a3610359b5
The indices for insert/compress were previously provided as a memref<?xindex> with proper rank, since that matched the argument for the runtime support libary better. However, with proper codegen coming, providing the indices as SSA values is much cleaner. This also brings the sparse_tensor.insert closer to unification with tensor.insert, planned in the longer run. Reviewed By: Peiming Differential Revision: https://reviews.llvm.org/D134404
57 lines
4.5 KiB
MLIR
57 lines
4.5 KiB
MLIR
// RUN: mlir-opt %s -sparsification | FileCheck %s
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#DCSR = #sparse_tensor.encoding<{
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dimLevelType = [ "compressed", "compressed" ]
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}>
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#transpose_trait = {
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indexing_maps = [
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affine_map<(i,j) -> (j,i)>, // A
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affine_map<(i,j) -> (i,j)> // X
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],
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iterator_types = ["parallel", "parallel"],
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doc = "X(i,j) = A(j,i)"
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}
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// TODO: improve auto-conversion followed by yield
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// CHECK-LABEL: func.func @sparse_transpose_auto(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>) -> tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> {
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// CHECK-DAG: %[[VAL_1:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 1 : index
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// CHECK: %[[VAL_3:.*]] = bufferization.alloc_tensor() : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: %[[VAL_4:.*]] = sparse_tensor.convert %[[VAL_0]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>> to tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
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// CHECK: %[[VAL_5:.*]] = sparse_tensor.pointers %[[VAL_4]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = sparse_tensor.indices %[[VAL_4]] {dimension = 0 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
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// CHECK: %[[VAL_7:.*]] = sparse_tensor.pointers %[[VAL_4]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = sparse_tensor.indices %[[VAL_4]] {dimension = 1 : index} : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = sparse_tensor.values %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xf64>
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// CHECK: %[[VAL_10:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_1]]] : memref<?xindex>
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// CHECK: %[[VAL_11:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_10]] to %[[VAL_11]] step %[[VAL_2]] {
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// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_12]]] : memref<?xindex>
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_12]]] : memref<?xindex>
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// CHECK: %[[VAL_15:.*]] = arith.addi %[[VAL_12]], %[[VAL_2]] : index
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// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_17:.*]] = %[[VAL_14]] to %[[VAL_16]] step %[[VAL_2]] {
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_17]]] : memref<?xindex>
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// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_17]]] : memref<?xf64>
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// CHECK: sparse_tensor.insert %[[VAL_19]] into %[[VAL_3]]{{\[}}%[[VAL_13]], %[[VAL_18]]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_20:.*]] = sparse_tensor.load %[[VAL_3]] hasInserts : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: bufferization.dealloc_tensor %[[VAL_4]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
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// CHECK: return %[[VAL_20]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: }
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func.func @sparse_transpose_auto(%arga: tensor<3x4xf64, #DCSR>)
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-> tensor<4x3xf64, #DCSR> {
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%i = bufferization.alloc_tensor() : tensor<4x3xf64, #DCSR>
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%0 = linalg.generic #transpose_trait
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ins(%arga: tensor<3x4xf64, #DCSR>)
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outs(%i: tensor<4x3xf64, #DCSR>) {
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^bb(%a: f64, %x: f64):
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linalg.yield %a : f64
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} -> tensor<4x3xf64, #DCSR>
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return %0 : tensor<4x3xf64, #DCSR>
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}
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