101 lines
5 KiB
MLIR
101 lines
5 KiB
MLIR
// RUN: mlir-opt %s -pre-sparsification-rewrite | FileCheck %s
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#SparseVector = #sparse_tensor.encoding<{
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map = (d0) -> (d0 : compressed)
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}>
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#SortedCOO = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)
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}>
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#DCSR = #sparse_tensor.encoding<{
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map = (d0, d1) -> (d0 : compressed, d1 : compressed)
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}>
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#Slice = #sparse_tensor.encoding<{
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map = (d0 : #sparse_tensor<slice(?, 1, 1)>, d1 : #sparse_tensor<slice(?, 3, 1)>) -> (d0 : compressed(nonunique), d1 : singleton)
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}>
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#sel_trait = {
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indexing_maps = [
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affine_map<(i,j) -> (i,j)>, // C (in)
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affine_map<(i,j) -> (i,j)>, // L (in)
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affine_map<(i,j) -> (i,j)>, // R (in)
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affine_map<(i,j) -> (i,j)> // X (out)
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],
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iterator_types = ["parallel", "parallel"]
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}
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// CHECK-LABEL: func @sparse_nop_cast(
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// CHECK-SAME: %[[A:.*]]: tensor<?xf32, #sparse{{[0-9]*}}>)
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// CHECK: return %[[A]] : tensor<?xf32, #sparse{{[0-9]*}}>
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func.func @sparse_nop_cast(%a : tensor<?xf32, #SparseVector>) -> tensor<?xf32, #SparseVector> {
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%0 = tensor.cast %a : tensor<?xf32, #SparseVector> to tensor<?xf32, #SparseVector>
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%1 = tensor.cast %0 : tensor<?xf32, #SparseVector> to tensor<?xf32, #SparseVector>
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%2 = tensor.cast %1 : tensor<?xf32, #SparseVector> to tensor<?xf32, #SparseVector>
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return %2 : tensor<?xf32, #SparseVector>
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}
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// CHECK-LABEL: func @sparse_repair_cast(
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// CHECK-SAME: %[[A:.*]]: tensor<?xf32>)
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// CHECK: %[[C:.*]] = sparse_tensor.convert %[[A]] : tensor<?xf32> to tensor<?xf32, #sparse{{[0-9]*}}>
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// CHECK: return %[[C]] : tensor<?xf32, #sparse{{[0-9]*}}>
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func.func @sparse_repair_cast(%a : tensor<?xf32>) -> tensor<?xf32, #SparseVector> {
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%0 = tensor.cast %a : tensor<?xf32> to tensor<?xf32, #SparseVector>
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return %0 : tensor<?xf32, #SparseVector>
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}
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// CHECK-LABEL: func @sparse_fuse_slice(
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// CHECK-SAME: %[[A:.*]]: tensor<2x3xi64, #sparse{{[0-9]*}}>)
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// CHECK: %[[E:.*]] = tensor.extract_slice %[[A]][1, 0] [1, 3] [1, 1] : tensor<2x3xi64, #sparse{{[0-9]*}}> to tensor<1x3xi64, #sparse{{[0-9]*}}>
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// CHECK: %[[C:.*]] = sparse_tensor.convert %[[E]] : tensor<1x3xi64, #sparse{{[0-9]*}}> to tensor<1x3xi64, #sparse{{[0-9]*}}>
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// CHECK: return %[[C]] : tensor<1x3xi64, #sparse{{[0-9]*}}>
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func.func @sparse_fuse_slice(%a : tensor<2x3xi64, #SortedCOO>) -> tensor<1x3xi64, #SortedCOO> {
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%extracted_slice = tensor.extract_slice %a[1, 0] [1, 3] [1, 1] : tensor<2x3xi64, #SortedCOO> to tensor<1x3xi64>
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%cast = tensor.cast %extracted_slice : tensor<1x3xi64> to tensor<1x3xi64, #Slice>
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%0 = sparse_tensor.convert %cast : tensor<1x3xi64, #Slice> to tensor<1x3xi64, #SortedCOO>
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return %0 : tensor<1x3xi64, #SortedCOO>
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}
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// CHECK-LABEL: func.func @sparse_select(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<4x4xi1>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<4x4xf64, #sparse{{[0-9]*}}>,
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// CHECK-SAME: %[[VAL_2:.*]]: tensor<4x4xf64, #sparse{{[0-9]*}}>) -> tensor<4x4xf64, #sparse{{[0-9]*}}> {
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0.000000e+00 : f64
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// CHECK-DAG: %[[VAL_4:.*]] = tensor.empty() : tensor<4x4xf64, #sparse{{[0-9]*}}>
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// CHECK-NEXT: %[[VAL_5:.*]] = linalg.generic {indexing_maps = [#map, #map, #map, #map], iterator_types = ["parallel", "parallel"]}
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// CHECK-SAME: ins(%[[VAL_0]], %[[VAL_1]], %[[VAL_2]]
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// CHECK-NEXT: ^bb0(%[[VAL_6:.*]]: i1, %[[VAL_7:.*]]: f64, %[[VAL_8:.*]]: f64, %[[VAL_9:.*]]: f64):
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// CHECK-NEXT: %[[VAL_10:.*]] = sparse_tensor.binary %[[VAL_7]], %[[VAL_8]] : f64, f64 to f64
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// CHECK-NEXT: overlap = {
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// CHECK-NEXT: ^bb0(%[[VAL_11:.*]]: f64, %[[VAL_12:.*]]: f64):
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// CHECK-NEXT: %[[VAL_13:.*]] = arith.select %[[VAL_6]], %[[VAL_11]], %[[VAL_12]] : f64
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// CHECK-NEXT: sparse_tensor.yield %[[VAL_13]] : f64
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// CHECK-NEXT: }
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// CHECK-NEXT: left = {
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// CHECK-NEXT: ^bb0(%[[VAL_14:.*]]: f64):
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// CHECK-NEXT: %[[VAL_15:.*]] = arith.select %[[VAL_6]], %[[VAL_14]], %[[VAL_3]] : f64
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// CHECK-NEXT: sparse_tensor.yield %[[VAL_15]] : f64
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// CHECK-NEXT: }
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// CHECK-NEXT: right = {
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// CHECK-NEXT: ^bb0(%[[VAL_16:.*]]: f64):
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// CHECK-NEXT: %[[VAL_17:.*]] = arith.select %[[VAL_6]], %[[VAL_3]], %[[VAL_16]] : f64
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// CHECK-NEXT: sparse_tensor.yield %[[VAL_17]] : f64
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// CHECK-NEXT: }
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// CHECK-NEXT: linalg.yield %[[VAL_10]] : f64
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// CHECK-NEXT: } -> tensor<4x4xf64, #sparse{{[0-9]*}}>
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// CHECK-NEXT: return %[[VAL_18:.*]] : tensor<4x4xf64, #sparse{{[0-9]*}}>
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// CHECK-NEXT: }
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func.func @sparse_select(%cond: tensor<4x4xi1>,
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%arga: tensor<4x4xf64, #DCSR>,
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%argb: tensor<4x4xf64, #DCSR>) -> tensor<4x4xf64, #DCSR> {
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%xv = tensor.empty() : tensor<4x4xf64, #DCSR>
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%0 = linalg.generic #sel_trait
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ins(%cond, %arga, %argb: tensor<4x4xi1>, tensor<4x4xf64, #DCSR>, tensor<4x4xf64, #DCSR>)
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outs(%xv: tensor<4x4xf64, #DCSR>) {
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^bb(%c: i1, %a: f64, %b: f64, %x: f64):
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%1 = arith.select %c, %a, %b : f64
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linalg.yield %1 : f64
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} -> tensor<4x4xf64, #DCSR>
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return %0 : tensor<4x4xf64, #DCSR>
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}
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