a0615d020a
This commit is part of the migration of towards the new STEA syntax/design. In particular, this commit includes the following changes:
* Renaming compiler-internal functions/methods:
* `SparseTensorEncodingAttr::{getDimLevelType => getLvlTypes}`
* `Merger::{getDimLevelType => getLvlType}` (for consistency)
* `sparse_tensor::{getDimLevelType => buildLevelType}` (to help reduce confusion vs actual getter methods)
* Renaming external facets to match:
* the STEA parser and printer
* the C and Python bindings
* PyTACO
However, the actual renaming of the `DimLevelType` itself (along with all the "dlt" names) will be handled in a separate commit.
Reviewed By: aartbik
Differential Revision: https://reviews.llvm.org/D150330
40 lines
1.5 KiB
MLIR
40 lines
1.5 KiB
MLIR
// RUN: mlir-opt %s -sparsification | FileCheck %s
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// The file contains examples that will be rejected by sparse compiler
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// (we expect the linalg.generic unchanged).
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#SparseVector = #sparse_tensor.encoding<{lvlTypes = ["compressed"]}>
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#trait = {
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indexing_maps = [
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affine_map<(i) -> (i)>, // a (in)
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affine_map<(i) -> ()> // x (out)
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],
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iterator_types = ["reduction"]
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}
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// CHECK-LABEL: func.func @sparse_reduction_subi(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<i32>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<?xi32, #sparse_tensor.encoding<{ lvlTypes = [ "compressed" ] }>>) -> tensor<i32> {
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// CHECK: %[[VAL_2:.*]] = linalg.generic
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// CHECK: ^bb0(%[[VAL_3:.*]]: i32, %[[VAL_4:.*]]: i32):
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// CHECK: %[[VAL_5:.*]] = arith.subi %[[VAL_3]], %[[VAL_4]] : i32
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// CHECK: linalg.yield %[[VAL_5]] : i32
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// CHECK: } -> tensor<i32>
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// CHECK: return %[[VAL_6:.*]] : tensor<i32>
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func.func @sparse_reduction_subi(%argx: tensor<i32>,
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%arga: tensor<?xi32, #SparseVector>)
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-> tensor<i32> {
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%0 = linalg.generic #trait
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ins(%arga: tensor<?xi32, #SparseVector>)
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outs(%argx: tensor<i32>) {
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^bb(%a: i32, %x: i32):
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// NOTE: `subi %a, %x` is the reason why the program is rejected by the sparse compiler.
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// It is because we do not allow `-outTensor` in reduction loops as it creates cyclic
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// dependences.
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%t = arith.subi %a, %x: i32
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linalg.yield %t : i32
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} -> tensor<i32>
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return %0 : tensor<i32>
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}
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