3dc621124f
**COO**
`lvlTypes = [ "compressed_nu", "singleton" ]` to `map = (d0, d1) -> (d0
: compressed(nonunique), d1 : singleton)`
`lvlTypes = [ "compressed_nu_no", "singleton_no" ]` to `map = (d0, d1)
-> (d0 : compressed(nonunique, nonordered), d1 : singleton(nonordered))`
**SortedCOO**
`lvlTypes = [ "compressed_nu", "singleton" ]` to `map = (d0, d1) -> (d0
: compressed(nonunique), d1 : singleton)`
**BCOO**
`lvlTypes = [ "dense", "compressed_hi_nu", "singleton" ]` to `map = (d0,
d1, d2) -> (d0 : dense, d1 : compressed(nonunique, high), d2 :
singleton)`
**BCSR**
`lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl =
affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod
3)>` to
`map = ( i, j ) ->
( i floordiv 2 : compressed,
j floordiv 3 : compressed,
i mod 2 : dense,
j mod 3 : dense
)`
**Tensor and other supported formats(e.g. CCC, CDC, CCCC)**
Currently, ELL and slice are not supported yet in the new syntax and the
CHECK tests will be updated once printing is set to output the new
syntax.
Previous PRs: #66146, #66309, #66443
92 lines
5.3 KiB
MLIR
92 lines
5.3 KiB
MLIR
// NOTE: Assertions have been autogenerated by utils/generate-test-checks.py
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// RUN: mlir-opt %s -sparsification | FileCheck %s
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#X = #sparse_tensor.encoding<{
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map = (d0, d1, d2) -> (d2 : dense, d0 : dense, d1 : dense)
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}>
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#trait = {
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indexing_maps = [
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affine_map<(i,j,k) -> (k,i,j)>, // A (in)
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affine_map<(i,j,k) -> (i,j,k)> // X (out)
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],
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iterator_types = ["parallel", "parallel", "parallel"]
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}
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// CHECK-LABEL: func @sparse_static_dims(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> {
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// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 20 : index
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 30 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 10 : index
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// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_7:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<10x20x30xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-DAG: %[[VAL_9:.*]] = bufferization.to_memref %[[VAL_1]] : memref<20x30x10xf32>
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// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_9]] : memref<20x30x10xf32>)
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// CHECK: scf.for %[[VAL_10:.*]] = %[[VAL_5]] to %[[VAL_3]] step %[[VAL_6]] {
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// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_5]] to %[[VAL_4]] step %[[VAL_6]] {
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// CHECK: %[[VAL_12:.*]] = arith.muli %[[VAL_10]], %[[VAL_4]] : index
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// CHECK: %[[VAL_13:.*]] = arith.addi %[[VAL_12]], %[[VAL_11]] : index
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// CHECK: scf.for %[[VAL_14:.*]] = %[[VAL_5]] to %[[VAL_2]] step %[[VAL_6]] {
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// CHECK: %[[VAL_15:.*]] = arith.muli %[[VAL_13]], %[[VAL_2]] : index
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// CHECK: %[[VAL_16:.*]] = arith.addi %[[VAL_15]], %[[VAL_14]] : index
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// CHECK: %[[VAL_17:.*]] = memref.load %[[VAL_7]]{{\[}}%[[VAL_16]]] : memref<?xf32>
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// CHECK: memref.store %[[VAL_17]], %[[VAL_9]]{{\[}}%[[VAL_14]], %[[VAL_10]], %[[VAL_11]]] : memref<20x30x10xf32>
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_18:.*]] = bufferization.to_tensor %[[VAL_9]] : memref<20x30x10xf32>
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// CHECK: return %[[VAL_18]] : tensor<20x30x10xf32>
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// CHECK: }
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func.func @sparse_static_dims(%arga: tensor<10x20x30xf32, #X>,
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%argx: tensor<20x30x10xf32>) -> tensor<20x30x10xf32> {
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%0 = linalg.generic #trait
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ins(%arga: tensor<10x20x30xf32, #X>)
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outs(%argx: tensor<20x30x10xf32>) {
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^bb(%a : f32, %x: f32):
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linalg.yield %a : f32
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} -> tensor<20x30x10xf32>
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return %0 : tensor<20x30x10xf32>
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}
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// CHECK-LABEL: func @sparse_dynamic_dims(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
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// CHECK-DAG: %[[ZERO:.*]] = arith.constant 0.000000e+00 : f32
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// CHECK-DAG: %[[VAL_2:.*]] = arith.constant 2 : index
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// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
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// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 1 : index
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// CHECK-DAG: %[[VAL_5:.*]] = sparse_tensor.values %[[VAL_0]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-DAG: %[[VAL_6:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-DAG: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-DAG: %[[VAL_8:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32, #sparse_tensor.encoding<{{{.*}}}>>
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// CHECK-DAG: %[[VAL_10:.*]] = bufferization.to_memref %[[VAL_1]] : memref<?x?x?xf32>
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// CHECK: linalg.fill ins(%[[ZERO]] : f32) outs(%[[VAL_10]] : memref<?x?x?xf32>)
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// CHECK: scf.for %[[VAL_11:.*]] = %[[VAL_3]] to %[[VAL_6]] step %[[VAL_4]] {
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// CHECK: scf.for %[[VAL_12:.*]] = %[[VAL_3]] to %[[VAL_7]] step %[[VAL_4]] {
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// CHECK: %[[VAL_13:.*]] = arith.muli %[[VAL_7]], %[[VAL_11]] : index
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// CHECK: %[[VAL_14:.*]] = arith.addi %[[VAL_13]], %[[VAL_12]] : index
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// CHECK: scf.for %[[VAL_15:.*]] = %[[VAL_3]] to %[[VAL_8]] step %[[VAL_4]] {
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// CHECK: %[[VAL_16:.*]] = arith.muli %[[VAL_8]], %[[VAL_14]] : index
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// CHECK: %[[VAL_17:.*]] = arith.addi %[[VAL_16]], %[[VAL_15]] : index
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// CHECK: %[[VAL_18:.*]] = memref.load %[[VAL_5]]{{\[}}%[[VAL_17]]] : memref<?xf32>
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// CHECK: memref.store %[[VAL_18]], %[[VAL_10]]{{\[}}%[[VAL_15]], %[[VAL_11]], %[[VAL_12]]] : memref<?x?x?xf32>
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// CHECK: }
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// CHECK: }
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// CHECK: }
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// CHECK: %[[VAL_19:.*]] = bufferization.to_tensor %[[VAL_10]] : memref<?x?x?xf32>
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// CHECK: return %[[VAL_19]] : tensor<?x?x?xf32>
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// CHECK: }
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func.func @sparse_dynamic_dims(%arga: tensor<?x?x?xf32, #X>,
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%argx: tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
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%0 = linalg.generic #trait
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ins(%arga: tensor<?x?x?xf32, #X>)
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outs(%argx: tensor<?x?x?xf32>) {
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^bb(%a : f32, %x: f32):
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linalg.yield %a : f32
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} -> tensor<?x?x?xf32>
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return %0 : tensor<?x?x?xf32>
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}
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