From: Aart Bik Date: Thu, 17 Feb 2022 00:30:46 +0000 (-0800) Subject: [mlir][sparse] avoid some codeup in sparsification transformation X-Git-Tag: upstream/15.0.7~16162 X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=34381a76c1a37ad316c650f290f5846f92cbd86c;p=platform%2Fupstream%2Fllvm.git [mlir][sparse] avoid some codeup in sparsification transformation A very small refactoring, but a big impact on tests that expect an exact order. This revision fixes the tests, but also makes them less brittle for similar minor changes in the future! Reviewed By: bixia Differential Revision: https://reviews.llvm.org/D119992 --- diff --git a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp index 7a6dd31..427ee30 100644 --- a/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp +++ b/mlir/lib/Dialect/SparseTensor/Transforms/Sparsification.cpp @@ -464,15 +464,15 @@ static Value genOutputBuffer(CodeGen &codegen, PatternRewriter &rewriter, // impact the running complexity of the sparse kernel. If the tensor // materializes into the computation, we need to preserve the zero // initialization assumption of all sparse output buffers. + Value alloc = rewriter.create(loc, denseTp, args); if (isMaterializing(tensor)) { - Value alloc = rewriter.create(loc, denseTp, args); Value zero = constantZero(rewriter, loc, denseTp.getElementType()); rewriter.create(loc, zero, alloc); - return alloc; + } else { + Value init = + rewriter.create(loc, denseTp, tensor); + rewriter.create(loc, init, alloc); } - Value init = rewriter.create(loc, denseTp, tensor); - Value alloc = rewriter.create(loc, denseTp, args); - rewriter.create(loc, init, alloc); return alloc; } diff --git a/mlir/test/Dialect/SparseTensor/dense.mlir b/mlir/test/Dialect/SparseTensor/dense.mlir index 012f968..25c6072 100644 --- a/mlir/test/Dialect/SparseTensor/dense.mlir +++ b/mlir/test/Dialect/SparseTensor/dense.mlir @@ -35,14 +35,14 @@ // CHECK-LABEL: func @dense1( // CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32> {linalg.inplaceable = false}) -> tensor<32x16xf32> { -// CHECK: %[[VAL_2:.*]] = arith.constant 1.000000e+00 : f32 -// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> -// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1.000000e+00 : f32 +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{{.*}}>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { diff --git a/mlir/test/Dialect/SparseTensor/sparse_1d.mlir b/mlir/test/Dialect/SparseTensor/sparse_1d.mlir index d56ac71..cf9ff82 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_1d.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_1d.mlir @@ -14,15 +14,15 @@ } // CHECK-LABEL: func @add_d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: f32, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: f32, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<32xf32> to memref<32xf32> // CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref @@ -44,8 +44,8 @@ func @add_d(%arga: tensor<32xf32, #DV>, %argb: f32, %argx: tensor<32xf32>) -> te } // CHECK-LABEL: func @add_d_init( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: f32) -> tensor<32xf32> { +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: f32) -> tensor<32xf32> { // CHECK: %[[VAL_2:.*]] = arith.constant 32 : index // CHECK: %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f32 // CHECK: %[[VAL_4:.*]] = arith.constant 0 : index @@ -74,15 +74,15 @@ func @add_d_init(%arga: tensor<32xf32, #DV>, %argb: f32) -> tensor<32xf32> { } // CHECK-LABEL: func @mul_d( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: f32, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: f32, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<32xf32> to memref<32xf32> // CHECK: scf.for %[[VAL_9:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_9]]] : memref @@ -104,18 +104,18 @@ func @mul_d(%arga: tensor<32xf32, #DV>, %argb: f32, %argx: tensor<32xf32>) -> te } // CHECK-LABEL: func @add_s( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: f32, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: f32, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref @@ -160,15 +160,15 @@ func @add_s(%arga: tensor<32xf32, #SV>, %argb: f32, %argx: tensor<32xf32>) -> te } // CHECK-LABEL: func @repeated_add_s( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> -// CHECK: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_2]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> +// CHECK-DAG: %[[VAL_8:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref @@ -200,16 +200,16 @@ func @repeated_add_s(%arga: tensor<32xf32, #SV>, %argx: tensor<32xf32>) -> tenso } // CHECK-LABEL: func @mul_s( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: f32, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: f32, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref @@ -244,16 +244,16 @@ func @mul_s(%arga: tensor<32xf32, #SV>, %argb: f32, %argx: tensor<32xf32>) -> te } // CHECK-LABEL: func @add_dd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32xf32> to memref<32xf32> // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref @@ -276,16 +276,16 @@ func @add_dd(%arga: tensor<32xf32, #DV>, %argb: tensor<32xf32>, %argx: tensor<32 } // CHECK-LABEL: func @mul_dd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<32xf32> to memref<32xf32> // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref @@ -308,19 +308,19 @@ func @mul_dd(%arga: tensor<32xf32, #DV>, %argb: tensor<32xf32>, %argx: tensor<32 } // CHECK-LABEL: func @add_ds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32> +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref @@ -368,17 +368,17 @@ func @add_ds(%arga: tensor<32xf32>, %argb: tensor<32xf32, #SV>, %argx: tensor<32 } // CHECK-LABEL: func @mul_ds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32> -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = bufferization.to_memref %[[VAL_0]] : memref<32xf32> +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref @@ -404,19 +404,19 @@ func @mul_ds(%arga: tensor<32xf32>, %argb: tensor<32xf32, #SV>, %argx: tensor<32 } // CHECK-LABEL: func @add_sd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref @@ -464,17 +464,17 @@ func @add_sd(%arga: tensor<32xf32, #SV>, %argb: tensor<32xf32>, %argx: tensor<32 } // CHECK-LABEL: func @mul_sd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref @@ -500,19 +500,19 @@ func @mul_sd(%arga: tensor<32xf32, #SV>, %argb: tensor<32xf32>, %argx: tensor<32 } // CHECK-LABEL: func @add_ss( -// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref @@ -584,19 +584,19 @@ func @add_ss(%arga: tensor<32xf32, #SV>, %argb: tensor<32xf32, #SV>, %argx: tens } // CHECK-LABEL: func @mul_ss( -// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> +// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32xf32>) -> tensor<32xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref @@ -646,20 +646,20 @@ func @mul_ss(%arga: tensor<32xf32, #SV>, %argb: tensor<32xf32, #SV>, %argx: tens } // CHECK-LABEL: func @two_way_inv( -// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*2]]: f32, -// CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> { -// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<16xf32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<16xf32> +// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*2]]: f32, +// CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> { +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<16xf32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<16xf32> to memref<16xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref @@ -740,20 +740,20 @@ func @two_way_inv(%arga: tensor<16xf32, #SV>, %argb: tensor<16xf32, #SV>, %argc: } // CHECK-LABEL: func @two_way_inv_alt( -// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*2]]: f32, -// CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> { -// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<16xf32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<16xf32> +// CHECK-SAME: %[[VAL_0:.*0]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*1]]: tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*2]]: f32, +// CHECK-SAME: %[[VAL_3:.*3]]: tensor<16xf32>) -> tensor<16xf32> { +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<16xf32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<16xf32> to memref<16xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref @@ -846,10 +846,10 @@ func @two_way_inv_alt(%arga: tensor<16xf32, #SV>, // CHECK-SAME: %[[VAL_1:.*]]: tensor) -> tensor { // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor> to memref -// CHECK: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref -// CHECK: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_1]] : memref -// CHECK: %[[VAL_7:.*]] = memref.alloc() : memref +// CHECK-DAG: %[[VAL_4:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor> to memref +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref +// CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK-DAG: %[[VAL_7:.*]] = memref.alloc() : memref // CHECK: memref.copy %[[VAL_6]], %[[VAL_7]] : memref to memref // CHECK-DAG: %[[VAL_8:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_2]]] : memref // CHECK-DAG: %[[VAL_9:.*]] = memref.load %[[VAL_4]]{{\[}}%[[VAL_3]]] : memref @@ -890,14 +890,14 @@ func @sum_reduction(%arga: tensor, %argx: tensor) -> tensor) -> tensor { // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref -// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref +// CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref to memref // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_12]][] : memref // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref @@ -999,15 +999,15 @@ func @sum_reduction_ss(%arga: tensor<16xf32, #SV>, // CHECK-SAME: %[[VAL_3:.*3]]: tensor) -> tensor { // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]] : memref -// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_4]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_2]] : tensor<16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_3]] : memref +// CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref to memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_14]][] : memref // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]][] : memref @@ -1110,25 +1110,25 @@ func @sum_reduction_inv(%arga: tensor<16xf32, #SV>, } // CHECK-LABEL: func @four_tensors_op( -// CHECK-SAME: %[[VAL_0:.*0]]: tensor, -// CHECK-SAME: %[[VAL_1:.*1]]: tensor>, -// CHECK-SAME: %[[VAL_2:.*2]]: tensor, -// CHECK-SAME: %[[VAL_3:.*3]]: tensor>, -// CHECK-SAME: %[[VAL_4:.*]]: tensor) -> tensor { -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> to memref -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_3]], %[[VAL_5]] : tensor> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_3]], %[[VAL_5]] : tensor> to memref -// CHECK: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_3]] : tensor> to memref -// CHECK: %[[VAL_16:.*]] = tensor.dim %[[VAL_4]], %[[VAL_5]] : tensor -// CHECK: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_4]] : memref -// CHECK: %[[VAL_18:.*]] = memref.alloc(%[[VAL_16]]) : memref +// CHECK-SAME: %[[VAL_0:.*0]]: tensor, +// CHECK-SAME: %[[VAL_1:.*1]]: tensor>, +// CHECK-SAME: %[[VAL_2:.*2]]: tensor, +// CHECK-SAME: %[[VAL_3:.*3]]: tensor>, +// CHECK-SAME: %[[VAL_4:.*]]: tensor) -> tensor { +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> to memref +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.pointers %[[VAL_3]], %[[VAL_5]] : tensor> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.indices %[[VAL_3]], %[[VAL_5]] : tensor> to memref +// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.values %[[VAL_3]] : tensor> to memref +// CHECK-DAG: %[[VAL_16:.*]] = tensor.dim %[[VAL_4]], %[[VAL_5]] : tensor +// CHECK-DAG: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_4]] : memref +// CHECK-DAG: %[[VAL_18:.*]] = memref.alloc(%[[VAL_16]]) : memref // CHECK: memref.copy %[[VAL_17]], %[[VAL_18]] : memref to memref // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_5]]] : memref // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref @@ -1295,17 +1295,17 @@ func @four_tensors_op(%arga: tensor, // CHECK-SAME: %[[VAL_3:.*3]]: tensor) -> tensor { // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_2]] : tensor> to memref -// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_3]] : memref -// CHECK: %[[VAL_16:.*]] = memref.alloc() : memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_2]] : tensor> to memref +// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_3]] : memref +// CHECK-DAG: %[[VAL_16:.*]] = memref.alloc() : memref // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref to memref // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_16]][] : memref // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref diff --git a/mlir/test/Dialect/SparseTensor/sparse_2d.mlir b/mlir/test/Dialect/SparseTensor/sparse_2d.mlir index d176015..7c318be 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_2d.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_2d.mlir @@ -17,17 +17,17 @@ } // CHECK-LABEL: func @add_dd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { @@ -54,17 +54,17 @@ func @add_dd(%arga: tensor<32x16xf32, #Tdd>, %argb: tensor<32x16xf32>, %argx: te } // CHECK-LABEL: func @mul_dd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { @@ -91,20 +91,20 @@ func @mul_dd(%arga: tensor<32x16xf32, #Tdd>, %argb: tensor<32x16xf32>, %argx: te } // CHECK-LABEL: func @add_ds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_7]] { // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref @@ -155,18 +155,18 @@ func @add_ds(%arga: tensor<32x16xf32, #Tds>, %argb: tensor<32x16xf32>, %argx: te } // CHECK-LABEL: func @mul_ds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref @@ -195,20 +195,20 @@ func @mul_ds(%arga: tensor<32x16xf32, #Tds>, %argb: tensor<32x16xf32>, %argx: te } // CHECK-LABEL: func @add_sd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref @@ -264,18 +264,18 @@ func @add_sd(%arga: tensor<32x16xf32, #Tsd>, %argb: tensor<32x16xf32>, %argx: te } // CHECK-LABEL: func @mul_sd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref @@ -305,22 +305,22 @@ func @mul_sd(%arga: tensor<32x16xf32, #Tsd>, %argb: tensor<32x16xf32>, %argx: te } // CHECK-LABEL: func @add_ss( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> -// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_15:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_15:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_6]]] : memref // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref @@ -400,19 +400,19 @@ func @add_ss(%arga: tensor<32x16xf32, #Tss>, %argb: tensor<32x16xf32>, %argx: te } // CHECK-LABEL: func @mul_ss( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref @@ -444,23 +444,23 @@ func @mul_ss(%arga: tensor<32x16xf32, #Tss>, %argb: tensor<32x16xf32>, %argx: te } // CHECK-LABEL: func @add_ss_ss( -// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_16:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_16:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref @@ -609,23 +609,23 @@ func @add_ss_ss(%arga: tensor<32x16xf32, #Tss>, %argb: tensor<32x16xf32, #Tss>, } // CHECK-LABEL: func @mul_ss_ss( -// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_16:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*0]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*1]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*2]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_16:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_4]]] : memref @@ -706,22 +706,22 @@ func @mul_ss_ss(%arga: tensor<32x16xf32, #Tss>, %argb: tensor<32x16xf32, #Tss>, } // CHECK-LABEL: func @add_sd_ds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_15:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_7]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_15:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_7]]] : memref @@ -813,20 +813,20 @@ func @add_sd_ds(%arga: tensor<32x16xf32, #Tsd>, %argb: tensor<32x16xf32, #Tds>, } // CHECK-LABEL: func @mul_sd_ds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16xf32>) -> tensor<32x16xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<32x16xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf32> to memref<32x16xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref @@ -876,12 +876,12 @@ func @mul_sd_ds(%arga: tensor<32x16xf32, #Tsd>, %argb: tensor<32x16xf32, #Tds>, // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<16xf32> -// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<16xf32> +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<16x32xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32xf32> +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<16xf32> +// CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<16xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<16xf32> to memref<16xf32> // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref @@ -928,10 +928,10 @@ func @matvec(%argA: tensor<16x32xf32, #Tds>, %argb: tensor<32xf32>, %argx: tenso // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 10 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref -// CHECK: %[[VAL_8:.*]] = memref.alloc() : memref +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK-DAG: %[[VAL_8:.*]] = memref.alloc() : memref // CHECK: memref.copy %[[VAL_7]], %[[VAL_8]] : memref to memref // CHECK: %[[VAL_9:.*]] = memref.load %[[VAL_8]][] : memref // CHECK: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[VAL_4]] to %[[VAL_2]] step %[[VAL_3]] iter_args(%[[VAL_12:.*]] = %[[VAL_9]]) -> (f32) { @@ -970,18 +970,18 @@ func @sum_reduction(%arga: tensor<10x20xf32, #Tds>, %argx: tensor) -> tenso } // CHECK-LABEL: func @scale( -// CHECK-SAME: %[[VAL_0:.*]]: tensor>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor) -> tensor { -// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f64 -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref -// CHECK: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor -// CHECK: %[[VAL_9:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref -// CHECK: %[[VAL_11:.*]] = memref.alloc(%[[VAL_8]], %[[VAL_9]]) : memref +// CHECK-SAME: %[[VAL_0:.*]]: tensor>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor) -> tensor { +// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2.000000e+00 : f64 +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref +// CHECK-DAG: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor +// CHECK-DAG: %[[VAL_9:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK-DAG: %[[VAL_11:.*]] = memref.alloc(%[[VAL_8]], %[[VAL_9]]) : memref // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref to memref // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] { // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_12]]] : memref @@ -1025,20 +1025,20 @@ func @scale(%arga: tensor, %argx: tensor) -> tensor, // CHECK-SAME: %[[VAL_2:.*2]]: tensor, // CHECK-SAME: %[[VAL_3:.*3]]: tensor) -> tensor { -// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref -// CHECK: %[[VAL_12:.*]] = tensor.dim %[[VAL_2]], %[[VAL_4]] : tensor -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref -// CHECK: %[[VAL_14:.*]] = tensor.dim %[[VAL_3]], %[[VAL_4]] : tensor -// CHECK: %[[VAL_15:.*]] = tensor.dim %[[VAL_3]], %[[VAL_5]] : tensor -// CHECK: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_3]] : memref -// CHECK: %[[VAL_17:.*]] = memref.alloc(%[[VAL_14]], %[[VAL_15]]) : memref +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK-DAG: %[[VAL_12:.*]] = tensor.dim %[[VAL_2]], %[[VAL_4]] : tensor +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref +// CHECK-DAG: %[[VAL_14:.*]] = tensor.dim %[[VAL_3]], %[[VAL_4]] : tensor +// CHECK-DAG: %[[VAL_15:.*]] = tensor.dim %[[VAL_3]], %[[VAL_5]] : tensor +// CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_3]] : memref +// CHECK-DAG: %[[VAL_17:.*]] = memref.alloc(%[[VAL_14]], %[[VAL_15]]) : memref // CHECK: memref.copy %[[VAL_16]], %[[VAL_17]] : memref to memref // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref @@ -1048,9 +1048,9 @@ func @scale(%arga: tensor, %argx: tensor) -> tensor // CHECK: scf.for %[[VAL_25:.*]] = %[[VAL_22]] to %[[VAL_24]] step %[[VAL_5]] { -// CHECK-DAG: %[[VAL_26:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_25]]] : memref -// CHECK-DAG: %[[VAL_27:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_25]]] : memref -// CHECK-DAG: %[[VAL_28:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_21]], %[[VAL_26]]] : memref +// CHECK-DAG: %[[VAL_26:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_25]]] : memref +// CHECK-DAG: %[[VAL_27:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_25]]] : memref +// CHECK-DAG: %[[VAL_28:.*]] = memref.load %[[VAL_17]]{{\[}}%[[VAL_21]], %[[VAL_26]]] : memref // CHECK: %[[VAL_29:.*]] = scf.for %[[VAL_30:.*]] = %[[VAL_4]] to %[[VAL_12]] step %[[VAL_5]] iter_args(%[[VAL_31:.*]] = %[[VAL_28]]) -> (f32) { // CHECK: %[[VAL_32:.*]] = memref.load %[[VAL_11]]{{\[}}%[[VAL_21]], %[[VAL_30]]] : memref // CHECK: %[[VAL_33:.*]] = memref.load %[[VAL_13]]{{\[}}%[[VAL_30]], %[[VAL_26]]] : memref @@ -1104,22 +1104,22 @@ func @sampled_dense_dense(%args: tensor, // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_8:.*]] = arith.constant true -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_7]] : tensor> to memref -// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_7]] : tensor> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_7]] : tensor> to memref +// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_7]] : tensor> to memref // CHECK: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> to memref -// CHECK: %[[VAL_17:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_7]] : tensor> to memref -// CHECK: %[[VAL_18:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_7]] : tensor> to memref -// CHECK: %[[VAL_19:.*]] = sparse_tensor.values %[[VAL_2]] : tensor> to memref -// CHECK: %[[VAL_20:.*]] = bufferization.to_memref %[[VAL_3]] : memref -// CHECK: %[[VAL_21:.*]] = bufferization.to_memref %[[VAL_4]] : memref -// CHECK: %[[VAL_22:.*]] = tensor.dim %[[VAL_5]], %[[VAL_6]] : tensor -// CHECK: %[[VAL_23:.*]] = bufferization.to_memref %[[VAL_5]] : memref -// CHECK: %[[VAL_24:.*]] = memref.alloc(%[[VAL_22]]) : memref +// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.pointers %[[VAL_2]], %[[VAL_7]] : tensor> to memref +// CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.indices %[[VAL_2]], %[[VAL_7]] : tensor> to memref +// CHECK-DAG: %[[VAL_19:.*]] = sparse_tensor.values %[[VAL_2]] : tensor> to memref +// CHECK-DAG: %[[VAL_20:.*]] = bufferization.to_memref %[[VAL_3]] : memref +// CHECK-DAG: %[[VAL_21:.*]] = bufferization.to_memref %[[VAL_4]] : memref +// CHECK-DAG: %[[VAL_22:.*]] = tensor.dim %[[VAL_5]], %[[VAL_6]] : tensor +// CHECK-DAG: %[[VAL_23:.*]] = bufferization.to_memref %[[VAL_5]] : memref +// CHECK-DAG: %[[VAL_24:.*]] = memref.alloc(%[[VAL_22]]) : memref // CHECK: memref.copy %[[VAL_23]], %[[VAL_24]] : memref to memref // CHECK: %[[VAL_25:.*]] = memref.load %[[VAL_21]][] : memref // CHECK: %[[VAL_26:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_6]]] : memref diff --git a/mlir/test/Dialect/SparseTensor/sparse_3d.mlir b/mlir/test/Dialect/SparseTensor/sparse_3d.mlir index aea77ac..648d4f7 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_3d.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_3d.mlir @@ -23,18 +23,18 @@ } // CHECK-LABEL: func @add_ddd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_6]] to %[[VAL_3]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] { @@ -65,18 +65,18 @@ func @add_ddd(%arga: tensor<32x16x8xf32, #Tddd>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @mul_ddd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_6]] to %[[VAL_3]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] { @@ -107,22 +107,22 @@ func @mul_ddd(%arga: tensor<32x16x8xf32, #Tddd>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @add_dds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_8:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_15:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_8:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_15:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: scf.for %[[VAL_16:.*]] = %[[VAL_7]] to %[[VAL_4]] step %[[VAL_9]] { // CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_7]] to %[[VAL_5]] step %[[VAL_9]] { @@ -177,20 +177,20 @@ func @add_dds(%arga: tensor<32x16x8xf32, #Tdds>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @mul_dds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 2 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 32 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 16 : index -// CHECK: %[[VAL_6:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_7:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_6]] to %[[VAL_4]] step %[[VAL_7]] { // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_6]] to %[[VAL_5]] step %[[VAL_7]] { @@ -223,21 +223,21 @@ func @mul_dds(%arga: tensor<32x16x8xf32, #Tdds>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @add_dsd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_7]] to %[[VAL_3]] step %[[VAL_8]] { // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_15]]] : memref @@ -296,19 +296,19 @@ func @add_dsd(%arga: tensor<32x16x8xf32, #Tdsd>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @mul_dsd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_13]]] : memref @@ -341,24 +341,24 @@ func @mul_dsd(%arga: tensor<32x16x8xf32, #Tdsd>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @add_dss( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_17:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_17:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_16]], %[[VAL_17]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_8]] to %[[VAL_4]] step %[[VAL_9]] { // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_18]]] : memref @@ -441,21 +441,21 @@ func @add_dss(%arga: tensor<32x16x8xf32, #Tdss>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @mul_dss( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_6]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref @@ -490,21 +490,21 @@ func @mul_dss(%arga: tensor<32x16x8xf32, #Tdss>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @add_sdd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_8]]] : memref @@ -568,19 +568,19 @@ func @add_sdd(%arga: tensor<32x16x8xf32, #Tsdd>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @mul_sdd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref @@ -614,24 +614,24 @@ func @mul_sdd(%arga: tensor<32x16x8xf32, #Tsdd>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @add_sds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_17:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_16:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_17:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_16]], %[[VAL_17]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref // CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_9]]] : memref @@ -719,21 +719,21 @@ func @add_sds(%arga: tensor<32x16x8xf32, #Tsds>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @mul_sds( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "dense", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_5]]] : memref // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_6]]] : memref @@ -769,23 +769,23 @@ func @mul_sds(%arga: tensor<32x16x8xf32, #Tsds>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @add_ssd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_16:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_7]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_16:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_7]]] : memref // CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_8]]] : memref @@ -877,20 +877,20 @@ func @add_ssd(%arga: tensor<32x16x8xf32, #Tssd>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @mul_ssd( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref @@ -926,26 +926,26 @@ func @mul_ssd(%arga: tensor<32x16x8xf32, #Tssd>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @add_sss( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true -// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_18:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_19:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 32 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 16 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 8 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant true +// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_8]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_9]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_17:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_18:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_19:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_18]], %[[VAL_19]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: %[[VAL_20:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_8]]] : memref // CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_9]]] : memref @@ -1061,22 +1061,22 @@ func @add_sss(%arga: tensor<32x16x8xf32, #Tsss>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @mul_sss( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { -// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> -// CHECK: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> -// CHECK: %[[VAL_15:.*]] = memref.alloc() : memref<32x16x8xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x16x8xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<32x16x8xf32>) -> tensor<32x16x8xf32> { +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16x8xf32, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed", "compressed" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_1]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_14:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16x8xf32> +// CHECK-DAG: %[[VAL_15:.*]] = memref.alloc() : memref<32x16x8xf32> // CHECK: memref.copy %[[VAL_14]], %[[VAL_15]] : memref<32x16x8xf32> to memref<32x16x8xf32> // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_5]]] : memref @@ -1125,23 +1125,23 @@ func @mul_sss(%arga: tensor<32x16x8xf32, #Tsss>, %argb: tensor<32x16x8xf32>, %ar } // CHECK-LABEL: func @kernel_3d( -// CHECK-SAME: %[[VAL_0:.*0]]: tensor, -// CHECK-SAME: %[[VAL_1:.*1]]: tensor>, -// CHECK-SAME: %[[VAL_2:.*2]]: tensor, -// CHECK-SAME: %[[VAL_3:.*3]]: tensor) -> tensor { -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor> to memref -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> to memref -// CHECK: %[[VAL_10:.*]] = tensor.dim %[[VAL_2]], %[[VAL_5]] : tensor -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref -// CHECK: %[[VAL_13:.*]] = tensor.dim %[[VAL_0]], %[[VAL_5]] : tensor -// CHECK: %[[VAL_14:.*]] = tensor.dim %[[VAL_0]], %[[VAL_6]] : tensor -// CHECK: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_0]] : memref -// CHECK: %[[VAL_16:.*]] = memref.alloc(%[[VAL_13]], %[[VAL_14]]) : memref +// CHECK-SAME: %[[VAL_0:.*0]]: tensor, +// CHECK-SAME: %[[VAL_1:.*1]]: tensor>, +// CHECK-SAME: %[[VAL_2:.*2]]: tensor, +// CHECK-SAME: %[[VAL_3:.*3]]: tensor) -> tensor { +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor> to memref +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> to memref +// CHECK-DAG: %[[VAL_10:.*]] = tensor.dim %[[VAL_2]], %[[VAL_5]] : tensor +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref +// CHECK-DAG: %[[VAL_13:.*]] = tensor.dim %[[VAL_0]], %[[VAL_5]] : tensor +// CHECK-DAG: %[[VAL_14:.*]] = tensor.dim %[[VAL_0]], %[[VAL_6]] : tensor +// CHECK-DAG: %[[VAL_15:.*]] = bufferization.to_memref %[[VAL_0]] : memref +// CHECK-DAG: %[[VAL_16:.*]] = memref.alloc(%[[VAL_13]], %[[VAL_14]]) : memref // CHECK: memref.copy %[[VAL_15]], %[[VAL_16]] : memref to memref // CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_5]] to %[[VAL_13]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_18:.*]] = %[[VAL_5]] to %[[VAL_10]] step %[[VAL_6]] { @@ -1194,17 +1194,17 @@ func @kernel_3d(%arga: tensor, } // CHECK-LABEL: func @sum_reduction( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>, +// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor) -> tensor { -// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 2 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>> -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>> -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref -// CHECK: %[[VAL_10:.*]] = memref.alloc() : memref +// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_2]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>> +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}>> +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK-DAG: %[[VAL_10:.*]] = memref.alloc() : memref // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref to memref // CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref @@ -1255,16 +1255,16 @@ func @sum_reduction(%arga: tensor<10x20x30xf32, #Tsss>, %argx: tensor) -> t // CHECK-SAME: %[[VAL_0:.*]]: tensor, // CHECK-SAME: %[[VAL_1:.*]]: tensor> // CHECK-SAME: %[[VAL_2:.*]]: tensor) -> tensor { -// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 2 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor -// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref -// CHECK: %[[VAL_9:.*]] = tensor.dim %[[VAL_1]], %[[VAL_5]] : tensor> -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref -// CHECK: %[[VAL_12:.*]] = memref.alloc() : memref +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 2 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor +// CHECK-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_0]] : memref +// CHECK-DAG: %[[VAL_9:.*]] = tensor.dim %[[VAL_1]], %[[VAL_5]] : tensor> +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_1]] : tensor> +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref +// CHECK-DAG: %[[VAL_12:.*]] = memref.alloc() : memref // CHECK: memref.copy %[[VAL_11]], %[[VAL_12]] : memref to memref // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_12]][] : memref // CHECK: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_9]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (f32) { @@ -1310,20 +1310,20 @@ func @sum_reduction_inv(%arga: tensor, } // CHECK-LABEL: func @invariants( -// CHECK-SAME: %[[VAL_0:.*]]: tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, -// CHECK-SAME: %[[VAL_1:.*]]: tensor<20xf32>, -// CHECK-SAME: %[[VAL_2:.*]]: tensor<30xf32>, -// CHECK-SAME: %[[VAL_3:.*]]: tensor<10x20x30xf32>) -> tensor<10x20x30xf32> { -// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index -// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 20 : index -// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 30 : index -// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index -// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20xf32> -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<30xf32> -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<10x20x30xf32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<10x20x30xf32> +// CHECK-SAME: %[[VAL_0:.*]]: tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, +// CHECK-SAME: %[[VAL_1:.*]]: tensor<20xf32>, +// CHECK-SAME: %[[VAL_2:.*]]: tensor<30xf32>, +// CHECK-SAME: %[[VAL_3:.*]]: tensor<10x20x30xf32>) -> tensor<10x20x30xf32> { +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 20 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 30 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20xf32> +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<30xf32> +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_3]] : memref<10x20x30xf32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<10x20x30xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<10x20x30xf32> to memref<10x20x30xf32> // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_7]] to %[[VAL_4]] step %[[VAL_8]] { // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_14]]] : memref diff --git a/mlir/test/Dialect/SparseTensor/sparse_affine.mlir b/mlir/test/Dialect/SparseTensor/sparse_affine.mlir index 551b2b6..8212f1c 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_affine.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_affine.mlir @@ -21,12 +21,12 @@ // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 3 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<4xf32> -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> -// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32xf32> +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<4xf32> +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf32> +// CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf32> to memref<32xf32> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_4]]] : memref<4xf32> // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref @@ -73,12 +73,12 @@ func @mul_inv_dense1d(%arga: tensor<32xf32, #SpVec>, // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 2 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34xi32> -// CHECK: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi32> -// CHECK: %[[VAL_11:.*]] = memref.alloc() : memref<32xi32> +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34xi32> +// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xi32> +// CHECK-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xi32> // CHECK: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xi32> to memref<32xi32> // CHECK: %[[VAL_12:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref @@ -125,12 +125,12 @@ func @and_affine_dense1d(%arga: tensor<32xi32, #SpVec>, // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_7:.*]] = arith.constant 3 : index -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34x19xf64> -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf64> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf64> +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x16xf64, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<34x19xf64> +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32x16xf64> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<32x16xf64> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<32x16xf64> to memref<32x16xf64> // CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_3]] { // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_14]]] : memref diff --git a/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir b/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir index 7d8461c..6d427d5 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_kernels.mlir @@ -12,14 +12,14 @@ // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 30 : index -// CHECK: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30xf32> -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x30xf32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<10x30xf32> +// CHECK-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_3]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_4]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30xf32> +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x30xf32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<10x30xf32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<10x30xf32> to memref<10x30xf32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_4]]] : memref @@ -159,14 +159,14 @@ func @matmul2(%A: tensor<4x8xf64, #DCSR>, // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 6 : index -// CHECK: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32> -// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<6x6xi32> -// CHECK: %[[VAL_13:.*]] = memref.alloc() : memref<6x6xi32> +// CHECK-DAG: %[[VAL_6:.*]] = bufferization.to_memref %[[VAL_0]] : memref<8x8xi32> +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x3xi32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_12:.*]] = bufferization.to_memref %[[VAL_2]] : memref<6x6xi32> +// CHECK-DAG: %[[VAL_13:.*]] = memref.alloc() : memref<6x6xi32> // CHECK: memref.copy %[[VAL_12]], %[[VAL_13]] : memref<6x6xi32> to memref<6x6xi32> // CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_3]]] : memref // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_4]]] : memref @@ -211,14 +211,14 @@ func @conv2d(%input: tensor<8x8xi32>, // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index // CHECK-DAG: %[[VAL_6:.*]] = arith.constant 5 : index -// CHECK: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8> -// CHECK: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<5x6xi64> -// CHECK: %[[VAL_14:.*]] = memref.alloc() : memref<5x6xi64> +// CHECK-DAG: %[[VAL_7:.*]] = bufferization.to_memref %[[VAL_0]] : memref<5x3xi8> +// CHECK-DAG: %[[VAL_8:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_9:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_10:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_11:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_5]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_12:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<3x6xi8, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_2]] : memref<5x6xi64> +// CHECK-DAG: %[[VAL_14:.*]] = memref.alloc() : memref<5x6xi64> // CHECK: memref.copy %[[VAL_13]], %[[VAL_14]] : memref<5x6xi64> to memref<5x6xi64> // CHECK: %[[VAL_15:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_4]]] : memref // CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_5]]] : memref diff --git a/mlir/test/Dialect/SparseTensor/sparse_lower.mlir b/mlir/test/Dialect/SparseTensor/sparse_lower.mlir index 22a8e3a..abde97e 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_lower.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_lower.mlir @@ -27,12 +27,12 @@ // CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-HIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK-HIR: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK-HIR: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK-HIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> -// CHECK-HIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> -// CHECK-HIR: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> +// CHECK-HIR-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-HIR-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-HIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> +// CHECK-HIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> +// CHECK-HIR-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> // CHECK-HIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64> // CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK-HIR-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref @@ -60,12 +60,12 @@ // CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-MIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-MIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK-MIR: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr, index) -> memref -// CHECK-MIR: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr, index) -> memref -// CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr) -> memref -// CHECK-MIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> -// CHECK-MIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> -// CHECK-MIR: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> +// CHECK-MIR-DAG: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr, index) -> memref +// CHECK-MIR-DAG: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr, index) -> memref +// CHECK-MIR-DAG: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr) -> memref +// CHECK-MIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> +// CHECK-MIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> +// CHECK-MIR-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> // CHECK-MIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64> // CHECK-MIR: scf.for %[[VAL_14:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK-MIR-DAG: %[[VAL_15:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_14]]] : memref @@ -93,10 +93,10 @@ // CHECK-LIR-DAG: %[[VAL_3:.*]] = arith.constant 32 : index // CHECK-LIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-LIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK-LIR: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr, index) -> memref -// CHECK-LIR: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr, index) -> memref -// CHECK-LIR: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr) -> memref -// CHECK-LIR: %[[VAL_9:.*]] = memref.alloc() : memref<32xf64> +// CHECK-LIR-DAG: %[[VAL_6:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr, index) -> memref +// CHECK-LIR-DAG: %[[VAL_7:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_5]]) : (!llvm.ptr, index) -> memref +// CHECK-LIR-DAG: %[[VAL_8:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr) -> memref +// CHECK-LIR-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<32xf64> // CHECK-LIR: memref.copy %[[VAL_2]], %[[VAL_9]] : memref<32xf64> to memref<32xf64> // CHECK-LIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK-LIR-DAG: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref diff --git a/mlir/test/Dialect/SparseTensor/sparse_lower_col.mlir b/mlir/test/Dialect/SparseTensor/sparse_lower_col.mlir index d06231b..1220218 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_lower_col.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_lower_col.mlir @@ -30,12 +30,12 @@ // CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index // CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index // CHECK-HIR-DAG: %[[VAL_5:.*]] = arith.constant 1 : index -// CHECK-HIR: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK-HIR: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK-HIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> -// CHECK-HIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> -// CHECK-HIR: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> +// CHECK-HIR-DAG: %[[VAL_6:.*]] = sparse_tensor.pointers %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-HIR-DAG: %[[VAL_7:.*]] = sparse_tensor.indices %[[VAL_0]], %[[VAL_5]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<32x64xf64, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)>, pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-HIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> +// CHECK-HIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> +// CHECK-HIR-DAG: %[[VAL_11:.*]] = memref.alloc() : memref<32xf64> // CHECK-HIR: memref.copy %[[VAL_10]], %[[VAL_11]] : memref<32xf64> to memref<32xf64> // CHECK-HIR: scf.for %[[VAL_12:.*]] = %[[VAL_4]] to %[[VAL_3]] step %[[VAL_5]] { // CHECK-HIR: %[[VAL_13:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_12]]] : memref<64xf64> @@ -62,12 +62,12 @@ // CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index // CHECK-MIR-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-MIR-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK-MIR: %[[VAL_7:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr, index) -> memref -// CHECK-MIR: %[[VAL_8:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr, index) -> memref -// CHECK-MIR: %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr) -> memref -// CHECK-MIR: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> -// CHECK-MIR: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> -// CHECK-MIR: %[[VAL_12:.*]] = memref.alloc() : memref<32xf64> +// CHECK-MIR-DAG: %[[VAL_7:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr, index) -> memref +// CHECK-MIR-DAG: %[[VAL_8:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr, index) -> memref +// CHECK-MIR-DAG: %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr) -> memref +// CHECK-MIR-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<64xf64> +// CHECK-MIR-DAG: %[[VAL_11:.*]] = bufferization.to_memref %[[VAL_2]] : memref<32xf64> +// CHECK-MIR-DAG: %[[VAL_12:.*]] = memref.alloc() : memref<32xf64> // CHECK-MIR: memref.copy %[[VAL_11]], %[[VAL_12]] : memref<32xf64> to memref<32xf64> // CHECK-MIR: scf.for %[[VAL_15:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK-MIR: %[[VAL_16:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_15]]] : memref<64xf64> @@ -94,10 +94,10 @@ // CHECK-LIR-DAG: %[[VAL_3:.*]] = arith.constant 64 : index // CHECK-LIR-DAG: %[[VAL_5:.*]] = arith.constant 0 : index // CHECK-LIR-DAG: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK-LIR: %[[VAL_7:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr, index) -> memref -// CHECK-LIR: %[[VAL_8:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr, index) -> memref -// CHECK-LIR: %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr) -> memref -// CHECK-LIR: %[[VAL_10:.*]] = memref.alloc() : memref<32xf64> +// CHECK-LIR-DAG: %[[VAL_7:.*]] = call @sparsePointers(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr, index) -> memref +// CHECK-LIR-DAG: %[[VAL_8:.*]] = call @sparseIndices(%[[VAL_0]], %[[VAL_6]]) : (!llvm.ptr, index) -> memref +// CHECK-LIR-DAG: %[[VAL_9:.*]] = call @sparseValuesF64(%[[VAL_0]]) : (!llvm.ptr) -> memref +// CHECK-LIR-DAG: %[[VAL_10:.*]] = memref.alloc() : memref<32xf64> // CHECK-LIR: memref.copy %[[VAL_2]], %[[VAL_10]] : memref<32xf64> to memref<32xf64> // CHECK-LIR: scf.for %[[VAL_13:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK-LIR: %[[VAL_14:.*]] = memref.load %[[VAL_1]]{{\[}}%[[VAL_13]]] : memref<64xf64> diff --git a/mlir/test/Dialect/SparseTensor/sparse_nd.mlir b/mlir/test/Dialect/SparseTensor/sparse_nd.mlir index 7c6e98f..5cf6430 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_nd.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_nd.mlir @@ -24,24 +24,24 @@ // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30x40x50x60x70x80xf32>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>>, // CHECK-SAME: %[[VAL_2:.*]]: tensor<10x20x30x40x50x60x70x80xf32>) -> tensor<10x20x30x40x50x60x70x80xf32> { -// CHECK: %[[VAL_3:.*]] = arith.constant 3 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 4 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 10 : index -// CHECK: %[[VAL_6:.*]] = arith.constant 20 : index -// CHECK: %[[VAL_7:.*]] = arith.constant 30 : index -// CHECK: %[[VAL_8:.*]] = arith.constant 60 : index -// CHECK: %[[VAL_9:.*]] = arith.constant 70 : index -// CHECK: %[[VAL_10:.*]] = arith.constant 80 : index -// CHECK: %[[VAL_11:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_12:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32> -// CHECK: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_17:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref -// CHECK: %[[VAL_19:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32> -// CHECK: %[[VAL_20:.*]] = memref.alloc() : memref<10x20x30x40x50x60x70x80xf32> +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 3 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 4 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 10 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 20 : index +// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 30 : index +// CHECK-DAG: %[[VAL_8:.*]] = arith.constant 60 : index +// CHECK-DAG: %[[VAL_9:.*]] = arith.constant 70 : index +// CHECK-DAG: %[[VAL_10:.*]] = arith.constant 80 : index +// CHECK-DAG: %[[VAL_11:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_12:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_13:.*]] = bufferization.to_memref %[[VAL_0]] : memref<10x20x30x40x50x60x70x80xf32> +// CHECK-DAG: %[[VAL_14:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_15:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_3]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_16:.*]] = sparse_tensor.pointers %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_17:.*]] = sparse_tensor.indices %[[VAL_1]], %[[VAL_4]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_18:.*]] = sparse_tensor.values %[[VAL_1]] : tensor<80x70x60x50x40x30x20x10xf32, #sparse_tensor.encoding<{ dimLevelType = [ "dense", "dense", "dense", "compressed", "compressed", "dense", "dense", "dense" ], pointerBitWidth = 0, indexBitWidth = 0 }>> to memref +// CHECK-DAG: %[[VAL_19:.*]] = bufferization.to_memref %[[VAL_2]] : memref<10x20x30x40x50x60x70x80xf32> +// CHECK-DAG: %[[VAL_20:.*]] = memref.alloc() : memref<10x20x30x40x50x60x70x80xf32> // CHECK: memref.copy %[[VAL_19]], %[[VAL_20]] : memref<10x20x30x40x50x60x70x80xf32> to memref<10x20x30x40x50x60x70x80xf32> // CHECK: scf.for %[[VAL_21:.*]] = %[[VAL_11]] to %[[VAL_10]] step %[[VAL_12]] { // CHECK: scf.for %[[VAL_22:.*]] = %[[VAL_11]] to %[[VAL_9]] step %[[VAL_12]] { diff --git a/mlir/test/Dialect/SparseTensor/sparse_perm.mlir b/mlir/test/Dialect/SparseTensor/sparse_perm.mlir index 463db3c..14c8b78 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_perm.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_perm.mlir @@ -17,14 +17,14 @@ // CHECK-LABEL: func @sparse_static_dims( // CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>, // CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> { -// CHECK: %[[VAL_2:.*]] = arith.constant 20 : index -// CHECK: %[[VAL_3:.*]] = arith.constant 30 : index -// CHECK: %[[VAL_4:.*]] = arith.constant 10 : index -// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index -// CHECK: %[[VAL_6:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>> -// CHECK: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30x10xf32> -// CHECK: %[[VAL_9:.*]] = memref.alloc() : memref<20x30x10xf32> +// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 20 : index +// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 30 : index +// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index +// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index +// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index +// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>> +// CHECK-DAG: %[[VAL_8:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30x10xf32> +// CHECK-DAG: %[[VAL_9:.*]] = memref.alloc() : memref<20x30x10xf32> // CHECK: memref.copy %[[VAL_8]], %[[VAL_9]] : memref<20x30x10xf32> to memref<20x30x10xf32> // CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] { // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] { @@ -58,12 +58,12 @@ func @sparse_static_dims(%arga: tensor<10x20x30xf32, #X>, // CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : index // CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index -// CHECK: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> -// CHECK: %[[VAL_6:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor -// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor -// CHECK: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_2]] : tensor -// CHECK: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref -// CHECK: %[[VAL_10:.*]] = memref.alloc(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) : memref +// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> +// CHECK-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_1]], %[[VAL_3]] : tensor +// CHECK-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_1]], %[[VAL_4]] : tensor +// CHECK-DAG: %[[VAL_8:.*]] = tensor.dim %[[VAL_1]], %[[VAL_2]] : tensor +// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK-DAG: %[[VAL_10:.*]] = memref.alloc(%[[VAL_6]], %[[VAL_7]], %[[VAL_8]]) : memref // CHECK: memref.copy %[[VAL_9]], %[[VAL_10]] : memref to memref // CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_4]] { // CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] { diff --git a/mlir/test/Dialect/SparseTensor/sparse_perm_lower.mlir b/mlir/test/Dialect/SparseTensor/sparse_perm_lower.mlir index a01e38c..b9b1fcb 100644 --- a/mlir/test/Dialect/SparseTensor/sparse_perm_lower.mlir +++ b/mlir/test/Dialect/SparseTensor/sparse_perm_lower.mlir @@ -22,12 +22,12 @@ // CHECK-HIR-DAG: %[[VAL_2:.*]] = arith.constant 1 : index // CHECK-HIR-DAG: %[[VAL_3:.*]] = arith.constant 0 : index // CHECK-HIR-DAG: %[[VAL_4:.*]] = arith.constant 2 : index -// CHECK-HIR: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor> -// CHECK-HIR: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor> -// CHECK-HIR: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor> -// CHECK-HIR: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> -// CHECK-HIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref -// CHECK-HIR: %[[VAL_10:.*]] = memref.alloc() : memref +// CHECK-HIR-DAG: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor> +// CHECK-HIR-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor> +// CHECK-HIR-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor> +// CHECK-HIR-DAG: %[[VAL_8:.*]] = sparse_tensor.values %[[VAL_0]] : tensor> +// CHECK-HIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK-HIR-DAG: %[[VAL_10:.*]] = memref.alloc() : memref // CHECK-HIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref to memref // CHECK-HIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref // CHECK-HIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_3]] to %[[VAL_5]] step %[[VAL_2]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) { @@ -56,12 +56,12 @@ // CHECK-MIR-DAG: %[[VAL_2:.*]] = arith.constant 2 : index // CHECK-MIR-DAG: %[[VAL_3:.*]] = arith.constant 1 : index // CHECK-MIR-DAG: %[[VAL_4:.*]] = arith.constant 0 : index -// CHECK-MIR: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr, index) -> index -// CHECK-MIR: %[[VAL_6:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_3]]) : (!llvm.ptr, index) -> index -// CHECK-MIR: %[[VAL_7:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_2]]) : (!llvm.ptr, index) -> index -// CHECK-MIR: %[[VAL_8:.*]] = call @sparseValuesF32(%[[VAL_0]]) : (!llvm.ptr) -> memref -// CHECK-MIR: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref -// CHECK-MIR: %[[VAL_10:.*]] = memref.alloc() : memref +// CHECK-MIR-DAG: %[[VAL_5:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_4]]) : (!llvm.ptr, index) -> index +// CHECK-MIR-DAG: %[[VAL_6:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_3]]) : (!llvm.ptr, index) -> index +// CHECK-MIR-DAG: %[[VAL_7:.*]] = call @sparseDimSize(%[[VAL_0]], %[[VAL_2]]) : (!llvm.ptr, index) -> index +// CHECK-MIR-DAG: %[[VAL_8:.*]] = call @sparseValuesF32(%[[VAL_0]]) : (!llvm.ptr) -> memref +// CHECK-MIR-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref +// CHECK-MIR-DAG: %[[VAL_10:.*]] = memref.alloc() : memref // CHECK-MIR: memref.copy %[[VAL_9]], %[[VAL_10]] : memref to memref // CHECK-MIR: %[[VAL_11:.*]] = memref.load %[[VAL_10]][] : memref // CHECK-MIR: %[[VAL_12:.*]] = scf.for %[[VAL_13:.*]] = %[[VAL_4]] to %[[VAL_5]] step %[[VAL_3]] iter_args(%[[VAL_14:.*]] = %[[VAL_11]]) -> (f32) {