e3d64ccf9f
This change omits default values from the sparse tensor type, saving considerable text real estate for the common cases. Reviewed By: Peiming Differential Revision: https://reviews.llvm.org/D132083
63 lines
5.0 KiB
MLIR
63 lines
5.0 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-DAG: %[[VAL_3:.*]] = arith.constant 2 : index
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// CHECK: %[[VAL_4:.*]] = bufferization.alloc_tensor() : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: %[[VAL_5:.*]] = 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_6:.*]] = sparse_tensor.pointers %[[VAL_5]], %[[VAL_1]] : 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.indices %[[VAL_5]], %[[VAL_1]] : 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.pointers %[[VAL_5]], %[[VAL_2]] : 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.indices %[[VAL_5]], %[[VAL_2]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xindex>
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// CHECK: %[[VAL_10:.*]] = sparse_tensor.values %[[VAL_5]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>> to memref<?xf64>
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// CHECK: %[[VAL_11:.*]] = memref.alloca(%[[VAL_3]]) : memref<?xindex>
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// CHECK: %[[VAL_12:.*]] = memref.alloca() : memref<f64>
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// CHECK: %[[VAL_13:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_1]]] : memref<?xindex>
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// CHECK: %[[VAL_14:.*]] = memref.load %[[VAL_6]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_13]] to %[[VAL_14]] step %[[VAL_2]] {
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// CHECK: %[[VAL_16:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK: memref.store %[[VAL_16]], %[[VAL_11]]{{\[}}%[[VAL_1]]] : memref<?xindex>
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_15]]] : memref<?xindex>
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// CHECK: %[[VAL_18:.*]] = arith.addi %[[VAL_15]], %[[VAL_2]] : index
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// CHECK: %[[VAL_19:.*]] = memref.load %[[VAL_8]]{{\[}}%[[VAL_18]]] : memref<?xindex>
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// CHECK: scf.for %[[VAL_20:.*]] = %[[VAL_17]] to %[[VAL_19]] step %[[VAL_2]] {
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// CHECK: %[[VAL_21:.*]] = memref.load %[[VAL_9]]{{\[}}%[[VAL_20]]] : memref<?xindex>
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// CHECK: memref.store %[[VAL_21]], %[[VAL_11]]{{\[}}%[[VAL_2]]] : memref<?xindex>
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// CHECK: %[[VAL_22:.*]] = memref.load %[[VAL_10]]{{\[}}%[[VAL_20]]] : memref<?xf64>
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// CHECK: memref.store %[[VAL_22]], %[[VAL_12]][] : memref<f64>
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// CHECK: sparse_tensor.lex_insert %[[VAL_4]], %[[VAL_11]], %[[VAL_12]] : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>, memref<?xindex>, memref<f64>
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_23:.*]] = sparse_tensor.load %[[VAL_4]] hasInserts : tensor<4x3xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ] }>>
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// CHECK: bufferization.dealloc_tensor %[[VAL_5]] : tensor<3x4xf64, #sparse_tensor.encoding<{ dimLevelType = [ "compressed", "compressed" ], dimOrdering = affine_map<(d0, d1) -> (d1, d0)> }>>
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// CHECK: return %[[VAL_23]] : 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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