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Mar 5, 2024
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[mlir][sparse] migrate tests to sparse_tensor.print #84055

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[mlir][sparse] migrate tests to sparse_tensor.print
aartbik Mar 5, 2024
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aartbik Mar 5, 2024
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aartbik Mar 5, 2024
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add missing //
aartbik Mar 5, 2024
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85 changes: 35 additions & 50 deletions mlir/test/Integration/Dialect/SparseTensor/CPU/concatenate_dim_0.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
// DEFINE: %{run_opts} = -e entry -entry-point-result=void
// DEFINE: %{run_opts} = -e main -entry-point-result=void
// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs}
//
Expand Down Expand Up @@ -51,11 +51,6 @@

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> {
Expand Down Expand Up @@ -85,30 +80,15 @@ module {
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>) -> ()

bufferization.dealloc_tensor %c : tensor<9x4xf64>
return
}

// Outputs dense matrix.
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() {
func.func @main() {
%m24 = arith.constant dense<
[ [ 1.0, 0.0, 3.0, 0.0],
[ 0.0, 2.0, 0.0, 0.0] ]> : tensor<2x4xf64>
Expand All @@ -126,22 +106,24 @@ module {
%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
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 18
// CHECK-NEXT: dim = ( 9, 4 )
// CHECK-NEXT: lvl = ( 9, 4 )
// CHECK-NEXT: pos[0] : ( 0, 9,
// CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7, 8,
// CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 7, 10, 12, 13, 16, 18,
// CHECK-NEXT: crd[1] : ( 0, 2, 1, 0, 2, 3, 1, 0, 1, 2, 2, 3, 1, 0, 1, 2, 0, 1,
// CHECK-NEXT: values : ( 1, 3, 2, 1, 1, 1, 0.5, 1, 5, 2, 1.5, 1, 3.5, 1, 5, 2, 1, 0.5,
// CHECK-NEXT: ----
//
%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>) -> ()
sparse_tensor.print %0 : tensor<9x4xf64, #MAT_C_C>

// CHECK: {{\[}}[1, 0, 3, 0],
//
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
Expand All @@ -150,26 +132,29 @@ module {
// 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
//
// CHECK: ---- Sparse Tensor ----
// CHECK-NEXT: nse = 18
// CHECK-NEXT: dim = ( 9, 4 )
// CHECK-NEXT: lvl = ( 9, 4 )
// CHECK-NEXT: pos[0] : ( 0, 9,
// CHECK-NEXT: crd[0] : ( 0, 1, 2, 3, 4, 5, 6, 7, 8,
// CHECK-NEXT: pos[1] : ( 0, 2, 3, 6, 7, 10, 12, 13, 16, 18,
// CHECK-NEXT: crd[1] : ( 0, 2, 1, 0, 2, 3, 1, 0, 1, 2, 2, 3, 1, 0, 1, 2, 0, 1,
// CHECK-NEXT: values : ( 1, 3, 2, 1, 1, 1, 0.5, 1, 5, 2, 1.5, 1, 3.5, 1, 5, 2, 1, 0.5,
// CHECK-NEXT: ----
//
%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>) -> ()
sparse_tensor.print %2 : tensor<9x4xf64, #MAT_C_C>

// CHECK: {{\[}}[1, 0, 3, 0],
//
// CHECK: {{\[}}[1, 0, 3, 0],
// CHECK-NEXT: [0, 2, 0, 0],
// CHECK-NEXT: [1, 0, 1, 1],
// CHECK-NEXT: [0, 0.5, 0, 0],
Expand All @@ -178,11 +163,11 @@ module {
// 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>
Expand Down
138 changes: 66 additions & 72 deletions mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_block_matmul.mlir
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
// DEFINE: %{compile} = mlir-opt %s --sparsifier="%{sparsifier_opts}"
// DEFINE: %{compile_sve} = mlir-opt %s --sparsifier="%{sparsifier_opts_sve}"
// DEFINE: %{run_libs} = -shared-libs=%mlir_c_runner_utils,%mlir_runner_utils
// DEFINE: %{run_opts} = -e entry -entry-point-result=void
// DEFINE: %{run_opts} = -e main -entry-point-result=void
// DEFINE: %{run} = mlir-cpu-runner %{run_opts} %{run_libs}
// DEFINE: %{run_sve} = %mcr_aarch64_cmd --march=aarch64 --mattr="+sve" %{run_opts} %{run_libs}
//
Expand Down Expand Up @@ -45,7 +45,6 @@
map = ( i, j ) -> (i : dense, j : compressed)
}>


#BSR = #sparse_tensor.encoding<{
map = ( i, j ) ->
( i floordiv 2 : dense,
Expand All @@ -65,67 +64,66 @@

module {

func.func @mul(%arg0: tensor<4x8xf64>,
%arg1: tensor<4x8xf64, #BSR>) -> tensor<4x4xf64> {
%out = arith.constant dense<0.0> : tensor<4x4xf64>
%0 = linalg.generic #trait_mul
ins(%arg0, %arg1: tensor<4x8xf64>, tensor<4x8xf64, #BSR>)
outs(%out: tensor<4x4xf64>) {
^bb(%x: f64, %y : f64, %z : f64):
%1 = arith.mulf %x, %y : f64
%2 = arith.addf %1, %z : f64
linalg.yield %2 : f64
} -> tensor<4x4xf64>
return %0 : tensor<4x4xf64>
}

func.func @mul_24(%arg0: tensor<4x8xf64>,
%arg1: tensor<4x8xf64, #NV_24>) -> tensor<4x4xf64> {
%out = arith.constant dense<0.0> : tensor<4x4xf64>
%0 = linalg.generic #trait_mul
ins(%arg0, %arg1: tensor<4x8xf64>, tensor<4x8xf64, #NV_24>)
outs(%out: tensor<4x4xf64>) {
^bb(%x: f64, %y : f64, %z : f64):
%1 = arith.mulf %x, %y : f64
%2 = arith.addf %1, %z : f64
linalg.yield %2 : f64
} -> tensor<4x4xf64>
return %0 : tensor<4x4xf64>
}
func.func @mul(%arg0: tensor<4x8xf64>,
%arg1: tensor<4x8xf64, #BSR>) -> tensor<4x4xf64> {
%out = arith.constant dense<0.0> : tensor<4x4xf64>
%0 = linalg.generic #trait_mul
ins(%arg0, %arg1: tensor<4x8xf64>, tensor<4x8xf64, #BSR>)
outs(%out: tensor<4x4xf64>) {
^bb(%x: f64, %y : f64, %z : f64):
%1 = arith.mulf %x, %y : f64
%2 = arith.addf %1, %z : f64
linalg.yield %2 : f64
} -> tensor<4x4xf64>
return %0 : tensor<4x4xf64>
}

func.func @mul_csr_bsr(%arg0: tensor<4x8xf64, #CSR>,
%arg1: tensor<4x8xf64, #BSR>) -> tensor<4x4xf64> {
%out = arith.constant dense<0.0> : tensor<4x4xf64>
%0 = linalg.generic #trait_mul
ins(%arg0, %arg1: tensor<4x8xf64, #CSR>, tensor<4x8xf64, #BSR>)
outs(%out: tensor<4x4xf64>) {
^bb(%x: f64, %y : f64, %z : f64):
%1 = arith.mulf %x, %y : f64
%2 = arith.addf %1, %z : f64
linalg.yield %2 : f64
} -> tensor<4x4xf64>
return %0 : tensor<4x4xf64>
}
func.func @mul_24(%arg0: tensor<4x8xf64>,
%arg1: tensor<4x8xf64, #NV_24>) -> tensor<4x4xf64> {
%out = arith.constant dense<0.0> : tensor<4x4xf64>
%0 = linalg.generic #trait_mul
ins(%arg0, %arg1: tensor<4x8xf64>, tensor<4x8xf64, #NV_24>)
outs(%out: tensor<4x4xf64>) {
^bb(%x: f64, %y : f64, %z : f64):
%1 = arith.mulf %x, %y : f64
%2 = arith.addf %1, %z : f64
linalg.yield %2 : f64
} -> tensor<4x4xf64>
return %0 : tensor<4x4xf64>
}

func.func @mul_dense(%arg0: tensor<4x8xf64>,
%arg1: tensor<4x8xf64>) -> tensor<4x4xf64> {
%out = arith.constant dense<0.0> : tensor<4x4xf64>
%0 = linalg.generic #trait_mul
ins(%arg0, %arg1: tensor<4x8xf64>, tensor<4x8xf64>)
outs(%out: tensor<4x4xf64>) {
^bb(%x: f64, %y : f64, %z : f64):
%1 = arith.mulf %x, %y : f64
%2 = arith.addf %1, %z : f64
linalg.yield %2 : f64
} -> tensor<4x4xf64>
return %0 : tensor<4x4xf64>
}
func.func @mul_csr_bsr(%arg0: tensor<4x8xf64, #CSR>,
%arg1: tensor<4x8xf64, #BSR>) -> tensor<4x4xf64> {
%out = arith.constant dense<0.0> : tensor<4x4xf64>
%0 = linalg.generic #trait_mul
ins(%arg0, %arg1: tensor<4x8xf64, #CSR>, tensor<4x8xf64, #BSR>)
outs(%out: tensor<4x4xf64>) {
^bb(%x: f64, %y : f64, %z : f64):
%1 = arith.mulf %x, %y : f64
%2 = arith.addf %1, %z : f64
linalg.yield %2 : f64
} -> tensor<4x4xf64>
return %0 : tensor<4x4xf64>
}

func.func @mul_dense(%arg0: tensor<4x8xf64>,
%arg1: tensor<4x8xf64>) -> tensor<4x4xf64> {
%out = arith.constant dense<0.0> : tensor<4x4xf64>
%0 = linalg.generic #trait_mul
ins(%arg0, %arg1: tensor<4x8xf64>, tensor<4x8xf64>)
outs(%out: tensor<4x4xf64>) {
^bb(%x: f64, %y : f64, %z : f64):
%1 = arith.mulf %x, %y : f64
%2 = arith.addf %1, %z : f64
linalg.yield %2 : f64
} -> tensor<4x4xf64>
return %0 : tensor<4x4xf64>
}

//
// Output utilities.
// Output utility.
//
func.func @dumpf64(%arg0: tensor<4x4xf64>) {
func.func @dump_dense_f64(%arg0: tensor<4x4xf64>) {
%c0 = arith.constant 0 : index
%d0 = arith.constant -1.0 : f64
%0 = vector.transfer_read %arg0[%c0, %c0], %d0: tensor<4x4xf64>, vector<4x4xf64>
Expand All @@ -136,36 +134,32 @@ func.func @mul_dense(%arg0: tensor<4x8xf64>,
//
// Main driver.
//
func.func @entry() {
func.func @main() {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%c2 = arith.constant 2 : index


%td = arith.constant dense<[[ 1.0, 2.0, 0.0, 0.0, 0.0, 0.0, 4.0, 5.0],
[ 6.0, 7.0, 0.0, 0.0, 0.0, 0.0, 10.0, 11.0],
[ 0.0, 0.0, 12.0, 13.0, 16.0, 17.0, 0.0, 0.0],
[ 0.0, 0.0, 18.0, 19.0, 22.0, 23.0, 0.0, 0.0]]> : tensor<4x8xf64>


%2 = sparse_tensor.convert %td : tensor<4x8xf64> to tensor<4x8xf64, #BSR>
%3 = sparse_tensor.convert %td : tensor<4x8xf64> to tensor<4x8xf64, #NV_24>
%4 = sparse_tensor.convert %td : tensor<4x8xf64> to tensor<4x8xf64, #CSR>
%a = sparse_tensor.convert %td : tensor<4x8xf64> to tensor<4x8xf64, #BSR>
%b = sparse_tensor.convert %td : tensor<4x8xf64> to tensor<4x8xf64, #NV_24>
%c = sparse_tensor.convert %td : tensor<4x8xf64> to tensor<4x8xf64, #CSR>

%d = call @mul_dense(%td, %td)
: (tensor<4x8xf64>, tensor<4x8xf64>) -> tensor<4x4xf64>
%s = call @mul(%td, %2)
%s = call @mul(%td, %a)
: (tensor<4x8xf64>, tensor<4x8xf64, #BSR>) -> tensor<4x4xf64>
%s24 = call @mul_24(%td, %3)
%s24 = call @mul_24(%td, %b)
: (tensor<4x8xf64>, tensor<4x8xf64, #NV_24>) -> tensor<4x4xf64>
%scsr = call @mul_csr_bsr(%4, %2)
%scsr = call @mul_csr_bsr(%c, %a)
: (tensor<4x8xf64, #CSR>, tensor<4x8xf64, #BSR>) -> tensor<4x4xf64>

// CHECK-COUNT-4: ( ( 46, 115, 0, 0 ), ( 115, 306, 0, 0 ), ( 0, 0, 858, 1206 ), ( 0, 0, 1206, 1698 ) )
call @dumpf64(%d) : (tensor<4x4xf64>) -> ()
call @dumpf64(%s) : (tensor<4x4xf64>) -> ()
call @dumpf64(%s24) : (tensor<4x4xf64>) -> ()
call @dumpf64(%scsr) : (tensor<4x4xf64>) -> ()
call @dump_dense_f64(%d) : (tensor<4x4xf64>) -> ()
call @dump_dense_f64(%s) : (tensor<4x4xf64>) -> ()
call @dump_dense_f64(%s24) : (tensor<4x4xf64>) -> ()
call @dump_dense_f64(%scsr) : (tensor<4x4xf64>) -> ()

return
}
Expand Down
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