[mlir][sparse] test four row/col major versions of BSR #72898
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@llvm/pr-subscribers-mlir Author: Aart Bik (aartbik) ChangesNote, this is a redo of #72712 which was reverted due to time outs in the bot. I have timed the tests on various settings, and it does not even hit the top 20 of integration tests. To be safe, I removed the SIMD version of the tests, just keeping libgen/direcIR paths (which are the most important to test for us). I will also keep an eye on https://lab.llvm.org/buildbot/#/builders/264/builds after submitting to make sure there is no repeat. Full diff: https://github.com/llvm/llvm-project/pull/72898.diff 1 Files Affected:
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/block_majors.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/block_majors.mlir
new file mode 100755
index 000000000000000..780c1e8b3f64a7a
--- /dev/null
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/block_majors.mlir
@@ -0,0 +1,174 @@
+//--------------------------------------------------------------------------------------------------
+// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
+//
+// Set-up that's shared across all tests in this directory. In principle, this
+// config could be moved to lit.local.cfg. However, there are downstream users that
+// do not use these LIT config files. Hence why this is kept inline.
+//
+// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
+// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
+// 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 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}
+//
+// DEFINE: %{env} =
+//--------------------------------------------------------------------------------------------------
+
+// RUN: %{compile} | %{run} | FileCheck %s
+//
+// Do the same run, but now with direct IR generation.
+// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false
+// RUN: %{compile} | %{run} | FileCheck %s
+
+#BSR_row_rowmajor = #sparse_tensor.encoding<{
+ map = (i, j) ->
+ ( i floordiv 3 : dense
+ , j floordiv 4 : compressed
+ , i mod 3 : dense
+ , j mod 4 : dense
+ )
+}>
+
+#BSR_row_colmajor = #sparse_tensor.encoding<{
+ map = (i, j) ->
+ ( i floordiv 3 : dense
+ , j floordiv 4 : compressed
+ , j mod 4 : dense
+ , i mod 3 : dense
+ )
+}>
+
+#BSR_col_rowmajor = #sparse_tensor.encoding<{
+ map = (i, j) ->
+ ( j floordiv 4 : dense
+ , i floordiv 3 : compressed
+ , i mod 3 : dense
+ , j mod 4 : dense
+ )
+}>
+
+#BSR_col_colmajor = #sparse_tensor.encoding<{
+ map = (i, j) ->
+ ( j floordiv 4 : dense
+ , i floordiv 3 : compressed
+ , j mod 4 : dense
+ , i mod 3 : dense
+ )
+}>
+
+//
+// Example 3x4 block storage of a 6x16 matrix:
+//
+// +---------+---------+---------+---------+
+// | 1 2 . . | . . . . | . . . . | . . . . |
+// | . . . . | . . . . | . . . . | . . . . |
+// | . . . 3 | . . . . | . . . . | . . . . |
+// +---------+---------+---------+---------+
+// | . . . . | . . . . | 4 5 . . | . . . . |
+// | . . . . | . . . . | . . . . | . . . . |
+// | . . . . | . . . . | . . 6 7 | . . . . |
+// +---------+---------+---------+---------+
+//
+// Storage for CSR block storage. Note that this essentially
+// provides CSR storage of 2x4 blocks with either row-major
+// or column-major storage within each 3x4 block of elements.
+//
+// positions[1] : 0 1 2
+// coordinates[1] : 0 2
+// values : 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
+// 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 [row-major]
+//
+// 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3,
+// 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 [col-major]
+//
+// Storage for CSC block storage. Note that this essentially
+// provides CSC storage of 4x2 blocks with either row-major
+// or column-major storage within each 3x4 block of elements.
+//
+// positions[1] : 0 1 1 2 2
+// coordinates[1] : 0 1
+// values : 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
+// 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 [row-major]
+//
+// 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3,
+// 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 [col-major]
+//
+module {
+
+ func.func @main() {
+ %c0 = arith.constant 0 : index
+ %f0 = arith.constant 0.0 : f64
+
+ %m = arith.constant sparse<
+ [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ],
+ [ 1., 2., 3., 4., 5., 6., 7.]
+ > : tensor<6x16xf64>
+ %s1 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_row_rowmajor>
+ %s2 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_row_colmajor>
+ %s3 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_col_rowmajor>
+ %s4 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_col_colmajor>
+
+ // CHECK: ( 0, 1, 2 )
+ // CHECK-NEXT: ( 0, 2 )
+ // CHECK-NEXT: ( 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 )
+ %pos1 = sparse_tensor.positions %s1 {level = 1 : index } : tensor<?x?xf64, #BSR_row_rowmajor> to memref<?xindex>
+ %vecp1 = vector.transfer_read %pos1[%c0], %c0 : memref<?xindex>, vector<3xindex>
+ vector.print %vecp1 : vector<3xindex>
+ %crd1 = sparse_tensor.coordinates %s1 {level = 1 : index } : tensor<?x?xf64, #BSR_row_rowmajor> to memref<?xindex>
+ %vecc1 = vector.transfer_read %crd1[%c0], %c0 : memref<?xindex>, vector<2xindex>
+ vector.print %vecc1 : vector<2xindex>
+ %val1 = sparse_tensor.values %s1 : tensor<?x?xf64, #BSR_row_rowmajor> to memref<?xf64>
+ %vecv1 = vector.transfer_read %val1[%c0], %f0 : memref<?xf64>, vector<24xf64>
+ vector.print %vecv1 : vector<24xf64>
+
+ // CHECK-NEXT: ( 0, 1, 2 )
+ // CHECK-NEXT: ( 0, 2 )
+ // CHECK-NEXT: ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 )
+ %pos2 = sparse_tensor.positions %s2 {level = 1 : index } : tensor<?x?xf64, #BSR_row_colmajor> to memref<?xindex>
+ %vecp2 = vector.transfer_read %pos2[%c0], %c0 : memref<?xindex>, vector<3xindex>
+ vector.print %vecp2 : vector<3xindex>
+ %crd2 = sparse_tensor.coordinates %s2 {level = 1 : index } : tensor<?x?xf64, #BSR_row_colmajor> to memref<?xindex>
+ %vecc2 = vector.transfer_read %crd2[%c0], %c0 : memref<?xindex>, vector<2xindex>
+ vector.print %vecc2 : vector<2xindex>
+ %val2 = sparse_tensor.values %s2 : tensor<?x?xf64, #BSR_row_colmajor> to memref<?xf64>
+ %vecv2 = vector.transfer_read %val2[%c0], %f0 : memref<?xf64>, vector<24xf64>
+ vector.print %vecv2 : vector<24xf64>
+
+ // CHECK-NEXT: ( 0, 1, 1, 2, 2 )
+ // CHECK-NEXT: ( 0, 1 )
+ // CHECK-NEXT: ( 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 )
+ %pos3 = sparse_tensor.positions %s3 {level = 1 : index } : tensor<?x?xf64, #BSR_col_rowmajor> to memref<?xindex>
+ %vecp3 = vector.transfer_read %pos3[%c0], %c0 : memref<?xindex>, vector<5xindex>
+ vector.print %vecp3 : vector<5xindex>
+ %crd3 = sparse_tensor.coordinates %s3 {level = 1 : index } : tensor<?x?xf64, #BSR_col_rowmajor> to memref<?xindex>
+ %vecc3 = vector.transfer_read %crd3[%c0], %c0 : memref<?xindex>, vector<2xindex>
+ vector.print %vecc3 : vector<2xindex>
+ %val3 = sparse_tensor.values %s3 : tensor<?x?xf64, #BSR_col_rowmajor> to memref<?xf64>
+ %vecv3 = vector.transfer_read %val3[%c0], %f0 : memref<?xf64>, vector<24xf64>
+ vector.print %vecv3 : vector<24xf64>
+
+ // CHECK-NEXT: ( 0, 1, 1, 2, 2 )
+ // CHECK-NEXT: ( 0, 1 )
+ // CHECK-NEXT: ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 )
+ %pos4 = sparse_tensor.positions %s4 {level = 1 : index } : tensor<?x?xf64, #BSR_col_colmajor> to memref<?xindex>
+ %vecp4 = vector.transfer_read %pos4[%c0], %c0 : memref<?xindex>, vector<5xindex>
+ vector.print %vecp4 : vector<5xindex>
+ %crd4 = sparse_tensor.coordinates %s4 {level = 1 : index } : tensor<?x?xf64, #BSR_col_colmajor> to memref<?xindex>
+ %vecc4 = vector.transfer_read %crd4[%c0], %c0 : memref<?xindex>, vector<2xindex>
+ vector.print %vecc4 : vector<2xindex>
+ %val4 = sparse_tensor.values %s4 : tensor<?x?xf64, #BSR_col_colmajor> to memref<?xf64>
+ %vecv4 = vector.transfer_read %val4[%c0], %f0 : memref<?xf64>, vector<24xf64>
+ vector.print %vecv4 : vector<24xf64>
+
+ // Release the resources.
+ bufferization.dealloc_tensor %s1: tensor<?x?xf64, #BSR_row_rowmajor>
+ bufferization.dealloc_tensor %s2: tensor<?x?xf64, #BSR_row_colmajor>
+ bufferization.dealloc_tensor %s3: tensor<?x?xf64, #BSR_col_rowmajor>
+ bufferization.dealloc_tensor %s4: tensor<?x?xf64, #BSR_col_colmajor>
+
+ return
+ }
+}
|
|
@llvm/pr-subscribers-mlir-sparse Author: Aart Bik (aartbik) ChangesNote, this is a redo of #72712 which was reverted due to time outs in the bot. I have timed the tests on various settings, and it does not even hit the top 20 of integration tests. To be safe, I removed the SIMD version of the tests, just keeping libgen/direcIR paths (which are the most important to test for us). I will also keep an eye on https://lab.llvm.org/buildbot/#/builders/264/builds after submitting to make sure there is no repeat. Full diff: https://github.com/llvm/llvm-project/pull/72898.diff 1 Files Affected:
diff --git a/mlir/test/Integration/Dialect/SparseTensor/CPU/block_majors.mlir b/mlir/test/Integration/Dialect/SparseTensor/CPU/block_majors.mlir
new file mode 100755
index 000000000000000..780c1e8b3f64a7a
--- /dev/null
+++ b/mlir/test/Integration/Dialect/SparseTensor/CPU/block_majors.mlir
@@ -0,0 +1,174 @@
+//--------------------------------------------------------------------------------------------------
+// WHEN CREATING A NEW TEST, PLEASE JUST COPY & PASTE WITHOUT EDITS.
+//
+// Set-up that's shared across all tests in this directory. In principle, this
+// config could be moved to lit.local.cfg. However, there are downstream users that
+// do not use these LIT config files. Hence why this is kept inline.
+//
+// DEFINE: %{sparsifier_opts} = enable-runtime-library=true
+// DEFINE: %{sparsifier_opts_sve} = enable-arm-sve=true %{sparsifier_opts}
+// 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 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}
+//
+// DEFINE: %{env} =
+//--------------------------------------------------------------------------------------------------
+
+// RUN: %{compile} | %{run} | FileCheck %s
+//
+// Do the same run, but now with direct IR generation.
+// REDEFINE: %{sparsifier_opts} = enable-runtime-library=false
+// RUN: %{compile} | %{run} | FileCheck %s
+
+#BSR_row_rowmajor = #sparse_tensor.encoding<{
+ map = (i, j) ->
+ ( i floordiv 3 : dense
+ , j floordiv 4 : compressed
+ , i mod 3 : dense
+ , j mod 4 : dense
+ )
+}>
+
+#BSR_row_colmajor = #sparse_tensor.encoding<{
+ map = (i, j) ->
+ ( i floordiv 3 : dense
+ , j floordiv 4 : compressed
+ , j mod 4 : dense
+ , i mod 3 : dense
+ )
+}>
+
+#BSR_col_rowmajor = #sparse_tensor.encoding<{
+ map = (i, j) ->
+ ( j floordiv 4 : dense
+ , i floordiv 3 : compressed
+ , i mod 3 : dense
+ , j mod 4 : dense
+ )
+}>
+
+#BSR_col_colmajor = #sparse_tensor.encoding<{
+ map = (i, j) ->
+ ( j floordiv 4 : dense
+ , i floordiv 3 : compressed
+ , j mod 4 : dense
+ , i mod 3 : dense
+ )
+}>
+
+//
+// Example 3x4 block storage of a 6x16 matrix:
+//
+// +---------+---------+---------+---------+
+// | 1 2 . . | . . . . | . . . . | . . . . |
+// | . . . . | . . . . | . . . . | . . . . |
+// | . . . 3 | . . . . | . . . . | . . . . |
+// +---------+---------+---------+---------+
+// | . . . . | . . . . | 4 5 . . | . . . . |
+// | . . . . | . . . . | . . . . | . . . . |
+// | . . . . | . . . . | . . 6 7 | . . . . |
+// +---------+---------+---------+---------+
+//
+// Storage for CSR block storage. Note that this essentially
+// provides CSR storage of 2x4 blocks with either row-major
+// or column-major storage within each 3x4 block of elements.
+//
+// positions[1] : 0 1 2
+// coordinates[1] : 0 2
+// values : 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
+// 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 [row-major]
+//
+// 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3,
+// 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 [col-major]
+//
+// Storage for CSC block storage. Note that this essentially
+// provides CSC storage of 4x2 blocks with either row-major
+// or column-major storage within each 3x4 block of elements.
+//
+// positions[1] : 0 1 1 2 2
+// coordinates[1] : 0 1
+// values : 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3,
+// 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 [row-major]
+//
+// 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3,
+// 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 [col-major]
+//
+module {
+
+ func.func @main() {
+ %c0 = arith.constant 0 : index
+ %f0 = arith.constant 0.0 : f64
+
+ %m = arith.constant sparse<
+ [ [0, 0], [0, 1], [2, 3], [3, 8], [3, 9], [5, 10], [5, 11] ],
+ [ 1., 2., 3., 4., 5., 6., 7.]
+ > : tensor<6x16xf64>
+ %s1 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_row_rowmajor>
+ %s2 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_row_colmajor>
+ %s3 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_col_rowmajor>
+ %s4 = sparse_tensor.convert %m : tensor<6x16xf64> to tensor<?x?xf64, #BSR_col_colmajor>
+
+ // CHECK: ( 0, 1, 2 )
+ // CHECK-NEXT: ( 0, 2 )
+ // CHECK-NEXT: ( 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 )
+ %pos1 = sparse_tensor.positions %s1 {level = 1 : index } : tensor<?x?xf64, #BSR_row_rowmajor> to memref<?xindex>
+ %vecp1 = vector.transfer_read %pos1[%c0], %c0 : memref<?xindex>, vector<3xindex>
+ vector.print %vecp1 : vector<3xindex>
+ %crd1 = sparse_tensor.coordinates %s1 {level = 1 : index } : tensor<?x?xf64, #BSR_row_rowmajor> to memref<?xindex>
+ %vecc1 = vector.transfer_read %crd1[%c0], %c0 : memref<?xindex>, vector<2xindex>
+ vector.print %vecc1 : vector<2xindex>
+ %val1 = sparse_tensor.values %s1 : tensor<?x?xf64, #BSR_row_rowmajor> to memref<?xf64>
+ %vecv1 = vector.transfer_read %val1[%c0], %f0 : memref<?xf64>, vector<24xf64>
+ vector.print %vecv1 : vector<24xf64>
+
+ // CHECK-NEXT: ( 0, 1, 2 )
+ // CHECK-NEXT: ( 0, 2 )
+ // CHECK-NEXT: ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 )
+ %pos2 = sparse_tensor.positions %s2 {level = 1 : index } : tensor<?x?xf64, #BSR_row_colmajor> to memref<?xindex>
+ %vecp2 = vector.transfer_read %pos2[%c0], %c0 : memref<?xindex>, vector<3xindex>
+ vector.print %vecp2 : vector<3xindex>
+ %crd2 = sparse_tensor.coordinates %s2 {level = 1 : index } : tensor<?x?xf64, #BSR_row_colmajor> to memref<?xindex>
+ %vecc2 = vector.transfer_read %crd2[%c0], %c0 : memref<?xindex>, vector<2xindex>
+ vector.print %vecc2 : vector<2xindex>
+ %val2 = sparse_tensor.values %s2 : tensor<?x?xf64, #BSR_row_colmajor> to memref<?xf64>
+ %vecv2 = vector.transfer_read %val2[%c0], %f0 : memref<?xf64>, vector<24xf64>
+ vector.print %vecv2 : vector<24xf64>
+
+ // CHECK-NEXT: ( 0, 1, 1, 2, 2 )
+ // CHECK-NEXT: ( 0, 1 )
+ // CHECK-NEXT: ( 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 5, 0, 0, 0, 0, 0, 0, 0, 0, 6, 7 )
+ %pos3 = sparse_tensor.positions %s3 {level = 1 : index } : tensor<?x?xf64, #BSR_col_rowmajor> to memref<?xindex>
+ %vecp3 = vector.transfer_read %pos3[%c0], %c0 : memref<?xindex>, vector<5xindex>
+ vector.print %vecp3 : vector<5xindex>
+ %crd3 = sparse_tensor.coordinates %s3 {level = 1 : index } : tensor<?x?xf64, #BSR_col_rowmajor> to memref<?xindex>
+ %vecc3 = vector.transfer_read %crd3[%c0], %c0 : memref<?xindex>, vector<2xindex>
+ vector.print %vecc3 : vector<2xindex>
+ %val3 = sparse_tensor.values %s3 : tensor<?x?xf64, #BSR_col_rowmajor> to memref<?xf64>
+ %vecv3 = vector.transfer_read %val3[%c0], %f0 : memref<?xf64>, vector<24xf64>
+ vector.print %vecv3 : vector<24xf64>
+
+ // CHECK-NEXT: ( 0, 1, 1, 2, 2 )
+ // CHECK-NEXT: ( 0, 1 )
+ // CHECK-NEXT: ( 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0, 0, 5, 0, 0, 0, 0, 6, 0, 0, 7 )
+ %pos4 = sparse_tensor.positions %s4 {level = 1 : index } : tensor<?x?xf64, #BSR_col_colmajor> to memref<?xindex>
+ %vecp4 = vector.transfer_read %pos4[%c0], %c0 : memref<?xindex>, vector<5xindex>
+ vector.print %vecp4 : vector<5xindex>
+ %crd4 = sparse_tensor.coordinates %s4 {level = 1 : index } : tensor<?x?xf64, #BSR_col_colmajor> to memref<?xindex>
+ %vecc4 = vector.transfer_read %crd4[%c0], %c0 : memref<?xindex>, vector<2xindex>
+ vector.print %vecc4 : vector<2xindex>
+ %val4 = sparse_tensor.values %s4 : tensor<?x?xf64, #BSR_col_colmajor> to memref<?xf64>
+ %vecv4 = vector.transfer_read %val4[%c0], %f0 : memref<?xf64>, vector<24xf64>
+ vector.print %vecv4 : vector<24xf64>
+
+ // Release the resources.
+ bufferization.dealloc_tensor %s1: tensor<?x?xf64, #BSR_row_rowmajor>
+ bufferization.dealloc_tensor %s2: tensor<?x?xf64, #BSR_row_colmajor>
+ bufferization.dealloc_tensor %s3: tensor<?x?xf64, #BSR_col_rowmajor>
+ bufferization.dealloc_tensor %s4: tensor<?x?xf64, #BSR_col_colmajor>
+
+ return
+ }
+}
|
PeimingLiu
approved these changes
Nov 20, 2023
yinying-lisa-li
approved these changes
Nov 20, 2023
|
Just to play it super safe, broke the test up into foo1,2,3,4 to make sure we are not hitting some IR size issues. |
|
Submitted... keeping an eye on https://lab.llvm.org/buildbot/#/builders/264 |
|
Looks like we are in good shape \o/ |
|
And all bots are happy with this change: |
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Note, this is a redo of #72712 which was reverted due to time outs in the bot. I have timed the tests on various settings, and it does not even hit the top 20 of integration tests. To be safe, I removed the SIMD version of the tests, just keeping libgen/direcIR paths (which are the most important to test for us).
I will also keep an eye on https://lab.llvm.org/buildbot/#/builders/264/builds after submitting to make sure there is no repeat.