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[mlir][sparse] move sparse2sparse conversion to own test file
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Rationale:
We were running *all* conversion tests two times, just to check the
difference of one indidivual test in that file. By splitting that test
out, we have a much more focused testing setup.

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D132757
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@aartbik
aartbik committed Aug 26, 2022
1 parent 59d246e commit a8166d8
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Showing 2 changed files with 52 additions and 23 deletions.
26 changes: 3 additions & 23 deletions mlir/test/Dialect/SparseTensor/conversion.mlir
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Original file line number Diff line number Diff line change
@@ -1,10 +1,4 @@
// First use with `kViaCOO` for sparse2sparse conversion (the old way).
// RUN: mlir-opt %s --sparse-tensor-conversion="s2s-strategy=1" \
// RUN: --canonicalize --cse | FileCheck %s
//
// Now again with `kAuto` (the new default).
// RUN: mlir-opt %s --sparse-tensor-conversion="s2s-strategy=0" \
// RUN: --canonicalize --cse | FileCheck %s -check-prefix=CHECKAUTO
// RUN: mlir-opt %s --sparse-tensor-conversion --canonicalize --cse | FileCheck %s

#SparseVector = #sparse_tensor.encoding<{
dimLevelType = ["compressed"]
Expand Down Expand Up @@ -233,29 +227,15 @@ func.func @sparse_convert_complex(%arg0: tensor<100xcomplex<f64>>) -> tensor<100

// CHECK-LABEL: func @sparse_convert_1d_ss(
// CHECK-SAME: %[[A:.*]]: !llvm.ptr<i8>)
// CHECK-DAG: %[[ToCOO:.*]] = arith.constant 5 : i32
// CHECK-DAG: %[[FromCOO:.*]] = arith.constant 2 : i32
// CHECK-DAG: %[[SparseToSparse:.*]] = arith.constant 3 : i32
// CHECK-DAG: %[[P:.*]] = memref.alloca() : memref<1xi8>
// CHECK-DAG: %[[Q:.*]] = memref.alloca() : memref<1xindex>
// CHECK-DAG: %[[R:.*]] = memref.alloca() : memref<1xindex>
// CHECK-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<1xi8> to memref<?xi8>
// CHECK-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<1xindex> to memref<?xindex>
// CHECK-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<1xindex> to memref<?xindex>
// CHECK: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %[[ToCOO]], %[[A]])
// CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %[[FromCOO]], %[[C]])
// CHECK: call @delSparseTensorCOOF32(%[[C]])
// CHECK: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %[[SparseToSparse]], %[[A]])
// CHECK: return %[[T]] : !llvm.ptr<i8>
// CHECKAUTO-LABEL: func @sparse_convert_1d_ss(
// CHECKAUTO-SAME: %[[A:.*]]: !llvm.ptr<i8>)
// CHECKAUTO-DAG: %[[SparseToSparse:.*]] = arith.constant 3 : i32
// CHECKAUTO-DAG: %[[P:.*]] = memref.alloca() : memref<1xi8>
// CHECKAUTO-DAG: %[[Q:.*]] = memref.alloca() : memref<1xindex>
// CHECKAUTO-DAG: %[[R:.*]] = memref.alloca() : memref<1xindex>
// CHECKAUTO-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<1xi8> to memref<?xi8>
// CHECKAUTO-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<1xindex> to memref<?xindex>
// CHECKAUTO-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<1xindex> to memref<?xindex>
// CHECKAUTO: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %[[SparseToSparse]], %[[A]])
// CHECKAUTO: return %[[T]] : !llvm.ptr<i8>
func.func @sparse_convert_1d_ss(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32, #SparseVector32> {
%0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseVector64> to tensor<?xf32, #SparseVector32>
return %0 : tensor<?xf32, #SparseVector32>
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49 changes: 49 additions & 0 deletions mlir/test/Dialect/SparseTensor/conversion_sparse2sparse.mlir
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Original file line number Diff line number Diff line change
@@ -0,0 +1,49 @@
// First use with `kViaCOO` for sparse2sparse conversion (the old way).
// RUN: mlir-opt %s --sparse-tensor-conversion="s2s-strategy=1" \
// RUN: --canonicalize --cse | FileCheck %s -check-prefix=CHECK-COO
//
// Now again with `kAuto` (the new default).
// RUN: mlir-opt %s --sparse-tensor-conversion="s2s-strategy=0" \
// RUN: --canonicalize --cse | FileCheck %s -check-prefix=CHECK-AUTO

#SparseVector64 = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
pointerBitWidth = 64,
indexBitWidth = 64
}>

#SparseVector32 = #sparse_tensor.encoding<{
dimLevelType = ["compressed"],
pointerBitWidth = 32,
indexBitWidth = 32
}>

// CHECK-COO-LABEL: func @sparse_convert(
// CHECK-COO-SAME: %[[A:.*]]: !llvm.ptr<i8>)
// CHECK-COO-DAG: %[[ToCOO:.*]] = arith.constant 5 : i32
// CHECK-COO-DAG: %[[FromCOO:.*]] = arith.constant 2 : i32
// CHECK-COO-DAG: %[[P:.*]] = memref.alloca() : memref<1xi8>
// CHECK-COO-DAG: %[[Q:.*]] = memref.alloca() : memref<1xindex>
// CHECK-COO-DAG: %[[R:.*]] = memref.alloca() : memref<1xindex>
// CHECK-COO-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<1xi8> to memref<?xi8>
// CHECK-COO-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<1xindex> to memref<?xindex>
// CHECK-COO-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<1xindex> to memref<?xindex>
// CHECK-COO: %[[C:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %[[ToCOO]], %[[A]])
// CHECK-COO: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %[[FromCOO]], %[[C]])
// CHECK-COO: call @delSparseTensorCOOF32(%[[C]])
// CHECK-COO: return %[[T]] : !llvm.ptr<i8>
// CHECK-AUTO-LABEL: func @sparse_convert(
// CHECK-AUTO-SAME: %[[A:.*]]: !llvm.ptr<i8>)
// CHECK-AUTO-DAG: %[[SparseToSparse:.*]] = arith.constant 3 : i32
// CHECK-AUTO-DAG: %[[P:.*]] = memref.alloca() : memref<1xi8>
// CHECK-AUTO-DAG: %[[Q:.*]] = memref.alloca() : memref<1xindex>
// CHECK-AUTO-DAG: %[[R:.*]] = memref.alloca() : memref<1xindex>
// CHECK-AUTO-DAG: %[[X:.*]] = memref.cast %[[P]] : memref<1xi8> to memref<?xi8>
// CHECK-AUTO-DAG: %[[Y:.*]] = memref.cast %[[Q]] : memref<1xindex> to memref<?xindex>
// CHECK-AUTO-DAG: %[[Z:.*]] = memref.cast %[[R]] : memref<1xindex> to memref<?xindex>
// CHECK-AUTO: %[[T:.*]] = call @newSparseTensor(%[[X]], %[[Y]], %[[Z]], %{{.*}}, %{{.*}}, %{{.*}}, %[[SparseToSparse]], %[[A]])
// CHECK-AUTO: return %[[T]] : !llvm.ptr<i8>
func.func @sparse_convert(%arg0: tensor<?xf32, #SparseVector64>) -> tensor<?xf32, #SparseVector32> {
%0 = sparse_tensor.convert %arg0 : tensor<?xf32, #SparseVector64> to tensor<?xf32, #SparseVector32>
return %0 : tensor<?xf32, #SparseVector32>
}

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