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Differential D144139

[mlir][sparse] split sparse concatenate test cases smaller ones.
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Authored by Peiming on Feb 15 2023, 2:20 PM.

Details

Reviewers
aartbik
nicolasvasilache
Commits
rG9ed587ea9bec: [mlir][sparse] split sparse concatenate test cases smaller ones.
Summary

To allow a better parallelization.

Diff Detail

Event Timeline

Peiming created this revision.Feb 15 2023, 2:20 PM
Herald added a reviewer: aartbik. · View Herald TranscriptFeb 15 2023, 2:20 PM
Herald added a project: Restricted Project. · View Herald Transcript
Herald added subscribers: hanchung, jsetoain, Moerafaat and 23 others. · View Herald Transcript
Peiming requested review of this revision.Feb 15 2023, 2:20 PM
Herald added a reviewer: nicolasvasilache. · View Herald TranscriptFeb 15 2023, 2:20 PM
Herald added a project: Restricted Project. · View Herald Transcript
Herald added subscribers: stephenneuendorffer, nicolasvasilache. · View Herald Transcript
aartbik accepted this revision.Feb 15 2023, 2:27 PM
aartbik added inline comments.
mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_0.mlir
74

while you are here, here and below, period at end

mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_0_permute.mlir
74

while you are here, period at end everyhere

mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_1.mlir
57

while you are here, typo: permutation

74

period at end

mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_1_permute.mlir
57

typo: permutation

74

period at end

This revision is now accepted and ready to land.Feb 15 2023, 2:27 PM
Peiming updated this revision to Diff 497809.Feb 15 2023, 2:35 PM
Peiming marked 3 inline comments as done.

address comments.

Peiming marked 3 inline comments as done.Feb 15 2023, 2:36 PM
Peiming updated this revision to Diff 497812.Feb 15 2023, 2:41 PM

rebase.

Peiming retitled this revision from [mlir][sparse] split sparse concatenate test cases two smaller ones. to [mlir][sparse] split sparse concatenate test cases smaller ones..Feb 15 2023, 2:41 PM
This revision was landed with ongoing or failed builds.Feb 15 2023, 2:54 PM
Closed by commit rG9ed587ea9bec: [mlir][sparse] split sparse concatenate test cases smaller ones. (authored by Peiming). · Explain Why
This revision was automatically updated to reflect the committed changes.
Peiming added a commit: rG9ed587ea9bec: [mlir][sparse] split sparse concatenate test cases smaller ones..
Harbormaster completed remote builds in B213998: Diff 497812.Feb 15 2023, 3:17 PM

Revision Contents

PathSize
mlir/
test/
Integration/
Dialect/
SparseTensor/
CPU/
195 lines
235 lines
178 lines
205 lines

Diff 497803

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

  • This file was deleted.
// 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_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true 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 --dlopen=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
module {
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
func.func private @printMemref1dF64(%ptr : memref<?xf64>) attributes { llvm.emit_c_interface }
//
// Tests without permutation.
//
// Concats all sparse matrices (with different encodings) to a sparse matrix.
func.func @concat_sparse_sparse(%arg0: tensor<2x4xf64, #MAT_C_C>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C>
return %0 : tensor<9x4xf64, #MAT_C_C>
}
// Concats all sparse matrices (with different encodings) to a dense matrix.
func.func @concat_sparse_dense(%arg0: tensor<2x4xf64, #MAT_C_C>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64>
return %0 : tensor<9x4xf64>
}
// Concats all sparse matrices (with different encodings) to a annotated all dense matrix.
func.func @concat_sparse_annotated_dense(%arg0: tensor<2x4xf64, #MAT_C_C>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_D_D> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_D_D>
return %0 : tensor<9x4xf64, #MAT_D_D>
}
// Concats mix sparse and dense matrices to a sparse matrix
func.func @concat_mix_sparse(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C>
return %0 : tensor<9x4xf64, #MAT_C_C>
}
// Concats mix sparse and dense matrices to a dense matrix
func.func @concat_mix_dense(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64>
return %0 : tensor<9x4xf64>
}
//
// Tests with permutation.
//
// Concats all sparse matrices (with different encodings) to a sparse matrix.
func.func @concat_sparse_sparse_perm(%arg0: tensor<2x4xf64, #MAT_C_C_P>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C_P> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C_P>
return %0 : tensor<9x4xf64, #MAT_C_C_P>
}
// Concats all sparse matrices (with different encodings) to a dense matrix.
func.func @concat_sparse_dense_perm(%arg0: tensor<2x4xf64, #MAT_C_C_P>, %arg1: tensor<3x4xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64>
return %0 : tensor<9x4xf64>
}
// Concats mix sparse and dense matrices to a sparse matrix
func.func @concat_mix_sparse_perm(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C>
return %0 : tensor<9x4xf64, #MAT_C_C>
}
// Concats mix sparse and dense matrices to a dense matrix
func.func @concat_mix_dense_perm(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C_P>) -> tensor<9x4xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P> to tensor<9x4xf64>
return %0 : tensor<9x4xf64>
}
//
// Tests without perumutation (concatenate on dimension 1)
//
// Concats all sparse matrices (with different encodings) to a sparse matrix.
func.func @concat_sparse_sparse_dim1(%arg0: tensor<4x2xf64, #MAT_C_C>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C>
return %0 : tensor<4x9xf64, #MAT_C_C>
}
// Concats all sparse matrices (with different encodings) to a dense matrix.
func.func @concat_sparse_dense_dim1(%arg0: tensor<4x2xf64, #MAT_C_C>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
// Concats mix sparse and dense matrices to a sparse matrix
func.func @concat_mix_sparse_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C>
return %0 : tensor<4x9xf64, #MAT_C_C>
}
// Concats mix sparse and dense matrices to a dense matrix
func.func @concat_mix_dense_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
//
// Tests with perumutation (concatenate on dimension 1)
//
// Concats all sparse matrices (with different encodings) to a sparse matrix.
func.func @concat_sparse_sparse_perm_dim1(%arg0: tensor<4x2xf64, #MAT_C_C_P>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C_P> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C_P>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C_P>
return %0 : tensor<4x9xf64, #MAT_C_C_P>
}
// Concats all sparse matrices (with different encodings) to a dense matrix.
func.func @concat_sparse_dense_perm_dim1(%arg0: tensor<4x2xf64, #MAT_C_C_P>, %arg1: tensor<4x3xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C_P>, tensor<4x3xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
// Concats mix sparse and dense matrices to a sparse matrix
func.func @concat_mix_sparse_perm_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C>
return %0 : tensor<4x9xf64, #MAT_C_C>
}
// Concats mix sparse and dense matrices to a dense matrix
func.func @concat_mix_dense_perm_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C_P>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
//
// Concats mix sparse and dense matrices to a sparse matrix (with dynamic sizes)
//
func.func @concat_mix_sparse_dyn(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<?x?xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<?x?xf64, #MAT_C_C>
return %0 : tensor<?x?xf64, #MAT_C_C>
}
func.func @dump_mat_9x4(%A: tensor<9x4xf64, #MAT_C_C>) {
%c = sparse_tensor.convert %A : tensor<9x4xf64, #MAT_C_C> to tensor<9x4xf64>
%cu = tensor.cast %c : tensor<9x4xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<9x4xf64, #MAT_C_C>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<9x4xf64, #MAT_C_C> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_perm_9x4(%A: tensor<9x4xf64, #MAT_C_C_P>) {
%c = sparse_tensor.convert %A : tensor<9x4xf64, #MAT_C_C_P> to tensor<9x4xf64>
%cu = tensor.cast %c : tensor<9x4xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<9x4xf64, #MAT_C_C_P>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<9x4xf64, #MAT_C_C_P> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_dense_9x4(%A: tensor<9x4xf64>) {
%u = tensor.cast %A : tensor<9x4xf64> to tensor<*xf64>
call @printMemrefF64(%u) : (tensor<*xf64>) -> ()
return
}
func.func @dump_mat_annotated_dense_9x4(%A: tensor<9x4xf64, #MAT_D_D>) {
%n = sparse_tensor.number_of_entries %A : tensor<9x4xf64, #MAT_D_D>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<9x4xf64, #MAT_D_D> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_4x9(%A: tensor<4x9xf64, #MAT_C_C>) {
%c = sparse_tensor.convert %A : tensor<4x9xf64, #MAT_C_C> to tensor<4x9xf64>
%cu = tensor.cast %c : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<4x9xf64, #MAT_C_C>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<4x9xf64, #MAT_C_C> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_dyn(%A: tensor<?x?xf64, #MAT_C_C>) {
%c = sparse_tensor.convert %A : tensor<?x?xf64, #MAT_C_C> to tensor<?x?xf64>
%cu = tensor.cast %c : tensor<?x?xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<?x?xf64, #MAT_C_C>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<?x?xf64, #MAT_C_C> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_perm_4x9(%A: tensor<4x9xf64, #MAT_C_C_P>) {
%c = sparse_tensor.convert %A : tensor<4x9xf64, #MAT_C_C_P> to tensor<4x9xf64>
%cu = tensor.cast %c : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<4x9xf64, #MAT_C_C_P>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<4x9xf64, #MAT_C_C_P> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_dense_4x9(%A: tensor<4x9xf64>) {
%1 = tensor.cast %A : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%1) : (tensor<*xf64>) -> ()
return
}
// Driver method to call and verify kernels.
func.func @entry() {
%m42 = arith.constant dense<
[ [ 1.0, 0.0 ],
[ 3.1, 0.0 ],
[ 0.0, 2.0 ],
[ 0.0, 0.0 ] ]> : tensor<4x2xf64>
%m43 = arith.constant dense<
[ [ 1.0, 0.0, 1.0 ],
[ 1.0, 0.0, 0.5 ],
[ 0.0, 0.0, 1.0 ],
[ 5.0, 2.0, 0.0 ] ]> : tensor<4x3xf64>
%m24 = arith.constant dense<
[ [ 1.0, 0.0, 3.0, 0.0],
[ 0.0, 2.0, 0.0, 0.0] ]> : tensor<2x4xf64>
%m34 = arith.constant dense<
[ [ 1.0, 0.0, 1.0, 1.0],
[ 0.0, 0.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0] ]> : tensor<3x4xf64>
%m44 = arith.constant dense<
[ [ 0.0, 0.0, 1.5, 1.0],
[ 0.0, 3.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0],
[ 1.0, 0.5, 0.0, 0.0] ]> : tensor<4x4xf64>
%sm24cc = sparse_tensor.convert %m24 : tensor<2x4xf64> to tensor<2x4xf64, #MAT_C_C>
%sm34cd = sparse_tensor.convert %m34 : tensor<3x4xf64> to tensor<3x4xf64, #MAT_C_D>
%sm42cc = sparse_tensor.convert %m42 : tensor<4x2xf64> to tensor<4x2xf64, #MAT_C_C>
%sm43cd = sparse_tensor.convert %m43 : tensor<4x3xf64> to tensor<4x3xf64, #MAT_C_D>
%sm44dc = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C>
%sm24ccp = sparse_tensor.convert %m24 : tensor<2x4xf64> to tensor<2x4xf64, #MAT_C_C_P>
%sm34cdp = sparse_tensor.convert %m34 : tensor<3x4xf64> to tensor<3x4xf64, #MAT_C_D_P>
%sm42ccp = sparse_tensor.convert %m42 : tensor<4x2xf64> to tensor<4x2xf64, #MAT_C_C_P>
%sm43cdp = sparse_tensor.convert %m43 : tensor<4x3xf64> to tensor<4x3xf64, #MAT_C_D_P>
%sm44dcp = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C_P>
%sm43cd_dyn = sparse_tensor.convert %m43 : tensor<4x3xf64> to tensor<?x?xf64, #MAT_C_D>
%sm44dc_dyn = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<?x?xf64, #MAT_D_C>
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 3, 2, 1, 1, 1, 0.5, 1, 5, 2, 1.5, 1, 3.5, 1, 5, 2, 1, 0.5
%0 = call @concat_sparse_sparse(%sm24cc, %sm34cd, %sm44dc)
: (tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C>
call @dump_mat_9x4(%0) : (tensor<9x4xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
%1 = call @concat_sparse_dense(%sm24cc, %sm34cd, %sm44dc)
: (tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64>
call @dump_mat_dense_9x4(%1) : (tensor<9x4xf64>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 3, 2, 1, 1, 1, 0.5, 1, 5, 2, 1.5, 1, 3.5, 1, 5, 2, 1, 0.5
%2 = call @concat_mix_sparse(%m24, %sm34cd, %sm44dc)
: (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C>
call @dump_mat_9x4(%2) : (tensor<9x4xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
%3 = call @concat_mix_dense(%m24, %sm34cd, %sm44dc)
: (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64>
call @dump_mat_dense_9x4(%3) : (tensor<9x4xf64>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1, 1, 2, 0.5, 5, 3.5, 5, 0.5, 3, 1, 2, 1.5, 2, 1, 1
%4 = call @concat_sparse_sparse_perm(%sm24ccp, %sm34cd, %sm44dc)
: (tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C_P>
call @dump_mat_perm_9x4(%4) : (tensor<9x4xf64, #MAT_C_C_P>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
%5 = call @concat_sparse_dense_perm(%sm24ccp, %sm34cdp, %sm44dc)
: (tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64>
call @dump_mat_dense_9x4(%5) : (tensor<9x4xf64>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 3, 2, 1, 1, 1, 0.5, 1, 5, 2, 1.5, 1, 3.5, 1, 5, 2, 1, 0.5
%6 = call @concat_mix_sparse_perm(%m24, %sm34cdp, %sm44dc)
: (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C>
call @dump_mat_9x4(%6) : (tensor<9x4xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
%7 = call @concat_mix_dense_perm(%m24, %sm34cd, %sm44dcp)
: (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P>) -> tensor<9x4xf64>
call @dump_mat_dense_9x4(%7) : (tensor<9x4xf64>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1.5, 1, 3.1, 1, 0.5, 3.5, 2, 1, 1, 5, 2, 5, 2, 1, 0.5
%8 = call @concat_sparse_sparse_dim1(%sm42cc, %sm43cd, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C>
call @dump_mat_4x9(%8) : (tensor<4x9xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%9 = call @concat_sparse_dense_dim1(%sm42cc, %sm43cd, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%9) : (tensor<4x9xf64>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1.5, 1, 3.1, 1, 0.5, 3.5, 2, 1, 1, 5, 2, 5, 2, 1, 0.5
%10 = call @concat_mix_sparse_dim1(%m42, %sm43cd, %sm44dc)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C>
call @dump_mat_4x9(%10) : (tensor<4x9xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%11 = call @concat_mix_dense_dim1(%m42, %sm43cd, %sm44dc)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%11) : (tensor<4x9xf64>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 3.1, 2, 1, 1, 5, 2, 1, 0.5, 1, 1, 1, 3.5, 5, 0.5, 1.5, 2, 1
%12 = call @concat_sparse_sparse_perm_dim1(%sm42ccp, %sm43cd, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C_P>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C_P>
call @dump_mat_perm_4x9(%12) : (tensor<4x9xf64, #MAT_C_C_P>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%13 = call @concat_sparse_dense_perm_dim1(%sm42ccp, %sm43cdp, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C_P>, tensor<4x3xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%13) : (tensor<4x9xf64>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1.5, 1, 3.1, 1, 0.5, 3.5, 2, 1, 1, 5, 2, 5, 2, 1, 0.5
%14 = call @concat_mix_sparse_perm_dim1(%m42, %sm43cdp, %sm44dc)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C>
call @dump_mat_4x9(%14) : (tensor<4x9xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%15 = call @concat_mix_dense_perm_dim1(%m42, %sm43cd, %sm44dcp)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%15) : (tensor<4x9xf64>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1.5, 1, 3.1, 1, 0.5, 3.5, 2, 1, 1, 5, 2, 5, 2, 1, 0.5
%16 = call @concat_mix_sparse_dyn(%m42, %sm43cd, %sm44dc)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<?x?xf64, #MAT_C_C>
call @dump_mat_dyn(%16) : (tensor<?x?xf64, #MAT_C_C>) -> ()
// CHECK-NEXT: 36
// CHECK: [1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 2, 0, 0.5, 5, 0, 3.5, 5, 0.5, 3, 0, 1, 0, 2, 1.5, 0, 2, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0
%17 = call @concat_sparse_annotated_dense(%sm24cc, %sm34cd, %sm44dc)
: (tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_D_D>
call @dump_mat_annotated_dense_9x4(%17) : (tensor<9x4xf64, #MAT_D_D>) -> ()
// Release resources.
bufferization.dealloc_tensor %sm24cc : tensor<2x4xf64, #MAT_C_C>
bufferization.dealloc_tensor %sm34cd : tensor<3x4xf64, #MAT_C_D>
bufferization.dealloc_tensor %sm42cc : tensor<4x2xf64, #MAT_C_C>
bufferization.dealloc_tensor %sm43cd : tensor<4x3xf64, #MAT_C_D>
bufferization.dealloc_tensor %sm44dc : tensor<4x4xf64, #MAT_D_C>
bufferization.dealloc_tensor %sm24ccp : tensor<2x4xf64, #MAT_C_C_P>
bufferization.dealloc_tensor %sm34cdp : tensor<3x4xf64, #MAT_C_D_P>
bufferization.dealloc_tensor %sm42ccp : tensor<4x2xf64, #MAT_C_C_P>
bufferization.dealloc_tensor %sm43cdp : tensor<4x3xf64, #MAT_C_D_P>
bufferization.dealloc_tensor %sm44dcp : tensor<4x4xf64, #MAT_D_C_P>
bufferization.dealloc_tensor %0 : tensor<9x4xf64, #MAT_C_C>
bufferization.dealloc_tensor %1 : tensor<9x4xf64>
bufferization.dealloc_tensor %2 : tensor<9x4xf64, #MAT_C_C>
bufferization.dealloc_tensor %3 : tensor<9x4xf64>
bufferization.dealloc_tensor %4 : tensor<9x4xf64, #MAT_C_C_P>
bufferization.dealloc_tensor %5 : tensor<9x4xf64>
bufferization.dealloc_tensor %6 : tensor<9x4xf64, #MAT_C_C>
bufferization.dealloc_tensor %7 : tensor<9x4xf64>
bufferization.dealloc_tensor %8 : tensor<4x9xf64, #MAT_C_C>
bufferization.dealloc_tensor %9 : tensor<4x9xf64>
bufferization.dealloc_tensor %10 : tensor<4x9xf64, #MAT_C_C>
bufferization.dealloc_tensor %11 : tensor<4x9xf64>
bufferization.dealloc_tensor %12 : tensor<4x9xf64, #MAT_C_C_P>
bufferization.dealloc_tensor %13 : tensor<4x9xf64>
bufferization.dealloc_tensor %14 : tensor<4x9xf64, #MAT_C_C>
bufferization.dealloc_tensor %15 : tensor<4x9xf64>
bufferization.dealloc_tensor %16 : tensor<?x?xf64, #MAT_C_C>
bufferization.dealloc_tensor %17 : tensor<9x4xf64, #MAT_D_D>
return
}
}

mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_0.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_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true 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 --dlopen=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
module {
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
func.func private @printMemref1dF64(%ptr : memref<?xf64>) attributes { llvm.emit_c_interface }
//
// Tests without permutation.
//
// Concats all sparse matrices (with different encodings) to a sparse matrix.
func.func @concat_sparse_sparse(%arg0: tensor<2x4xf64, #MAT_C_C>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C>
return %0 : tensor<9x4xf64, #MAT_C_C>
}
// Concats all sparse matrices (with different encodings) to a dense matrix.
func.func @concat_sparse_dense(%arg0: tensor<2x4xf64, #MAT_C_C>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64>
return %0 : tensor<9x4xf64>
}
// Concats mix sparse and dense matrices to a sparse matrix
aartbikUnsubmitted
Done
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func.func @concat_mix_sparse(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C>
return %0 : tensor<9x4xf64, #MAT_C_C>
}
// Concats mix sparse and dense matrices to a dense matrix
func.func @concat_mix_dense(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64>
return %0 : tensor<9x4xf64>
}
func.func @dump_mat_9x4(%A: tensor<9x4xf64, #MAT_C_C>) {
%c = sparse_tensor.convert %A : tensor<9x4xf64, #MAT_C_C> to tensor<9x4xf64>
%cu = tensor.cast %c : tensor<9x4xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<9x4xf64, #MAT_C_C>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<9x4xf64, #MAT_C_C> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_dense_9x4(%A: tensor<9x4xf64>) {
%u = tensor.cast %A : tensor<9x4xf64> to tensor<*xf64>
call @printMemrefF64(%u) : (tensor<*xf64>) -> ()
return
}
// Driver method to call and verify kernels.
func.func @entry() {
%m24 = arith.constant dense<
[ [ 1.0, 0.0, 3.0, 0.0],
[ 0.0, 2.0, 0.0, 0.0] ]> : tensor<2x4xf64>
%m34 = arith.constant dense<
[ [ 1.0, 0.0, 1.0, 1.0],
[ 0.0, 0.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0] ]> : tensor<3x4xf64>
%m44 = arith.constant dense<
[ [ 0.0, 0.0, 1.5, 1.0],
[ 0.0, 3.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0],
[ 1.0, 0.5, 0.0, 0.0] ]> : tensor<4x4xf64>
%sm24cc = sparse_tensor.convert %m24 : tensor<2x4xf64> to tensor<2x4xf64, #MAT_C_C>
%sm34cd = sparse_tensor.convert %m34 : tensor<3x4xf64> to tensor<3x4xf64, #MAT_C_D>
%sm44dc = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C>
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 3, 2, 1, 1, 1, 0.5, 1, 5, 2, 1.5, 1, 3.5, 1, 5, 2, 1, 0.5
%0 = call @concat_sparse_sparse(%sm24cc, %sm34cd, %sm44dc)
: (tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C>
call @dump_mat_9x4(%0) : (tensor<9x4xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
%1 = call @concat_sparse_dense(%sm24cc, %sm34cd, %sm44dc)
: (tensor<2x4xf64, #MAT_C_C>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64>
call @dump_mat_dense_9x4(%1) : (tensor<9x4xf64>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 3, 2, 1, 1, 1, 0.5, 1, 5, 2, 1.5, 1, 3.5, 1, 5, 2, 1, 0.5
%2 = call @concat_mix_sparse(%m24, %sm34cd, %sm44dc)
: (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C>
call @dump_mat_9x4(%2) : (tensor<9x4xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
%3 = call @concat_mix_dense(%m24, %sm34cd, %sm44dc)
: (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64>
call @dump_mat_dense_9x4(%3) : (tensor<9x4xf64>) -> ()
// Release resources.
bufferization.dealloc_tensor %sm24cc : tensor<2x4xf64, #MAT_C_C>
bufferization.dealloc_tensor %sm34cd : tensor<3x4xf64, #MAT_C_D>
bufferization.dealloc_tensor %sm44dc : tensor<4x4xf64, #MAT_D_C>
bufferization.dealloc_tensor %0 : tensor<9x4xf64, #MAT_C_C>
bufferization.dealloc_tensor %1 : tensor<9x4xf64>
bufferization.dealloc_tensor %2 : tensor<9x4xf64, #MAT_C_C>
bufferization.dealloc_tensor %3 : tensor<9x4xf64>
return
}
}

mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_0_permute.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_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true 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 --dlopen=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
module {
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
func.func private @printMemref1dF64(%ptr : memref<?xf64>) attributes { llvm.emit_c_interface }
//
// Tests with permutation.
//
// Concats all sparse matrices (with different encodings) to a sparse matrix.
func.func @concat_sparse_sparse_perm(%arg0: tensor<2x4xf64, #MAT_C_C_P>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C_P> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C_P>
return %0 : tensor<9x4xf64, #MAT_C_C_P>
}
// Concats all sparse matrices (with different encodings) to a dense matrix.
func.func @concat_sparse_dense_perm(%arg0: tensor<2x4xf64, #MAT_C_C_P>, %arg1: tensor<3x4xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64>
return %0 : tensor<9x4xf64>
}
// Concats mix sparse and dense matrices to a sparse matrix
aartbikUnsubmitted
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func.func @concat_mix_sparse_perm(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<9x4xf64, #MAT_C_C>
return %0 : tensor<9x4xf64, #MAT_C_C>
}
// Concats mix sparse and dense matrices to a dense matrix
func.func @concat_mix_dense_perm(%arg0: tensor<2x4xf64>, %arg1: tensor<3x4xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C_P>) -> tensor<9x4xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 0 : index}
: tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P> to tensor<9x4xf64>
return %0 : tensor<9x4xf64>
}
func.func @dump_mat_9x4(%A: tensor<9x4xf64, #MAT_C_C>) {
%c = sparse_tensor.convert %A : tensor<9x4xf64, #MAT_C_C> to tensor<9x4xf64>
%cu = tensor.cast %c : tensor<9x4xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<9x4xf64, #MAT_C_C>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<9x4xf64, #MAT_C_C> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_perm_9x4(%A: tensor<9x4xf64, #MAT_C_C_P>) {
%c = sparse_tensor.convert %A : tensor<9x4xf64, #MAT_C_C_P> to tensor<9x4xf64>
%cu = tensor.cast %c : tensor<9x4xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<9x4xf64, #MAT_C_C_P>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<9x4xf64, #MAT_C_C_P> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_dense_9x4(%A: tensor<9x4xf64>) {
%u = tensor.cast %A : tensor<9x4xf64> to tensor<*xf64>
call @printMemrefF64(%u) : (tensor<*xf64>) -> ()
return
}
func.func @dump_mat_annotated_dense_9x4(%A: tensor<9x4xf64, #MAT_D_D>) {
%n = sparse_tensor.number_of_entries %A : tensor<9x4xf64, #MAT_D_D>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<9x4xf64, #MAT_D_D> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
// Driver method to call and verify kernels.
func.func @entry() {
%m42 = arith.constant dense<
[ [ 1.0, 0.0 ],
[ 3.1, 0.0 ],
[ 0.0, 2.0 ],
[ 0.0, 0.0 ] ]> : tensor<4x2xf64>
%m43 = arith.constant dense<
[ [ 1.0, 0.0, 1.0 ],
[ 1.0, 0.0, 0.5 ],
[ 0.0, 0.0, 1.0 ],
[ 5.0, 2.0, 0.0 ] ]> : tensor<4x3xf64>
%m24 = arith.constant dense<
[ [ 1.0, 0.0, 3.0, 0.0],
[ 0.0, 2.0, 0.0, 0.0] ]> : tensor<2x4xf64>
%m34 = arith.constant dense<
[ [ 1.0, 0.0, 1.0, 1.0],
[ 0.0, 0.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0] ]> : tensor<3x4xf64>
%m44 = arith.constant dense<
[ [ 0.0, 0.0, 1.5, 1.0],
[ 0.0, 3.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0],
[ 1.0, 0.5, 0.0, 0.0] ]> : tensor<4x4xf64>
%sm24cc = sparse_tensor.convert %m24 : tensor<2x4xf64> to tensor<2x4xf64, #MAT_C_C>
%sm34cd = sparse_tensor.convert %m34 : tensor<3x4xf64> to tensor<3x4xf64, #MAT_C_D>
%sm44dc = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C>
%sm24ccp = sparse_tensor.convert %m24 : tensor<2x4xf64> to tensor<2x4xf64, #MAT_C_C_P>
%sm34cdp = sparse_tensor.convert %m34 : tensor<3x4xf64> to tensor<3x4xf64, #MAT_C_D_P>
%sm44dcp = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C_P>
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1, 1, 2, 0.5, 5, 3.5, 5, 0.5, 3, 1, 2, 1.5, 2, 1, 1
%4 = call @concat_sparse_sparse_perm(%sm24ccp, %sm34cd, %sm44dc)
: (tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C_P>
call @dump_mat_perm_9x4(%4) : (tensor<9x4xf64, #MAT_C_C_P>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
%5 = call @concat_sparse_dense_perm(%sm24ccp, %sm34cdp, %sm44dc)
: (tensor<2x4xf64, #MAT_C_C_P>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64>
call @dump_mat_dense_9x4(%5) : (tensor<9x4xf64>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 3, 2, 1, 1, 1, 0.5, 1, 5, 2, 1.5, 1, 3.5, 1, 5, 2, 1, 0.5
%6 = call @concat_mix_sparse_perm(%m24, %sm34cdp, %sm44dc)
: (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<9x4xf64, #MAT_C_C>
call @dump_mat_9x4(%6) : (tensor<9x4xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 1.5, 1],
// CHECK-NEXT: [0, 3.5, 0, 0],
// CHECK-NEXT: [1, 5, 2, 0],
// CHECK-NEXT: [1, 0.5, 0, 0]]
%7 = call @concat_mix_dense_perm(%m24, %sm34cd, %sm44dcp)
: (tensor<2x4xf64>, tensor<3x4xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P>) -> tensor<9x4xf64>
call @dump_mat_dense_9x4(%7) : (tensor<9x4xf64>) -> ()
// Release resources.
bufferization.dealloc_tensor %sm24cc : tensor<2x4xf64, #MAT_C_C>
bufferization.dealloc_tensor %sm34cd : tensor<3x4xf64, #MAT_C_D>
bufferization.dealloc_tensor %sm44dc : tensor<4x4xf64, #MAT_D_C>
bufferization.dealloc_tensor %sm24ccp : tensor<2x4xf64, #MAT_C_C_P>
bufferization.dealloc_tensor %sm34cdp : tensor<3x4xf64, #MAT_C_D_P>
bufferization.dealloc_tensor %sm44dcp : tensor<4x4xf64, #MAT_D_C_P>
bufferization.dealloc_tensor %4 : tensor<9x4xf64, #MAT_C_C_P>
bufferization.dealloc_tensor %5 : tensor<9x4xf64>
bufferization.dealloc_tensor %6 : tensor<9x4xf64, #MAT_C_C>
bufferization.dealloc_tensor %7 : tensor<9x4xf64>
return
}
}

mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_1.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_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true 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 --dlopen=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
module {
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
func.func private @printMemref1dF64(%ptr : memref<?xf64>) attributes { llvm.emit_c_interface }
//
// Tests without perumutation (concatenate on dimension 1)
aartbikUnsubmitted
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while you are here, typo: permutation

aartbik: while you are here, typo: permutation
//
// Concats all sparse matrices (with different encodings) to a sparse matrix.
func.func @concat_sparse_sparse_dim1(%arg0: tensor<4x2xf64, #MAT_C_C>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C>
return %0 : tensor<4x9xf64, #MAT_C_C>
}
// Concats all sparse matrices (with different encodings) to a dense matrix.
func.func @concat_sparse_dense_dim1(%arg0: tensor<4x2xf64, #MAT_C_C>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
// Concats mix sparse and dense matrices to a sparse matrix
aartbikUnsubmitted
Done
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period at end

aartbik: period at end
func.func @concat_mix_sparse_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C>
return %0 : tensor<4x9xf64, #MAT_C_C>
}
// Concats mix sparse and dense matrices to a dense matrix
func.func @concat_mix_dense_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
func.func @dump_mat_4x9(%A: tensor<4x9xf64, #MAT_C_C>) {
%c = sparse_tensor.convert %A : tensor<4x9xf64, #MAT_C_C> to tensor<4x9xf64>
%cu = tensor.cast %c : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<4x9xf64, #MAT_C_C>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<4x9xf64, #MAT_C_C> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_dense_4x9(%A: tensor<4x9xf64>) {
%1 = tensor.cast %A : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%1) : (tensor<*xf64>) -> ()
return
}
// Driver method to call and verify kernels.
func.func @entry() {
%m42 = arith.constant dense<
[ [ 1.0, 0.0 ],
[ 3.1, 0.0 ],
[ 0.0, 2.0 ],
[ 0.0, 0.0 ] ]> : tensor<4x2xf64>
%m43 = arith.constant dense<
[ [ 1.0, 0.0, 1.0 ],
[ 1.0, 0.0, 0.5 ],
[ 0.0, 0.0, 1.0 ],
[ 5.0, 2.0, 0.0 ] ]> : tensor<4x3xf64>
%m44 = arith.constant dense<
[ [ 0.0, 0.0, 1.5, 1.0],
[ 0.0, 3.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0],
[ 1.0, 0.5, 0.0, 0.0] ]> : tensor<4x4xf64>
%sm42cc = sparse_tensor.convert %m42 : tensor<4x2xf64> to tensor<4x2xf64, #MAT_C_C>
%sm43cd = sparse_tensor.convert %m43 : tensor<4x3xf64> to tensor<4x3xf64, #MAT_C_D>
%sm44dc = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C>
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1.5, 1, 3.1, 1, 0.5, 3.5, 2, 1, 1, 5, 2, 5, 2, 1, 0.5
%8 = call @concat_sparse_sparse_dim1(%sm42cc, %sm43cd, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C>
call @dump_mat_4x9(%8) : (tensor<4x9xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%9 = call @concat_sparse_dense_dim1(%sm42cc, %sm43cd, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%9) : (tensor<4x9xf64>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1.5, 1, 3.1, 1, 0.5, 3.5, 2, 1, 1, 5, 2, 5, 2, 1, 0.5
%10 = call @concat_mix_sparse_dim1(%m42, %sm43cd, %sm44dc)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C>
call @dump_mat_4x9(%10) : (tensor<4x9xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%11 = call @concat_mix_dense_dim1(%m42, %sm43cd, %sm44dc)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%11) : (tensor<4x9xf64>) -> ()
// Release resources.
bufferization.dealloc_tensor %sm42cc : tensor<4x2xf64, #MAT_C_C>
bufferization.dealloc_tensor %sm43cd : tensor<4x3xf64, #MAT_C_D>
bufferization.dealloc_tensor %sm44dc : tensor<4x4xf64, #MAT_D_C>
bufferization.dealloc_tensor %8 : tensor<4x9xf64, #MAT_C_C>
bufferization.dealloc_tensor %9 : tensor<4x9xf64>
bufferization.dealloc_tensor %10 : tensor<4x9xf64, #MAT_C_C>
bufferization.dealloc_tensor %11 : tensor<4x9xf64>
return
}
}

mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_1_permute.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_lib_dir/libmlir_c_runner_utils%shlibext,%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// DEFINE: FileCheck %s
//
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true"
// RUN: %{compile} | %{run}
//
// Do the same run, but now with direct IR generation and vectorization.
// REDEFINE: %{option} = "enable-runtime-library=false enable-buffer-initialization=true 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 --dlopen=%mlir_lib_dir/libmlir_runner_utils%shlibext | \
// REDEFINE: FileCheck %s
// RUN: %{compile} | mlir-translate -mlir-to-llvmir | %{run}
#MAT_C_C = #sparse_tensor.encoding<{dimLevelType = ["compressed", "compressed"]}>
#MAT_D_C = #sparse_tensor.encoding<{dimLevelType = ["dense", "compressed"]}>
#MAT_C_D = #sparse_tensor.encoding<{dimLevelType = ["compressed", "dense"]}>
#MAT_D_D = #sparse_tensor.encoding<{
dimLevelType = ["dense", "dense"],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_C_D_P = #sparse_tensor.encoding<{
dimLevelType = [ "compressed", "dense" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
#MAT_D_C_P = #sparse_tensor.encoding<{
dimLevelType = [ "dense", "compressed" ],
dimOrdering = affine_map<(i,j) -> (j,i)>
}>
module {
func.func private @printMemrefF64(%ptr : tensor<*xf64>)
func.func private @printMemref1dF64(%ptr : memref<?xf64>) attributes { llvm.emit_c_interface }
//
// Tests with perumutation (concatenate on dimension 1)
aartbikUnsubmitted
Done
ReplyInline Actions

typo: permutation

aartbik: typo: permutation
//
// Concats all sparse matrices (with different encodings) to a sparse matrix.
func.func @concat_sparse_sparse_perm_dim1(%arg0: tensor<4x2xf64, #MAT_C_C_P>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C_P> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C_P>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C_P>
return %0 : tensor<4x9xf64, #MAT_C_C_P>
}
// Concats all sparse matrices (with different encodings) to a dense matrix.
func.func @concat_sparse_dense_perm_dim1(%arg0: tensor<4x2xf64, #MAT_C_C_P>, %arg1: tensor<4x3xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64, #MAT_C_C_P>, tensor<4x3xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
// Concats mix sparse and dense matrices to a sparse matrix
aartbikUnsubmitted
Done
ReplyInline Actions

period at end

aartbik: period at end
func.func @concat_mix_sparse_perm_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D_P>, %arg2: tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C> to tensor<4x9xf64, #MAT_C_C>
return %0 : tensor<4x9xf64, #MAT_C_C>
}
// Concats mix sparse and dense matrices to a dense matrix
func.func @concat_mix_dense_perm_dim1(%arg0: tensor<4x2xf64>, %arg1: tensor<4x3xf64, #MAT_C_D>, %arg2: tensor<4x4xf64, #MAT_D_C_P>) -> tensor<4x9xf64> {
%0 = sparse_tensor.concatenate %arg0, %arg1, %arg2 {dimension = 1 : index}
: tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P> to tensor<4x9xf64>
return %0 : tensor<4x9xf64>
}
func.func @dump_mat_4x9(%A: tensor<4x9xf64, #MAT_C_C>) {
%c = sparse_tensor.convert %A : tensor<4x9xf64, #MAT_C_C> to tensor<4x9xf64>
%cu = tensor.cast %c : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<4x9xf64, #MAT_C_C>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<4x9xf64, #MAT_C_C> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_perm_4x9(%A: tensor<4x9xf64, #MAT_C_C_P>) {
%c = sparse_tensor.convert %A : tensor<4x9xf64, #MAT_C_C_P> to tensor<4x9xf64>
%cu = tensor.cast %c : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%cu) : (tensor<*xf64>) -> ()
%n = sparse_tensor.number_of_entries %A : tensor<4x9xf64, #MAT_C_C_P>
vector.print %n : index
%1 = sparse_tensor.values %A : tensor<4x9xf64, #MAT_C_C_P> to memref<?xf64>
call @printMemref1dF64(%1) : (memref<?xf64>) -> ()
return
}
func.func @dump_mat_dense_4x9(%A: tensor<4x9xf64>) {
%1 = tensor.cast %A : tensor<4x9xf64> to tensor<*xf64>
call @printMemrefF64(%1) : (tensor<*xf64>) -> ()
return
}
// Driver method to call and verify kernels.
func.func @entry() {
%m42 = arith.constant dense<
[ [ 1.0, 0.0 ],
[ 3.1, 0.0 ],
[ 0.0, 2.0 ],
[ 0.0, 0.0 ] ]> : tensor<4x2xf64>
%m43 = arith.constant dense<
[ [ 1.0, 0.0, 1.0 ],
[ 1.0, 0.0, 0.5 ],
[ 0.0, 0.0, 1.0 ],
[ 5.0, 2.0, 0.0 ] ]> : tensor<4x3xf64>
%m24 = arith.constant dense<
[ [ 1.0, 0.0, 3.0, 0.0],
[ 0.0, 2.0, 0.0, 0.0] ]> : tensor<2x4xf64>
%m34 = arith.constant dense<
[ [ 1.0, 0.0, 1.0, 1.0],
[ 0.0, 0.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0] ]> : tensor<3x4xf64>
%m44 = arith.constant dense<
[ [ 0.0, 0.0, 1.5, 1.0],
[ 0.0, 3.5, 0.0, 0.0],
[ 1.0, 5.0, 2.0, 0.0],
[ 1.0, 0.5, 0.0, 0.0] ]> : tensor<4x4xf64>
%sm42cc = sparse_tensor.convert %m42 : tensor<4x2xf64> to tensor<4x2xf64, #MAT_C_C>
%sm43cd = sparse_tensor.convert %m43 : tensor<4x3xf64> to tensor<4x3xf64, #MAT_C_D>
%sm44dc = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C>
%sm42ccp = sparse_tensor.convert %m42 : tensor<4x2xf64> to tensor<4x2xf64, #MAT_C_C_P>
%sm43cdp = sparse_tensor.convert %m43 : tensor<4x3xf64> to tensor<4x3xf64, #MAT_C_D_P>
%sm44dcp = sparse_tensor.convert %m44 : tensor<4x4xf64> to tensor<4x4xf64, #MAT_D_C_P>
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 3.1, 2, 1, 1, 5, 2, 1, 0.5, 1, 1, 1, 3.5, 5, 0.5, 1.5, 2, 1
%12 = call @concat_sparse_sparse_perm_dim1(%sm42ccp, %sm43cd, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C_P>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C_P>
call @dump_mat_perm_4x9(%12) : (tensor<4x9xf64, #MAT_C_C_P>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%13 = call @concat_sparse_dense_perm_dim1(%sm42ccp, %sm43cdp, %sm44dc)
: (tensor<4x2xf64, #MAT_C_C_P>, tensor<4x3xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%13) : (tensor<4x9xf64>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
// CHECK-NEXT: 18
// CHECK: [1, 1, 1, 1.5, 1, 3.1, 1, 0.5, 3.5, 2, 1, 1, 5, 2, 5, 2, 1, 0.5
%14 = call @concat_mix_sparse_perm_dim1(%m42, %sm43cdp, %sm44dc)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D_P>, tensor<4x4xf64, #MAT_D_C>) -> tensor<4x9xf64, #MAT_C_C>
call @dump_mat_4x9(%14) : (tensor<4x9xf64, #MAT_C_C>) -> ()
// CHECK: {{\[}}[1, 0, 1, 0, 1, 0, 0, 1.5, 1],
// CHECK-NEXT: [3.1, 0, 1, 0, 0.5, 0, 3.5, 0, 0],
// CHECK-NEXT: [0, 2, 0, 0, 1, 1, 5, 2, 0],
// CHECK-NEXT: [0, 0, 5, 2, 0, 1, 0.5, 0, 0]]
%15 = call @concat_mix_dense_perm_dim1(%m42, %sm43cd, %sm44dcp)
: (tensor<4x2xf64>, tensor<4x3xf64, #MAT_C_D>, tensor<4x4xf64, #MAT_D_C_P>) -> tensor<4x9xf64>
call @dump_mat_dense_4x9(%15) : (tensor<4x9xf64>) -> ()
// Release resources.
bufferization.dealloc_tensor %sm42cc : tensor<4x2xf64, #MAT_C_C>
bufferization.dealloc_tensor %sm43cd : tensor<4x3xf64, #MAT_C_D>
bufferization.dealloc_tensor %sm44dc : tensor<4x4xf64, #MAT_D_C>
bufferization.dealloc_tensor %sm42ccp : tensor<4x2xf64, #MAT_C_C_P>
bufferization.dealloc_tensor %sm43cdp : tensor<4x3xf64, #MAT_C_D_P>
bufferization.dealloc_tensor %sm44dcp : tensor<4x4xf64, #MAT_D_C_P>
bufferization.dealloc_tensor %12 : tensor<4x9xf64, #MAT_C_C_P>
bufferization.dealloc_tensor %13 : tensor<4x9xf64>
bufferization.dealloc_tensor %14 : tensor<4x9xf64, #MAT_C_C>
bufferization.dealloc_tensor %15 : tensor<4x9xf64>
return
}
}