[mlir][sparse] More allocate -> empty tensor migration #66720
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This also allows tensor.empty in the "conversion" path of the sparse compiler, further paving the way to deprecate the bufferization.allocated_tensor() op.
aartbik requested review from PeimingLiu, maerhart, matthias-springer and yinying-lisa-li 
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| @llvm/pr-subscribers-mlir-sparse @llvm/pr-subscribers-mlir ChangesThis also allows tensor.empty in the "conversion" path of the sparse compiler, further paving the way to Patch is 37.16 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/66720.diff 9 Files Affected:
diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp index 871686a4ada0f70..d75601e369a0d25 100644 --- a/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp +++ b/mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorConversion.cpp @@ -830,6 +830,7 @@ class SparseTensorNewConverter : public OpConversionPattern<NewOp> { }; /// Sparse conversion rule for the alloc operator. +/// TODO(springerm): remove when bufferization.alloc_tensor is gone class SparseTensorAllocConverter : public OpConversionPattern<bufferization::AllocTensorOp> { public: @@ -864,6 +865,37 @@ class SparseTensorAllocConverter } }; +/// Sparse conversion rule for the empty tensor. +class SparseTensorEmptyConverter : public OpConversionPattern<tensor::EmptyOp> { +public: + using OpConversionPattern::OpConversionPattern; + LogicalResult + matchAndRewrite(tensor::EmptyOp op, OpAdaptor adaptor, + ConversionPatternRewriter &rewriter) const override { + Location loc = op.getLoc(); + const auto stt = getSparseTensorType(op); + if (!stt.hasEncoding()) + return failure(); + // Gather all dimension sizes as SSA values. + const Dimension dimRank = stt.getDimRank(); + SmallVector<Value> dimSizes; + dimSizes.reserve(dimRank); + auto shape = op.getType().getShape(); + unsigned operandCtr = 0; + for (Dimension d = 0; d < dimRank; ++d) { + dimSizes.push_back(stt.isDynamicDim(d) + ? adaptor.getOperands()[operandCtr++] + : constantIndex(rewriter, loc, shape[d])); + } + // Generate the call to construct empty tensor. The sizes are + // explicitly defined by the arguments to the alloc operator. + rewriter.replaceOp(op, NewCallParams(rewriter, loc) + .genBuffers(stt, dimSizes) + .genNewCall(Action::kEmpty)); + return success(); + } +}; + /// Sparse conversion rule for the convert operator. class SparseTensorConvertConverter : public OpConversionPattern<ConvertOp> { public: @@ -1503,19 +1535,19 @@ mlir::SparseTensorTypeToPtrConverter::SparseTensorTypeToPtrConverter() { void mlir::populateSparseTensorConversionPatterns( TypeConverter &typeConverter, RewritePatternSet &patterns, const SparseTensorConversionOptions &options) { - patterns.add<SparseReturnConverter, SparseTensorToDimSizeConverter, - SparseCastConverter, SparseTensorNewConverter, - SparseReshapeConverter<tensor::ExpandShapeOp>, - SparseReshapeConverter<tensor::CollapseShapeOp>, - SparseTensorConcatConverter, SparseTensorAllocConverter, - SparseTensorDeallocConverter, SparseTensorToPositionsConverter, - SparseTensorToCoordinatesConverter, - SparseTensorToValuesConverter, SparseNumberOfEntriesConverter, - SparseTensorLoadConverter, SparseTensorInsertConverter, - SparseTensorExpandConverter, SparseTensorCompressConverter, - SparseTensorOutConverter, SparseTensorPackConverter>( - typeConverter, patterns.getContext()); - + patterns + .add<SparseReturnConverter, SparseTensorToDimSizeConverter, + SparseCastConverter, SparseTensorNewConverter, + SparseReshapeConverter<tensor::ExpandShapeOp>, + SparseReshapeConverter<tensor::CollapseShapeOp>, + SparseTensorConcatConverter, SparseTensorAllocConverter, + SparseTensorEmptyConverter, SparseTensorDeallocConverter, + SparseTensorToPositionsConverter, SparseTensorToCoordinatesConverter, + SparseTensorToValuesConverter, SparseNumberOfEntriesConverter, + SparseTensorLoadConverter, SparseTensorInsertConverter, + SparseTensorExpandConverter, SparseTensorCompressConverter, + SparseTensorOutConverter, SparseTensorPackConverter>( + typeConverter, patterns.getContext()); patterns.add<SparseTensorConvertConverter>(typeConverter, patterns.getContext(), options); } diff --git a/mlir/test/Dialect/SparseTensor/constant_index_map.mlir b/mlir/test/Dialect/SparseTensor/constant_index_map.mlir index bfb4503edbc4e40..9eb535385790146 100644 --- a/mlir/test/Dialect/SparseTensor/constant_index_map.mlir +++ b/mlir/test/Dialect/SparseTensor/constant_index_map.mlir @@ -13,7 +13,7 @@ // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 77 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK-DAG: %[[VAL_5:.*]] = bufferization.alloc_tensor() : tensor<77xi1, #{{.*}}> +// CHECK-DAG: %[[VAL_5:.*]] = tensor.empty() : tensor<77xi1, #{{.*}}> // CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<1x77xi1> // CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<1x77xi1> // CHECK: %[[VAL_8:.*]] = scf.for %[[VAL_9:.*]] = %[[VAL_3]] to %[[VAL_2]] step %[[VAL_4]] iter_args(%[[VAL_10:.*]] = %[[VAL_5]]) -> (tensor<77xi1, #{{.*}}>) { @@ -27,7 +27,7 @@ // CHECK: return %[[VAL_15]] : tensor<77xi1, #{{.*}}> // CHECK: } func.func @main(%arg0: tensor<1x77xi1>, %arg1: tensor<1x77xi1>) -> tensor<77xi1, #SpVec> { - %0 = bufferization.alloc_tensor() : tensor<77xi1, #SpVec> + %0 = tensor.empty() : tensor<77xi1, #SpVec> %1 = linalg.generic { indexing_maps = [#map1, #map1, #map2], iterator_types = ["parallel"]} diff --git a/mlir/test/Dialect/SparseTensor/sparse_affine.mlir b/mlir/test/Dialect/SparseTensor/sparse_affine.mlir index b3f6ae9f12ee4d6..fc97685b8378bf5 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_affine.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_affine.mlir @@ -1,4 +1,3 @@ -// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py // RUN: mlir-opt %s -sparsification | FileCheck %s #SpVec = #sparse_tensor.encoding<{ map = (d0) -> (d0 : compressed) }> @@ -17,9 +16,9 @@ } // CHECK-LABEL: func @mul_inv_dense1d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 3 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index @@ -57,13 +56,13 @@ func.func @mul_inv_dense1d(%arga: tensor<32xf32, #SpVec>, } // CHECK-LABEL: func.func @mul_inv_sparse1d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>>) +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>>) // CHECK: %[[VAL_2:.*]] = arith.constant 0 : index // CHECK: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK: %[[VAL_4:.*]] = arith.constant 3 : index // CHECK: %[[VAL_5:.*]] = arith.constant 0.000000e+00 : f32 -// CHECK: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_6:.*]] = tensor.empty() : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK: %[[VAL_7:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> // CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32> // CHECK: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_1]] {level = 0 : index} : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> @@ -95,7 +94,7 @@ func.func @mul_inv_dense1d(%arga: tensor<32xf32, #SpVec>, // CHECK: return %[[VAL_32]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> func.func @mul_inv_sparse1d(%arga: tensor<32xf32, #SpVec>, %argb: tensor<4xf32, #SpVec>) -> tensor<32xf32, #SpVec> { - %argx = bufferization.alloc_tensor() : tensor<32xf32, #SpVec> + %argx = tensor.empty() : tensor<32xf32, #SpVec> %0 = linalg.generic #trait1 ins(%arga, %argb: tensor<32xf32, #SpVec>, tensor<4xf32, #SpVec>) outs(%argx: tensor<32xf32, #SpVec>) { @@ -109,13 +108,13 @@ func.func @mul_inv_sparse1d(%arga: tensor<32xf32, #SpVec>, // CHECK-LABEL: func.func @mul_inv_enc_dense1d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> { +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> { // CHECK: %[[VAL_2:.*]] = arith.constant 32 : index // CHECK: %[[VAL_3:.*]] = arith.constant 3 : index // CHECK: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = bufferization.alloc_tensor() : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_6:.*]] = tensor.empty() : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32> // CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<4xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32> // CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_6]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf32> @@ -132,7 +131,7 @@ func.func @mul_inv_sparse1d(%arga: tensor<32xf32, #SpVec>, // CHECK: } func.func @mul_inv_enc_dense1d(%arga: tensor<32xf32, #EncDenseVec>, %argb: tensor<4xf32, #EncDenseVec>) -> tensor<32xf32, #EncDenseVec> { - %argx = bufferization.alloc_tensor() : tensor<32xf32, #EncDenseVec> + %argx = tensor.empty() : tensor<32xf32, #EncDenseVec> %0 = linalg.generic #trait1 ins(%arga, %argb: tensor<32xf32, #EncDenseVec>, tensor<4xf32, #EncDenseVec>) outs(%argx: tensor<32xf32, #EncDenseVec>) { @@ -155,9 +154,9 @@ func.func @mul_inv_enc_dense1d(%arga: tensor<32xf32, #EncDenseVec>, } // CHECK-LABEL: func @and_affine_dense1d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<34xi32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xi32>) -> tensor<32xi32> { +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<34xi32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xi32>) -> tensor<32xi32> { // CHECK-DAG: %[[ZERO:.*]] = arith.constant 0 : i32 // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index @@ -195,12 +194,12 @@ func.func @and_affine_dense1d(%arga: tensor<32xi32, #SpVec>, } // CHECK-LABEL: func.func @and_affine_sparse1d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>>) +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<34xi32, #sparse_tensor.encoding<{{{.*}}}>>) // CHECK: %[[VAL_2:.*]] = arith.constant 0 : index // CHECK: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK: %[[VAL_4:.*]] = arith.constant 2 : index -// CHECK: %[[VAL_5:.*]] = bufferization.alloc_tensor() : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_5:.*]] = tensor.empty() : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> // CHECK: %[[VAL_6:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> // CHECK: %[[VAL_7:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 0 : index} : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> // CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xi32> @@ -234,7 +233,7 @@ func.func @and_affine_dense1d(%arga: tensor<32xi32, #SpVec>, // CHECK: return %[[VAL_33]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> func.func @and_affine_sparse1d(%arga: tensor<32xi32, #SpVec>, %argb: tensor<34xi32, #SpVec>) -> tensor<32xi32, #SpVec> { - %argx = bufferization.alloc_tensor() : tensor<32xi32, #SpVec> + %argx = tensor.empty() : tensor<32xi32, #SpVec> %0 = linalg.generic #trait2 ins(%arga, %argb: tensor<32xi32, #SpVec>, tensor<34xi32, #SpVec>) outs(%argx: tensor<32xi32, #SpVec>) { @@ -256,9 +255,9 @@ func.func @and_affine_sparse1d(%arga: tensor<32xi32, #SpVec>, } // CHECK-LABEL: func @mul_affine_dense2d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<34x19xf64>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> { +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<34x19xf64>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> { // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index @@ -304,8 +303,8 @@ func.func @mul_affine_dense2d(%arga: tensor<32x16xf64, #CSR>, // CHECK-LABEL: func.func @mul_affine_sparse2d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> { +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<34x19xf64, #sparse_tensor.encoding<{{{.*}}}>>) -> tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> { // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 32 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index @@ -314,7 +313,7 @@ func.func @mul_affine_dense2d(%arga: tensor<32x16xf64, #CSR>, // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 3 : index // CHECK-DAG: %[[VAL_TRUE:.*]] = arith.constant true // CHECK-DAG: %[[VAL_FALSE:.*]] = arith.constant false -// CHECK: %[[VAL_8:.*]] = bufferization.alloc_tensor() : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK: %[[VAL_8:.*]] = tensor.empty() : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> // CHECK: %[[VAL_9:.*]] = sparse_tensor.positions %[[VAL_0]] {level = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> // CHECK: %[[VAL_10:.*]] = sparse_tensor.coordinates %[[VAL_0]] {level = 1 : index} : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xindex> // CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> to memref<?xf64> @@ -360,7 +359,7 @@ func.func @mul_affine_dense2d(%arga: tensor<32x16xf64, #CSR>, // CHECK: return %[[VAL_45]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> func.func @mul_affine_sparse2d(%arga: tensor<32x16xf64, #CSR>, %argb: tensor<34x19xf64, #CSR>) -> tensor<32x16xf64, #CSR> { - %argx = bufferization.alloc_tensor() : tensor<32x16xf64, #CSR> + %argx = tensor.empty() : tensor<32x16xf64, #CSR> %0 = linalg.generic #trait3 ins(%arga, %argb: tensor<32x16xf64, #CSR>, tensor<34x19xf64, #CSR>) outs(%argx: tensor<32x16xf64, #CSR>) { @@ -383,9 +382,9 @@ func.func @mul_affine_sparse2d(%arga: tensor<32x16xf64, #CSR>, } // CHECK-LABEL: func.func @mul_affine_dense_dim_2d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<34x16xf64, #sparse_tensor.encoding -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> { +// CHECK-SAME: %[[VAL_0:.*]]: tensor<34x16xf64, #sparse_tensor.encoding +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> { // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 19 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index @@ -447,9 +446,9 @@ func.func @mul_affine_dense_dim_2d(%arga: tensor<34x16xf64, #CSR>, } // CHECK-LABEL: func.func @mul_const_affine_dense_dim_2d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<34x16xf64, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> { +// CHECK-SAME: %[[VAL_0:.*]]: tensor<34x16xf64, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x19xf64, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf64>) -> tensor<32x16xf64> { // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 19 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index diff --git a/mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir b/mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir index ae5b941259f6515..1a5f79d23cba29a 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_broadcast.mlir @@ -16,7 +16,7 @@ // CHECK-DAG: %[[TMP_c3:.*]] = arith.constant 3 : index // CHECK-DAG: %[[TMP_c0:.*]] = arith.constant 0 : index // CHECK-DAG: %[[TMP_c1:.*]] = arith.constant 1 : index -// CHECK: %[[TMP_0:.*]] = bufferization.alloc_tensor() +// CHECK: %[[TMP_0:.*]] = tensor.empty() // CHECK: %[[TMP_1:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 0 : index} // CHECK: %[[TMP_2:.*]] = sparse_tensor.coordinates %[[TMP_arg0]] {level = 0 : index} // CHECK: %[[TMP_3:.*]] = sparse_tensor.positions %[[TMP_arg0]] {level = 1 : index} @@ -44,7 +44,7 @@ // CHECK: return %[[TMP_8]] module @func_sparse { func.func public @main(%arg0: tensor<4x5xi32, #DCSR>) -> tensor<4x3x5xi32, #SparseTensor> { - %0 = bufferization.alloc_tensor() : tensor<4x3x5xi32, #SparseTensor> + %0 = tensor.empty() : tensor<4x3x5xi32, #SparseTensor> %1 = linalg.generic #trait ins(%arg0 : tensor<4x5xi32, #DCSR>) outs(%0 : tensor<4x3x5xi32, #SparseTensor>) { ^bb0(%in: i32, %out: i32): diff --git a/mlir/test/Dialect/SparseTensor/sparse_expand.mlir b/mlir/test/Dialect/SparseTensor/sparse_expand.mlir index ee3613a268def5e..d19d7fe2871d674 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_expand.mlir +++ b/mlir/test/Dialect/SparseTen... [truncated] |
yinying-lisa-li approved these changes Sep 18, 2023
This was referenced Sep 19, 2023
This was referenced Sep 20, 2023
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This also allows tensor.empty in the "conversion" path of the sparse compiler, further paving the way to
deprecate the bufferization.allocated_tensor() op.