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// DEFINE: %{option} = enable-runtime-library=false
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = TENSOR0="%mlir_src_dir/test/Integration/data/test.mtx" \
// DEFINE: mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=2 reassociate-fp-reductions=true enable-index-optimizations=true"
// RUN: %{compile} | %{run}
#COO_2D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
#COO_3D = #sparse_tensor.encoding<{ dimLevelType = [ "compressed-nu", "singleton-nu", "singleton" ], posWidth = 32, crdWidth = 32 }>
module {
func.func private @printMemref3dF32(%ptr : tensor<?x?x?xf32>) attributes { llvm.emit_c_interface }
func.func private @printMemref2dF32(%ptr : tensor<?x?xf32>) attributes { llvm.emit_c_interface }
func.func @test_sparse_rhs(%arg0: tensor<5x6xf32>, %arg1: tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> {
%collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32, #COO_3D> into tensor<6x6xf32, #COO_2D>
%0 = tensor.empty() : tensor<5x6xf32>
%cst = arith.constant 0.000000e+00 : f32
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>
%2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>
%expanded = tensor.expand_shape %2 [[0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
%ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>
return %ret1 : tensor<?x?x?xf32>
}
func.func @test_sparse_all(%arg0: tensor<5x6xf32, #COO_2D>, %arg1: tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32> {
%collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32, #COO_3D> into tensor<6x6xf32, #COO_2D>
%0 = tensor.empty() : tensor<5x6xf32>
%cst = arith.constant 0.000000e+00 : f32
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>
%2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32, #COO_2D>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>
%expanded = tensor.expand_shape %2 [[0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
%ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>
return %ret1 : tensor<?x?x?xf32>
}
func.func @test_dense(%arg0: tensor<5x6xf32>, %arg1: tensor<6x2x3xf32>) -> tensor<?x?x?xf32> {
%collapsed = tensor.collapse_shape %arg1 [[0], [1, 2]] : tensor<6x2x3xf32> into tensor<6x6xf32>
%0 = tensor.empty() : tensor<5x6xf32>
%cst = arith.constant 0.000000e+00 : f32
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>
%2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32>, tensor<6x6xf32>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>
%expanded = tensor.expand_shape %2 [[0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
%ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>
return %ret1 : tensor<?x?x?xf32>
}
func.func @test_sparse_all_2(%arg0: tensor<5x6xf32, #COO_2D>, %arg1: tensor<2x3x6xf32, #COO_3D>) -> tensor<?x?x?xf32> {
// collapse the first two level this time, as this is the level requires coiterations.
%collapsed = tensor.collapse_shape %arg1 [[0, 1], [2]] : tensor<2x3x6xf32, #COO_3D> into tensor<6x6xf32, #COO_2D>
%0 = tensor.empty() : tensor<5x6xf32>
%cst = arith.constant 0.000000e+00 : f32
%1 = linalg.fill ins(%cst : f32) outs(%0 : tensor<5x6xf32>) -> tensor<5x6xf32>
%2 = linalg.matmul ins(%arg0, %collapsed : tensor<5x6xf32, #COO_2D>, tensor<6x6xf32, #COO_2D>) outs(%1 : tensor<5x6xf32>) -> tensor<5x6xf32>
%expanded = tensor.expand_shape %2 [[0], [1, 2]] : tensor<5x6xf32> into tensor<5x2x3xf32>
%ret1 = tensor.cast %expanded : tensor<5x2x3xf32> to tensor<?x?x?xf32>
return %ret1 : tensor<?x?x?xf32>
}
func.func @entry() {
// Setup two sparse vectors.
%d1 = arith.constant sparse<
[ [0, 0], [1, 1], [2, 2], [2, 3], [4, 5] ],
[1.0, 2.0, 3.0, 4.0, 5.0]
> : tensor<5x6xf32>
%d2 = arith.constant sparse<
[ [0, 0, 0], [1, 1, 1], [2, 1, 1] ],
[ 6.0, 7.0, 8.0]
> : tensor<6x2x3xf32>
%shape = arith.constant dense<[2, 3, 6]> : tensor<3xi32>
%d3 = tensor.reshape %d2(%shape): (tensor<6x2x3xf32>, tensor<3xi32>) -> tensor<2x3x6xf32>
%s1 = sparse_tensor.convert %d1 : tensor<5x6xf32> to tensor<5x6xf32, #COO_2D>
%s2 = sparse_tensor.convert %d2 : tensor<6x2x3xf32> to tensor<6x2x3xf32, #COO_3D>
%s3 = sparse_tensor.convert %d3 : tensor<2x3x6xf32> to tensor<2x3x6xf32, #COO_3D>
// CHECK: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data =
// CHECK-NEXT:[
// CHECK-SAME: [
// CHECK-SAME: [6, 0, 0],
// CHECK-NEXT: [0, 0, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 14, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 24, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0]]]
%do1 = call @test_dense(%d1, %d2) : (tensor<5x6xf32>, tensor<6x2x3xf32>) -> tensor<?x?x?xf32>
call @printMemref3dF32(%do1) : (tensor<?x?x?xf32>) -> ()
// Same results.
// CHECK-NEXT: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data =
// CHECK-NEXT:[
// CHECK-SAME: [
// CHECK-SAME: [6, 0, 0],
// CHECK-NEXT: [0, 0, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 14, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 24, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0]]]
%so1 = call @test_sparse_rhs(%d1, %s2): (tensor<5x6xf32>, tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32>
call @printMemref3dF32(%so1) : (tensor<?x?x?xf32>) -> ()
// Same results.
// CHECK-NEXT: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data =
// CHECK-NEXT:[
// CHECK-SAME: [
// CHECK-SAME: [6, 0, 0],
// CHECK-NEXT: [0, 0, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 14, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 24, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0]]]
%so2 = call @test_sparse_all(%s1, %s2): (tensor<5x6xf32, #COO_2D>, tensor<6x2x3xf32, #COO_3D>) -> tensor<?x?x?xf32>
call @printMemref3dF32(%so2) : (tensor<?x?x?xf32>) -> ()
// Same results.
// CHECK-NEXT: Memref base@ = {{.*}} rank = 3 offset = 0 sizes = [5, 2, 3] strides = [6, 3, 1] data =
// CHECK-NEXT:[
// CHECK-SAME: [
// CHECK-SAME: [6, 0, 0],
// CHECK-NEXT: [0, 0, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 14, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 24, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0]],
// CHECK-NEXT: [
// CHECK-SAME: [0, 0, 0],
// CHECK-NEXT: [0, 0, 0]]]
%so3 = call @test_sparse_all_2(%s1, %s3): (tensor<5x6xf32, #COO_2D>, tensor<2x3x6xf32, #COO_3D>) -> tensor<?x?x?xf32>
call @printMemref3dF32(%so2) : (tensor<?x?x?xf32>) -> ()
bufferization.dealloc_tensor %s1 : tensor<5x6xf32, #COO_2D>
bufferization.dealloc_tensor %s2 : tensor<6x2x3xf32, #COO_3D>
bufferization.dealloc_tensor %s3 : tensor<2x3x6xf32, #COO_3D>
bufferization.dealloc_tensor %do1 : tensor<?x?x?xf32>
bufferization.dealloc_tensor %so1 : tensor<?x?x?xf32>
bufferization.dealloc_tensor %so2 : tensor<?x?x?xf32>
bufferization.dealloc_tensor %so3 : tensor<?x?x?xf32>
return
}
}
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