dim = ( 4, 4, 4 )
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// CHECK-NEXT: lvl = ( 2, 2, 2, 2, 2, 2 )
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// CHECK-NEXT: pos[0] : ( 0, 2 )
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// CHECK-NEXT: crd[0] : ( 0, 1 )
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// CHECK-NEXT: pos[1] : ( 0, 2, 4 )
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// CHECK-NEXT: crd[1] : ( 0, 1, 0, 1 )
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// CHECK-NEXT: pos[2] : ( 0, 1, 2, 3, 4 )
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// CHECK-NEXT: crd[2] : ( 0, 1, 0, 1 )
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// CHECK-NEXT: values : ( 1, 0, 0, 0, 2, 0, 0, 0, 0, 0, 5, 0, 0, 0, 6, 0, 3, 0, 0, 0, 4, 0, 0, 0, 0, 0, 7, 0, 0, 0, 8, 0 )
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// CHECK-NEXT: ----
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//
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sparse_tensor.print %b : tensor<4x4x4xi32, #Sparse2>
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// Release the resources.
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bufferization.dealloc_tensor %a : tensor<4x4x4xi32, #Sparse1>
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bufferization.dealloc_tensor %b : tensor<4x4x4xi32, #Sparse2>
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return
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}
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}
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