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
45 lines
3.1 KiB
MLIR
45 lines
3.1 KiB
MLIR
// RUN: mlir-opt %s --sparse-tensor-codegen --canonicalize --cse | FileCheck %s
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#CSR = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : dense, d1 : compressed)}>
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#COO = #sparse_tensor.encoding<{ map = (d0, d1) -> (d0 : compressed(nonunique), d1 : singleton)}>
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// CHECK-LABEL: func.func @sparse_alloc_copy_CSR(
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// CHECK-SAME: %[[VAL_0:.*0]]: memref<?xindex>,
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// CHECK-SAME: %[[VAL_1:.*1]]: memref<?xindex>,
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// CHECK-SAME: %[[VAL_2:.*2]]: memref<?xf32>,
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// CHECK-SAME: %[[VAL_3:.*]]: !sparse_tensor.storage_specifier<#{{.*}}>) -> (memref<?xindex>, memref<?xindex>, memref<?xf32>, !sparse_tensor.storage_specifier<#{{.*}}>) {
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// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index
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// CHECK: %[[VAL_5:.*]] = memref.dim %[[VAL_0]], %[[VAL_4]] : memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = memref.alloc(%[[VAL_5]]) : memref<?xindex>
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// CHECK: memref.copy %[[VAL_0]], %[[VAL_6]] : memref<?xindex> to memref<?xindex>
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// CHECK: %[[VAL_7:.*]] = memref.dim %[[VAL_1]], %[[VAL_4]] : memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = memref.alloc(%[[VAL_7]]) : memref<?xindex>
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// CHECK: memref.copy %[[VAL_1]], %[[VAL_8]] : memref<?xindex> to memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = memref.dim %[[VAL_2]], %[[VAL_4]] : memref<?xf32>
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// CHECK: %[[VAL_10:.*]] = memref.alloc(%[[VAL_9]]) : memref<?xf32>
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// CHECK: memref.copy %[[VAL_2]], %[[VAL_10]] : memref<?xf32> to memref<?xf32>
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func.func @sparse_alloc_copy_CSR(%arg0: tensor<2x2xf32, #CSR>) -> tensor<2x2xf32, #CSR> {
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%0 = bufferization.alloc_tensor() copy(%arg0) : tensor<2x2xf32, #CSR>
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"test.sink"(%0) : (tensor<2x2xf32, #CSR>) -> ()
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}
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// CHECK-LABEL: func.func @sparse_alloc_copy_COO(
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// CHECK-SAME: %[[VAL_0:.*0]]: memref<?xindex>,
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// CHECK-SAME: %[[VAL_1:.*1]]: memref<?xindex>,
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// CHECK-SAME: %[[VAL_2:.*2]]: memref<?xf32>,
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// CHECK-SAME: %[[VAL_3:.*]]: !sparse_tensor.storage_specifier<#{{.*}}>) -> (memref<?xindex>, memref<?xindex>, memref<?xf32>, !sparse_tensor.storage_specifier<#{{.*}}>) {
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// CHECK: %[[VAL_4:.*]] = arith.constant 0 : index
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// CHECK: %[[VAL_5:.*]] = memref.dim %[[VAL_0]], %[[VAL_4]] : memref<?xindex>
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// CHECK: %[[VAL_6:.*]] = memref.alloc(%[[VAL_5]]) : memref<?xindex>
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// CHECK: memref.copy %[[VAL_0]], %[[VAL_6]] : memref<?xindex> to memref<?xindex>
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// CHECK: %[[VAL_7:.*]] = memref.dim %[[VAL_1]], %[[VAL_4]] : memref<?xindex>
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// CHECK: %[[VAL_8:.*]] = memref.alloc(%[[VAL_7]]) : memref<?xindex>
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// CHECK: memref.copy %[[VAL_1]], %[[VAL_8]] : memref<?xindex> to memref<?xindex>
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// CHECK: %[[VAL_9:.*]] = memref.dim %[[VAL_2]], %[[VAL_4]] : memref<?xf32>
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// CHECK: %[[VAL_10:.*]] = memref.alloc(%[[VAL_9]]) : memref<?xf32>
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// CHECK: memref.copy %[[VAL_2]], %[[VAL_10]] : memref<?xf32> to memref<?xf32>
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func.func @sparse_alloc_copy_COO(%arg0: tensor<2x2xf32, #COO>) -> tensor<2x2xf32, #COO> {
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%0 = bufferization.alloc_tensor() copy(%arg0) : tensor<2x2xf32, #COO>
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"test.sink"(%0) : (tensor<2x2xf32, #COO>) -> ()
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}
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