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Differential D144231

[mlir][sparse] split reshape.mlir into expand/collapse_shape.mlir.
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Authored by Peiming on Feb 16 2023, 3:58 PM.

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Reviewers
aartbik
nicolasvasilache
wrengr
bixia
Commits
rG0af5f84a13be: [mlir][sparse] split reshape.mlir into expand/collapse_shape.mlir.

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Peiming created this revision.Feb 16 2023, 3:58 PM
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Peiming added reviewers: wrengr, bixia.Feb 16 2023, 4:00 PM
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Closed by commit rG0af5f84a13be: [mlir][sparse] split reshape.mlir into expand/collapse_shape.mlir. (authored by Peiming). · Explain Why
This revision was automatically updated to reflect the committed changes.
Peiming added a commit: rG0af5f84a13be: [mlir][sparse] split reshape.mlir into expand/collapse_shape.mlir..
Harbormaster completed remote builds in B214286: Diff 498181.Feb 16 2023, 5:17 PM

Revision Contents

PathSize
mlir/
test/
Integration/
Dialect/
SparseTensor/
CPU/
238 lines
220 lines

Diff 498186

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_collapse_shape.mlir

  • This file was added.
PropertyOld ValueNew Value
File Modenull100755
// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// 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}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"]
}>
#Sparse3dTensor = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed", "compressed"]
}>
#Sparse4dTensor = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed", "compressed", "compressed"]
}>
//
// Test with various forms of the two most elementary reshape
// operations: collapse.
//
module {
func.func @collapse_dense(%arg0: tensor<3x4xf64>) -> tensor<12xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64> into tensor<12xf64>
return %0 : tensor<12xf64>
}
func.func @collapse_from_sparse(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64, #SparseMatrix> into tensor<12xf64>
return %0 : tensor<12xf64>
}
func.func @collapse_to_sparse(%arg0: tensor<3x4xf64>) -> tensor<12xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64> into tensor<12xf64, #SparseVector>
return %0 : tensor<12xf64, #SparseVector>
}
func.func @collapse_sparse2sparse(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64, #SparseMatrix> into tensor<12xf64, #SparseVector>
return %0 : tensor<12xf64, #SparseVector>
}
func.func @collapse_dense_6x10(%arg0: tensor<2x3x5x2xf64>) -> tensor<6x10xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<2x3x5x2xf64> into tensor<6x10xf64>
return %0 : tensor<6x10xf64>
}
func.func @collapse_from_sparse_6x10(%arg0: tensor<2x3x5x2xf64, #Sparse4dTensor>) -> tensor<6x10xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<2x3x5x2xf64, #Sparse4dTensor> into tensor<6x10xf64>
return %0 : tensor<6x10xf64>
}
func.func @collapse_to_sparse_6x10(%arg0: tensor<2x3x5x2xf64>) -> tensor<6x10xf64, #SparseMatrix> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<2x3x5x2xf64> into tensor<6x10xf64, #SparseMatrix>
return %0 : tensor<6x10xf64, #SparseMatrix>
}
func.func @collapse_sparse2sparse_6x10(%arg0: tensor<2x3x5x2xf64, #Sparse4dTensor>) -> tensor<6x10xf64, #SparseMatrix> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<2x3x5x2xf64, #Sparse4dTensor> into tensor<6x10xf64, #SparseMatrix>
return %0 : tensor<6x10xf64, #SparseMatrix>
}
func.func @collapse_dense_dyn(%arg0: tensor<?x?x?x?xf64>) -> tensor<?x?xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?x?x?xf64> into tensor<?x?xf64>
return %0 : tensor<?x?xf64>
}
func.func @collapse_from_sparse_dyn(%arg0: tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?x?x?xf64, #Sparse4dTensor> into tensor<?x?xf64>
return %0 : tensor<?x?xf64>
}
func.func @collapse_to_sparse_dyn(%arg0: tensor<?x?x?x?xf64>) -> tensor<?x?xf64, #SparseMatrix> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?x?x?xf64> into tensor<?x?xf64, #SparseMatrix>
return %0 : tensor<?x?xf64, #SparseMatrix>
}
func.func @collapse_sparse2sparse_dyn(%arg0: tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64, #SparseMatrix> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?x?x?xf64, #Sparse4dTensor> into tensor<?x?xf64, #SparseMatrix>
return %0 : tensor<?x?xf64, #SparseMatrix>
}
//
// Main driver.
//
func.func @entry() {
%c0 = arith.constant 0 : index
%df = arith.constant -1.0 : f64
// Setup test vectors and matrices..
%m = arith.constant dense <[ [ 1.1, 0.0, 1.3, 0.0 ],
[ 2.1, 0.0, 2.3, 0.0 ],
[ 3.1, 0.0, 3.3, 0.0 ]]> : tensor<3x4xf64>
%n = arith.constant dense <[
[ [[ 1.0, 0.0], [ 3.0, 0.0], [ 5.0, 0.0], [ 7.0, 0.0], [ 9.0, 0.0]],
[[ 0.0, 0.0], [ 0.0, 0.0], [ 0.0, 0.0], [ 0.0, 0.0], [ 0.0, 0.0]],
[[21.0, 0.0], [23.0, 0.0], [25.0, 0.0], [27.0, 0.0], [29.0, 0.0]] ],
[ [[ 0.0, 0.0], [ 0.0, 0.0], [ 0.0, 0.0], [ 0.0, 0.0], [ 0.0, 0.0]],
[[41.0, 0.0], [43.0, 0.0], [45.0, 0.0], [47.0, 0.0], [49.0, 0.0]],
[[ 0.0, 0.0], [ 0.0, 0.0], [ 0.0, 0.0], [ 0.0, 0.0], [ 0.0, 0.0]] ] ]> : tensor<2x3x5x2xf64>
%sm = sparse_tensor.convert %m : tensor<3x4xf64> to tensor<3x4xf64, #SparseMatrix>
%sn = sparse_tensor.convert %n : tensor<2x3x5x2xf64> to tensor<2x3x5x2xf64, #Sparse4dTensor>
%dm = tensor.cast %m : tensor<3x4xf64> to tensor<?x?xf64>
%dn = tensor.cast %n : tensor<2x3x5x2xf64> to tensor<?x?x?x?xf64>
%sdn = sparse_tensor.convert %dn : tensor<?x?x?x?xf64> to tensor<?x?x?x?xf64, #Sparse4dTensor>
// Call the kernels.
%collapse0 = call @collapse_dense(%m) : (tensor<3x4xf64>) -> tensor<12xf64>
%collapse1 = call @collapse_from_sparse(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64>
%collapse2 = call @collapse_to_sparse(%m) : (tensor<3x4xf64>) -> tensor<12xf64, #SparseVector>
%collapse3 = call @collapse_sparse2sparse(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector>
%collapse4 = call @collapse_dense_6x10(%n) : (tensor<2x3x5x2xf64>) -> tensor<6x10xf64>
%collapse5 = call @collapse_from_sparse_6x10(%sn) : (tensor<2x3x5x2xf64, #Sparse4dTensor>) -> tensor<6x10xf64>
%collapse6 = call @collapse_to_sparse_6x10(%n) : (tensor<2x3x5x2xf64>) -> tensor<6x10xf64, #SparseMatrix>
%collapse7 = call @collapse_sparse2sparse_6x10(%sn) : (tensor<2x3x5x2xf64, #Sparse4dTensor>) -> tensor<6x10xf64, #SparseMatrix>
%collapse8 = call @collapse_dense_dyn(%dn) : (tensor<?x?x?x?xf64>) -> tensor<?x?xf64>
%collapse9 = call @collapse_from_sparse_dyn(%sdn) : (tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64>
%collapse10 = call @collapse_to_sparse_dyn(%dn) : (tensor<?x?x?x?xf64>) -> tensor<?x?xf64, #SparseMatrix>
%collapse11 = call @collapse_sparse2sparse_dyn(%sdn) : (tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64, #SparseMatrix>
//
// Verify results of collapse
//
// CHECK: ( 1.1, 0, 1.3, 0, 2.1, 0, 2.3, 0, 3.1, 0, 3.3, 0 )
// CHECK-NEXT: ( 1.1, 0, 1.3, 0, 2.1, 0, 2.3, 0, 3.1, 0, 3.3, 0 )
// CHECK-NEXT: ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3
// CHECK-NEXT: ( 1.1, 1.3, 2.1, 2.3, 3.1, 3.3
// CHECK-NEXT: ( ( 1, 0, 3, 0, 5, 0, 7, 0, 9, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 21, 0, 23, 0, 25, 0, 27, 0, 29, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 41, 0, 43, 0, 45, 0, 47, 0, 49, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ) )
// CHECK-NEXT: ( ( 1, 0, 3, 0, 5, 0, 7, 0, 9, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 21, 0, 23, 0, 25, 0, 27, 0, 29, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 41, 0, 43, 0, 45, 0, 47, 0, 49, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ) )
// CHECK-NEXT: ( 1, 3, 5, 7, 9, 21, 23, 25, 27, 29, 41, 43, 45, 47
// CHECK-NEXT: ( 1, 3, 5, 7, 9, 21, 23, 25, 27, 29, 41, 43, 45, 47
// CHECK-NEXT: ( ( 1, 0, 3, 0, 5, 0, 7, 0, 9, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 21, 0, 23, 0, 25, 0, 27, 0, 29, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 41, 0, 43, 0, 45, 0, 47, 0, 49, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ) )
// CHECK-NEXT: ( ( 1, 0, 3, 0, 5, 0, 7, 0, 9, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 21, 0, 23, 0, 25, 0, 27, 0, 29, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ),
// CHECK-SAME: ( 41, 0, 43, 0, 45, 0, 47, 0, 49, 0 ),
// CHECK-SAME: ( 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ) )
// CHECK-NEXT: ( 1, 3, 5, 7, 9, 21, 23, 25, 27, 29, 41, 43, 45, 47, 49
// CHECK-NEXT: ( 1, 3, 5, 7, 9, 21, 23, 25, 27, 29, 41, 43, 45, 47, 49
%v0 = vector.transfer_read %collapse0[%c0], %df: tensor<12xf64>, vector<12xf64>
vector.print %v0 : vector<12xf64>
%v1 = vector.transfer_read %collapse1[%c0], %df: tensor<12xf64>, vector<12xf64>
vector.print %v1 : vector<12xf64>
%b2 = sparse_tensor.values %collapse2 : tensor<12xf64, #SparseVector> to memref<?xf64>
%v2 = vector.transfer_read %b2[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %v2 : vector<12xf64>
%b3 = sparse_tensor.values %collapse3 : tensor<12xf64, #SparseVector> to memref<?xf64>
%v3 = vector.transfer_read %b3[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %v3 : vector<12xf64>
%v4 = vector.transfer_read %collapse4[%c0, %c0], %df: tensor<6x10xf64>, vector<6x10xf64>
vector.print %v4 : vector<6x10xf64>
%v5 = vector.transfer_read %collapse5[%c0, %c0], %df: tensor<6x10xf64>, vector<6x10xf64>
vector.print %v5 : vector<6x10xf64>
%b6 = sparse_tensor.values %collapse6 : tensor<6x10xf64, #SparseMatrix> to memref<?xf64>
%v6 = vector.transfer_read %b6[%c0], %df: memref<?xf64>, vector<60xf64>
vector.print %v6 : vector<60xf64>
%b7 = sparse_tensor.values %collapse7 : tensor<6x10xf64, #SparseMatrix> to memref<?xf64>
%v7 = vector.transfer_read %b7[%c0], %df: memref<?xf64>, vector<60xf64>
vector.print %v7 : vector<60xf64>
%v8 = vector.transfer_read %collapse8[%c0, %c0], %df: tensor<?x?xf64>, vector<6x10xf64>
vector.print %v8 : vector<6x10xf64>
%v9 = vector.transfer_read %collapse9[%c0, %c0], %df: tensor<?x?xf64>, vector<6x10xf64>
vector.print %v9 : vector<6x10xf64>
%b10 = sparse_tensor.values %collapse10 : tensor<?x?xf64, #SparseMatrix> to memref<?xf64>
%v10 = vector.transfer_read %b10[%c0], %df: memref<?xf64>, vector<60xf64>
vector.print %v10 : vector<60xf64>
%b11 = sparse_tensor.values %collapse11 : tensor<?x?xf64, #SparseMatrix> to memref<?xf64>
%v11 = vector.transfer_read %b11[%c0], %df: memref<?xf64>, vector<60xf64>
vector.print %v11 : vector<60xf64>
// Release sparse resources.
bufferization.dealloc_tensor %sm : tensor<3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %sn : tensor<2x3x5x2xf64, #Sparse4dTensor>
bufferization.dealloc_tensor %sdn : tensor<?x?x?x?xf64, #Sparse4dTensor>
bufferization.dealloc_tensor %collapse2 : tensor<12xf64, #SparseVector>
bufferization.dealloc_tensor %collapse3 : tensor<12xf64, #SparseVector>
bufferization.dealloc_tensor %collapse6 : tensor<6x10xf64, #SparseMatrix>
bufferization.dealloc_tensor %collapse7 : tensor<6x10xf64, #SparseMatrix>
bufferization.dealloc_tensor %collapse10 : tensor<?x?xf64, #SparseMatrix>
bufferization.dealloc_tensor %collapse11 : tensor<?x?xf64, #SparseMatrix>
// Release dense resources.
bufferization.dealloc_tensor %collapse1 : tensor<12xf64>
bufferization.dealloc_tensor %collapse5 : tensor<6x10xf64>
bufferization.dealloc_tensor %collapse9: tensor<?x?xf64>
return
}
}

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_expand_shape.mlir

  • This file was added.
// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// 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}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"]
}>
#Sparse3dTensor = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed", "compressed"]
}>
#Sparse4dTensor = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed", "compressed", "compressed"]
}>
//
// Test with various forms of the two most elementary reshape
// operations: expand
//
module {
func.func @expand_dense(%arg0: tensor<12xf64>) -> tensor<3x4xf64> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64> into tensor<3x4xf64>
return %0 : tensor<3x4xf64>
}
func.func @expand_from_sparse(%arg0: tensor<12xf64, #SparseVector>) -> tensor<3x4xf64> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64, #SparseVector> into tensor<3x4xf64>
return %0 : tensor<3x4xf64>
}
func.func @expand_to_sparse(%arg0: tensor<12xf64>) -> tensor<3x4xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64> into tensor<3x4xf64, #SparseMatrix>
return %0 : tensor<3x4xf64, #SparseMatrix>
}
func.func @expand_sparse2sparse(%arg0: tensor<12xf64, #SparseVector>) -> tensor<3x4xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64, #SparseVector> into tensor<3x4xf64, #SparseMatrix>
return %0 : tensor<3x4xf64, #SparseMatrix>
}
func.func @expand_dense_3x2x2(%arg0: tensor<3x4xf64>) -> tensor<3x2x2xf64> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64> into tensor<3x2x2xf64>
return %0 : tensor<3x2x2xf64>
}
func.func @expand_from_sparse_3x2x2(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64, #SparseMatrix> into tensor<3x2x2xf64>
return %0 : tensor<3x2x2xf64>
}
func.func @expand_to_sparse_3x2x2(%arg0: tensor<3x4xf64>) -> tensor<3x2x2xf64, #Sparse3dTensor> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64> into tensor<3x2x2xf64, #Sparse3dTensor>
return %0 : tensor<3x2x2xf64, #Sparse3dTensor>
}
func.func @expand_sparse2sparse_3x2x2(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64, #Sparse3dTensor> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64, #SparseMatrix> into tensor<3x2x2xf64, #Sparse3dTensor>
return %0 : tensor<3x2x2xf64, #Sparse3dTensor>
}
func.func @expand_dense_dyn(%arg0: tensor<?x?xf64>) -> tensor<?x2x?xf64> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64> into tensor<?x2x?xf64>
return %0 : tensor<?x2x?xf64>
}
func.func @expand_from_sparse_dyn(%arg0: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64, #SparseMatrix> into tensor<?x2x?xf64>
return %0 : tensor<?x2x?xf64>
}
func.func @expand_to_sparse_dyn(%arg0: tensor<?x?xf64>) -> tensor<?x2x?xf64, #Sparse3dTensor> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64> into tensor<?x2x?xf64, #Sparse3dTensor>
return %0 : tensor<?x2x?xf64, #Sparse3dTensor>
}
func.func @expand_sparse2sparse_dyn(%arg0: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64, #Sparse3dTensor> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64, #SparseMatrix> into tensor<?x2x?xf64, #Sparse3dTensor>
return %0 : tensor<?x2x?xf64, #Sparse3dTensor>
}
//
// Main driver.
//
func.func @entry() {
%c0 = arith.constant 0 : index
%df = arith.constant -1.0 : f64
// Setup test vectors and matrices..
%v = arith.constant dense <[ 1.0, 0.0, 3.0, 0.0, 5.0, 0.0,
7.0, 0.0, 9.0, 0.0, 11.0, 0.0]> : tensor<12xf64>
%m = arith.constant dense <[ [ 1.1, 1.2, 1.3, 1.4 ],
[ 2.1, 2.2, 2.3, 2.4 ],
[ 3.1, 3.2, 3.3, 3.4 ]]> : tensor<3x4xf64>
%sv = sparse_tensor.convert %v : tensor<12xf64> to tensor<12xf64, #SparseVector>
%sm = sparse_tensor.convert %m : tensor<3x4xf64> to tensor<3x4xf64, #SparseMatrix>
%dm = tensor.cast %m : tensor<3x4xf64> to tensor<?x?xf64>
%sdm = sparse_tensor.convert %dm : tensor<?x?xf64> to tensor<?x?xf64, #SparseMatrix>
// Call the kernels.
%expand0 = call @expand_dense(%v) : (tensor<12xf64>) -> tensor<3x4xf64>
%expand1 = call @expand_from_sparse(%sv) : (tensor<12xf64, #SparseVector>) -> tensor<3x4xf64>
%expand2 = call @expand_to_sparse(%v) : (tensor<12xf64>) -> tensor<3x4xf64, #SparseMatrix>
%expand3 = call @expand_sparse2sparse(%sv) : (tensor<12xf64, #SparseVector>) -> tensor<3x4xf64, #SparseMatrix>
%expand4 = call @expand_dense_3x2x2(%m) : (tensor<3x4xf64>) -> tensor<3x2x2xf64>
%expand5 = call @expand_from_sparse_3x2x2(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64>
%expand6 = call @expand_to_sparse_3x2x2(%m) : (tensor<3x4xf64>) -> tensor<3x2x2xf64, #Sparse3dTensor>
%expand7 = call @expand_sparse2sparse_3x2x2(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64, #Sparse3dTensor>
%expand8 = call @expand_dense_dyn(%dm) : (tensor<?x?xf64>) -> tensor<?x2x?xf64>
%expand9 = call @expand_from_sparse_dyn(%sdm) : (tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64>
%expand10 = call @expand_to_sparse_dyn(%dm) : (tensor<?x?xf64>) -> tensor<?x2x?xf64, #Sparse3dTensor>
%expand11 = call @expand_sparse2sparse_dyn(%sdm) : (tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64, #Sparse3dTensor>
//
// Verify results of expand
//
// CHECK: ( ( 1, 0, 3, 0 ), ( 5, 0, 7, 0 ), ( 9, 0, 11, 0 ) )
// CHECK-NEXT: ( ( 1, 0, 3, 0 ), ( 5, 0, 7, 0 ), ( 9, 0, 11, 0 ) )
// CHECK-NEXT: ( 1, 3, 5, 7, 9,
// CHECK-NEXT: ( 1, 3, 5, 7, 9,
// CHECK-NEXT: ( ( ( 1.1, 1.2 ), ( 1.3, 1.4 ) ), ( ( 2.1, 2.2 ), ( 2.3, 2.4 ) ), ( ( 3.1, 3.2 ), ( 3.3, 3.4 ) ) )
// CHECK-NEXT: ( ( ( 1.1, 1.2 ), ( 1.3, 1.4 ) ), ( ( 2.1, 2.2 ), ( 2.3, 2.4 ) ), ( ( 3.1, 3.2 ), ( 3.3, 3.4 ) ) )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( ( ( 1.1, 1.2 ), ( 1.3, 1.4 ) ), ( ( 2.1, 2.2 ), ( 2.3, 2.4 ) ), ( ( 3.1, 3.2 ), ( 3.3, 3.4 ) ) )
// CHECK-NEXT: ( ( ( 1.1, 1.2 ), ( 1.3, 1.4 ) ), ( ( 2.1, 2.2 ), ( 2.3, 2.4 ) ), ( ( 3.1, 3.2 ), ( 3.3, 3.4 ) ) )
// CHECK-NEXT: 12
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: 12
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
//
%m0 = vector.transfer_read %expand0[%c0, %c0], %df: tensor<3x4xf64>, vector<3x4xf64>
vector.print %m0 : vector<3x4xf64>
%m1 = vector.transfer_read %expand1[%c0, %c0], %df: tensor<3x4xf64>, vector<3x4xf64>
vector.print %m1 : vector<3x4xf64>
%a2 = sparse_tensor.values %expand2 : tensor<3x4xf64, #SparseMatrix> to memref<?xf64>
%m2 = vector.transfer_read %a2[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m2 : vector<12xf64>
%a3 = sparse_tensor.values %expand3 : tensor<3x4xf64, #SparseMatrix> to memref<?xf64>
%m3 = vector.transfer_read %a3[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m3 : vector<12xf64>
%m4 = vector.transfer_read %expand4[%c0, %c0, %c0], %df: tensor<3x2x2xf64>, vector<3x2x2xf64>
vector.print %m4 : vector<3x2x2xf64>
%m5 = vector.transfer_read %expand5[%c0, %c0, %c0], %df: tensor<3x2x2xf64>, vector<3x2x2xf64>
vector.print %m5 : vector<3x2x2xf64>
%a6 = sparse_tensor.values %expand6 : tensor<3x2x2xf64, #Sparse3dTensor> to memref<?xf64>
%m6 = vector.transfer_read %a6[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m6 : vector<12xf64>
%a7 = sparse_tensor.values %expand7 : tensor<3x2x2xf64, #Sparse3dTensor> to memref<?xf64>
%m7 = vector.transfer_read %a7[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m7 : vector<12xf64>
%m8 = vector.transfer_read %expand8[%c0, %c0, %c0], %df: tensor<?x2x?xf64>, vector<3x2x2xf64>
vector.print %m8 : vector<3x2x2xf64>
%m9 = vector.transfer_read %expand9[%c0, %c0, %c0], %df: tensor<?x2x?xf64>, vector<3x2x2xf64>
vector.print %m9 : vector<3x2x2xf64>
%n10 = sparse_tensor.number_of_entries %expand10 : tensor<?x2x?xf64, #Sparse3dTensor>
vector.print %n10 : index
%a10 = sparse_tensor.values %expand10 : tensor<?x2x?xf64, #Sparse3dTensor> to memref<?xf64>
%m10 = vector.transfer_read %a10[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m10 : vector<12xf64>
%n11 = sparse_tensor.number_of_entries %expand11 : tensor<?x2x?xf64, #Sparse3dTensor>
vector.print %n11 : index
%a11 = sparse_tensor.values %expand11 : tensor<?x2x?xf64, #Sparse3dTensor> to memref<?xf64>
%m11 = vector.transfer_read %a11[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m11 : vector<12xf64>
// Release sparse resources.
bufferization.dealloc_tensor %sv : tensor<12xf64, #SparseVector>
bufferization.dealloc_tensor %sm : tensor<3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %sdm : tensor<?x?xf64, #SparseMatrix>
bufferization.dealloc_tensor %expand2 : tensor<3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %expand3 : tensor<3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %expand6 : tensor<3x2x2xf64, #Sparse3dTensor>
bufferization.dealloc_tensor %expand7 : tensor<3x2x2xf64, #Sparse3dTensor>
bufferization.dealloc_tensor %expand10 : tensor<?x2x?xf64, #Sparse3dTensor>
bufferization.dealloc_tensor %expand11 : tensor<?x2x?xf64, #Sparse3dTensor>
// Release dense resources.
bufferization.dealloc_tensor %expand1 : tensor<3x4xf64>
bufferization.dealloc_tensor %expand5 : tensor<3x2x2xf64>
bufferization.dealloc_tensor %expand9 : tensor<?x2x?xf64>
return
}
}

mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_reshape.mlir

  • This file was deleted.
PropertyOld ValueNew Value
File Mode100755null
// DEFINE: %{option} = enable-runtime-library=true
// DEFINE: %{compile} = mlir-opt %s --sparse-compiler=%{option}
// DEFINE: %{run} = mlir-cpu-runner \
// DEFINE: -e entry -entry-point-result=void \
// DEFINE: -shared-libs=%mlir_c_runner_utils | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = enable-runtime-library=false
// 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}
// Do the same run, but now with direct IR generation and, if available, VLA
// vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false vl=4 enable-arm-sve=%ENABLE_VLA"
// REDEFINE: %{run} = %lli \
// REDEFINE: --entry-function=entry_lli \
// REDEFINE: --extra-module=%S/Inputs/main_for_lli.ll \
// REDEFINE: %VLA_ARCH_ATTR_OPTIONS \
// REDEFINE: --dlopen=%mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
}>
#SparseMatrix = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed"]
}>
#Sparse3dTensor = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed", "compressed"]
}>
#Sparse4dTensor = #sparse_tensor.encoding<{
dimLevelType = ["compressed", "compressed", "compressed", "compressed"]
}>
//
// Test with various forms of the two most elementary reshape
// operations: expand/collapse.
//
module {
func.func @expand_dense(%arg0: tensor<12xf64>) -> tensor<3x4xf64> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64> into tensor<3x4xf64>
return %0 : tensor<3x4xf64>
}
func.func @expand_from_sparse(%arg0: tensor<12xf64, #SparseVector>) -> tensor<3x4xf64> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64, #SparseVector> into tensor<3x4xf64>
return %0 : tensor<3x4xf64>
}
func.func @expand_to_sparse(%arg0: tensor<12xf64>) -> tensor<3x4xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64> into tensor<3x4xf64, #SparseMatrix>
return %0 : tensor<3x4xf64, #SparseMatrix>
}
func.func @expand_sparse2sparse(%arg0: tensor<12xf64, #SparseVector>) -> tensor<3x4xf64, #SparseMatrix> {
%0 = tensor.expand_shape %arg0 [[0, 1]] : tensor<12xf64, #SparseVector> into tensor<3x4xf64, #SparseMatrix>
return %0 : tensor<3x4xf64, #SparseMatrix>
}
func.func @collapse_dense(%arg0: tensor<3x4xf64>) -> tensor<12xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64> into tensor<12xf64>
return %0 : tensor<12xf64>
}
func.func @collapse_from_sparse(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64, #SparseMatrix> into tensor<12xf64>
return %0 : tensor<12xf64>
}
func.func @collapse_to_sparse(%arg0: tensor<3x4xf64>) -> tensor<12xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64> into tensor<12xf64, #SparseVector>
return %0 : tensor<12xf64, #SparseVector>
}
func.func @collapse_sparse2sparse(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector> {
%0 = tensor.collapse_shape %arg0 [[0, 1]] : tensor<3x4xf64, #SparseMatrix> into tensor<12xf64, #SparseVector>
return %0 : tensor<12xf64, #SparseVector>
}
func.func @expand_dense_3x2x2(%arg0: tensor<3x4xf64>) -> tensor<3x2x2xf64> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64> into tensor<3x2x2xf64>
return %0 : tensor<3x2x2xf64>
}
func.func @expand_from_sparse_3x2x2(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64, #SparseMatrix> into tensor<3x2x2xf64>
return %0 : tensor<3x2x2xf64>
}
func.func @expand_to_sparse_3x2x2(%arg0: tensor<3x4xf64>) -> tensor<3x2x2xf64, #Sparse3dTensor> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64> into tensor<3x2x2xf64, #Sparse3dTensor>
return %0 : tensor<3x2x2xf64, #Sparse3dTensor>
}
func.func @expand_sparse2sparse_3x2x2(%arg0: tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64, #Sparse3dTensor> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<3x4xf64, #SparseMatrix> into tensor<3x2x2xf64, #Sparse3dTensor>
return %0 : tensor<3x2x2xf64, #Sparse3dTensor>
}
func.func @collapse_dense_6x10(%arg0: tensor<2x3x5x2xf64>) -> tensor<6x10xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<2x3x5x2xf64> into tensor<6x10xf64>
return %0 : tensor<6x10xf64>
}
func.func @collapse_from_sparse_6x10(%arg0: tensor<2x3x5x2xf64, #Sparse4dTensor>) -> tensor<6x10xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<2x3x5x2xf64, #Sparse4dTensor> into tensor<6x10xf64>
return %0 : tensor<6x10xf64>
}
func.func @collapse_to_sparse_6x10(%arg0: tensor<2x3x5x2xf64>) -> tensor<6x10xf64, #SparseMatrix> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<2x3x5x2xf64> into tensor<6x10xf64, #SparseMatrix>
return %0 : tensor<6x10xf64, #SparseMatrix>
}
func.func @collapse_sparse2sparse_6x10(%arg0: tensor<2x3x5x2xf64, #Sparse4dTensor>) -> tensor<6x10xf64, #SparseMatrix> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<2x3x5x2xf64, #Sparse4dTensor> into tensor<6x10xf64, #SparseMatrix>
return %0 : tensor<6x10xf64, #SparseMatrix>
}
func.func @expand_dense_dyn(%arg0: tensor<?x?xf64>) -> tensor<?x2x?xf64> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64> into tensor<?x2x?xf64>
return %0 : tensor<?x2x?xf64>
}
func.func @expand_from_sparse_dyn(%arg0: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64, #SparseMatrix> into tensor<?x2x?xf64>
return %0 : tensor<?x2x?xf64>
}
func.func @expand_to_sparse_dyn(%arg0: tensor<?x?xf64>) -> tensor<?x2x?xf64, #Sparse3dTensor> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64> into tensor<?x2x?xf64, #Sparse3dTensor>
return %0 : tensor<?x2x?xf64, #Sparse3dTensor>
}
func.func @expand_sparse2sparse_dyn(%arg0: tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64, #Sparse3dTensor> {
%0 = tensor.expand_shape %arg0 [[0], [1, 2]] : tensor<?x?xf64, #SparseMatrix> into tensor<?x2x?xf64, #Sparse3dTensor>
return %0 : tensor<?x2x?xf64, #Sparse3dTensor>
}
func.func @collapse_dense_dyn(%arg0: tensor<?x?x?x?xf64>) -> tensor<?x?xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?x?x?xf64> into tensor<?x?xf64>
return %0 : tensor<?x?xf64>
}
func.func @collapse_from_sparse_dyn(%arg0: tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?x?x?xf64, #Sparse4dTensor> into tensor<?x?xf64>
return %0 : tensor<?x?xf64>
}
func.func @collapse_to_sparse_dyn(%arg0: tensor<?x?x?x?xf64>) -> tensor<?x?xf64, #SparseMatrix> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?x?x?xf64> into tensor<?x?xf64, #SparseMatrix>
return %0 : tensor<?x?xf64, #SparseMatrix>
}
func.func @collapse_sparse2sparse_dyn(%arg0: tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64, #SparseMatrix> {
%0 = tensor.collapse_shape %arg0 [[0, 1], [2, 3]] : tensor<?x?x?x?xf64, #Sparse4dTensor> into tensor<?x?xf64, #SparseMatrix>
return %0 : tensor<?x?xf64, #SparseMatrix>
}
//
// Main driver.
//
func.func @entry() {
%c0 = arith.constant 0 : index
%df = arith.constant -1.0 : f64
// Setup test vectors and matrices..
%v = arith.constant dense <[ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0,
7.0, 8.0, 9.0, 10.0, 11.0, 12.0]> : tensor<12xf64>
%m = arith.constant dense <[ [ 1.1, 1.2, 1.3, 1.4 ],
[ 2.1, 2.2, 2.3, 2.4 ],
[ 3.1, 3.2, 3.3, 3.4 ]]> : tensor<3x4xf64>
%n = arith.constant dense <[
[ [[1.0, 2.0], [3.0, 4.0], [5.0, 6.0], [7.0, 8.0], [9.0, 10.0]],
[[11.0, 12.0], [13.0, 14.0], [15.0, 16.0], [17.0, 18.0], [19.0, 20.0]],
[[21.0, 22.0], [23.0, 24.0], [25.0, 26.0], [27.0, 28.0], [29.0, 30.0]] ],
[ [[31.0, 32.0], [33.0, 34.0], [35.0, 36.0], [37.0, 38.0], [39.0, 40.0]],
[[41.0, 42.0], [43.0, 44.0], [45.0, 26.0], [47.0, 48.0], [49.0, 50.0]],
[[51.0, 52.0], [53.0, 54.0], [55.0, 56.0], [57.0, 58.0], [59.0, 60.0]] ] ]> : tensor<2x3x5x2xf64>
%sv = sparse_tensor.convert %v : tensor<12xf64> to tensor<12xf64, #SparseVector>
%sm = sparse_tensor.convert %m : tensor<3x4xf64> to tensor<3x4xf64, #SparseMatrix>
%sn = sparse_tensor.convert %n : tensor<2x3x5x2xf64> to tensor<2x3x5x2xf64, #Sparse4dTensor>
%dm = tensor.cast %m : tensor<3x4xf64> to tensor<?x?xf64>
%sdm = sparse_tensor.convert %dm : tensor<?x?xf64> to tensor<?x?xf64, #SparseMatrix>
%dn = tensor.cast %n : tensor<2x3x5x2xf64> to tensor<?x?x?x?xf64>
%sdn = sparse_tensor.convert %dn : tensor<?x?x?x?xf64> to tensor<?x?x?x?xf64, #Sparse4dTensor>
// Call the kernels.
%expand0 = call @expand_dense(%v) : (tensor<12xf64>) -> tensor<3x4xf64>
%expand1 = call @expand_from_sparse(%sv) : (tensor<12xf64, #SparseVector>) -> tensor<3x4xf64>
%expand2 = call @expand_to_sparse(%v) : (tensor<12xf64>) -> tensor<3x4xf64, #SparseMatrix>
%expand3 = call @expand_sparse2sparse(%sv) : (tensor<12xf64, #SparseVector>) -> tensor<3x4xf64, #SparseMatrix>
%expand4 = call @expand_dense_3x2x2(%m) : (tensor<3x4xf64>) -> tensor<3x2x2xf64>
%expand5 = call @expand_from_sparse_3x2x2(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64>
%expand6 = call @expand_to_sparse_3x2x2(%m) : (tensor<3x4xf64>) -> tensor<3x2x2xf64, #Sparse3dTensor>
%expand7 = call @expand_sparse2sparse_3x2x2(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<3x2x2xf64, #Sparse3dTensor>
%expand8 = call @expand_dense_dyn(%dm) : (tensor<?x?xf64>) -> tensor<?x2x?xf64>
%expand9 = call @expand_from_sparse_dyn(%sdm) : (tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64>
%expand10 = call @expand_to_sparse_dyn(%dm) : (tensor<?x?xf64>) -> tensor<?x2x?xf64, #Sparse3dTensor>
%expand11 = call @expand_sparse2sparse_dyn(%sdm) : (tensor<?x?xf64, #SparseMatrix>) -> tensor<?x2x?xf64, #Sparse3dTensor>
%collapse0 = call @collapse_dense(%m) : (tensor<3x4xf64>) -> tensor<12xf64>
%collapse1 = call @collapse_from_sparse(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64>
%collapse2 = call @collapse_to_sparse(%m) : (tensor<3x4xf64>) -> tensor<12xf64, #SparseVector>
%collapse3 = call @collapse_sparse2sparse(%sm) : (tensor<3x4xf64, #SparseMatrix>) -> tensor<12xf64, #SparseVector>
%collapse4 = call @collapse_dense_6x10(%n) : (tensor<2x3x5x2xf64>) -> tensor<6x10xf64>
%collapse5 = call @collapse_from_sparse_6x10(%sn) : (tensor<2x3x5x2xf64, #Sparse4dTensor>) -> tensor<6x10xf64>
%collapse6 = call @collapse_to_sparse_6x10(%n) : (tensor<2x3x5x2xf64>) -> tensor<6x10xf64, #SparseMatrix>
%collapse7 = call @collapse_sparse2sparse_6x10(%sn) : (tensor<2x3x5x2xf64, #Sparse4dTensor>) -> tensor<6x10xf64, #SparseMatrix>
%collapse8 = call @collapse_dense_dyn(%dn) : (tensor<?x?x?x?xf64>) -> tensor<?x?xf64>
%collapse9 = call @collapse_from_sparse_dyn(%sdn) : (tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64>
%collapse10 = call @collapse_to_sparse_dyn(%dn) : (tensor<?x?x?x?xf64>) -> tensor<?x?xf64, #SparseMatrix>
%collapse11 = call @collapse_sparse2sparse_dyn(%sdn) : (tensor<?x?x?x?xf64, #Sparse4dTensor>) -> tensor<?x?xf64, #SparseMatrix>
//
// Verify results of expand
//
// CHECK: ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) )
// CHECK-NEXT: ( ( 1, 2, 3, 4 ), ( 5, 6, 7, 8 ), ( 9, 10, 11, 12 ) )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 )
// CHECK-NEXT: ( ( ( 1.1, 1.2 ), ( 1.3, 1.4 ) ), ( ( 2.1, 2.2 ), ( 2.3, 2.4 ) ), ( ( 3.1, 3.2 ), ( 3.3, 3.4 ) ) )
// CHECK-NEXT: ( ( ( 1.1, 1.2 ), ( 1.3, 1.4 ) ), ( ( 2.1, 2.2 ), ( 2.3, 2.4 ) ), ( ( 3.1, 3.2 ), ( 3.3, 3.4 ) ) )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( ( ( 1.1, 1.2 ), ( 1.3, 1.4 ) ), ( ( 2.1, 2.2 ), ( 2.3, 2.4 ) ), ( ( 3.1, 3.2 ), ( 3.3, 3.4 ) ) )
// CHECK-NEXT: ( ( ( 1.1, 1.2 ), ( 1.3, 1.4 ) ), ( ( 2.1, 2.2 ), ( 2.3, 2.4 ) ), ( ( 3.1, 3.2 ), ( 3.3, 3.4 ) ) )
// CHECK-NEXT: 12
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: 12
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
//
%m0 = vector.transfer_read %expand0[%c0, %c0], %df: tensor<3x4xf64>, vector<3x4xf64>
vector.print %m0 : vector<3x4xf64>
%m1 = vector.transfer_read %expand1[%c0, %c0], %df: tensor<3x4xf64>, vector<3x4xf64>
vector.print %m1 : vector<3x4xf64>
%a2 = sparse_tensor.values %expand2 : tensor<3x4xf64, #SparseMatrix> to memref<?xf64>
%m2 = vector.transfer_read %a2[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m2 : vector<12xf64>
%a3 = sparse_tensor.values %expand3 : tensor<3x4xf64, #SparseMatrix> to memref<?xf64>
%m3 = vector.transfer_read %a3[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m3 : vector<12xf64>
%m4 = vector.transfer_read %expand4[%c0, %c0, %c0], %df: tensor<3x2x2xf64>, vector<3x2x2xf64>
vector.print %m4 : vector<3x2x2xf64>
%m5 = vector.transfer_read %expand5[%c0, %c0, %c0], %df: tensor<3x2x2xf64>, vector<3x2x2xf64>
vector.print %m5 : vector<3x2x2xf64>
%a6 = sparse_tensor.values %expand6 : tensor<3x2x2xf64, #Sparse3dTensor> to memref<?xf64>
%m6 = vector.transfer_read %a6[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m6 : vector<12xf64>
%a7 = sparse_tensor.values %expand7 : tensor<3x2x2xf64, #Sparse3dTensor> to memref<?xf64>
%m7 = vector.transfer_read %a7[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m7 : vector<12xf64>
%m8 = vector.transfer_read %expand8[%c0, %c0, %c0], %df: tensor<?x2x?xf64>, vector<3x2x2xf64>
vector.print %m8 : vector<3x2x2xf64>
%m9 = vector.transfer_read %expand9[%c0, %c0, %c0], %df: tensor<?x2x?xf64>, vector<3x2x2xf64>
vector.print %m9 : vector<3x2x2xf64>
%n10 = sparse_tensor.number_of_entries %expand10 : tensor<?x2x?xf64, #Sparse3dTensor>
vector.print %n10 : index
%a10 = sparse_tensor.values %expand10 : tensor<?x2x?xf64, #Sparse3dTensor> to memref<?xf64>
%m10 = vector.transfer_read %a10[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m10 : vector<12xf64>
%n11 = sparse_tensor.number_of_entries %expand11 : tensor<?x2x?xf64, #Sparse3dTensor>
vector.print %n11 : index
%a11 = sparse_tensor.values %expand11 : tensor<?x2x?xf64, #Sparse3dTensor> to memref<?xf64>
%m11 = vector.transfer_read %a11[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %m11 : vector<12xf64>
//
// Verify results of collapse
//
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( 1.1, 1.2, 1.3, 1.4, 2.1, 2.2, 2.3, 2.4, 3.1, 3.2, 3.3, 3.4 )
// CHECK-NEXT: ( ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ( 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ), ( 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ), ( 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 ), ( 41, 42, 43, 44, 45, 26, 47, 48, 49, 50 ), ( 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 ) )
// CHECK-NEXT: ( ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ( 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ), ( 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ), ( 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 ), ( 41, 42, 43, 44, 45, 26, 47, 48, 49, 50 ), ( 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 ) )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 26, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 26, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 )
// CHECK-NEXT: ( ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ( 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ), ( 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ), ( 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 ), ( 41, 42, 43, 44, 45, 26, 47, 48, 49, 50 ), ( 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 ) )
// CHECK-NEXT: ( ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ), ( 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ), ( 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ), ( 31, 32, 33, 34, 35, 36, 37, 38, 39, 40 ), ( 41, 42, 43, 44, 45, 26, 47, 48, 49, 50 ), ( 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 ) )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 26, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 )
// CHECK-NEXT: ( 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 26, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60 )
//
%v0 = vector.transfer_read %collapse0[%c0], %df: tensor<12xf64>, vector<12xf64>
vector.print %v0 : vector<12xf64>
%v1 = vector.transfer_read %collapse1[%c0], %df: tensor<12xf64>, vector<12xf64>
vector.print %v1 : vector<12xf64>
%b2 = sparse_tensor.values %collapse2 : tensor<12xf64, #SparseVector> to memref<?xf64>
%v2 = vector.transfer_read %b2[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %v2 : vector<12xf64>
%b3 = sparse_tensor.values %collapse3 : tensor<12xf64, #SparseVector> to memref<?xf64>
%v3 = vector.transfer_read %b3[%c0], %df: memref<?xf64>, vector<12xf64>
vector.print %v3 : vector<12xf64>
%v4 = vector.transfer_read %collapse4[%c0, %c0], %df: tensor<6x10xf64>, vector<6x10xf64>
vector.print %v4 : vector<6x10xf64>
%v5 = vector.transfer_read %collapse5[%c0, %c0], %df: tensor<6x10xf64>, vector<6x10xf64>
vector.print %v5 : vector<6x10xf64>
%b6 = sparse_tensor.values %collapse6 : tensor<6x10xf64, #SparseMatrix> to memref<?xf64>
%v6 = vector.transfer_read %b6[%c0], %df: memref<?xf64>, vector<60xf64>
vector.print %v6 : vector<60xf64>
%b7 = sparse_tensor.values %collapse7 : tensor<6x10xf64, #SparseMatrix> to memref<?xf64>
%v7 = vector.transfer_read %b7[%c0], %df: memref<?xf64>, vector<60xf64>
vector.print %v7 : vector<60xf64>
%v8 = vector.transfer_read %collapse8[%c0, %c0], %df: tensor<?x?xf64>, vector<6x10xf64>
vector.print %v8 : vector<6x10xf64>
%v9 = vector.transfer_read %collapse9[%c0, %c0], %df: tensor<?x?xf64>, vector<6x10xf64>
vector.print %v9 : vector<6x10xf64>
%b10 = sparse_tensor.values %collapse10 : tensor<?x?xf64, #SparseMatrix> to memref<?xf64>
%v10 = vector.transfer_read %b10[%c0], %df: memref<?xf64>, vector<60xf64>
vector.print %v10 : vector<60xf64>
%b11 = sparse_tensor.values %collapse11 : tensor<?x?xf64, #SparseMatrix> to memref<?xf64>
%v11 = vector.transfer_read %b11[%c0], %df: memref<?xf64>, vector<60xf64>
vector.print %v11 : vector<60xf64>
// Release sparse resources.
bufferization.dealloc_tensor %sv : tensor<12xf64, #SparseVector>
bufferization.dealloc_tensor %sm : tensor<3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %sn : tensor<2x3x5x2xf64, #Sparse4dTensor>
bufferization.dealloc_tensor %sdm : tensor<?x?xf64, #SparseMatrix>
bufferization.dealloc_tensor %sdn : tensor<?x?x?x?xf64, #Sparse4dTensor>
bufferization.dealloc_tensor %expand2 : tensor<3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %expand3 : tensor<3x4xf64, #SparseMatrix>
bufferization.dealloc_tensor %expand6 : tensor<3x2x2xf64, #Sparse3dTensor>
bufferization.dealloc_tensor %expand7 : tensor<3x2x2xf64, #Sparse3dTensor>
bufferization.dealloc_tensor %expand10 : tensor<?x2x?xf64, #Sparse3dTensor>
bufferization.dealloc_tensor %expand11 : tensor<?x2x?xf64, #Sparse3dTensor>
bufferization.dealloc_tensor %collapse2 : tensor<12xf64, #SparseVector>
bufferization.dealloc_tensor %collapse3 : tensor<12xf64, #SparseVector>
bufferization.dealloc_tensor %collapse6 : tensor<6x10xf64, #SparseMatrix>
bufferization.dealloc_tensor %collapse7 : tensor<6x10xf64, #SparseMatrix>
bufferization.dealloc_tensor %collapse10 : tensor<?x?xf64, #SparseMatrix>
bufferization.dealloc_tensor %collapse11 : tensor<?x?xf64, #SparseMatrix>
// Release dense resources.
bufferization.dealloc_tensor %expand1 : tensor<3x4xf64>
bufferization.dealloc_tensor %collapse1 : tensor<12xf64>
bufferization.dealloc_tensor %expand5 : tensor<3x2x2xf64>
bufferization.dealloc_tensor %collapse5 : tensor<6x10xf64>
bufferization.dealloc_tensor %expand9 : tensor<?x2x?xf64>
bufferization.dealloc_tensor %collapse9: tensor<?x?xf64>
return
}
}
mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_expand_shape.mlir